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Sample records for pruned extreme learning

  1. Peramalan Beban Listrik Jangka Pendek Menggunakan Optimally Pruned Extreme Learning Machine (OPELM pada Sistem Kelistrikan Jawa Timur

    Directory of Open Access Journals (Sweden)

    Januar Adi Perdana

    2012-09-01

    Full Text Available Peramalan beban listrik jangka pendek merupakan faktor yang sangat penting dalam perencanaan dan pengoperasian sistem tenaga listrik. Tujuan dari peramalan beban listrik adalah agar permintaan listrik dan penyediaan listrik dapat seimbang. Karakteristik beban di wilayah Jawa Timur sangat fluktuatif sehingga pada penelitian ini digunakan metode Optimally Pruned Extreme Learning Machine (OPELM untuk meramalkan beban listrik. Kelebihan OPELM ada pada learning speed yang cepat dan pemilihan model yang tepat meskipun datanya mempunyai pola non linier. Keakuratan metode OPELM dapat diketahui dengan menggunakan metode pembanding yaitu metode ELM. Kriteria keakuratan yang digunakan adalah MAPE. Hasil dari perbandingan kriteria keakuratan menunjukkan bahwa hasil peramalan OPELM lebih baik dari ELM. Error rata-rata hasil pengujian peramalan paling minimum menunjukkan MAPE sebesar 1,3579% terjadi pada peramalan hari Jumat, sementara pada hari yang sama dengan metode ELM menghasilkan MAPE sebesar 2,2179%.

  2. Structure learning by pruning in independent component analysis

    DEFF Research Database (Denmark)

    Nielsen, Andreas Brinch; Hansen, Lars Kai

    2008-01-01

    We discuss pruning as a means of structure learning in independent component analysis (ICA). Learning the structure is attractive in both signal processing and in analysis of abstract data, where it can assist model interpretation, generalizability and reduce computation. We derive the relevant...

  3. Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP Model Using Degradation Data

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    2017-11-01

    Full Text Available The insulated gate bipolar transistor (IGBT is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN and least squares estimation (LSE method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

  4. Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data

    Science.gov (United States)

    Mei, Wenjuan; Zeng, Xianping; Yang, Chenglin; Zhou, Xiuyun

    2017-01-01

    The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches. PMID:29099811

  5. Remaining Useful Life Estimation of Insulated Gate Biploar Transistors (IGBTs) Based on a Novel Volterra k-Nearest Neighbor Optimally Pruned Extreme Learning Machine (VKOPP) Model Using Degradation Data.

    Science.gov (United States)

    Liu, Zhen; Mei, Wenjuan; Zeng, Xianping; Yang, Chenglin; Zhou, Xiuyun

    2017-11-03

    The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs' RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT's degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs' ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

  6. Structure Learning by Pruning in Independent Component Analysis

    DEFF Research Database (Denmark)

    Kjems, Andreas; Hansen, Lars Kai

    2006-01-01

    We discuss pruning as a means of structure learning in independent component analysis. Sparse models are attractive in both signal processing and in analysis of abstract data, they can assist model interpretation, generalizability and reduce computation. We derive the relevant saliency expressions...

  7. An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity

    DEFF Research Database (Denmark)

    Fu, Bin; Wang, Zhihai; Pan, Rong

    2013-01-01

    Ensemble pruning is an important issue in the field of ensemble learning. Diversity is a key criterion to determine how the pruning process has been done and measure what result has been derived. However, there is few formal definitions of diversity yet. Hence, three important factors that should......-climbing search, compared with other definitions of diversity and other criteria....

  8. Online transfer learning with extreme learning machine

    Science.gov (United States)

    Yin, Haibo; Yang, Yun-an

    2017-05-01

    In this paper, we propose a new transfer learning algorithm for online training. The proposed algorithm, which is called Online Transfer Extreme Learning Machine (OTELM), is based on Online Sequential Extreme Learning Machine (OSELM) while it introduces Semi-Supervised Extreme Learning Machine (SSELM) to transfer knowledge from the source to the target domain. With the manifold regularization, SSELM picks out instances from the source domain that are less relevant to those in the target domain to initialize the online training, so as to improve the classification performance. Experimental results demonstrate that the proposed OTELM can effectively use instances in the source domain to enhance the learning performance.

  9. An empirical solution for over-pruning with a novel ensemble-learning method for fMRI decoding.

    Science.gov (United States)

    Hirose, Satoshi; Nambu, Isao; Naito, Eiichi

    2015-01-15

    Recent functional magnetic resonance imaging (fMRI) decoding techniques allow us to predict the contents of sensory and motor events or participants' mental states from multi-voxel patterns of fMRI signals. Sparse logistic regression (SLR) is a useful pattern classification algorithm that has the advantage of being able to automatically select voxels to avoid over-fitting. However, SLR suffers from over-pruning, in which many voxels that are potentially useful for prediction are discarded. We propose an ensemble solution for over-pruning, called "Iterative Recycling" (iRec), in which sparse classifiers are trained iteratively by recycling over-pruned voxels. Our simulation demonstrates that iRec can effectively rectify over-pruning in SLR and improve its classification accuracy. We also conduct an fMRI experiment in which eight healthy volunteers perform a finger-tapping task with their index or middle fingers. The results indicate that SLR with iRec (iSLR) can predict the finger used more accurately than SLR. Further, iSLR is able to identify a voxel cluster representing the finger movements in the biologically plausible contralateral primary sensory-motor cortices in each participant. We also successfully dissociated the regularly arranged representation for each finger in the cluster. To the best of our knowledge, ours is the first study to propose a solution for over-pruning with ensemble-learning that is applicable to any sparse algorithm. In addition, from the viewpoint of machine learning, we provide the novel idea of using the sparse classification algorithm to generate accurate divergent base classifiers. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

  10. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.

    Science.gov (United States)

    Vuković, Najdan; Miljković, Zoran

    2013-10-01

    Radial basis function (RBF) neural network is constructed of certain number of RBF neurons, and these networks are among the most used neural networks for modeling of various nonlinear problems in engineering. Conventional RBF neuron is usually based on Gaussian type of activation function with single width for each activation function. This feature restricts neuron performance for modeling the complex nonlinear problems. To accommodate limitation of a single scale, this paper presents neural network with similar but yet different activation function-hyper basis function (HBF). The HBF allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The HBF is based on generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. Compared to the RBF, the HBF neuron has more parameters to optimize, but HBF neural network needs less number of HBF neurons to memorize relationship between input and output sets in order to achieve good generalization property. However, recent research results of HBF neural network performance have shown that optimal way of constructing this type of neural network is needed; this paper addresses this issue and modifies sequential learning algorithm for HBF neural network that exploits the concept of neuron's significance and allows growing and pruning of HBF neuron during learning process. Extensive experimental study shows that HBF neural network, trained with developed learning algorithm, achieves lower prediction error and more compact neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. An Incremental Type-2 Meta-Cognitive Extreme Learning Machine.

    Science.gov (United States)

    Pratama, Mahardhika; Zhang, Guangquan; Er, Meng Joo; Anavatti, Sreenatha

    2017-02-01

    Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

  12. Sparse Bayesian extreme learning machine for multi-classification.

    Science.gov (United States)

    Luo, Jiahua; Vong, Chi-Man; Wong, Pak-Kin

    2014-04-01

    Extreme learning machine (ELM) has become a popular topic in machine learning in recent years. ELM is a new kind of single-hidden layer feedforward neural network with an extremely low computational cost. ELM, however, has two evident drawbacks: 1) the output weights solved by Moore-Penrose generalized inverse is a least squares minimization issue, which easily suffers from overfitting and 2) the accuracy of ELM is drastically sensitive to the number of hidden neurons so that a large model is usually generated. This brief presents a sparse Bayesian approach for learning the output weights of ELM in classification. The new model, called Sparse Bayesian ELM (SBELM), can resolve these two drawbacks by estimating the marginal likelihood of network outputs and automatically pruning most of the redundant hidden neurons during learning phase, which results in an accurate and compact model. The proposed SBELM is evaluated on wide types of benchmark classification problems, which verifies that the accuracy of SBELM model is relatively insensitive to the number of hidden neurons; and hence a much more compact model is always produced as compared with other state-of-the-art neural network classifiers.

  13. Graph Embedded Extreme Learning Machine.

    Science.gov (United States)

    Iosifidis, Alexandros; Tefas, Anastasios; Pitas, Ioannis

    2016-01-01

    In this paper, we propose a novel extension of the extreme learning machine (ELM) algorithm for single-hidden layer feedforward neural network training that is able to incorporate subspace learning (SL) criteria on the optimization process followed for the calculation of the network's output weights. The proposed graph embedded ELM (GEELM) algorithm is able to naturally exploit both intrinsic and penalty SL criteria that have been (or will be) designed under the graph embedding framework. In addition, we extend the proposed GEELM algorithm in order to be able to exploit SL criteria in arbitrary (even infinite) dimensional ELM spaces. We evaluate the proposed approach on eight standard classification problems and nine publicly available datasets designed for three problems related to human behavior analysis, i.e., the recognition of human face, facial expression, and activity. Experimental results denote the effectiveness of the proposed approach, since it outperforms other ELM-based classification schemes in all the cases.

  14. BELM: Bayesian extreme learning machine.

    Science.gov (United States)

    Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J

    2011-03-01

    The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.

  15. Extreme Learning Machine for land cover classification

    OpenAIRE

    Pal, Mahesh

    2008-01-01

    This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution. ETM+ multispectral data set (England) was used to judge the suitability of extreme learning machine for remote sensing classifications...

  16. Extreme learning machines 2013 algorithms and applications

    CERN Document Server

    Toh, Kar-Ann; Romay, Manuel; Mao, Kezhi

    2014-01-01

    In recent years, ELM has emerged as a revolutionary technique of computational intelligence, and has attracted considerable attentions. An extreme learning machine (ELM) is a single layer feed-forward neural network alike learning system, whose connections from the input layer to the hidden layer are randomly generated, while the connections from the hidden layer to the output layer are learned through linear learning methods. The outstanding merits of extreme learning machine (ELM) are its fast learning speed, trivial human intervene and high scalability.   This book contains some selected papers from the International Conference on Extreme Learning Machine 2013, which was held in Beijing China, October 15-17, 2013. This conference aims to bring together the researchers and practitioners of extreme learning machine from a variety of fields including artificial intelligence, biomedical engineering and bioinformatics, system modelling and control, and signal and image processing, to promote research and discu...

  17. International Conference on Extreme Learning Machine 2015

    CERN Document Server

    Mao, Kezhi; Wu, Jonathan; Lendasse, Amaury; ELM 2015; Theory, Algorithms and Applications (I); Theory, Algorithms and Applications (II)

    2016-01-01

    This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. .

  18. International Conference on Extreme Learning Machines 2014

    CERN Document Server

    Mao, Kezhi; Cambria, Erik; Man, Zhihong; Toh, Kar-Ann

    2015-01-01

    This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”.  The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.  

  19. Two-Dimensional Extreme Learning Machine

    OpenAIRE

    Bo Jia; Dong Li; Zhisong Pan; Guyu Hu

    2015-01-01

    Extreme learning machine (ELM) has achieved wide attention due to faster learning speed compared with conventional neural network models like support vector machine (SVM) and back-propagation (BP) networks. However, like many other methods, ELM is originally proposed to handle vector pattern while nonvector patterns in real applications need to be explored, such as image data. We propose the two-dimensional extreme learning machine (2DELM) based on the very natural idea to deal with matrix...

  20. Parsimonious kernel extreme learning machine in primal via Cholesky factorization.

    Science.gov (United States)

    Zhao, Yong-Ping

    2016-08-01

    Recently, extreme learning machine (ELM) has become a popular topic in machine learning community. By replacing the so-called ELM feature mappings with the nonlinear mappings induced by kernel functions, two kernel ELMs, i.e., P-KELM and D-KELM, are obtained from primal and dual perspectives, respectively. Unfortunately, both P-KELM and D-KELM possess the dense solutions in direct proportion to the number of training data. To this end, a constructive algorithm for P-KELM (CCP-KELM) is first proposed by virtue of Cholesky factorization, in which the training data incurring the largest reductions on the objective function are recruited as significant vectors. To reduce its training cost further, PCCP-KELM is then obtained with the application of a probabilistic speedup scheme into CCP-KELM. Corresponding to CCP-KELM, a destructive P-KELM (CDP-KELM) is presented using a partial Cholesky factorization strategy, where the training data incurring the smallest reductions on the objective function after their removals are pruned from the current set of significant vectors. Finally, to verify the efficacy and feasibility of the proposed algorithms in this paper, experiments on both small and large benchmark data sets are investigated. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. PP prune users guide.

    Science.gov (United States)

    N.A. Bolon; R.D. Fight; J.M. Cahill

    1992-01-01

    The PP PRUNE program allows users to conduct a financial analysis of pruning ponderosa pine (Pinus ponderosa Dougl. ex Laws.). The increase in product value and rate of return from pruning the butt 16.5-foot log can be estimated. Lumber recovery information is based on actual mill experience with pruned and unpruned logs. Users supply lumber prices...

  2. Protein sequence classification with improved extreme learning machine algorithms.

    Science.gov (United States)

    Cao, Jiuwen; Xiong, Lianglin

    2014-01-01

    Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

  3. How to Prune Trees

    Science.gov (United States)

    Peter Bedker; Joseph O' Brien; Manfred Mielke

    2012-01-01

    The objective of pruning is to produce strong, healthy, attractive plants. By understanding how, when and why to prune, and by following a few simple principles, this objective can be achievedHow to Prune Trees (Revised 2012) Agency Publisher: Agriculture Dept., Forest Service, Northeastern Area State and Price forestry USA List Price:$4.00 Sale...

  4. Parsimonious Wavelet Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wang Qin

    2015-11-01

    Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

  5. Extreme Learning Machines for spatial environmental data

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2015-12-01

    The use of machine learning algorithms has increased in a wide variety of domains (from finance to biocomputing and astronomy), and nowadays has a significant impact on the geoscience community. In most real cases geoscience data modelling problems are multivariate, high dimensional, variable at several spatial scales, and are generated by non-linear processes. For such complex data, the spatial prediction of continuous (or categorical) variables is a challenging task. The aim of this paper is to investigate the potential of the recently developed Extreme Learning Machine (ELM) for environmental data analysis, modelling and spatial prediction purposes. An important contribution of this study deals with an application of a generic self-consistent methodology for environmental data driven modelling based on Extreme Learning Machine. Both real and simulated data are used to demonstrate applicability of ELM at different stages of the study to understand and justify the results.

  6. Dimension Reduction with Extreme Learning Machine.

    Science.gov (United States)

    Liyanaarachchi Lekamalage, Chamara; Yang, Yan; Huang, Guang-Bin; Zhang, Zhengyou

    2016-05-18

    Data may often contain noise or irrelevant information which negatively affect the generalization capability of machine learning algorithms. The objective of dimension reduction algorithms such as Principal Component Analysis (PCA), Non-negative Matrix Factorization (NMF), random projection (RP) and auto-encoder (AE) is to reduce the noise or irrelevant information of the data. The features of PCA (eigenvectors) and linear AE is not able to represent data as parts (e.g. nose in a face image); On the other hand, NMF and non-linear AE is maimed by slow learning speed and RP only represents a subspace of original data. This paper introduces a dimension reduction framework which to some extend represents data as parts, has fast learning speed and learns the between-class scatter subspace. To this end, this paper investigates a linear and nonlinear dimension reduction framework referred to as Extreme Learning Machine Auto-Encoder (ELM-AE) and Sparse Extreme Learning Machine Auto-Encoder (SELM-AE). In contrast to tied weight auto-encoder (TAE), the hidden neurons in ELMAE and SELM-AE need not be tuned, their parameters (e.g, input weights in additive neurons) are initialized using orthogonal and sparse random weights respectively. Experimental results on USPS handwritten digit recognition dataset, CIFAR-10 object recognition and NORB object recognition data set show the efficacy of linear and non-linear ELM-AE and SELM-AE in terms of discriminative capability, sparsity, training time and Normalized Mean Square Error (NMSE).

  7. Deep Extreme Learning Machine and Its Application in EEG Classification

    OpenAIRE

    Shifei Ding; Nan Zhang; Xinzheng Xu; Lili Guo; Jian Zhang

    2015-01-01

    Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM appr...

  8. Regularized minimum class variance extreme learning machine for language recognition

    National Research Council Canada - National Science Library

    Xu, Jiaming; Zhang, Wei-Qiang; Liu, Jia; Xia, Shanhong

    2015-01-01

    .... The recently developed extreme learning machine (ELM) tends to have better scalability and achieve similar or much better generalization performance at much faster learning speed than traditional SVM...

  9. Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning.

    Science.gov (United States)

    Yang, Yimin; Wu, Q M Jonathan

    2016-11-01

    The extreme learning machine (ELM), which was originally proposed for "generalized" single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

  10. Discriminative clustering via extreme learning machine.

    Science.gov (United States)

    Huang, Gao; Liu, Tianchi; Yang, Yan; Lin, Zhiping; Song, Shiji; Wu, Cheng

    2015-10-01

    Discriminative clustering is an unsupervised learning framework which introduces the discriminative learning rule of supervised classification into clustering. The underlying assumption is that a good partition (clustering) of the data should yield high discrimination, namely, the partitioned data can be easily classified by some classification algorithms. In this paper, we propose three discriminative clustering approaches based on Extreme Learning Machine (ELM). The first algorithm iteratively trains weighted ELM (W-ELM) classifier to gradually maximize the data discrimination. The second and third methods are both built on Fisher's Linear Discriminant Analysis (LDA); but one approach adopts alternative optimization, while the other leverages kernel k-means. We show that the proposed algorithms can be easily implemented, and yield competitive clustering accuracy on real world data sets compared to state-of-the-art clustering methods. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. Extreme Learning Machine for Multilayer Perceptron.

    Science.gov (United States)

    Tang, Jiexiong; Deng, Chenwei; Huang, Guang-Bin

    2016-04-01

    Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via l1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

  12. Pruning Allegheny hardwoods

    Science.gov (United States)

    W. D. Zeedyk; A. F. Hough

    1958-01-01

    The continuing heavy demand for high-quality Allegheny hardwoods, particularly black cherry and sugar maple, impresses on us the need for more information responses of hardwoods to pruning. Pruning may have beneficial effects: it may increase quality without sacrificing growth. Or it may have detrimental effects: it may cause dieback of cambium, decay, staining and...

  13. Robust Matching Pursuit Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Zejian Yuan

    2018-01-01

    Full Text Available Extreme learning machine (ELM is a popular learning algorithm for single hidden layer feedforward networks (SLFNs. It was originally proposed with the inspiration from biological learning and has attracted massive attentions due to its adaptability to various tasks with a fast learning ability and efficient computation cost. As an effective sparse representation method, orthogonal matching pursuit (OMP method can be embedded into ELM to overcome the singularity problem and improve the stability. Usually OMP recovers a sparse vector by minimizing a least squares (LS loss, which is efficient for Gaussian distributed data, but may suffer performance deterioration in presence of non-Gaussian data. To address this problem, a robust matching pursuit method based on a novel kernel risk-sensitive loss (in short KRSLMP is first proposed in this paper. The KRSLMP is then applied to ELM to solve the sparse output weight vector, and the new method named the KRSLMP-ELM is developed for SLFN learning. Experimental results on synthetic and real-world data sets confirm the effectiveness and superiority of the proposed method.

  14. Hierarchical extreme learning machine based reinforcement learning for goal localization

    Science.gov (United States)

    AlDahoul, Nouar; Zaw Htike, Zaw; Akmeliawati, Rini

    2017-03-01

    The objective of goal localization is to find the location of goals in noisy environments. Simple actions are performed to move the agent towards the goal. The goal detector should be capable of minimizing the error between the predicted locations and the true ones. Few regions need to be processed by the agent to reduce the computational effort and increase the speed of convergence. In this paper, reinforcement learning (RL) method was utilized to find optimal series of actions to localize the goal region. The visual data, a set of images, is high dimensional unstructured data and needs to be represented efficiently to get a robust detector. Different deep Reinforcement models have already been used to localize a goal but most of them take long time to learn the model. This long learning time results from the weights fine tuning stage that is applied iteratively to find an accurate model. Hierarchical Extreme Learning Machine (H-ELM) was used as a fast deep model that doesn’t fine tune the weights. In other words, hidden weights are generated randomly and output weights are calculated analytically. H-ELM algorithm was used in this work to find good features for effective representation. This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB.

  15. Automated Flare Prediction Using Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Yuqing Bian

    2013-01-01

    Full Text Available Extreme learning machine (ELM is a fast learning algorithm of single-hidden layer feedforward neural networks (SLFNs. Compared with the traditional neural networks, the ELM algorithm has the advantages of fast learning speed and good generalization. At the same time, an ordinal logistic regression (LR is a statistical method which is conceptually simple and algorithmically fast. In this paper, in order to improve the real-time performance, a flare forecasting method is introduced which is the combination of the LR model and the ELM algorithm. The predictive variables are three photospheric magnetic parameters, that is, the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The LR model is used to map these three magnetic parameters of each active region into four probabilities. Consequently, the ELM is used to map the four probabilities into a binary label which is the final output. The proposed model is used to predict the occurrence of flares with a certain level over 24 hours following the time when the magnetogram is recorded. The experimental results show that the cascade algorithm not only improves learning speed to realize timely prediction but also has higher accuracy of X-class flare prediction in comparison with other methods.

  16. Online Sequential Extreme Learning Machine With Kernels.

    Science.gov (United States)

    Scardapane, Simone; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio

    2015-09-01

    The extreme learning machine (ELM) was recently proposed as a unifying framework for different families of learning algorithms. The classical ELM model consists of a linear combination of a fixed number of nonlinear expansions of the input vector. Learning in ELM is hence equivalent to finding the optimal weights that minimize the error on a dataset. The update works in batch mode, either with explicit feature mappings or with implicit mappings defined by kernels. Although an online version has been proposed for the former, no work has been done up to this point for the latter, and whether an efficient learning algorithm for online kernel-based ELM exists remains an open problem. By explicating some connections between nonlinear adaptive filtering and ELM theory, in this brief, we present an algorithm for this task. In particular, we propose a straightforward extension of the well-known kernel recursive least-squares, belonging to the kernel adaptive filtering (KAF) family, to the ELM framework. We call the resulting algorithm the kernel online sequential ELM (KOS-ELM). Moreover, we consider two different criteria used in the KAF field to obtain sparse filters and extend them to our context. We show that KOS-ELM, with their integration, can result in a highly efficient algorithm, both in terms of obtained generalization error and training time. Empirical evaluations demonstrate interesting results on some benchmarking datasets.

  17. Deep Extreme Learning Machine and Its Application in EEG Classification

    Directory of Open Access Journals (Sweden)

    Shifei Ding

    2015-01-01

    Full Text Available Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM put forward deep extreme learning machine (DELM and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

  18. Prune belly syndrome

    Science.gov (United States)

    The causes of prune belly syndrome are unknown. The condition affects mostly boys. While in the womb, the developing baby's abdomen swells with fluid. Often, the cause is a problem in the urinary tract. The fluid disappears after birth, leading ...

  19. Evolutionary Cost-Sensitive Extreme Learning Machine.

    Science.gov (United States)

    Zhang, Lei; Zhang, David

    2017-12-01

    Conventional extreme learning machines (ELMs) solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition-based access control system, where misclassifying a stranger as a family member may result in more serious disaster than misclassifying a family member as a stranger. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another, in many realistic cases, the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive ELM, with the following merits: 1) to the best of our knowledge, it is the first proposal of ELM in evolutionary cost-sensitive classification scenario; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; and 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Experiments in a variety of cost-sensitive tasks well demonstrate the effectiveness of the proposed approaches, with about 5%-10% improvements.

  20. Distributed Extreme Learning Machine for Nonlinear Learning over Network

    Directory of Open Access Journals (Sweden)

    Songyan Huang

    2015-02-01

    Full Text Available Distributed data collection and analysis over a network are ubiquitous, especially over a wireless sensor network (WSN. To our knowledge, the data model used in most of the distributed algorithms is linear. However, in real applications, the linearity of systems is not always guaranteed. In nonlinear cases, the single hidden layer feedforward neural network (SLFN with radial basis function (RBF hidden neurons has the ability to approximate any continuous functions and, thus, may be used as the nonlinear learning system. However, confined by the communication cost, using the distributed version of the conventional algorithms to train the neural network directly is usually prohibited. Fortunately, based on the theorems provided in the extreme learning machine (ELM literature, we only need to compute the output weights of the SLFN. Computing the output weights itself is a linear learning problem, although the input-output mapping of the overall SLFN is still nonlinear. Using the distributed algorithmto cooperatively compute the output weights of the SLFN, we obtain a distributed extreme learning machine (dELM for nonlinear learning in this paper. This dELM is applied to the regression problem and classification problem to demonstrate its effectiveness and advantages.

  1. A Fast Reduced Kernel Extreme Learning Machine.

    Science.gov (United States)

    Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

    2016-04-01

    In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. 7 CFR 993.7 - French prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false French prunes. 993.7 Section 993.7 Agriculture... Order Regulating Handling Definitions § 993.7 French prunes. French prunes means: (a) Prunes produced from plums of the following varieties of plums: French (Prune d'Agen, Petite Prune d'Agen), Coates (Cox...

  3. Water demand forecasting using extreme learning machines

    Directory of Open Access Journals (Sweden)

    Tiwari Mukesh

    2016-03-01

    Full Text Available The capacity of recently-developed extreme learning machine (ELM modelling approaches in forecasting daily urban water demand from limited data, alone or in concert with wavelet analysis (W or bootstrap (B methods (i.e., ELM, ELMW, ELMB, was assessed, and compared to that of equivalent traditional artificial neural network-based models (i.e., ANN, ANNW, ANNB. The urban water demand forecasting models were developed using 3-year water demand and climate datasets for the city of Calgary, Alberta, Canada. While the hybrid ELMB and ANNB models provided satisfactory 1-day lead-time forecasts of similar accuracy, the ANNW and ELMW models provided greater accuracy, with the ELMW model outperforming the ANNW model. Significant improvement in peak urban water demand prediction was only achieved with the ELMW model. The superiority of the ELMW model over both the ANNW or ANNB models demonstrated the significant role of wavelet transformation in improving the overall performance of the urban water demand model.

  4. Training and Pruning Apple Trees

    OpenAIRE

    Marini, Richard P. (Richard Paul), 1952-

    2009-01-01

    Discusses the pruning and training of apple trees, placing emphasis on proper training of young trees to save time and the expense of future pruning, and to produce earlier profitable crops. Advises about the best techniques for pruning in relation to age of the apple tree.

  5. Coast redwood responses to pruning

    Science.gov (United States)

    Kevin L. O' Hara

    2012-01-01

    A large-scale pruning study was established in the winter of 1999 to 2000 at seven different sites on Green Diamond Resource Company forestlands in Humboldt County. The objective of this study was to determine the effects of pruning on increment, epicormic sprouting, stem taper, heartwood formation, and bear damage on these young trees. Pruning treatments varied...

  6. Hydrological data assimilation using Extreme Learning Machines

    Science.gov (United States)

    Boucher, Marie-Amélie; Quilty, John; Adamowski, Jan

    2017-04-01

    Data assimilation refers to any process that allows for updating state variables in a model to represent reality more accurately than the initial (open loop) simulation. In hydrology, data assimilation is often a pre-requisite for forecasting. In practice, many operational agencies rely on "manual" data assimilation: perturbations are added manually to meteorological inputs or directly to state variables based on "expert knowledge" until the simulated streamflow matches the observed streamflow closely. The corrected state variables are then considered as representative of the "true", unknown, state of the watershed just before the forecasting period. However, manual data assimilation raises concerns, mainly regarding reproducibility and high reliance on "expert knowledge". For those reasons, automatic data assimilation methods have been proposed in the literature. Automatic data assimilation also allows for the assessment and reduction of state variable uncertainty, which is predominant for short-term streamflow forecasts (e.g. Thiboult et al. 2016). The goal of this project is to explore the potential of Extreme Learning Machines (ELM, Zang and Liu 2015) for data assimilation. ELMs are an emerging type of neural network that does not require iterative optimisation of their weights and biases and therefore are much faster to calibrate than typical feed-forward backpropagation neural networks. We explore ELM for updating state variables of the lumped conceptual hydrological model GR4J. The GR4J model has two state variables: the level of water in the production and routing reservoirs. Although these two variables are sufficient to describe the state of a snow-free watershed, they are modelling artifices that are not measurable. Consequently, their "true" values can only be verified indirectly through a comparison of simulated and observed streamflow and their values are highly uncertain. GR4J can also be coupled with the snow model CemaNeige, which adds two other

  7. Prune Belly Syndrome

    African Journals Online (AJOL)

    User

    a rare case and review of literature. Europe- an Journal of Plastic Surgery 35, 241-243. Moerman P., Fryns J.P., Goddeeris P. and Lau- weryns J.M. (1984) Pathogenesis of the. Prune-Belly Syndrome: A Functional Ure- thral Obstruction Caused by Prostatic Hy- poplasia. PEDIATRICS 73, 470-475. Okeniyi J.A., Ogunlesi T.A, ...

  8. Efficient Pruning Method for Ensemble Self-Generating Neural Networks

    Directory of Open Access Journals (Sweden)

    Hirotaka Inoue

    2003-12-01

    Full Text Available Recently, multiple classifier systems (MCS have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.

  9. Extreme Kernel Sparse Learning for Tactile Object Recognition.

    Science.gov (United States)

    Liu, Huaping; Qin, Jie; Sun, Fuchun; Guo, Di

    2016-10-19

    Tactile sensors play very important role for robot perception in the dynamic or unknown environment. However, the tactile object recognition exhibits great challenges in practical scenarios. In this paper, we address this problem by developing an extreme kernel sparse learning methodology. This method combines the advantages of extreme learning machine and kernel sparse learning by simultaneously addressing the dictionary learning and the classifier design problems. Furthermore, to tackle the intrinsic difficulties which are introduced by the representer theorem, we develop a reduced kernel dictionary learning method by introducing row-sparsity constraint. A globally convergent algorithm is developed to solve the optimization problem and the theoretical proof is provided. Finally, we perform extensive experimental validations on some public available tactile sequence datasets and show the advantages of the proposed method.

  10. Learning problems in kindergarten students with extremely preterm birth.

    Science.gov (United States)

    Taylor, H Gerry; Klein, Nancy; Anselmo, Marcia G; Minich, Nori; Espy, Kimberly A; Hack, Maureen

    2011-09-01

    To assess learning problems among kindergarten students with extremely preterm birth and to identify risk factors. Cohort study. Children's hospital. A cohort of 148 children born between January 1, 2001, and December 31, 2003, with extremely preterm birth, defined as less than 28 weeks' gestation or having a birth weight of less than 1000 g, and 111 classmate control individuals born at term with normal birth weight. The children were enrolled in the study during their first year in kindergarten and were assessed on measures of learning progress. Achievement testing, teacher ratings of learning progress, and individual educational assistance. Children with extremely preterm birth had lower mean standard scores than controls on achievement tests of spelling (8.52; 95% confidence interval, 4.58-12.46) and applied mathematics (11.02; 6.76-15.28). They had higher rates of substandard learning progress by teacher report in written language (odds ratio, 4.23; 95% CI, 2.32-7.73) and mathematics (7.08; 2.79-17.95). Group differences in mathematics achievement and in teacher ratings of learning progress were statistically significant even in children without neurosensory deficits or low global cognitive ability. Neonatal risk factors, early childhood neurodevelopmental impairment, and socioeconomic status predicted learning problems in children with extremely preterm birth; however, many children with problems were not enrolled in a special education program. Learning problems in children with extremely preterm birth are evident in kindergarten and are associated with neonatal and early childhood risk factors. Our findings support efforts to provide more extensive monitoring and interventions before and during the first year of school.

  11. Classification and Localization of Extreme Weather Patterns with Deep Learning

    Science.gov (United States)

    Prabhat, M.; Liu, Y.; Racah, E.; Kunkel, K.; Lavers, D. A.; Wehner, M. F.; Collins, W. D.

    2016-12-01

    Extreme weather events pose great potential risk on ecosystem, infrastructure and human health. Analyzing extreme weather in the observed record (satellite, reanalysis products) and characterizing changes in extremes in simulations of future climate regimes is an important task. Thus far, extreme weather events have been typically specified by the community through hand-coded, multi-variate threshold conditions. Such criteria are usually subjective, and often there is no agreement in the community on the specific algorithm that should be used. We propose a completely different approach: machine learning to solve this problem. If human experts can provide spatio-temporal patches of a climate dataset, and associated labels, we can turn to a machine learning system to learn the underlying feature representation. The `trained' ML system can then be applied to novel datasets, thereby automating the pattern detection step. Summary statistics, such as location, intensity and frequency of such events can be easily computed as a post-process. This talk will touch upon Deep Learning: the most powerful machine learning method at this point in time. We will report compelling results from the successful application of Deep Learning to classify tropical cyclones, atmospheric rivers and weather front events. For all of these events, we observe 90-99% classification accuracy by the Deep Learning system. We will also report on progress in localizing such events: namely drawing a bounding box (of the correct size and scale) around the weather pattern of interest. Both tasks currently utilize multi-layer convolutional networks in conjunction with hyper-parameter optimization. We utilize HPC systems at NERSC to perform the optimization across multiple nodes, and utilize highly-tuned libraries to utilize multiple cores on a single node. We will conclude with thoughts on the frontier of Deep Learning: can we train networks in a semi-supervised, or completely unsupervised manner?

  12. Aerial pruning mechanism, initial real environment test.

    Science.gov (United States)

    Molina, Javier; Hirai, Shinichi

    2017-01-01

    In this research, a pruning mechanism for aerial pruning tasks is tested in a real environment. Since the final goal of the aerial pruning robot will be to prune tree branches close to power lines, some experiments related to wireless communication and pruning performance were conducted. The experiments consisted of testing the communication between two XBee RF modules for monitoring purposes as well as testing the speed control of the circular saw used for pruning tree branches. Results show that both the monitoring and the pruning tasks were successfully done in a real environment.

  13. One-Class Classification with Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Qian Leng

    2015-01-01

    Full Text Available One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM. The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

  14. Regularization with a pruning prior

    DEFF Research Database (Denmark)

    Goutte, Cyril; Hansen, Lars Kai

    1997-01-01

    We investigate the use of a regularization priorthat we show has pruning properties. Analyses areconducted both using a Bayesian framework and withthe generalization method, on a simple toyproblem. Results are thoroughly compared withthose obtained with a traditional weight decay.......We investigate the use of a regularization priorthat we show has pruning properties. Analyses areconducted both using a Bayesian framework and withthe generalization method, on a simple toyproblem. Results are thoroughly compared withthose obtained with a traditional weight decay....

  15. Building Energy Consumption Prediction: An Extreme Deep Learning Approach

    Directory of Open Access Journals (Sweden)

    Chengdong Li

    2017-10-01

    Full Text Available Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs with the extreme learning machine (ELM to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN, support vector regression (SVR, the generalized radial basis function neural network (GRBFNN and multiple linear regression (MLR. Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption.

  16. DANNP: an efficient artificial neural network pruning tool

    Directory of Open Access Journals (Sweden)

    Mona Alshahrani

    2017-11-01

    Full Text Available Background Artificial neural networks (ANNs are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge

  17. DANNP: an efficient artificial neural network pruning tool

    KAUST Repository

    Alshahrani, Mona

    2017-11-06

    Background Artificial neural networks (ANNs) are a robust class of machine learning models and are a frequent choice for solving classification problems. However, determining the structure of the ANNs is not trivial as a large number of weights (connection links) may lead to overfitting the training data. Although several ANN pruning algorithms have been proposed for the simplification of ANNs, these algorithms are not able to efficiently cope with intricate ANN structures required for complex classification problems. Methods We developed DANNP, a web-based tool, that implements parallelized versions of several ANN pruning algorithms. The DANNP tool uses a modified version of the Fast Compressed Neural Network software implemented in C++ to considerably enhance the running time of the ANN pruning algorithms we implemented. In addition to the performance evaluation of the pruned ANNs, we systematically compared the set of features that remained in the pruned ANN with those obtained by different state-of-the-art feature selection (FS) methods. Results Although the ANN pruning algorithms are not entirely parallelizable, DANNP was able to speed up the ANN pruning up to eight times on a 32-core machine, compared to the serial implementations. To assess the impact of the ANN pruning by DANNP tool, we used 16 datasets from different domains. In eight out of the 16 datasets, DANNP significantly reduced the number of weights by 70%–99%, while maintaining a competitive or better model performance compared to the unpruned ANN. Finally, we used a naïve Bayes classifier derived with the features selected as a byproduct of the ANN pruning and demonstrated that its accuracy is comparable to those obtained by the classifiers trained with the features selected by several state-of-the-art FS methods. The FS ranking methodology proposed in this study allows the users to identify the most discriminant features of the problem at hand. To the best of our knowledge, DANNP (publicly

  18. Study of Environmental Data Complexity using Extreme Learning Machine

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2017-04-01

    The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

  19. Robust Extreme Learning Machine With its Application to Indoor Positioning.

    Science.gov (United States)

    Lu, Xiaoxuan; Zou, Han; Zhou, Hongming; Xie, Lihua; Huang, Guang-Bin

    2016-01-01

    The increasing demands of location-based services have spurred the rapid development of indoor positioning system and indoor localization system interchangeably (IPSs). However, the performance of IPSs suffers from noisy measurements. In this paper, two kinds of robust extreme learning machines (RELMs), corresponding to the close-to-mean constraint, and the small-residual constraint, have been proposed to address the issue of noisy measurements in IPSs. Based on whether the feature mapping in extreme learning machine is explicit, we respectively provide random-hidden-nodes and kernelized formulations of RELMs by second order cone programming. Furthermore, the computation of the covariance in feature space is discussed. Simulations and real-world indoor localization experiments are extensively carried out and the results demonstrate that the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviation and worst case error of IPSs compared with other baseline algorithms.

  20. Inverse-Free Extreme Learning Machine With Optimal Information Updating.

    Science.gov (United States)

    Li, Shuai; You, Zhu-Hong; Guo, Hongliang; Luo, Xin; Zhao, Zhong-Qiu

    2016-05-01

    The extreme learning machine (ELM) has drawn insensitive research attentions due to its effectiveness in solving many machine learning problems. However, the matrix inversion operation involved in the algorithm is computational prohibitive and limits the wide applications of ELM in many scenarios. To overcome this problem, in this paper, we propose an inverse-free ELM to incrementally increase the number of hidden nodes, and update the connection weights progressively and optimally. Theoretical analysis proves the monotonic decrease of the training error with the proposed updating procedure and also proves the optimality in every updating step. Extensive numerical experiments show the effectiveness and accuracy of the proposed algorithm.

  1. Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

    National Research Council Canada - National Science Library

    Junjie Lu; Jinquan Huang; Feng Lu

    2017-01-01

    A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper...

  2. Extremely Randomized Machine Learning Methods for Compound Activity Prediction.

    Science.gov (United States)

    Czarnecki, Wojciech M; Podlewska, Sabina; Bojarski, Andrzej J

    2015-11-09

    Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called 'extremely randomized methods'-Extreme Entropy Machine and Extremely Randomized Trees-for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their 'non-extreme' competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  3. Relative optical navigation around small bodies via Extreme Learning Machine

    Science.gov (United States)

    Law, Andrew M.

    To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

  4. Extremely Randomized Machine Learning Methods for Compound Activity Prediction

    Directory of Open Access Journals (Sweden)

    Wojciech M. Czarnecki

    2015-11-01

    Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

  5. Fault diagnosis of wind bearing based on multi-scale wavelet kernel extreme learning machine

    Science.gov (United States)

    Zhu, Siwen; Jiao, Bin

    2017-08-01

    The principle of kernel Extreme Learning Machine (ELM) is demonstrated. On this basis, a multi - scale wavelet kernel extreme learning machine is proposed. The multi-scale wavelet kernel is used as the kernel function of the extreme learning machine. The test shows that it is an achievable extreme learning machine. Experiments show that, using the multi-scale wavelet kernel extreme learning machine in the wind turbine bearing fault diagnosis has higher classification accuracy and speed than the support vector machine classification algorithm, and has excellent application value.

  6. Alumina Concentration Detection Based on the Kernel Extreme Learning Machine

    Science.gov (United States)

    Zhang, Tao; Yin, Yixin; Xiao, Wendong

    2017-01-01

    The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM. PMID:28862685

  7. Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

    Science.gov (United States)

    Zhang, Sen; Zhang, Tao; Yin, Yixin; Xiao, Wendong

    2017-09-01

    The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

  8. Financial analysis of pruning ponderosa pine.

    Science.gov (United States)

    Roger D. Fight; Natalie A. Bolon; James M. Cahill

    1992-01-01

    A recent lumber recovery study of pruned and unpruned ponderosa pine (Pinus ponderosa Dougl. ex Laws.) was used to project the financial return from pruning ponderosa pine in the Medford District of the Bureau of Land Management and in the Ochoco and Deschutes National Forests. The cost of pruning at which the investment would yield an expected 4-...

  9. 7 CFR 993.5 - Prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Prunes. 993.5 Section 993.5 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements and... Regulating Handling Definitions § 993.5 Prunes. Prunes means and includes all sun-dried or artificially...

  10. Semi-supervised and unsupervised extreme learning machines.

    Science.gov (United States)

    Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

    2014-12-01

    Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

  11. Development of a Grapevine Pruning Algorithm for Using in Pruning

    Directory of Open Access Journals (Sweden)

    S. M Hosseini

    2017-10-01

    Full Text Available Introduction Great areas of the orchards in the world are dedicated to cultivation of the grapevine. Normally grape vineyards are pruned twice a year. Among the operations of grape production, winter pruning of the bushes is the only operation that still has not been fully mechanized while it is known as the most laborious jobs in the farm. Some of the grape producing countries use various mechanical machines to prune the grapevines, but in most cases, these machines do not have a good performance. Therefore intelligent pruning machine seems to be necessary in this regard and this intelligent pruning machines can reduce the labor required to prune the vineyards. It this study in was attempted to develop an algorithm that uses image processing techniques to identify which parts of the grapevine should be cut. Stereo vision technique was used to obtain three dimensional images from the bare bushes whose leaves were fallen in autumn. Stereo vision systems are used to determine the depth from two images taken at the same time but from slightly different viewpoints using two cameras. Each pair of images of a common scene is related by a popular geometry, and corresponding points in the images pairs are constrained to lie on pairs of conjugate popular lines. Materials and Methods Photos were taken from gardens of the Research Center for Agriculture and Natural Resources of Fars province, Iran. At first, the distance between the plants and the cameras should be determined. The distance between the plants and cameras can be obtained by using the stereo vision techniques. Therefore, this method was used in this paper by two pictures taken from each plant with the left and right cameras. The algorithm was written in MATLAB. To facilitate the segmentation of the branches from the rows at the back, a blue plate with dimensions of 2×2 m2 were used at the background. After invoking the images, branches were segmented from the background to produce the binary

  12. Classifying BCI signals from novice users with extreme learning machine

    Directory of Open Access Journals (Sweden)

    Rodríguez-Bermúdez Germán

    2017-07-01

    Full Text Available Brain computer interface (BCI allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  13. Classifying BCI signals from novice users with extreme learning machine

    Science.gov (United States)

    Rodríguez-Bermúdez, Germán; Bueno-Crespo, Andrés; José Martinez-Albaladejo, F.

    2017-07-01

    Brain computer interface (BCI) allows to control external devices only with the electrical activity of the brain. In order to improve the system, several approaches have been proposed. However it is usual to test algorithms with standard BCI signals from experts users or from repositories available on Internet. In this work, extreme learning machine (ELM) has been tested with signals from 5 novel users to compare with standard classification algorithms. Experimental results show that ELM is a suitable method to classify electroencephalogram signals from novice users.

  14. Probabilistic forecasting of wind power generation using extreme learning machine

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Pinson, Pierre

    2014-01-01

    an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrapmethods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified...... with the best performance. Consequently, a new method for prediction intervals formulation based on theELMand the pairs bootstrap is developed.Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results...

  15. Epileptic seizure detection based on the kernel extreme learning machine.

    Science.gov (United States)

    Liu, Qi; Zhao, Xiaoguang; Hou, Zengguang; Liu, Hongguang

    2017-07-20

    This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis. After simple preprocessing, temporal- and wavelet-based features are extracted from epileptic EEG signals. A combined kernel-function-based ELM approach is then proposed for feature classification. To further reduce the computation, Cholesky decomposition is introduced during the process of calculating the output weights. The experimental results show that the proposed method can achieve satisfactory accuracy with less computation time.

  16. Extreme learning machine for ranking: generalization analysis and applications.

    Science.gov (United States)

    Chen, Hong; Peng, Jiangtao; Zhou, Yicong; Li, Luoqing; Pan, Zhibin

    2014-05-01

    The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Comparison between extreme learning machine and wavelet neural networks in data classification

    Science.gov (United States)

    Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2017-03-01

    Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

  18. Mapping of Estimations and Prediction Intervals Using Extreme Learning Machines

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2015-04-01

    Due to the large amount and complexity of data available nowadays in environmental sciences, we face the need to apply more robust methodology allowing analyses and understanding of the phenomena under study. One particular but very important aspect of this understanding is the reliability of generated prediction models. From the data collection to the prediction map, several sources of error can occur and affect the final result. Theses sources are mainly identified as uncertainty in data (data noise), and uncertainty in the model. Their combination leads to the so-called prediction interval. Quantifying these two categories of uncertainty allows a finer understanding of phenomena under study and a better assessment of the prediction accuracy. The present research deals with a methodology combining a machine learning algorithm (ELM - Extreme Learning Machine) with a bootstrap-based procedure. Developed by G.-B. Huang et al. (2006), ELM is an artificial neural network following the structure of a multilayer perceptron (MLP) with one single hidden layer. Compared to classical MLP, ELM has the ability to learn faster without loss of accuracy, and need only one hyper-parameter to be fitted (that is the number of nodes in the hidden layer). The key steps of the proposed method are as following: sample from the original data a variety of subsets using bootstrapping; from these subsets, train and validate ELM models; and compute residuals. Then, the same procedure is performed a second time with only the squared training residuals. Finally, taking into account the two modeling levels allows developing the mean prediction map, the model uncertainty variance, and the data noise variance. The proposed approach is illustrated using geospatial data. References Efron B., and Tibshirani R. 1986, Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical accuracy, Statistical Science, vol. 1: 54-75. Huang G.-B., Zhu Q.-Y., and Siew C.-K. 2006

  19. Bidirectional extreme learning machine for regression problem and its learning effectiveness.

    Science.gov (United States)

    Yang, Yimin; Wang, Yaonan; Yuan, Xiaofang

    2012-09-01

    It is clear that the learning effectiveness and learning speed of neural networks are in general far slower than required, which has been a major bottleneck for many applications. Recently, a simple and efficient learning method, referred to as extreme learning machine (ELM), was proposed by Huang , which has shown that, compared to some conventional methods, the training time of neural networks can be reduced by a thousand times. However, one of the open problems in ELM research is whether the number of hidden nodes can be further reduced without affecting learning effectiveness. This brief proposes a new learning algorithm, called bidirectional extreme learning machine (B-ELM), in which some hidden nodes are not randomly selected. In theory, this algorithm tends to reduce network output error to 0 at an extremely early learning stage. Furthermore, we find a relationship between the network output error and the network output weights in the proposed B-ELM. Simulation results demonstrate that the proposed method can be tens to hundreds of times faster than other incremental ELM algorithms.

  20. NMF-Based Image Quality Assessment Using Extreme Learning Machine.

    Science.gov (United States)

    Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

    2017-01-01

    Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

  1. Recursive least mean p-power Extreme Learning Machine.

    Science.gov (United States)

    Yang, Jing; Ye, Feng; Rong, Hai-Jun; Chen, Badong

    2017-07-01

    As real industrial processes have measurement samples with noises of different statistical characteristics and obtain the sample one by one usually, on-line sequential learning algorithms which can achieve better learning performance for systems with noises of various statistics are necessary. This paper proposes a new online Extreme Learning Machine (ELM, of Huang et al.) algorithm, namely recursive least mean p-power ELM (RLMP-ELM). In RLMP-ELM, a novel error criterion for cost function, namely the least mean p-power (LMP) error criterion, provides a mechanism to update the output weights sequentially. The LMP error criterion aims to minimize the mean p-power of the error that is the generalization of the mean square error criterion used in the ELM. The proposed on-line learning algorithm is able to provide on-line predictions of variables with noises of different statistics and obtains better performance than ELM and online sequential ELM (OS-ELM) while the non-Gaussian noises impact the processes. Simulations are reported to demonstrate the performance and effectiveness of the proposed methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

    Directory of Open Access Journals (Sweden)

    Xinran Zhou

    2014-01-01

    Full Text Available To apply the single hidden-layer feedforward neural networks (SLFN to identify time-varying system, online regularized extreme learning machine (ELM with forgetting mechanism (FORELM and online kernelized ELM with forgetting mechanism (FOKELM are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM and regularized online sequential ELM (ReOS-ELM; that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM under certain conditions.

  3. Multivariate Mapping of Environmental Data Using Extreme Learning Machines

    Science.gov (United States)

    Leuenberger, Michael; Kanevski, Mikhail

    2014-05-01

    In most real cases environmental data are multivariate, highly variable at several spatio-temporal scales, and are generated by nonlinear and complex phenomena. Mapping - spatial predictions of such data, is a challenging problem. Machine learning algorithms, being universal nonlinear tools, have demonstrated their efficiency in modelling of environmental spatial and space-time data (Kanevski et al. 2009). Recently, a new approach in machine learning - Extreme Learning Machine (ELM), has gained a great popularity. ELM is a fast and powerful approach being a part of the machine learning algorithm category. Developed by G.-B. Huang et al. (2006), it follows the structure of a multilayer perceptron (MLP) with one single-hidden layer feedforward neural networks (SLFNs). The learning step of classical artificial neural networks, like MLP, deals with the optimization of weights and biases by using gradient-based learning algorithm (e.g. back-propagation algorithm). Opposed to this optimization phase, which can fall into local minima, ELM generates randomly the weights between the input layer and the hidden layer and also the biases in the hidden layer. By this initialization, it optimizes just the weight vector between the hidden layer and the output layer in a single way. The main advantage of this algorithm is the speed of the learning step. In a theoretical context and by growing the number of hidden nodes, the algorithm can learn any set of training data with zero error. To avoid overfitting, cross-validation method or "true validation" (by randomly splitting data into training, validation and testing subsets) are recommended in order to find an optimal number of neurons. With its universal property and solid theoretical basis, ELM is a good machine learning algorithm which can push the field forward. The present research deals with an extension of ELM to multivariate output modelling and application of ELM to the real data case study - pollution of the sediments in

  4. Visual tracking based on extreme learning machine and sparse representation.

    Science.gov (United States)

    Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen

    2015-10-22

    The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.

  5. Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Qiong Li

    2017-01-01

    Full Text Available The morphology of wear particles reflects the complex properties of wear processes involved in particle formation. Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations. This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap. With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them. However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily. Recently, extreme learning machine (ELM for single-hidden-layer feedforward neural networks (SLFN has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms. In this paper, we propose to employ ELM for ferrography wear particles image recognition. We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier. Therefore, the novel ferrography wear particle classifier is founded based on ELM.

  6. Effective feature selection for image steganalysis using extreme learning machine

    Science.gov (United States)

    Feng, Guorui; Zhang, Haiyan; Zhang, Xinpeng

    2014-11-01

    Image steganography delivers secret data by slight modifications of the cover. To detect these data, steganalysis tries to create some features to embody the discrepancy between the cover and steganographic images. Therefore, the urgent problem is how to design an effective classification architecture for given feature vectors extracted from the images. We propose an approach to automatically select effective features based on the well-known JPEG steganographic methods. This approach, referred to as extreme learning machine revisited feature selection (ELM-RFS), can tune input weights in terms of the importance of input features. This idea is derived from cross-validation learning and one-dimensional (1-D) search. While updating input weights, we seek the energy decreasing direction using the leave-one-out (LOO) selection. Furthermore, we optimize the 1-D energy function instead of directly discarding the least significant feature. Since recent Liu features can gain considerable low detection errors compared to a previous JPEG steganalysis, the experimental results demonstrate that the new approach results in less classification error than other classifiers such as SVM, Kodovsky ensemble classifier, direct ELM-LOO learning, kernel ELM, and conventional ELM in Liu features. Furthermore, ELM-RFS achieves a similar performance with a deep Boltzmann machine using less training time.

  7. Unsupervised Feature Learning Classification With Radial Basis Function Extreme Learning Machine Using Graphic Processors.

    Science.gov (United States)

    Lam, Dao; Wunsch, Donald

    2017-01-01

    Ever-increasing size and complexity of data sets create challenges and potential tradeoffs of accuracy and speed in learning algorithms. This paper offers progress on both fronts. It presents a mechanism to train the unsupervised learning features learned from only one layer to improve performance in both speed and accuracy. The features are learned by an unsupervised feature learning (UFL) algorithm. Then, those features are trained by a fast radial basis function (RBF) extreme learning machine (ELM). By exploiting the massive parallel computing attribute of modern graphics processing unit, a customized compute unified device architecture (CUDA) kernel is developed to further speed up the computing of the RBF kernel in the ELM. Results tested on Canadian Institute for Advanced Research and Mixed National Institute of Standards and Technology data sets confirm the UFL RBF ELM achieves high accuracy, and the CUDA implementation is up to 20 times faster than CPU and the naive parallel approach.

  8. Is extreme learning machine feasible? A theoretical assessment (part II).

    Science.gov (United States)

    Lin, Shaobo; Liu, Xia; Fang, Jian; Xu, Zongben

    2015-01-01

    An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural network (FNN) learning system that randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons in the second stage. Therefore, ELM training is essentially a linear learning problem, which significantly reduces the computational burden. Numerous applications show that such a computation burden reduction does not degrade the generalization capability. It has, however, been open that whether this is true in theory. The aim of this paper is to study the theoretical feasibility of ELM by analyzing the pros and cons of ELM. In the previous part of this topic, we pointed out that via appropriately selected activation functions, ELM does not degrade the generalization capability in the sense of expectation. In this paper, we launch the study in a different direction and show that the randomness of ELM also leads to certain negative consequences. On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning. On the other hand, we theoretically justify that there also exist activation functions such that the corresponding ELM degrades the generalization capability. In particular, we prove that the generalization capability of ELM with Gaussian kernel is essentially worse than that of FNN with Gaussian kernel. To facilitate the use of ELM, we also provide a remedy to such a degradation. We find that the well-developed coefficient regularization technique can essentially improve the generalization capability. The obtained results reveal the essential characteristic of ELM in a certain sense and give theoretical guidance concerning how to use ELM.

  9. Extreme Learning Machine for the Predictions of Length of Day

    Science.gov (United States)

    Yu, Lei; Zhao, Danning; Cai, Hongbing

    2015-03-01

    This work presents short- and medium-term predictions of length of day (LOD) up to 500 days by means of extreme learning machine (ELM). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. The influences of the solid Earth and ocean tides and seasonal atmospheric variations are removed from the C04 series. The residuals are used for training of the ELM. The results of the prediction are compared with those from other prediction methods. The accuracy of the prediction is equal to or even better than that by other approaches. The most striking advantages of employing ELM instead of other algorithms are its noticeably reduced complexity and high computational efficiency.

  10. Calibration transfer via an extreme learning machine auto-encoder.

    Science.gov (United States)

    Chen, Wo-Ruo; Bin, Jun; Lu, Hong-Mei; Zhang, Zhi-Min; Liang, Yi-Zeng

    2016-03-21

    In order to solve the spectra standardization problem in near-infrared (NIR) spectroscopy, a Transfer via Extreme learning machine Auto-encoder Method (TEAM) has been proposed in this study. A comparative study among TEAM, piecewise direct standardization (PDS), generalized least squares (GLS) and calibration transfer methods based on canonical correlation analysis (CCA) was conducted, and the performances of these algorithms were benchmarked with three spectral datasets: corn, tobacco and pharmaceutical tablet spectra. The results show that TEAM is a stable method and can significantly reduce prediction errors compared with PDS, GLS and CCA. TEAM can also achieve the best RMSEPs in most cases with a small number of calibration sets. TEAM is implemented in Python language and available as an open source package at https://github.com/zmzhang/TEAM.

  11. Liver vessel segmentation based on extreme learning machine.

    Science.gov (United States)

    Zeng, Ye Zhan; Zhao, Yu Qian; Liao, Miao; Zou, Bei Ji; Wang, Xiao Fang; Wang, Wei

    2016-05-01

    Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity. Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  12. An Extreme Learning Machine Approach to Density Estimation Problems.

    Science.gov (United States)

    Cervellera, Cristiano; Maccio, Danilo

    2017-10-01

    In this paper, we discuss how the extreme learning machine (ELM) framework can be effectively employed in the unsupervised context of multivariate density estimation. In particular, two algorithms are introduced, one for the estimation of the cumulative distribution function underlying the observed data, and one for the estimation of the probability density function. The algorithms rely on the concept of F -discrepancy, which is closely related to the Kolmogorov-Smirnov criterion for goodness of fit. Both methods retain the key feature of the ELM of providing the solution through random assignment of the hidden feature map and a very light computational burden. A theoretical analysis is provided, discussing convergence under proper hypotheses on the chosen activation functions. Simulation tests show how ELMs can be successfully employed in the density estimation framework, as a possible alternative to other standard methods.

  13. Neural architecture design based on extreme learning machine.

    Science.gov (United States)

    Bueno-Crespo, Andrés; García-Laencina, Pedro J; Sancho-Gómez, José-Luis

    2013-12-01

    Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages. Copyright © 2013 Elsevier Ltd. All rights reserved.

  14. Hematocrit estimation using online sequential extreme learning machine.

    Science.gov (United States)

    Huynh, Hieu Trung; Won, Yonggwan; Kim, Jinsul

    2015-01-01

    Hematocrit is a blood test that is defined as the volume percentage of red blood cells in the whole blood. It is one of the important indicators for clinical decision making and the most effective factor in glucose measurement using handheld devices. In this paper, a method for hematocrit estimation that is based upon the transduced current curve and the neural network is presented. The salient points of this method are that (1) the neural network is trained by the online sequential extreme learning machine (OS-ELM) in which the devices can be still trained with new samples during the using process and (2) the extended features are used to reduce the number of current points which can save the battery power of devices and speed up the measurement process.

  15. Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Xinyi Yang

    2016-01-01

    Full Text Available A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine.

  16. Forecasting daily streamflow using online sequential extreme learning machines

    Science.gov (United States)

    Lima, Aranildo R.; Cannon, Alex J.; Hsieh, William W.

    2016-06-01

    While nonlinear machine methods have been widely used in environmental forecasting, in situations where new data arrive continually, the need to make frequent model updates can become cumbersome and computationally costly. To alleviate this problem, an online sequential learning algorithm for single hidden layer feedforward neural networks - the online sequential extreme learning machine (OSELM) - is automatically updated inexpensively as new data arrive (and the new data can then be discarded). OSELM was applied to forecast daily streamflow at two small watersheds in British Columbia, Canada, at lead times of 1-3 days. Predictors used were weather forecast data generated by the NOAA Global Ensemble Forecasting System (GEFS), and local hydro-meteorological observations. OSELM forecasts were tested with daily, monthly or yearly model updates. More frequent updating gave smaller forecast errors, including errors for data above the 90th percentile. Larger datasets used in the initial training of OSELM helped to find better parameters (number of hidden nodes) for the model, yielding better predictions. With the online sequential multiple linear regression (OSMLR) as benchmark, we concluded that OSELM is an attractive approach as it easily outperformed OSMLR in forecast accuracy.

  17. Parallel multiple instance learning for extremely large histopathology image analysis.

    Science.gov (United States)

    Xu, Yan; Li, Yeshu; Shen, Zhengyang; Wu, Ziwei; Gao, Teng; Fan, Yubo; Lai, Maode; Chang, Eric I-Chao

    2017-08-03

    Histopathology images are critical for medical diagnosis, e.g., cancer and its treatment. A standard histopathology slice can be easily scanned at a high resolution of, say, 200,000×200,000 pixels. These high resolution images can make most existing imaging processing tools infeasible or less effective when operated on a single machine with limited memory, disk space and computing power. In this paper, we propose an algorithm tackling this new emerging "big data" problem utilizing parallel computing on High-Performance-Computing (HPC) clusters. Experimental results on a large-scale data set (1318 images at a scale of 10 billion pixels each) demonstrate the efficiency and effectiveness of the proposed algorithm for low-latency real-time applications. The framework proposed an effective and efficient system for extremely large histopathology image analysis. It is based on the multiple instance learning formulation for weakly-supervised learning for image classification, segmentation and clustering. When a max-margin concept is adopted for different clusters, we obtain further improvement in clustering performance.

  18. Extreme learning machine and adaptive sparse representation for image classification.

    Science.gov (United States)

    Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping

    2016-09-01

    Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. A Novel NeutrosophicWeighted Extreme Learning Machine for Imbalanced Data Set

    OpenAIRE

    Yaman Akbulut; Abdulkadir Sengür; Yanhui Guo; Florentin Smarandache

    2017-01-01

    Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications.

  20. Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

    National Research Council Canada - National Science Library

    Chen Chen; Wei Li; Hongjun Su; Kui Liu

    2014-01-01

      Extreme learning machine (ELM) is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization...

  1. Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

    Science.gov (United States)

    Heddam, Salim; Kisi, Ozgur

    2017-07-01

    In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R

  2. Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Ying Yin

    2016-05-01

    Full Text Available Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1 the user-specific parameter for the number of clusters may incur the effective problem; (2 SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML-extreme learning machine (ELM, to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

  3. A novel multiple instance learning method based on extreme learning machine.

    Science.gov (United States)

    Wang, Jie; Cai, Liangjian; Peng, Jinzhu; Jia, Yuheng

    2015-01-01

    Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper, a novel efficient method based on extreme learning machine (ELM) is proposed to address MIL problem. First, the most qualified instance is selected in each bag through a single hidden layer feedforward network (SLFN) whose input and output weights are both initialed randomly, and the single selected instance is used to represent every bag. Second, the modified ELM model is trained by using the selected instances to update the output weights. Experiments on several benchmark data sets and multiple instance regression data sets show that the ELM-MIL achieves good performance; moreover, it runs several times or even hundreds of times faster than other similar MIL algorithms.

  4. Prediction of length-of-day using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Yu Lei

    2015-03-01

    Full Text Available Traditional artificial neural networks (ANN such as back-propagation neural networks (BPNN provide good predictions of length-of-day (LOD. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine (ELM, to improve the efficiency of LOD predictions. Earth orientation parameters (EOP C04 time-series provides daily values from International Earth Rotation and Reference Systems Service (IERS, which serves as our database. First, the known predictable effects that can be described by functional models—such as the effects of solid earth, ocean tides, or seasonal atmospheric variations—are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data (i.e., the irregular and quasi-periodic variations are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks (GRNN, and adaptive network-based fuzzy inference systems (ANFIS. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction techniques, the mean-absolute-error (MAE from the proposed method is compared with that from the EOP prediction comparison campaign (EOP PCC. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.

  5. Is extreme learning machine feasible? A theoretical assessment (part I).

    Science.gov (United States)

    Liu, Xia; Lin, Shaobo; Fang, Jian; Xu, Zongben

    2015-01-01

    An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have demonstrated the feasibility and high efficiency of ELM-like systems. It has, however, been open if this is true for any general applications. In this two-part paper, we conduct a comprehensive feasibility analysis of ELM. In Part I, we provide an answer to the question by theoretically justifying the following: 1) for some suitable activation functions, such as polynomials, Nadaraya-Watson and sigmoid functions, the ELM-like systems can attain the theoretical generalization bound of the FNNs with all connections adjusted, i.e., they do not degrade the generalization capability of the FNNs even when the connections with and within hidden neurons are randomly fixed; 2) the number of hidden neurons needed for an ELM-like system to achieve the theoretical bound can be estimated; and 3) whenever the activation function is taken as polynomial, the deduced hidden layer output matrix is of full column-rank, therefore the generalized inverse technique can be efficiently applied to yield the solution of an ELM-like system, and, furthermore, for the nonpolynomial case, the Tikhonov regularization can be applied to guarantee the weak regularity while not sacrificing the generalization capability. In Part II, however, we reveal a different aspect of the feasibility of ELM: there also exists some activation functions, which makes the corresponding ELM degrade the generalization capability. The obtained results underlie the feasibility and efficiency of ELM-like systems, and yield various generalizations and improvements of the systems as well.

  6. Some Tricks in Parameter Selection for Extreme Learning Machine

    Science.gov (United States)

    Cao, Weipeng; Gao, Jinzhu; Ming, Zhong; Cai, Shubin

    2017-10-01

    Extreme learning machine (ELM) is a widely used neural network with random weights (NNRW), which has made great contributions to many fields. However, the relationship between the parameters and the performance of ELM has not been fully investigated yet, i.e. the impact of the number of hidden layer nodes, the randomization range of the weights between the input layer and hidden layer, the randomization range of the threshold of hidden nodes, and the type of activation functions. In this paper, eight benchmark functions are used to study this relationship. We have some interesting findings, such as more hidden layer nodes cannot guarantee the best performance of ELM, the empirical randomization range of the hidden weights (i.e., [-1, 1]) and the empirical randomization range of the threshold of hidden nodes (i.e., [0, 1]) may not lead to the optimal performance of ELM models, and ELM with sigmoid as the activation function always achieves better performance on some regression problems than ELM with tribas as the activation function. We hope the findings from our work could provide a useful guidance for researchers to select right parameters for ELM.

  7. Band selection for hyperspectral image classification using extreme learning machine

    Science.gov (United States)

    Li, Jiaojiao; Kingsdorf, Benjamin; Du, Qian

    2017-05-01

    Extreme learning machine (ELM) is a feedforward neural network with one hidden layer, which is similar to a multilayer perceptron (MLP). To reduce the complexity in the training process of MLP using the traditional backpropagation algorithm, the weights in ELM between input and hidden layers are random variables. The output layer in the ELM is linear, as in a radial basis function neural network (RBFNN), so the output weights can be easily estimated with a least squares solution. It has been demonstrated in our previous work that the computational cost of ELM is much lower than the standard support vector machine (SVM), and a kernel version of ELM can offer comparable performance as SVM. In our previous work, we also investigate the impact of the number of hidden neurons to the performance of ELM. Basically, more hidden neurons are needed if the number of training samples and data dimensionality are large, which results in a very large matrix inversion problem. To avoid handling such a large matrix, we propose to conduct band selection to reduce data dimensionality (i.e., the number of input neurons), thereby reducing network complexity. Experimental results show that ELM using selected bands can yield similar or even better classification accuracy than using all the original bands.

  8. The Over-Pruning Hypothesis of Autism

    Science.gov (United States)

    Thomas, Michael S. C.; Davis, Rachael; Karmiloff-Smith, Annette; Knowland, Victoria C. P.; Charman, Tony

    2016-01-01

    This article outlines the "over-pruning hypothesis" of autism. The hypothesis originates in a neurocomputational model of the regressive sub-type (Thomas, Knowland & Karmiloff-Smith, 2011a, 2011b). Here we develop a more general version of the over-pruning hypothesis to address heterogeneity in the timing of manifestation of ASD,…

  9. Improved Extreme Learning Machine and Its Application in Image Quality Assessment

    OpenAIRE

    Li Mao; Lidong Zhang; Xingyang Liu; Chaofeng Li; Hong Yang

    2014-01-01

    Extreme learning machine (ELM) is a new class of single-hidden layer feedforward neural network (SLFN), which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting...

  10. Representation learning with deep extreme learning machines for efficient image set classification

    KAUST Repository

    Uzair, Muhammad

    2016-12-09

    Efficient and accurate representation of a collection of images, that belong to the same class, is a major research challenge for practical image set classification. Existing methods either make prior assumptions about the data structure, or perform heavy computations to learn structure from the data itself. In this paper, we propose an efficient image set representation that does not make any prior assumptions about the structure of the underlying data. We learn the nonlinear structure of image sets with deep extreme learning machines that are very efficient and generalize well even on a limited number of training samples. Extensive experiments on a broad range of public datasets for image set classification show that the proposed algorithm consistently outperforms state-of-the-art image set classification methods both in terms of speed and accuracy.

  11. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

    Directory of Open Access Journals (Sweden)

    C. V. Subbulakshmi

    2015-01-01

    Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  12. Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier.

    Science.gov (United States)

    Subbulakshmi, C V; Deepa, S N

    2015-01-01

    Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

  13. Improved Extreme Learning Machine and Its Application in Image Quality Assessment

    Directory of Open Access Journals (Sweden)

    Li Mao

    2014-01-01

    Full Text Available Extreme learning machine (ELM is a new class of single-hidden layer feedforward neural network (SLFN, which is simple in theory and fast in implementation. Zong et al. propose a weighted extreme learning machine for learning data with imbalanced class distribution, which maintains the advantages from original ELM. However, the current reported ELM and its improved version are only based on the empirical risk minimization principle, which may suffer from overfitting. To solve the overfitting troubles, in this paper, we incorporate the structural risk minimization principle into the (weighted ELM, and propose a modified (weighted extreme learning machine (M-ELM and M-WELM. Experimental results show that our proposed M-WELM outperforms the current reported extreme learning machine algorithm in image quality assessment.

  14. Financial analysis of pruning coast Douglas-fir.

    Science.gov (United States)

    Roger D. Fight; James M. Cahlll; Thomas D. Fahey; Thomas A. Snellgrove

    1987-01-01

    Pruning of coast Douglas-fir was evaluated; recent product recovery information for pruned and unpruned logs for both sawn and peeled products was used. Dimensions of pruned and unpruned trees were simulated with the Douglas-fir stand simulator (DFSIM). Results are presented for a range of sites, ages at time of pruning, ages at time of harvest, product prices, and...

  15. 7 CFR 993.6 - Non-French prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Non-French prunes. 993.6 Section 993.6 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements... Order Regulating Handling Definitions § 993.6 Non-French prunes. Non-French prunes means prunes commonly...

  16. Active learning for bird sound classification via a kernel-based extreme learning machine.

    Science.gov (United States)

    Qian, Kun; Zhang, Zixing; Baird, Alice; Schuller, Björn

    2017-10-01

    In recent years, research fields, including ecology, bioacoustics, signal processing, and machine learning, have made bird sound recognition a part of their focus. This has led to significant advancements within the field of ornithology, such as improved understanding of evolution, local biodiversity, mating rituals, and even the implications and realities associated to climate change. The volume of unlabeled bird sound data is now overwhelming, and comparatively little exploration is being made into methods for how best to handle them. In this study, two active learning (AL) methods are proposed, sparse-instance-based active learning (SI-AL), and least-confidence-score-based active learning (LCS-AL), both effectively reducing the need for expert human annotation. To both of these AL paradigms, a kernel-based extreme learning machine (KELM) is then integrated, and a comparison is made to the conventional support vector machine (SVM). Experimental results demonstrate that, when the classifier capacity is improved from an unweighted average recall of 60%-80%, KELM can outperform SVM even when a limited proportion of human annotations are used from the pool of data in both cases of SI-AL (minimum 34.5% vs minimum 59.0%) and LCS-AL (minimum 17.3% vs minimum 28.4%).

  17. Creating a learning organization for state, local, and tribal law enforcement to combat violent extremism

    OpenAIRE

    Powell, John Eric

    2016-01-01

    Approved for public release; distribution is unlimited This is a proof-of-concept project for an online law enforcement learning organization dedicated to combating violent extremism (CVE), specifically, counter-radicalization techniques to be implemented by state, local, and tribal law enforcement agencies. Although there are many different forms of violent extremism, examples in this paper reflect those threats from Islamic violent extremism. Even so, this proposed law enforcement learni...

  18. Debriefing to Learn from Extreme Events: The Case of Utøya

    Science.gov (United States)

    Firing, Kristian; Moen, Alexander; Skarsvåg, Kåre Inge

    2015-01-01

    The objective of this study was to discover potential ways to enhance debriefing so that more can be learned from the experience of extreme events. In order to reach this aim, we explored how personnel in the Explosive Ordnance Disposal team from the Norwegian Armed Forces experienced debriefing after an extreme event. That event was a terror…

  19. Megalourethra associated with prune-belly syndrome.

    Science.gov (United States)

    Gökalp, A; Gültekin, E Y

    1993-01-01

    A 14-day-old male infant with megalourethra is presented because of the rarity of the anomaly and its association with prune-belly syndrome. The lax, wrinkled appearance of the abdomen, bilateral cryptorchidism and severe dilatation of the urinary system are features included in the classic triad of the prune-belly syndrome. Our patient had the scaphoid variety of megalourethra since the penis appeared elongated and floppy in the fusiform form.

  20. Sequential Nonlinear Learning for Distributed Multiagent Systems via Extreme Learning Machines.

    Science.gov (United States)

    Vanli, Nuri Denizcan; Sayin, Muhammed O; Delibalta, Ibrahim; Kozat, Suleyman Serdar

    2017-03-01

    We study online nonlinear learning over distributed multiagent systems, where each agent employs a single hidden layer feedforward neural network (SLFN) structure to sequentially minimize arbitrary loss functions. In particular, each agent trains its own SLFN using only the data that is revealed to itself. On the other hand, the aim of the multiagent system is to train the SLFN at each agent as well as the optimal centralized batch SLFN that has access to all the data, by exchanging information between neighboring agents. We address this problem by introducing a distributed subgradient-based extreme learning machine algorithm. The proposed algorithm provides guaranteed upper bounds on the performance of the SLFN at each agent and shows that each of these individual SLFNs asymptotically achieves the performance of the optimal centralized batch SLFN. Our performance guarantees explicitly distinguish the effects of data- and network-dependent parameters on the convergence rate of the proposed algorithm. The experimental results illustrate that the proposed algorithm achieves the oracle performance significantly faster than the state-of-the-art methods in the machine learning and signal processing literature. Hence, the proposed method is highly appealing for the applications involving big data.

  1. Rotor Position Estimation for Switched Reluctance Wind Generator Using Extreme Learning Machine

    DEFF Research Database (Denmark)

    Wang, Chao; Liu, Xiao; Chen, Zhe

    2014-01-01

    Reluctance Wind Generator (SRWG) based on Extreme Learning Machine (ELM) which could build a nonlinear mapping between flux linkage-current and rotor position. The learning data are derived from magnetization curves of the SRWG which are obtained from Finite Element Analysis (FEA) of an SRG with 8/6 stator...

  2. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  3. Abdominoplasty in Prune Belly Syndrome.

    Science.gov (United States)

    Dénes, F T; Park, R; Lopes, R I; Moscardi, P R M; Srougi, M

    2015-10-01

    Many patients with Prune Belly Syndrome (PBS) require abdominoplasty alone or in combination with correction of any urogenital abnormalities. This video presents a simplified technique with which to treat the abdominal flaccidity in PBS. A longitudinal xypho-pubic fusiform figure is drawn on the abdomen, based on the area of skin and subcutaneous tissue to be removed. This is performed with preservation of the musculo-fascial layer and the umbilicus. A lateral elliptical single xypho-pubic line is drawn in the most lax side of the fascia, which is incised along this line. After urinary tract reconstruction and orchidopexy, closure is initiated by suturing the medial edge of the wider fascial flap laterally to the peritoneal side of the contralateral flap. Next, the now outer fascial flap is laid over the inner flap, and a buttonhole is made to expose the umbilicus. The subcutaneous tissue of the inner flap is laterally undermined to gain extra distance for the suture of the outer flap over the inner flap. The subcutaneous tissue and skin are sutured in the midline, incorporating the umbilicus. In a 30-year period, 43 PBS patients underwent this procedure with good cosmetic and long-term functional results. This abdominoplasty technique is simple and presents good functional and cosmetic results in PBS patients. Copyright © 2015 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

  4. Cross-person activity recognition using reduced kernel extreme learning machine.

    Science.gov (United States)

    Deng, Wan-Yu; Zheng, Qing-Hua; Wang, Zhong-Min

    2014-05-01

    Activity recognition based on mobile embedded accelerometer is very important for developing human-centric pervasive applications such as healthcare, personalized recommendation and so on. However, the distribution of accelerometer data is heavily affected by varying users. The performance will degrade when the model trained on one person is used to others. To solve this problem, we propose a fast and accurate cross-person activity recognition model, known as TransRKELM (Transfer learning Reduced Kernel Extreme Learning Machine) which uses RKELM (Reduced Kernel Extreme Learning Machine) to realize initial activity recognition model. In the online phase OS-RKELM (Online Sequential Reduced Kernel Extreme Learning Machine) is applied to update the initial model and adapt the recognition model to new device users based on recognition results with high confidence level efficiently. Experimental results show that, the proposed model can adapt the classifier to new device users quickly and obtain good recognition performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

  5. Pruning in poplar plantations by mechanized device Stihl HT-75

    Directory of Open Access Journals (Sweden)

    Danilović Milorad

    2009-01-01

    Full Text Available The effects of branch pruning device Stihl HT-75 were researched on sample plots in FA Kupinovo and FA Klenak, in poplar plantations of Populus×euramericana 'I-214', Populus×euramericana 'M1' and Populus deltoides of different planting spaces and different ages. The analysed factors were: pruning method, site conditions, number of pruned branches, pruning height, branch diameter, etc. Time measurement was performed by the flow method, and the required number of measurements was calculated by variation statistics. The results of the analysis of variance show the statistical significance of the differences between pruning times of different clone species, different planting spaces and different plantation ages. The results of the analysis of variance and statistical tests show that there are no statistically significant differences between the average time of poplar pruning in plantations of the same age and different planting spaces. The correlation of branch pruning time and the number of pruned branches is represented by the power function model, which according to the results of the regression analyses, is the best representation of the nature of this dependence. Exponential function represents the correlation of the average diameter of pruned branches and the time of pruning. Also, there is a correlation of the average diameter of pruned branches and fuel consumption. Pruning time of poplar trees increases with the increase of the average diameter of pruned branches.

  6. Robust Visual Knowledge Transfer via Extreme Learning Machine Based Domain Adaptation.

    Science.gov (United States)

    Zhang, Lei; Zhang, David

    2016-08-10

    We address the problem of visual knowledge adaptation by leveraging labeled patterns from source domain and a very limited number of labeled instances in target domain to learn a robust classifier for visual categorization. This paper proposes a new extreme learning machine based cross-domain network learning framework, that is called Extreme Learning Machine (ELM) based Domain Adaptation (EDA). It allows us to learn a category transformation and an ELM classifier with random projection by minimizing the -norm of the network output weights and the learning error simultaneously. The unlabeled target data, as useful knowledge, is also integrated as a fidelity term to guarantee the stability during cross domain learning. It minimizes the matching error between the learned classifier and a base classifier, such that many existing classifiers can be readily incorporated as base classifiers. The network output weights cannot only be analytically determined, but also transferrable. Additionally, a manifold regularization with Laplacian graph is incorporated, such that it is beneficial to semi-supervised learning. Extensively, we also propose a model of multiple views, referred as MvEDA. Experiments on benchmark visual datasets for video event recognition and object recognition, demonstrate that our EDA methods outperform existing cross-domain learning methods.

  7. Enhanced Context Recognition by Sensitivity Pruned Vocabularies

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2004-01-01

    Language independent `bag-of-words' representations are surprisingly effective for text classification. The generic BOW approach is based on a high-dimensional vocabulary which may reduce the generalization performance of subsequent classifiers, e.g., based on ill-posed principal component...... transformations. In this communication our aim is to study the effect of sensitivity based pruning of the bag-of-words representation. We consider neural network based sensitivity maps for determination of term relevancy, when pruning the vocabularies. With reduced vocabularies documents are classified using...... a latent semantic indexing representation and a probabilistic neural network classifier. Pruning the vocabularies to approximately 20% of the original size, we find consistent context recognition enhancement for two mid size data-sets for a range of training set sizes. We also study the applicability...

  8. Application of artificial neural network with extreme learning machine for economic growth estimation

    Science.gov (United States)

    Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

    2017-01-01

    The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

  9. Research into Financial Position of Listed Companies following Classification via Extreme Learning Machine Based upon DE Optimization

    OpenAIRE

    Fu Yu; Mu Jiong; Duan Xu Liang

    2016-01-01

    By means of the model of extreme learning machine based upon DE optimization, this article particularly centers on the optimization thinking of such a model as well as its application effect in the field of listed company’s financial position classification. It proves that the improved extreme learning machine algorithm based upon DE optimization eclipses the traditional extreme learning machine algorithm following comparison. Meanwhile, this article also intends to introduce certain research...

  10. Effect of different winter pruning systems on grapes produced

    Directory of Open Access Journals (Sweden)

    Claudio Caprara

    2013-09-01

    Full Text Available The purpose of these trials was to evaluate possible effects on properties of grapes, particularly the physical and mechanical features, depending on the winter pruning system. The following pruning techniques were carried out: manual pruning (m; mechanical pruning (M; mechanical pre-pruning and subsequent manual finishing (Mm; mechanical pre-pruning and contemporary fast manual finishing, using a wagon facility with two operators equipped with pneumatic scissors (Mw. The trials were carried out on Sangiovese trained to spurred cordon. During the trials were measured: time and cost of pruning, quality of pruning and the vegetative-productive response of vines. During grape harvesting a consolidated analytical method of texture analysis was applied to evaluate the physical parameters of grapevine cultivar: pedicel detachment, skin perforation, skin thickness, grape features as hardness, cohesiveness, springiness. Analysis of working time showed that the manual pruning (m determined a greater commitment of time, while the mechanized pruning (M presented a time reduction of 95%. The two mechanized pruning associated with manual finishing reduced the time of 21% (Mm and 69% (Mw. The lowering cost is less evident but important anyway. Regarding the quality of pruning, the increase in the level of mechanization has produced an increase of spurs and buds density. It was also detected a higher percentage of damaged spurs and in wrong position. The increasing of mechanization levels of pruning also has produced smaller and sparser bunches with smaller berries. The study of mechanical properties of berries showed significant differences in the mechanical behaviours of the different pruning tests. The mechanized pruning presented higher values for the pedicel detachment, skin perforation and cohesiveness, while it gave lower values for thickness of skin and springiness. The results showed that mechanical pruning can modify properties of the berries which

  11. Fault Diagnosis for Engine Based on Single-Stage Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2016-01-01

    Full Text Available Single-Stage Extreme Learning Machine (SS-ELM is presented to dispose of the mechanical fault diagnosis in this paper. Based on it, the traditional mapping type of extreme learning machine (ELM has been changed and the eigenvectors extracted from signal processing methods are directly regarded as outputs of the network’s hidden layer. Then the uncertainty that training data transformed from the input space to the ELM feature space with the ELM mapping and problem of the selection of the hidden nodes are avoided effectively. The experiment results of diesel engine fault diagnosis show good performance of the SS-ELM algorithm.

  12. A Case of Prune Belly Syndrome

    Directory of Open Access Journals (Sweden)

    Wei Xu

    2015-06-01

    Full Text Available Prune belly syndrome (PBS is a rare congenital disorder characterized by deficient abdominal wall muscles, urinary tract malformation, and, in males, cryptorchidism. We present a case of PBS in China. The patient was a newborn baby boy who had wrinkled, “prune-like” abdominal skin, bilateral cryptorchidism, and urinary system malformation, complicated with hypoplasia of the lung and branch of the coronary artery–right ventricular fistula. His kidney function was inadequate. The patient subsequently died at age 28 days due to septicemia from a severe urinary tract infection.

  13. Veneer grade yield from pruned Douglas-fir.

    Science.gov (United States)

    Edward J. II Dimock; Henry H. Haskell

    1962-01-01

    This paper reports actual veneer yields obtained from 10 trees pruned at age 38 and harvested 20 years later. Information of this kind is needed to help determine if and when to prune and ultimately will be essential to a thorough economic analysis of expected returns from pruning.

  14. Effects of root pruning in sour cherry (Prunus cersus) "Stevnsbaer"

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben; Jensen, Nauja Lisa; Dencker, Ivar Blücher

    2007-01-01

    (May), initial and final fruit set (June) were recorded. Root pruning had little effect on fruit set, fruit size and yield in the year of pruning. Only in one plot with a severe root pruning (26 cm from the trunks), negative effects were found. The effects on growth, flowering and fruit set in 2003...

  15. 7 CFR 993.149 - Receiving of prunes by handlers.

    Science.gov (United States)

    2010-01-01

    ... same procedure shall apply as set forth in paragraph (d)(1) of this section. For each day on which a... and usually received by a handler in any considerable volume as ranch deliveries, and at which there... samples of prunes drawn as prune plums and dehydrated in the same manner as the prunes to which they are...

  16. Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion

    OpenAIRE

    Le Cadre, Hélène; Aravena, Ignacio; Papavasiliou, Anthony

    2015-01-01

    International audience; We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the ag-gregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d'Azur, to evaluate ...

  17. Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine

    OpenAIRE

    Yang Liu; Bo He; Diya Dong; Yue Shen,; Tianhong Yan; Rui Nian; Amaury Lendasse

    2015-01-01

    A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, inclu...

  18. Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification.

    Science.gov (United States)

    Mirza, Bilal; Lin, Zhiping

    2016-08-01

    In this paper, a meta-cognitive online sequential extreme learning machine (MOS-ELM) is proposed for class imbalance and concept drift learning. In MOS-ELM, meta-cognition is used to self-regulate the learning by selecting suitable learning strategies for class imbalance and concept drift problems. MOS-ELM is the first sequential learning method to alleviate the imbalance problem for both binary class and multi-class data streams with concept drift. In MOS-ELM, a new adaptive window approach is proposed for concept drift learning. A single output update equation is also proposed which unifies various application specific OS-ELM methods. The performance of MOS-ELM is evaluated under different conditions and compared with methods each specific to some of the conditions. On most of the datasets in comparison, MOS-ELM outperforms the competing methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

  19. Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification.

    Science.gov (United States)

    Yang, Yimin; Wu, Q M Jonathan

    2016-12-01

    As demonstrated earlier, the learning effectiveness and learning speed of single-hidden-layer feedforward neural networks are in general far slower than required, which has been a major bottleneck for many applications. Huang et al. proposed extreme learning machine (ELM) which improves the training speed by hundreds of times as compared to its predecessor learning techniques. This paper offers an ELM-based learning method that can grow subnetwork hidden nodes by pulling back residual network error to the hidden layer. Furthermore, the proposed method provides a similar or better generalization performance with remarkably fewer hidden nodes as compared to other ELM methods employing huge number of hidden nodes. Thus, the learning speed of the proposed technique is hundred times faster compared to other ELMs as well as to back propagation and support vector machines. The experimental validations for all methods are carried out on 32 data sets.

  20. Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram.

    Science.gov (United States)

    Min, Beomjun; Kim, Jongin; Park, Hyeong-Jun; Lee, Boreom

    2016-01-01

    The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems.

  1. Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

    Science.gov (United States)

    Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

    2015-01-01

    The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

  2. Pruning the vocabulary for better context recognition

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2004-01-01

    of term relevancy, when pruning the vocabularies. With reduced vocabularies, documents are classified using a latent semantic indexing representation and a probabilistic neural network classifier. Reducing the bag-of-words vocabularies with 90%-98%, we find consistent classification improvement using two...

  3. Vocabulary Pruning for Improved Context Recognition

    DEFF Research Database (Denmark)

    Madsen, Rasmus Elsborg; Sigurdsson, Sigurdur; Hansen, Lars Kai

    2004-01-01

    of term relevancy, when pruning the vocabularies. With reduced vocabularies documents are classified using a latent semantic indexing representation and a probabilistic neural network classifier. Reducing the bag-of-words vocabularies with 90%-98%, we find consistent classification improvement using two...

  4. 21 CFR 145.190 - Canned prunes.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 2 2010-04-01 2010-04-01 false Canned prunes. 145.190 Section 145.190 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN...: (1) Natural and artificial flavors. (2) Spice. (3) Vinegar, lemon juice, or organic acids. (4...

  5. A Pruning Neural Network Model in Credit Classification Analysis

    Directory of Open Access Journals (Sweden)

    Yajiao Tang

    2018-01-01

    Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

  6. Quasilinear Extreme Learning Machine Model Based Internal Model Control for Nonlinear Process

    Directory of Open Access Journals (Sweden)

    Dazi Li

    2015-01-01

    Full Text Available A new strategy for internal model control (IMC is proposed using a regression algorithm of quasilinear model with extreme learning machine (QL-ELM. Aimed at the chemical process with nonlinearity, the learning process of the internal model and inverse model is derived. The proposed QL-ELM is constructed as a linear ARX model with a complicated nonlinear coefficient. It shows some good approximation ability and fast convergence. The complicated coefficients are separated into two parts. The linear part is determined by recursive least square (RLS, while the nonlinear part is identified through extreme learning machine. The parameters of linear part and the output weights of ELM are estimated iteratively. The proposed internal model control is applied to CSTR process. The effectiveness and accuracy of the proposed method are extensively verified through numerical results.

  7. Sample Selected Extreme Learning Machine Based Intrusion Detection in Fog Computing and MEC

    Directory of Open Access Journals (Sweden)

    Xingshuo An

    2018-01-01

    Full Text Available Fog computing, as a new paradigm, has many characteristics that are different from cloud computing. Due to the resources being limited, fog nodes/MEC hosts are vulnerable to cyberattacks. Lightweight intrusion detection system (IDS is a key technique to solve the problem. Because extreme learning machine (ELM has the characteristics of fast training speed and good generalization ability, we present a new lightweight IDS called sample selected extreme learning machine (SS-ELM. The reason why we propose “sample selected extreme learning machine” is that fog nodes/MEC hosts do not have the ability to store extremely large amounts of training data sets. Accordingly, they are stored, computed, and sampled by the cloud servers. Then, the selected sample is given to the fog nodes/MEC hosts for training. This design can bring down the training time and increase the detection accuracy. Experimental simulation verifies that SS-ELM performs well in intrusion detection in terms of accuracy, training time, and the receiver operating characteristic (ROC value.

  8. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

    Science.gov (United States)

    Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

    2017-03-01

    Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

    OpenAIRE

    Qingsong Xu

    2014-01-01

    Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have b...

  10. Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

    KAUST Repository

    AlShahrani, Mona

    2015-05-24

    Artificial Neural Networks (ANNs) are known to be among the most effective and expressive machine learning models. Their impressive abilities to learn have been reflected in many broad application domains such as image recognition, medical diagnosis, online banking, robotics, dynamic systems, and many others. ANNs with multiple layers of complex non-linear transformations (a.k.a Deep ANNs) have shown recently successful results in the area of computer vision and speech recognition. ANNs are parametric models that approximate unknown functions in which parameter values (weights) are adapted during training. ANN’s weights can be large in number and thus render the trained model more complex with chances for “overfitting” training data. In this study, we explore the effects of network pruning on performance of ANNs and ranking of features that describe the data. Simplified ANN model results in fewer parameters, less computation and faster training. We investigate the use of Hessian-based pruning algorithms as well as simpler ones (i.e. non Hessian-based) on nine datasets with varying number of input features and ANN parameters. The Hessian-based Optimal Brain Surgeon algorithm (OBS) is robust but slow. Therefore a faster parallel Hessian- approximation is provided. An additional speedup is provided using a variant we name ‘Simple n Optimal Brain Surgeon’ (SNOBS), which represents a good compromise between robustness and time efficiency. For some of the datasets, the ANN pruning experiments show on average 91% reduction in the number of ANN parameters and about 60% - 90% in the number of ANN input features, while maintaining comparable or better accuracy to the case when no pruning is applied. Finally, we show through a comprehensive comparison with seven state-of-the art feature filtering methods that the feature selection and ranking obtained as a byproduct of the ANN pruning is comparable in accuracy to these methods.

  11. 7 CFR 993.109 - Modified definition of non-French prunes.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 8 2010-01-01 2010-01-01 false Modified definition of non-French prunes. 993.109... definition of non-French prunes. The definition of non-French prunes set forth in § 993.6 is modified to read as follows: Non-French Prunes means prunes commonly known as Imperial, Sugar, Robe de Sargent, Burton...

  12. Prune Belly syndrome: A rare case report

    OpenAIRE

    Samal, Sunil Kumar; Rathod, Setu

    2015-01-01

    Prune Belly syndrome (PBS) is a rare congenital anomaly of uncertain etiology almost exclusive to males. We report a case of term male baby born to a 39-year-old grand multipara with previous four normal vaginal births. There was no history of genetic or congenital anomaly in her family. Examination of the baby revealed hypotonia, deficient abdominal muscle, cryptorchidism, palpable kidney, and bladder. Ultrasound examination of the abdomen revealed bilateral gross hydronephrosis and megauret...

  13. Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Junjie Lu

    2017-03-01

    Full Text Available A novel adaptive weight online sequential extreme learning machine (AWOS-ELM is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process. The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly. Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm. In addition, a real-world mechanical system identification problem is considered to test the feasibility and efficacy of the AWOS-ELM algorithm.

  14. An efficient abnormal cervical cell detection system based on multi-instance extreme learning machine

    Science.gov (United States)

    Zhao, Lili; Yin, Jianping; Yuan, Lihuan; Liu, Qiang; Li, Kuan; Qiu, Minghui

    2017-07-01

    Automatic detection of abnormal cells from cervical smear images is extremely demanded in annual diagnosis of women's cervical cancer. For this medical cell recognition problem, there are three different feature sections, namely cytology morphology, nuclear chromatin pathology and region intensity. The challenges of this problem come from feature combination s and classification accurately and efficiently. Thus, we propose an efficient abnormal cervical cell detection system based on multi-instance extreme learning machine (MI-ELM) to deal with above two questions in one unified framework. MI-ELM is one of the most promising supervised learning classifiers which can deal with several feature sections and realistic classification problems analytically. Experiment results over Herlev dataset demonstrate that the proposed method outperforms three traditional methods for two-class classification in terms of well accuracy and less time.

  15. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    OpenAIRE

    Qiang Li; Huiling Chen; Hui Huang; Xuehua Zhao; ZhenNao Cai; Changfei Tong; Wenbin Liu; Xin Tian

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update ...

  16. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

    Directory of Open Access Journals (Sweden)

    Jiangshe Zhang

    2017-01-01

    Full Text Available With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  17. Infrared image segmentation using HOG feature and kernel extreme learning machine

    Science.gov (United States)

    Liang, Ying; Wang, Luping; Zhang, Luping

    2015-10-01

    Image segmentation is an important application in computer vision. Nowadays, image segmentation of infrared image has not gain as much attention as image segmentation of visible light image. But this application is very useful. For example, searching and tracking targets with infrared search and track system (IRST) has been widely used these days due to its special passive mode. So it can be used as a kind of supplementary equipment for radar. Infrared image segmentation can help computers identify backgrounds of the image, and help it automatically adjust the related parameters for the next work, such as targets recognition or targets detection. Our work proposed a new image segmentation method for infrared image using histogram of oriented gradients (HOG) feature and kernel extreme learning machine (kernel ELM). HOG are feature descriptors which can be used in computer vision and image processing for the purpose of object detection. In this paper, we extract HOG feature of infrared image, and use this feature as the basis for classification. After having feature, we use kernel extreme learning machine to do the segmentation. Kernel extreme learning machine has shown many excellent characteristics in classification. By testing our algorithm proposed in our paper, we demonstrated that our algorithm is effective and feasible.

  18. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

    Science.gov (United States)

    Zhang, Jiangshe; Ding, Weifu

    2017-01-24

    With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  19. Unusual presentation of prune belly syndrome: a case report

    OpenAIRE

    Demisse, Abayneh Girma; Berhanu, Ashenafi; Tadesse, Temesgen

    2017-01-01

    Background Prune belly syndrome is a rare congenital malformation of unknown etiology, with the following triad of findings: abdominal muscle wall weakness, undescended testes, and urinary tract abnormalities. In most cases, detection of prune belly syndrome occurs during neonatal or infancy period. In this case report, we describe a 12-year-old boy from Ethiopia with the triad of findings of prune belly syndrome along with skeletal malformations. We are unaware of any previous report of prun...

  20. Multi-model convolutional extreme learning machine with kernel for RGB-D object recognition

    Science.gov (United States)

    Yin, Yunhua; Li, Huifang; Wen, Xinling

    2017-11-01

    With new depth sensing technology such as Kinect providing high quality synchronized RGB and depth images (RGB-D data), learning rich representations efficiently plays an important role in multi-modal recognition task, which is crucial to achieve high generalization performance. To address this problem, in this paper, we propose an effective multi-modal convolutional extreme learning machine with kernel (MMC-KELM) structure, which combines advantages both the power of CNN and fast training of ELM. In this model, CNN uses multiple alternate convolution layers and stochastic pooling layers to effectively abstract high level features from each modality (RGB and depth) separately without adjusting parameters. And then, the shared layer is developed by combining these features from each modality. Finally, the abstracted features are fed to the extreme learning machine with kernel (KELM), which leads to better generalization performance with faster learning speed. Experimental results on Washington RGB-D Object Dataset show that the proposed multiple modality fusion method achieves state-of-the-art performance with much less complexity.

  1. Multi-category classification using an Extreme Learning Machine for microarray gene expression cancer diagnosis.

    Science.gov (United States)

    Zhang, Runxuan; Huang, Guang-Bin; Sundararajan, Narasimhan; Saratchandran, P

    2007-01-01

    In this paper, the recently developed Extreme Learning Machine (ELM) is used for direct multicategory classification problems in the cancer diagnosis area. ELM avoids problems like local minima, improper learning rate and overfitting commonly faced by iterative learning methods and completes the training very fast. We have evaluated the multi-category classification performance of ELM on three benchmark microarray datasets for cancer diagnosis, namely, the GCM dataset, the Lung dataset and the Lymphoma dataset. The results indicate that ELM produces comparable or better classification accuracies with reduced training time and implementation complexity compared to artificial neural networks methods like conventional back-propagation ANN, Linder's SANN, and Support Vector Machine methods like SVM-OVO and Ramaswamy's SVM-OVA. ELM also achieves better accuracies for classification of individual categories.

  2. Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Yang Liu

    2015-01-01

    Full Text Available A novel particle swarm optimization based selective ensemble (PSOSEN of online sequential extreme learning machine (OS-ELM is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM, genetic algorithm based selective ensemble (GASEN of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.

  3. Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

    Science.gov (United States)

    Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

    2017-12-01

    In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

  4. Hybrid approach using fuzzy sets and extreme learning machine for classifying clinical datasets

    Directory of Open Access Journals (Sweden)

    Kindie Biredagn Nahato

    Full Text Available Data mining techniques play a major role in developing computer aided diagnosis systems and expert systems that will aid a physician in clinical decision making. In this work, a classifier that combines the relative merits of fuzzy sets and extreme learning machine (FELM for clinical datasets is proposed. The three major subsystems in the FELM framework are preprocessing subsystem, fuzzification subsystem and classification subsystem. Missing value imputation and outlier elimination are handled by the preprocessing subsystem. The fuzzification subsystem maps each feature to a fuzzy set and the classification subsystem uses extreme learning machine for classification.Cleveland heart disease (CHD, Statlog heart disease (SHD and Pima Indian diabetes (PID datasets from the University of California Irvine (UCI machine learning repository have been used for experimentation. The CHD and SHD datasets have been experimented with two class labels one indicating the absence and the other indicating the presence of heart disease. The CHD dataset has also been experimented with five class labels, one class label indicating the absence of heart disease and the other four class labels indicating the severity of heart disease namely low risk, medium risk, high risk and serious. The PID data set has been experimented with two class labels one indicating the absence and the other indicating the presence of gestational diabetes.The classifier has achieved an accuracy of 93.55% for CHD data set with two class labels; 73.77% for CHD data set with five class labels; 94.44% for SHD data set and 92.54% for PID dataset. Keywords: Extreme learning machine, Fuzzification, Fuzzy set, Classification, Euclidean distance, Membership function

  5. Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction

    National Research Council Canada - National Science Library

    Yan, Qisheng; Wang, Shitong; Li, Bingqing

    2014-01-01

    ...), and extreme learning machine (ELM) for international uranium resource prices is proposed. In the first stage, the original uranium resource price series are first decomposed into a finite number of independent intrinsic mode functions (IMFs...

  6. Extinction procedure induces pruning of dendritic spines in CA1 hippocampal field depending on strength of training in rats

    Science.gov (United States)

    Garín-Aguilar, María E.; Díaz-Cintra, Sofía; Quirarte, Gina L.; Aguilar-Vázquez, Azucena; Medina, Andrea C.; Prado-Alcalá, Roberto A.

    2012-01-01

    Numerous reports indicate that learning and memory of conditioned responses are accompanied by genesis of dendritic spines in the hippocampus, although there is a conspicuous lack of information regarding spine modifications after behavioral extinction. There is ample evidence that treatments that typically produce amnesia become innocuous when animals are submitted to a procedure of enhanced training. We now report that extinction of inhibitory avoidance (IA), trained with relatively low foot-shock intensities, induces pruning of dendritic spines along the length of the apical dendrites of hippocampal CA1 neurons. When animals are trained with a relatively high foot-shock there is a high resistance to extinction, and pruning in the proximal and medial segments of the apical dendrite are seen, while spine count in the distal dendrite remains normal. These results indicate that pruning is involved in behavioral extinction, while maintenance of spines is a probable mechanism that mediates the protecting effect against amnesic treatments produced by enhanced training. PMID:22438840

  7. Extinction procedure induces pruning of dendritic spines in CA1 hippocampal field depending on strength of training in rats

    Directory of Open Access Journals (Sweden)

    María Eugenia Garín-Aguilar

    2012-03-01

    Full Text Available Numerous reports indicate that learning and memory of conditioned responses are accompanied by genesis of dendritic spines in the hippocampus, although there is a conspicuous lack of information regarding spine modifications after behavioral extinction. There is ample evidence that treatments that typically produce amnesia become innocuous when animals are submitted to a procedure of enhanced training. We now report that extinction of inhibitory avoidance, trained with relatively low foot-shock intensities, induces pruning of dendritic spines along the length of the apical dendrites of hippocampal CA1 neurons. When animals are trained with a relatively high foot-shock there is a high resistance to extinction, and pruning in the proximal and medial segments of the apical dendrite are seen, while spine count in the distal dendrite remains normal. These results indicate that pruning is involved in behavioral extinction, while maintenance of spines is a probable mechanism that mediates the protecting effect against amnesic treatments produced by enhanced training.

  8. Effect of mechanical pruning on the yield and quality of ‘Fortune’ mandarins

    Directory of Open Access Journals (Sweden)

    Bernardo Martin-Gorriz

    2014-11-01

    Full Text Available This work compares mechanical pruning followed up by hand pruning versus manual pruning in the case of ‘Fortune’ mandarins. Yield and fruit quality were measured over a three-year period. Two mechanical pruning intensities were tested, these intensities being measured as the width of the row middles left free after mechanical pruning. Although there were differences in biomass and diameter of the branches that were cut, no differences were observed between the two mechanical pruning intensities in terms of yield or fruit quality. In all pruning treatments, fruit size reached the highest category. The pruning treatments consisted in: (i hand pruning every year, (ii mechanical pruning followed up by hand pruning every year, and (iii alternating the two previous methods over the years studied. On analysing the accumulated fruit production of the three years, it can be observed that there were no significant differences in yield when mechanical pruning was alternated with hand pruning over the years. When only mechanical pruning was used for the three years, however, a 22% reduction in yield was observed with respect to the treatment involving hand pruning alone. From the economic point of view, mechanical pruning shortened the time needed to complete the follow-up hand pruning by 13% with respect to just hand pruning, but this reduction in labour does not offset the cost of the mechanical equipment.

  9. Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle

    Directory of Open Access Journals (Sweden)

    Junjie Lu

    2017-01-01

    Full Text Available The on-board sensor fault detection and isolation (FDI system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming data chunk usually possesses a characteristic of timeliness, and the overdue training data may mislead the subsequent learning process. The proposed MOS-ELM can improve the training process by introducing the concept of memory principle into the online sequential extreme learning machine (OS-ELM to tackle the timeliness of the data chunk. Simulations on some time series problems and some benchmark databases show that MOS-ELM performs better in generalization performance, stability, and prediction accuracy than OS-ELM. The experiment results of the MOS-ELM-based sensor fault diagnosis system also verify the excellent generalization performance of MOS-ELM and indicate the effectiveness and feasibility of the developed diagnosis system.

  10. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

    Directory of Open Access Journals (Sweden)

    Shan Pang

    2016-01-01

    Full Text Available In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN and deep belief network (DBN. However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM, which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

  11. Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification.

    Science.gov (United States)

    Pang, Shan; Yang, Xinyi

    2016-01-01

    In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

  12. Prune belly syndrome with pouch colon and absent dermatome ...

    African Journals Online (AJOL)

    Prune belly syndrome with pouch colon and absent dermatome. AA Baba, SA Hussain, AH Shera, R Patnaik. Abstract. Prune belly syndrome (PBS) is a rare congenital constellation of defects in pediatric surgical practice. Although anorectal anomalies have been reported in association with PBS, only few case of pouch ...

  13. Prune belly syndrome: Early management outcome of nine ...

    African Journals Online (AJOL)

    Prune belly syndrome: Early management outcome of nine consecutive cases. ... Background: Prune belly syndrome (PBS) is a rare congenital malformation of unclear etiology. The disease progress and ... The degree of the anterior abdominal wall and the urinary tract morphology varies from patient to patient. Urinary tract ...

  14. Pruning quality affects infection of Acacia mangium and A ...

    African Journals Online (AJOL)

    Pruning (singling) is a common silvicultural practice in commercial Acacia plantations because these trees tend to have multiple stems. The wounds resulting from pruning are susceptible to infection by pathogens. Ceratocystis acaciivora and Lasiodiplodia theobromae have been shown recently to be important pathogens ...

  15. Prune Belly Syndrome | Hammond | South African Medical Journal

    African Journals Online (AJOL)

    Two cases of prune belly syndrome in Black infants are presented. The prune belly syndrome, or congenital absence of abdominal muscles, is accompanied by hydro-ureter, hydronephrosis, megalocystis and usually undescended testes. Other associated congenital defects occur, of which orthopaedic defects appear to be ...

  16. Effect of mycorrhiza and pruning regimes on seasonality of ...

    African Journals Online (AJOL)

    Effect of mycorrhiza and pruning regimes on seasonality of hedgerow tree mulch contribution to alley-cropped cassava in Ibadan, Nigeria. ... promoted dry season pruning production which was masked in Leucaena at 3 months by biomass diversion into flowering and in Gliricidia with both flowering and mite infestation.

  17. Pruning Chinese trees : an experimental and modelling approach

    NARCIS (Netherlands)

    Zeng, Bo

    2001-01-01

    Pruning of trees, in which some branches are removed from the lower crown of a tree, has been extensively used in China in silvicultural management for many purposes. With an experimental and modelling approach, the effects of pruning on tree growth and on the harvest of plant material were studied.

  18. An Extreme Learning Machine Based on the Mixed Kernel Function of Triangular Kernel and Generalized Hermite Dirichlet Kernel

    Directory of Open Access Journals (Sweden)

    Senyue Zhang

    2016-01-01

    Full Text Available According to the characteristics that the kernel function of extreme learning machine (ELM and its performance have a strong correlation, a novel extreme learning machine based on a generalized triangle Hermitian kernel function was proposed in this paper. First, the generalized triangle Hermitian kernel function was constructed by using the product of triangular kernel and generalized Hermite Dirichlet kernel, and the proposed kernel function was proved as a valid kernel function of extreme learning machine. Then, the learning methodology of the extreme learning machine based on the proposed kernel function was presented. The biggest advantage of the proposed kernel is its kernel parameter values only chosen in the natural numbers, which thus can greatly shorten the computational time of parameter optimization and retain more of its sample data structure information. Experiments were performed on a number of binary classification, multiclassification, and regression datasets from the UCI benchmark repository. The experiment results demonstrated that the robustness and generalization performance of the proposed method are outperformed compared to other extreme learning machines with different kernels. Furthermore, the learning speed of proposed method is faster than support vector machine (SVM methods.

  19. Complex extreme learning machine applications in terahertz pulsed signals feature sets.

    Science.gov (United States)

    Yin, X-X; Hadjiloucas, S; Zhang, Y

    2014-11-01

    This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed

  20. Optimized extreme learning machine for urban land cover classification using hyperspectral imagery

    Science.gov (United States)

    Su, Hongjun; Tian, Shufang; Cai, Yue; Sheng, Yehua; Chen, Chen; Najafian, Maryam

    2017-12-01

    This work presents a new urban land cover classification framework using the firefly algorithm (FA) optimized extreme learning machine (ELM). FA is adopted to optimize the regularization coefficient C and Gaussian kernel σ for kernel ELM. Additionally, effectiveness of spectral features derived from an FA-based band selection algorithm is studied for the proposed classification task. Three sets of hyperspectral databases were recorded using different sensors, namely HYDICE, HyMap, and AVIRIS. Our study shows that the proposed method outperforms traditional classification algorithms such as SVM and reduces computational cost significantly.

  1. EEG classification approach based on the extreme learning machine and wavelet transform.

    Science.gov (United States)

    Yuan, Qi; Zhou, Weidong; Zhang, Jing; Li, Shufang; Cai, Dongmei; Zeng, Yanjun

    2012-04-01

    Automatic detection and classification of electroencephalogram (EEG) epileptic activity aid diagnosis and relieve the heavy workload of doctors. This article presents a new EEG classification approach based on the extreme learning machine (ELM) and wavelet transform (WT). First, the WT is used to extract useful features when certain scales cover abnormal components of the EEG. Second, the ELM algorithm is used to train a single hidden layer of feedforward neural network (SLFN) features. Finally, the SLFN is tested with interictal and ictal EEGs. The experiments demonstrated that the proposed approach achieved a satisfactory classification rate of 99.25% for interictal and ictal EEGs.

  2. Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

    Directory of Open Access Journals (Sweden)

    Dhiadeen Mohammed Salih

    2015-01-01

    Full Text Available A single hidden layer feedforward neural network (SLFN with online sequential extreme learning machine (OSELM algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.

  3. A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training.

    Science.gov (United States)

    Yang, Zhiyong; Zhang, Taohong; Zhang, Dezheng

    2016-02-01

    Extreme learning machine (ELM) is a novel and fast learning method to train single layer feed-forward networks. However due to the demand for larger number of hidden neurons, the prediction speed of ELM is not fast enough. An evolutionary based ELM with differential evolution (DE) has been proposed to reduce the prediction time of original ELM. But it may still get stuck at local optima. In this paper, a novel algorithm hybridizing DE and metaheuristic coral reef optimization (CRO), which is called differential evolution coral reef optimization (DECRO), is proposed to balance the explorative power and exploitive power to reach better performance. The thought and the implement of DECRO algorithm are discussed in this article with detail. DE, CRO and DECRO are applied to ELM training respectively. Experimental results show that DECRO-ELM can reduce the prediction time of original ELM, and obtain better performance for training ELM than both DE and CRO.

  4. Probabilistic Forecasting of Traffic Flow Using Multikernel Based Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Yiming Xing

    2017-01-01

    Full Text Available Real-time and accurate prediction of traffic flow is the key to intelligent transportation systems (ITS. However, due to the nonstationarity of traffic flow data, traditional point forecasting can hardly be accurate, so probabilistic forecasting methods are essential for quantification of the potential risks and uncertainties for traffic management. A probabilistic forecasting model of traffic flow based on a multikernel extreme learning machine (MKELM is proposed. Moreover, the optimal output weights of MKELM are obtained by utilizing Quantum-behaved particle swarm optimization (QPSO algorithm. To verify its effectiveness, traffic flow probabilistic prediction using QPSO-MKELM was compared with other learning methods. Experimental results show that QPSO-MKELM is more effective for practical applications. And it will help traffic managers to make right decisions.

  5. Classification of BMI control commands from rat's neural signals using extreme learning machine

    Directory of Open Access Journals (Sweden)

    Shin Hyung-Cheul

    2009-10-01

    Full Text Available Abstract A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34 of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.

  6. Extreme Learning Machines on High Dimensional and Large Data Applications: A Survey

    Directory of Open Access Journals (Sweden)

    Jiuwen Cao

    2015-01-01

    Full Text Available Extreme learning machine (ELM has been developed for single hidden layer feedforward neural networks (SLFNs. In ELM algorithm, the connections between the input layer and the hidden neurons are randomly assigned and remain unchanged during the learning process. The output connections are then tuned via minimizing the cost function through a linear system. The computational burden of ELM has been significantly reduced as the only cost is solving a linear system. The low computational complexity attracted a great deal of attention from the research community, especially for high dimensional and large data applications. This paper provides an up-to-date survey on the recent developments of ELM and its applications in high dimensional and large data. Comprehensive reviews on image processing, video processing, medical signal processing, and other popular large data applications with ELM are presented in the paper.

  7. Probabilistic Regularized Extreme Learning Machine for Robust Modeling of Noise Data.

    Science.gov (United States)

    Lu, XinJiang; Ming, Li; Liu, WenBo; Li, Han-Xiong

    2017-08-17

    The extreme learning machine (ELM) has been extensively studied in the machine learning field and has been widely implemented due to its simplified algorithm and reduced computational costs. However, it is less effective for modeling data with non-Gaussian noise or data containing outliers. Here, a probabilistic regularized ELM is proposed to improve modeling performance with data containing non-Gaussian noise and/or outliers. While traditional ELM minimizes modeling error by using a worst-case scenario principle, the proposed method constructs a new objective function to minimize both mean and variance of this modeling error. Thus, the proposed method considers the modeling error distribution. A solution method is then developed for this new objective function and the proposed method is further proved to be more robust when compared with traditional ELM, even when subject to noise or outliers. Several experimental cases demonstrate that the proposed method has better modeling performance for problems with non-Gaussian noise or outliers.

  8. Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines Approach

    Directory of Open Access Journals (Sweden)

    Zhi-Xin Yang

    2016-05-01

    Full Text Available Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extreme learning machines (ELM in a hierarchical structure, where a forwarding list of ELM layers is concatenated and each of them is processed independently for its corresponding role. The framework enables both representational feature learning and fault classification. The multi-layered ELM based representational learning covers functions including data preprocessing, feature extraction and dimension reduction. An ELM based autoencoder is trained to generate a hidden layer output weight matrix, which is then used to transform the input dataset into a new feature representation. Compared with the traditional feature extraction methods which may empirically wipe off some “insignificant’ feature information that in fact conveys certain undiscovered important knowledge, the introduced representational learning method could overcome the loss of information content. The computed output weight matrix projects the high dimensional input vector into a compressed and orthogonally weighted distribution. The last single layer of ELM is applied for fault classification. Unlike the greedy layer wise learning method adopted in back propagation based deep learning (DL, the proposed framework does not need iterative fine-tuning of parameters. To evaluate its experimental performance, comparison tests are carried out on a wind turbine generator simulator. The results show that the proposed diagnostic framework achieves the best performance among the compared approaches in terms of accuracy and efficiency in multiple faults detection of wind turbines.

  9. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines

    Science.gov (United States)

    Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

    2016-01-01

    The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures. PMID:27023563

  10. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

    Directory of Open Access Journals (Sweden)

    Heng Liu

    2017-01-01

    Full Text Available In recent years, learning based machine intelligence has aroused a lot of attention across science and engineering. Particularly in the field of automatic industry inspection, the machine learning based vision inspection plays a more and more important role in defect identification and feature extraction. Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect or not. However, the robustness and the quickness are not easily achieved in such inspection way. In this work, for solar panel vision inspection, we present an extreme learning machine (ELM and moving least square regression based approach to identify solder joint defect and detect the panel position. Firstly, histogram peaks distribution (HPD and fractional calculus are applied for image preprocessing. Then an ELM-based defective solder joints identification is discussed in detail. Finally, moving least square regression (MLSR algorithm is introduced for solar panel position determination. Experimental results and comparisons show that the proposed ELM and MLSR based inspection method is efficient not only in detection accuracy but also in processing speed.

  11. Concrete Condition Assessment Using Impact-Echo Method and Extreme Learning Machines.

    Science.gov (United States)

    Zhang, Jing-Kui; Yan, Weizhong; Cui, De-Mi

    2016-03-26

    The impact-echo (IE) method is a popular non-destructive testing (NDT) technique widely used for measuring the thickness of plate-like structures and for detecting certain defects inside concrete elements or structures. However, the IE method is not effective for full condition assessment (i.e., defect detection, defect diagnosis, defect sizing and location), because the simple frequency spectrum analysis involved in the existing IE method is not sufficient to capture the IE signal patterns associated with different conditions. In this paper, we attempt to enhance the IE technique and enable it for full condition assessment of concrete elements by introducing advanced machine learning techniques for performing comprehensive analysis and pattern recognition of IE signals. Specifically, we use wavelet decomposition for extracting signatures or features out of the raw IE signals and apply extreme learning machine, one of the recently developed machine learning techniques, as classification models for full condition assessment. To validate the capabilities of the proposed method, we build a number of specimens with various types, sizes, and locations of defects and perform IE testing on these specimens in a lab environment. Based on analysis of the collected IE signals using the proposed machine learning based IE method, we demonstrate that the proposed method is effective in performing full condition assessment of concrete elements or structures.

  12. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. SU-E-J-191: Motion Prediction Using Extreme Learning Machine in Image Guided Radiotherapy

    Energy Technology Data Exchange (ETDEWEB)

    Jia, J; Cao, R; Pei, X; Wang, H; Hu, L [Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031 (China); Engineering Technology Research Center of Accurate Radiotherapy of Anhui Province, Hefei 230031 (China); Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, SuZhou (China)

    2015-06-15

    Purpose: Real-time motion tracking is a critical issue in image guided radiotherapy due to the time latency caused by image processing and system response. It is of great necessity to fast and accurately predict the future position of the respiratory motion and the tumor location. Methods: The prediction of respiratory position was done based on the positioning and tracking module in ARTS-IGRT system which was developed by FDS Team (www.fds.org.cn). An approach involving with the extreme learning machine (ELM) was adopted to predict the future respiratory position as well as the tumor’s location by training the past trajectories. For the training process, a feed-forward neural network with one single hidden layer was used for the learning. First, the number of hidden nodes was figured out for the single layered feed forward network (SLFN). Then the input weights and hidden layer biases of the SLFN were randomly assigned to calculate the hidden neuron output matrix. Finally, the predicted movement were obtained by applying the output weights and compared with the actual movement. Breathing movement acquired from the external infrared markers was used to test the prediction accuracy. And the implanted marker movement for the prostate cancer was used to test the implementation of the tumor motion prediction. Results: The accuracy of the predicted motion and the actual motion was tested. Five volunteers with different breathing patterns were tested. The average prediction time was 0.281s. And the standard deviation of prediction accuracy was 0.002 for the respiratory motion and 0.001 for the tumor motion. Conclusion: The extreme learning machine method can provide an accurate and fast prediction of the respiratory motion and the tumor location and therefore can meet the requirements of real-time tumor-tracking in image guided radiotherapy.

  14. Facilitating mathematics learning for students with upper extremity disabilities using touch-input system.

    Science.gov (United States)

    Choi, Kup-Sze; Chan, Tak-Yin

    2015-03-01

    To investigate the feasibility of using tablet device as user interface for students with upper extremity disabilities to input mathematics efficiently into computer. A touch-input system using tablet device as user interface was proposed to assist these students to write mathematics. User-switchable and context-specific keyboard layouts were designed to streamline the input process. The system could be integrated with conventional computer systems only with minor software setup. A two-week pre-post test study involving five participants was conducted to evaluate the performance of the system and collect user feedback. The mathematics input efficiency of the participants was found to improve during the experiment sessions. In particular, their performance in entering trigonometric expressions by using the touch-input system was significantly better than that by using conventional mathematics editing software with keyboard and mouse. The participants rated the touch-input system positively and were confident that they could operate at ease with more practice. The proposed touch-input system provides a convenient way for the students with hand impairment to write mathematics and has the potential to facilitate their mathematics learning. Implications for Rehabilitation Students with upper extremity disabilities often face barriers to learning mathematics which is largely based on handwriting. Conventional computer user interfaces are inefficient for them to input mathematics into computer. A touch-input system with context-specific and user-switchable keyboard layouts was designed to improve the efficiency of mathematics input. Experimental results and user feedback suggested that the system has the potential to facilitate mathematics learning for the students.

  15. Single NMR image super-resolution based on extreme learning machine.

    Science.gov (United States)

    Wang, Zhiqiong; Xin, Junchang; Wang, Zhongyang; Tian, Shuo; Qiu, Xuejun

    2016-10-01

    The performance limitation of MRI equipment and higher resolution demand of NMR images from radiologists have formed a strong contrast. Therefore, it is important to study the super resolution algorithm suitable for NMR images, using low costs software to replace the expensive equipment-updating. Firstly, a series of NMR images are obtained from original NMR images with original noise to the lowest resolution images with the highest noise. Then, based on extreme learning machine, the mapping relation model is constructed from lower resolution NMR images with higher noise to higher resolution NMR images with lower noise in each pair of adjacent images in the obtained image sequence. Finally, the optimal mapping model is established by the ensemble way to reconstruct the higher resolution NMR images with lower noise on the basis of original resolution NMR images with original noise. Experiments are carried out by 990111 NMR brain images in datasets NITRC, REMBRANDT, RIDER NEURO MRI, TCGA-GBM and TCGA-LGG. The performance of proposed method is compared with three approaches through 7 indexes, and the experimental results show that our proposed method has a significant improvement. Since our method considers the influence of the noise, it has 20% higher in Peak-Signal-to-Noise-Ratio comparison. As our method is sensitive to details, and has a better characteristic retention, it has higher image quality upgrade of 15% in the additional evaluation. Finally, since extreme learning machine has a celerity learning speed, our method is 46.1% faster. Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

  16. A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

    Science.gov (United States)

    Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

    2017-06-19

    A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

  17. [Drug discrimination by near infrared spectroscopy based on summation wavelet extreme learning machine].

    Science.gov (United States)

    Liu, Zhen-Bing; Jiang, Shu-Jie; Yang, Hui-Hua; Zhang, Xue-Bo

    2014-10-01

    As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM' s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near in- frared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper

  18. Prune Belly syndrome: A rare case report.

    Science.gov (United States)

    Samal, Sunil Kumar; Rathod, Setu

    2015-01-01

    Prune Belly syndrome (PBS) is a rare congenital anomaly of uncertain etiology almost exclusive to males. We report a case of term male baby born to a 39-year-old grand multipara with previous four normal vaginal births. There was no history of genetic or congenital anomaly in her family. Examination of the baby revealed hypotonia, deficient abdominal muscle, cryptorchidism, palpable kidney, and bladder. Ultrasound examination of the abdomen revealed bilateral gross hydronephrosis and megaureter. Provisional diagnosis of PBS was made and the baby was admitted in neonatal intensive care units for further management. Routine antenatal care with ultrasonography will help in detecting renal anomalies, which can be followed postnatally. Early diagnosis of this syndrome and determining its optimal treatment are very important in helping to avoid its fatal course.

  19. Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images.

    Science.gov (United States)

    Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Jiang, Mengying; Ling, Wing-Kuen

    2017-11-13

    As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are an improvement of linear ELM (LELM). However, based on lots of experiments and much analysis, it is found that the LELM is a better choice than nonlinear ELM for the spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learns such a distribution using the LBP. The proposed method not only maintains the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines, and Pavia University, demonstrate the good performance of the proposed method.

  20. An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

    Directory of Open Access Journals (Sweden)

    Marjan Mansourvar

    Full Text Available Assessing skeletal age is a subjective and tedious examination process. Hence, automated assessment methods have been developed to replace manual evaluation in medical applications. In this study, a new fully automated method based on content-based image retrieval and using extreme learning machines (ELM is designed and adapted to assess skeletal maturity. The main novelty of this approach is it overcomes the segmentation problem as suffered by existing systems. The estimation results of ELM models are compared with those of genetic programming (GP and artificial neural networks (ANNs models. The experimental results signify improvement in assessment accuracy over GP and ANN, while generalization capability is possible with the ELM approach. Moreover, the results are indicated that the ELM model developed can be used confidently in further work on formulating novel models of skeletal age assessment strategies. According to the experimental results, the new presented method has the capacity to learn many hundreds of times faster than traditional learning methods and it has sufficient overall performance in many aspects. It has conclusively been found that applying ELM is particularly promising as an alternative method for evaluating skeletal age.

  1. Online prediction of glucose concentration in type 1 diabetes using extreme learning machines.

    Science.gov (United States)

    Georga, Eleni I; Protopappas, Vasilios C; Polyzos, Demosthenes; Fotiadis, Dimitrios I

    2015-01-01

    We propose an online machine-learning solution to the problem of nonlinear glucose time series prediction in type 1 diabetes. Recently, extreme learning machine (ELM) has been proposed for training single hidden layer feed-forward neural networks. The high accuracy and fast learning speed of ELM drive us to investigate its applicability to the glucose prediction problem. Given that diabetes self-monitoring data are received sequentially, we focus on online sequential ELM (OS-ELM) and online sequential ELM kernels (KOS-ELM). A multivariate feature set is utilized concerning subcutaneous glucose, insulin therapy, carbohydrates intake and physical activity. The dataset comes from the continuous multi-day recordings of 15 type 1 patients in free-living conditions. Assuming stationarity and evaluating the performance of the proposed method by 10-fold cross- validation, KOS-ELM were found to perform better than OS-ELM in terms of prediction error, temporal gain and regularity of predictions for a 30-min prediction horizon.

  2. Modification of Hidden Layer Weight in Extreme Learning Machine Using Gain Ratio

    Directory of Open Access Journals (Sweden)

    Anggraeny Fetty Tri

    2016-01-01

    Full Text Available Extreme Learning Machine (ELM is a method of learning feed forward neural network quickly and has a fairly good accuracy. This method is devoted to a feed forward neural network with one hidden layer where the parameters (i.e. weight and bias are adjusted one time randomly at the beginning of the learning process. In neural network, the input layer is connected to all characteristics/features, and the output layer is connected to all classes of species. This research used three datasets from UCI database, which were Iris, Breast Wisconsin, and Dermatology, with each dataset having several features. Each characteristic/feature of the data has a role in the process of classification levels, starting from the most influencing role to non-influencing at all. Gain ratio was used to extract each feature role on each datasets. Gain ratio is a method to extract feature role in order to develop a decision tree structure. In this study, ELM structure has been modified, where the random weights of the hidden layer were adjusted to the level of each feature role in determining the species class, so as to improve the level of training and testing accuracy. The proposed method has higher classification accuracy rate than basic ELM on all three datasets, which were 99%, 96%, and 82%, respectively.

  3. A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine †

    Science.gov (United States)

    Zou, Han; Lu, Xiaoxuan; Jiang, Hao; Xie, Lihua

    2015-01-01

    Nowadays, developing indoor positioning systems (IPSs) has become an attractive research topic due to the increasing demands on location-based service (LBS) in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM) to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics. PMID:25599427

  4. A Fast and Precise Indoor Localization Algorithm Based on an Online Sequential Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Han Zou

    2015-01-01

    Full Text Available Nowadays, developing indoor positioning systems (IPSs has become an attractive research topic due to the increasing demands on location-based service (LBS in indoor environments. WiFi technology has been studied and explored to provide indoor positioning service for years in view of the wide deployment and availability of existing WiFi infrastructures in indoor environments. A large body of WiFi-based IPSs adopt fingerprinting approaches for localization. However, these IPSs suffer from two major problems: the intensive costs of manpower and time for offline site survey and the inflexibility to environmental dynamics. In this paper, we propose an indoor localization algorithm based on an online sequential extreme learning machine (OS-ELM to address the above problems accordingly. The fast learning speed of OS-ELM can reduce the time and manpower costs for the offline site survey. Meanwhile, its online sequential learning ability enables the proposed localization algorithm to adapt in a timely manner to environmental dynamics. Experiments under specific environmental changes, such as variations of occupancy distribution and events of opening or closing of doors, are conducted to evaluate the performance of OS-ELM. The simulation and experimental results show that the proposed localization algorithm can provide higher localization accuracy than traditional approaches, due to its fast adaptation to various environmental dynamics.

  5. A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces.

    Science.gov (United States)

    Chen, Yi; Yao, Enyi; Basu, Arindam

    2016-06-01

    Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35- μm CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by ≈ 2X.

  6. The National Integrated Heat Health Information System (NIHHIS) as a Learning System for Extreme Heat: Evolving Future Resilience from Present Climate Extremes

    Science.gov (United States)

    Jones, H.; Trtanj, J.; Pulwarty, R. S.; Higgins, W.

    2016-12-01

    There is presently no consensus indicator for the effect of extreme heat on human health. At the early warning timescale, a variety of approaches to setting temperature thresholds (minimum, maximum, time-lagged) or more complex approaches (Heat Index, Thermal Comfort, etc...) for issuing alerts and warnings have been recommended by literature and implemented, leading to much heterogeneity. At longer timescales, efforts have been made to quantify potential future health outcomes using climate projections, but nonstationarity of the climate system, economy, and demography may invalidate many of the assumptions which were necessarily made in these studies. Furthermore, in our pursuit of developing the best models and indicators to represent the impacts of climate extremes, perhaps we have not paid enough attention to what makes them policy-relevant, responsive to changing assumptions, and targeted at elements that can actually be predicted. In response to this concern, a comprehensive approach to improving the impactfulness of these indicators is underway as part of the National Integrated Heat Health Information System (NIHHIS), which was initiated by NOAA and CDC, but has grown to include many other federal agency and non-governmental partners. NIHHIS is a framework that integrates what we know about extreme heat and health outcomes within a learning system - simultaneously informing early warning and long-term risk reduction prior to, during, and while recovering from extreme heat events. NIHHIS develops impactful evolutionary responses to climate extremes. Through ongoing regional engagements, we are applying the lessons of impact modeling studies to create learning systems in the Southwest, Northeast, Midwest, and soon other regions of the U.S. This session will provide a view of this process as it has been carried out in the Southwest region - focused on the transboundary (US-Mexico) region around El Paso, Texas, and the NIHHIS approach to indicators overall.

  7. Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition

    Directory of Open Access Journals (Sweden)

    Zheng Chang

    2015-01-01

    Full Text Available Based on the traditional machine vision recognition technology and traditional artificial neural networks about body movement trajectory, this paper finds out the shortcomings of the traditional recognition technology. By combining the invariant moments of the three-dimensional motion history image (computed as the eigenvector of body movements and the extreme learning machine (constructed as the classification artificial neural network of body movements, the paper applies the method to the machine vision of the body movement trajectory. In detail, the paper gives a detailed introduction about the algorithm and realization scheme of the body movement trajectory recognition based on the three-dimensional motion history image and the extreme learning machine. Finally, by comparing with the results of the recognition experiments, it attempts to verify that the method of body movement trajectory recognition technology based on the three-dimensional motion history image and extreme learning machine has a more accurate recognition rate and better robustness.

  8. Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Xue-cun Yang

    2015-01-01

    Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

  9. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction

    Directory of Open Access Journals (Sweden)

    Jie Zhang

    2017-04-01

    Full Text Available Device-free localization (DFL is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF DFL system, radio transmitters (RTs and radio receivers (RXs are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN, support vector machine (SVM, back propagation neural network (BPNN, as well as the well-known radio tomographic imaging (RTI DFL approach.

  10. Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.

    Science.gov (United States)

    Zhang, Jie; Xiao, Wendong; Zhang, Sen; Huang, Shoudong

    2017-04-17

    Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.

  11. A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine.

    Science.gov (United States)

    Duan, Mingxing; Li, Kenli; Liao, Xiangke; Li, Keqin

    2017-04-24

    As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs. In this paper, an efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification. By partitioning the corresponding data sets reasonably, the hidden layer output matrix calculation algorithm, matrix Û decomposition algorithm, and matrix V decomposition algorithm perform most of the computations locally. At the same time, they retain the intermediate results in distributed memory and cache the diagonal matrix as broadcast variables instead of several copies for each task to reduce a large amount of the costs, and these actions strengthen the learning ability of the SELM. Finally, we implement our SELM algorithm to classify large data sets. Extensive experiments have been conducted to validate the effectiveness of the proposed algorithms. As shown, our SELM achieves an 8.71x speedup on a cluster with ten nodes, and reaches a 13.79x speedup with 15 nodes, an 18.74x speedup with 20 nodes, a 23.79x speedup with 25 nodes, a 28.89x speedup with 30 nodes, and a 33.81x speedup with 35 nodes.

  12. Extreme learning machines for regression based on V-matrix method.

    Science.gov (United States)

    Yang, Zhiyong; Zhang, Taohong; Lu, Jingcheng; Su, Yuan; Zhang, Dezheng; Duan, Yaowu

    2017-10-01

    This paper studies the joint effect of V-matrix, a recently proposed framework for statistical inferences, and extreme learning machine (ELM) on regression problems. First of all, a novel algorithm is proposed to efficiently evaluate the V-matrix. Secondly, a novel weighted ELM algorithm called V-ELM is proposed based on the explicit kernel mapping of ELM and the V-matrix method. Though V-matrix method could capture the geometrical structure of training data, it tends to assign a higher weight to instance with smaller input value. In order to avoid this bias, a novel method called VI-ELM is proposed by minimizing both the regression error and the V-matrix weighted error simultaneously. Finally, experiment results on 12 real world benchmark datasets show the effectiveness of our proposed methods.

  13. Extreme learning machine based spatiotemporal modeling of lithium-ion battery thermal dynamics

    Science.gov (United States)

    Liu, Zhen; Li, Han-Xiong

    2015-03-01

    Due to the overwhelming complexity of the electrochemical related behaviors and internal structure of lithium ion batteries, it is difficult to obtain an accurate mathematical expression of their thermal dynamics based on the physical principal. In this paper, a data based thermal model which is suitable for online temperature distribution estimation is proposed for lithium-ion batteries. Based on the physics based model, a simple but effective low order model is obtained using the Karhunen-Loeve decomposition method. The corresponding uncertain chemical related heat generation term in the low order model is approximated using extreme learning machine. All uncertain parameters in the low order model can be determined analytically in a linear way. Finally, the temperature distribution of the whole battery can be estimated in real time based on the identified low order model. Simulation results demonstrate the effectiveness of the proposed model. The simple training process of the model makes it superior for onboard application.

  14. New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

    Directory of Open Access Journals (Sweden)

    Xiguang Li

    2017-01-01

    Full Text Available Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA, is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent.

  15. Prediction Interval Construction for Byproduct Gas Flow Forecasting Using Optimized Twin Extreme Learning Machine

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    Xueying Sun

    2017-01-01

    Full Text Available Prediction of byproduct gas flow is of great significance to gas system scheduling in iron and steel plants. To quantify the associated prediction uncertainty, a two-step approach based on optimized twin extreme learning machine (ELM is proposed to construct prediction intervals (PIs. In the first step, the connection weights of the twin ELM are pretrained using a pair of symmetric weighted objective functions. In the second step, output weights of the twin ELM are further optimized by particle swarm optimization (PSO. The objective function is designed to comprehensively evaluate PIs based on their coverage probability, width, and deviation. The capability of the proposed method is validated using four benchmark datasets and two real-world byproduct gas datasets. The results demonstrate that the proposed approach constructs higher quality prediction intervals than the other three conventional methods.

  16. Extreme Learning Machine for Reservoir Parameter Estimation in Heterogeneous Sandstone Reservoir

    Directory of Open Access Journals (Sweden)

    Jianhua Cao

    2015-01-01

    Full Text Available This study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by conventional petrophysical methods in wells without core data. 4950 samples from 8 wells with core data have been used to train and validate the neural network, and robust ELM algorithm provides fast and accurate prediction results, which is also testified by comparison with BP (back propagation network and SVM (support vector machine approaches. The network model is then applied to estimate porosity and permeability for the remaining wells. The predicted attributes match well with the oil test conclusions. Based on the estimations, reservoir porosity and permeability have been mapped and analyzed. Two favorable zones have been suggested for further research in the survey.

  17. Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Guangyong Gao

    2015-01-01

    Full Text Available Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE technique. Firstly, the extreme learning machine (ELM with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED predictor and gradient-adjusted predictor (GAP, the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches.

  18. A Distributed Algorithm for the Cluster-Based Outlier Detection Using Unsupervised Extreme Learning Machines

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    Xite Wang

    2017-01-01

    Full Text Available Outlier detection is an important data mining task, whose target is to find the abnormal or atypical objects from a given dataset. The techniques for detecting outliers have a lot of applications, such as credit card fraud detection and environment monitoring. Our previous work proposed the Cluster-Based (CB outlier and gave a centralized method using unsupervised extreme learning machines to compute CB outliers. In this paper, we propose a new distributed algorithm for the CB outlier detection (DACB. On the master node, we collect a small number of points from the slave nodes to obtain a threshold. On each slave node, we design a new filtering method that can use the threshold to efficiently speed up the computation. Furthermore, we also propose a ranking method to optimize the order of cluster scanning. At last, the effectiveness and efficiency of the proposed approaches are verified through a plenty of simulation experiments.

  19. Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors

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    Yanjun Zhang

    2015-01-01

    Full Text Available A new optimized extreme learning machine- (ELM- based method for power system transient stability prediction (TSP using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.

  20. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis.

    Science.gov (United States)

    Li, Qiang; Chen, Huiling; Huang, Hui; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

  1. Non-invasive hypoglycemia monitoring system using extreme learning machine for Type 1 diabetes.

    Science.gov (United States)

    Ling, Sai Ho; San, Phyo Phyo; Nguyen, Hung T

    2016-09-01

    Hypoglycemia is a very common in type 1 diabetic persons and can occur at any age. It is always threatening to the well-being of patients with Type 1 diabetes mellitus (T1DM) since hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Because of that, an accurate continuing hypoglycemia monitoring system is a very important medical device for diabetic patients. In this paper, we proposed a non-invasive hypoglycemia monitoring system using the physiological parameters of electrocardiography (ECG) signal. To enhance the detection accuracy, extreme learning machine (ELM) is developed to recognize the presence of hypoglycemia. A clinical study of 16 children with T1DM is given to illustrate the good performance of ELM. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  2. New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

    Science.gov (United States)

    Li, Xiguang; Zhao, Liang; Gong, Changqing; Liu, Xiaojing

    2017-01-01

    Inspired by the behavior of dandelion sowing, a new novel swarm intelligence algorithm, namely, dandelion algorithm (DA), is proposed for global optimization of complex functions in this paper. In DA, the dandelion population will be divided into two subpopulations, and different subpopulations will undergo different sowing behaviors. Moreover, another sowing method is designed to jump out of local optimum. In order to demonstrate the validation of DA, we compare the proposed algorithm with other existing algorithms, including bat algorithm, particle swarm optimization, and enhanced fireworks algorithm. Simulations show that the proposed algorithm seems much superior to other algorithms. At the same time, the proposed algorithm can be applied to optimize extreme learning machine (ELM) for biomedical classification problems, and the effect is considerable. At last, we use different fusion methods to form different fusion classifiers, and the fusion classifiers can achieve higher accuracy and better stability to some extent. PMID:29085425

  3. Prediction of laser cutting heat affected zone by extreme learning machine

    Science.gov (United States)

    Anicic, Obrad; Jović, Srđan; Skrijelj, Hivzo; Nedić, Bogdan

    2017-01-01

    Heat affected zone (HAZ) of the laser cutting process may be developed based on combination of different factors. In this investigation the HAZ forecasting, based on the different laser cutting parameters, was analyzed. The main goal was to predict the HAZ according to three inputs. The purpose of this research was to develop and apply the Extreme Learning Machine (ELM) to predict the HAZ. The ELM results were compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and by using several statistical indicators. Based upon simulation results, it was demonstrated that ELM can be utilized effectively in applications of HAZ forecasting.

  4. Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

    Science.gov (United States)

    Jongin Kim; Boreom Lee

    2017-07-01

    The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

  5. Learning Disabilities in Extremely Low Birth Weight Children and Neurodevelopmental Profiles at Preschool Age.

    Science.gov (United States)

    Squarza, Chiara; Picciolini, Odoardo; Gardon, Laura; Giannì, Maria L; Murru, Alessandra; Gangi, Silvana; Cortinovis, Ivan; Milani, Silvano; Mosca, Fabio

    2016-01-01

    At school age extremely low birth weight (ELBW) and extremely low gestational age (ELGAN) children are more likely to show Learning Disabilities (LDs) and difficulties in emotional regulation. The aim of this study was to investigate the incidence of LDs at school age and to detect neurodevelopmental indicators of risk for LDs at preschool ages in a cohort of ELBW/ELGAN children with broadly average intelligence. All consecutively newborns 2001-2006 admitted to the same Institution entered the study. Inclusion criteria were BW learning disabilities at school age was investigated through a parent-report questionnaire at children's age range 9-10 years. Neurodevelopmental profiles were assessed through the Griffiths Mental Development Scales at 1 and 2 years of corrected age and at 3, 4, 5, and 6 years of chronological age and were analyzed comparing two groups of children: those with LDs and those without. At school age 24 on 102 (23.5%) of our ELBW/ELGAN children met criteria for LDs in one or more areas, with 70.8% comorbidity with emotional/attention difficulties. Children with LDs scored significantly lower in the Griffiths Locomotor and Language subscales at 2 years of corrected age and in the Personal-social, Performance and Practical Reasoning subscales at 5 years of chronological age. Our findings suggest that, among the early developmental indicators of adverse school outcome, there is a poor motor experimentation, language delay, and personal-social immaturity. Cognitive rigidity and poor ability to manage practical situations also affect academic attainment. Timely detection of these early indicators of risk is crucial to assist the transition to school.

  6. A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.

    Science.gov (United States)

    Deng, Wan-Yu; Bai, Zuo; Huang, Guang-Bin; Zheng, Qing-Hua

    2016-05-01

    Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine.

    Science.gov (United States)

    Huang, Zhiyong; Yu, Yuanlong; Gu, Jason; Liu, Huaping

    2017-04-01

    This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.

  8. A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron.

    Science.gov (United States)

    Ortín, S; Soriano, M C; Pesquera, L; Brunner, D; San-Martín, D; Fischer, I; Mirasso, C R; Gutiérrez, J M

    2015-10-08

    In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of "virtual" neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.

  9. Partial prune belly syndrome: A rare case report

    OpenAIRE

    Aditya Pratap Singh; Vinay Mathur; Ramesh Tanger; Arun Kumar Gupta

    2017-01-01

    Prune belly syndrome (PBS) is characterized by deficient development of abdominal muscles that causes the skin of the abdomen to wrinkle like a prune, bilateral cryptorchidism, abnormalities of the urinary tract. The etiology of PBS is unclear and possible familial genetic inheritance was reported in some of the studies. We are presenting here a case with the absence of the muscle in the right side of the abdomen as hernia, thinning of the muscle on left side with bilateral cryptorchidism, an...

  10. A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

    Directory of Open Access Journals (Sweden)

    Qingsong Xu

    2014-01-01

    Full Text Available Extreme learning machine (ELM is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have been developed for the location prediction. Comparative studies of the basic ELM, kernel-based ELM, and LSSVM models are carried out. Results show that the kernel-based ELM requires the shortest learning time and it is capable of producing suboptimal localization accuracy among the three models. Hence, ELM paves a promising way in structural impact detection.

  11. RMSE-ELM: Recursive Model Based Selective Ensemble of Extreme Learning Machines for Robustness Improvement

    Directory of Open Access Journals (Sweden)

    Bo Han

    2014-01-01

    Full Text Available For blended data, the robustness of extreme learning machine (ELM is so weak because the coefficients (weights and biases of hidden nodes are set randomly and the noisy data exert a negative effect. To solve this problem, a new framework called “RMSE-ELM” is proposed in this paper. It is a two-layer recursive model. In the first layer, the framework trains lots of ELMs in different ensemble groups concurrently and then employs selective ensemble approach to pick out an optimal set of ELMs in each group, which can be merged into a large group of ELMs called candidate pool. In the second layer, selective ensemble approach is recursively used on candidate pool to acquire the final ensemble. In the experiments, we apply UCI blended datasets to confirm the robustness of our new approach in two key aspects (mean square error and standard deviation. The space complexity of our method is increased to some degree, but the result has shown that RMSE-ELM significantly improves robustness with a rapid learning speed compared to representative methods (ELM, OP-ELM, GASEN-ELM, GASEN-BP, and E-GASEN. It becomes a potential framework to solve robustness issue of ELM for high-dimensional blended data in the future.

  12. Color-texture based extreme learning machines for tissue tumor classification

    Science.gov (United States)

    Yang, X.; Yeo, S. Y.; Wong, S. T.; Lee, G.; Su, Y.; Hong, J. M.; Choo, A.; Chen, S.

    2016-03-01

    In histopathological classification and diagnosis of cancer cases, pathologists perform visual assessments of immunohistochemistry (IHC)-stained biomarkers in cells to determine tumor versus non-tumor tissues. One of the prerequisites for such assessments is the correct identification of regions-of-interest (ROIs) with relevant histological features. Advances in image processing and machine learning give rise to the possibility of full automation in ROI identification by identifying image features such as colors and textures. Such computer-aided diagnostic systems could enhance research output and efficiency in identifying the pathology (normal, non-tumor or tumor) of a tissue pattern from ROI images. In this paper, a computational method using color-texture based extreme learning machines (ELM) is proposed for automatic tissue tumor classification. Our approach consists of three steps: (1) ROIs are manually identified and annotated from individual cores of tissue microarrays (TMAs); (2) color and texture features are extracted from the ROIs images; (3) ELM is applied to the extracted features to classify the ROIs into non-tumor or tumor categories. The proposed approach is tested on 100 sets of images from a kidney cancer TMA and the results show that ELM is able to achieve classification accuracies of 91.19% and 88.72% with a Gaussian radial basis function (RBF) and linear kernel, respectively, which is superior to using SVM with the same kernels.

  13. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines.

    Science.gov (United States)

    Abuassba, Adnan O M; Zhang, Dezheng; Luo, Xiong; Shaheryar, Ahmad; Ali, Hazrat

    2017-01-01

    Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble). The validity of AELME is confirmed through classification on several real-world benchmark datasets.

  14. Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Chen Chen

    2014-06-01

    Full Text Available Extreme learning machine (ELM is a single-layer feedforward neural network based classifier that has attracted significant attention in computer vision and pattern recognition due to its fast learning speed and strong generalization. In this paper, we propose to integrate spectral-spatial information for hyperspectral image classification and exploit the benefits of using spatial features for the kernel based ELM (KELM classifier. Specifically, Gabor filtering and multihypothesis (MH prediction preprocessing are two approaches employed for spatial feature extraction. Gabor features have currently been successfully applied for hyperspectral image analysis due to the ability to represent useful spatial information. MH prediction preprocessing makes use of the spatial piecewise-continuous nature of hyperspectral imagery to integrate spectral and spatial information. The proposed Gabor-filtering-based KELM classifier and MH-prediction-based KELM classifier have been validated on two real hyperspectral datasets. Classification results demonstrate that the proposed methods outperform the conventional pixel-wise classifiers as well as Gabor-filtering-based support vector machine (SVM and MH-prediction-based SVM in challenging small training sample size conditions.

  15. Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure

    Directory of Open Access Journals (Sweden)

    Shan Pang

    2016-01-01

    Full Text Available A new aero gas turbine engine gas path component fault diagnosis method based on multi-hidden-layer extreme learning machine with optimized structure (OM-ELM was proposed. OM-ELM employs quantum-behaved particle swarm optimization to automatically obtain the optimal network structure according to both the root mean square error on training data set and the norm of output weights. The proposed method is applied to handwritten recognition data set and a gas turbine engine diagnostic application and is compared with basic ELM, multi-hidden-layer ELM, and two state-of-the-art deep learning algorithms: deep belief network and the stacked denoising autoencoder. Results show that, with optimized network structure, OM-ELM obtains better test accuracy in both applications and is more robust to sensor noise. Meanwhile it controls the model complexity and needs far less hidden nodes than multi-hidden-layer ELM, thus saving computer memory and making it more efficient to implement. All these advantages make our method an effective and reliable tool for engine component fault diagnosis tool.

  16. Random feature subspace ensemble based Extreme Learning Machine for liver tumor detection and segmentation.

    Science.gov (United States)

    Huang, Weimin; Yang, Yongzhong; Lin, Zhiping; Huang, Guang-Bin; Zhou, Jiayin; Duan, Yuping; Xiong, Wei

    2014-01-01

    This paper presents a new approach to detect and segment liver tumors. The detection and segmentation of liver tumors can be formulized as novelty detection or two-class classification problem. Each voxel is characterized by a rich feature vector, and a classifier using random feature subspace ensemble is trained to classify the voxels. Since Extreme Learning Machine (ELM) has advantages of very fast learning speed and good generalization ability, it is chosen to be the base classifier in the ensemble. Besides, majority voting is incorporated for fusion of classification results from the ensemble of base classifiers. In order to further increase testing accuracy, ELM autoencoder is implemented as a pre-training step. In automatic liver tumor detection, ELM is trained as a one-class classifier with only healthy liver samples, and the performance is compared with two-class ELM. In liver tumor segmentation, a semi-automatic approach is adopted by selecting samples in 3D space to train the classifier. The proposed method is tested and evaluated on a group of patients' CT data and experiment show promising results.

  17. Depth-based human fall detection via shape features and improved extreme learning machine.

    Science.gov (United States)

    Ma, Xin; Wang, Haibo; Xue, Bingxia; Zhou, Mingang; Ji, Bing; Li, Yibin

    2014-11-01

    Falls are one of the major causes leading to injury of elderly people. Using wearable devices for fall detection has a high cost and may cause inconvenience to the daily lives of the elderly. In this paper, we present an automated fall detection approach that requires only a low-cost depth camera. Our approach combines two computer vision techniques-shape-based fall characterization and a learning-based classifier to distinguish falls from other daily actions. Given a fall video clip, we extract curvature scale space (CSS) features of human silhouettes at each frame and represent the action by a bag of CSS words (BoCSS). Then, we utilize the extreme learning machine (ELM) classifier to identify the BoCSS representation of a fall from those of other actions. In order to eliminate the sensitivity of ELM to its hyperparameters, we present a variable-length particle swarm optimization algorithm to optimize the number of hidden neurons, corresponding input weights, and biases of ELM. Using a low-cost Kinect depth camera, we build an action dataset that consists of six types of actions (falling, bending, sitting, squatting, walking, and lying) from ten subjects. Experimenting with the dataset shows that our approach can achieve up to 91.15% sensitivity, 77.14% specificity, and 86.83% accuracy. On a public dataset, our approach performs comparably to state-of-the-art fall detection methods that need multiple cameras.

  18. Daily sea level prediction at Chiayi coast, Taiwan using extreme learning machine and relevance vector machine

    Science.gov (United States)

    Imani, Moslem; Kao, Huan-Chin; Lan, Wen-Hau; Kuo, Chung-Yen

    2018-02-01

    The analysis and the prediction of sea level fluctuations are core requirements of marine meteorology and operational oceanography. Estimates of sea level with hours-to-days warning times are especially important for low-lying regions and coastal zone management. The primary purpose of this study is to examine the applicability and capability of extreme learning machine (ELM) and relevance vector machine (RVM) models for predicting sea level variations and compare their performances with powerful machine learning methods, namely, support vector machine (SVM) and radial basis function (RBF) models. The input dataset from the period of January 2004 to May 2011 used in the study was obtained from the Dongshi tide gauge station in Chiayi, Taiwan. Results showed that the ELM and RVM models outperformed the other methods. The performance of the RVM approach was superior in predicting the daily sea level time series given the minimum root mean square error of 34.73 mm and the maximum determination coefficient of 0.93 (R2) during the testing periods. Furthermore, the obtained results were in close agreement with the original tide-gauge data, which indicates that RVM approach is a promising alternative method for time series prediction and could be successfully used for daily sea level forecasts.

  19. Automated melanoma recognition in dermoscopic images based on extreme learning machine (ELM)

    Science.gov (United States)

    Rahman, Md. Mahmudur; Alpaslan, Nuh

    2017-03-01

    Melanoma is considered a major health problem since it is the deadliest form of skin cancer. The early diagnosis through periodic screening with dermoscopic images can significantly improve the survival rate as well as reduce the treatment cost and consequent suffering of patients. Dermoscopy or skin surface microscopy provides in vivo inspection of color and morphologic structures of pigmented skin lesions (PSLs), rendering higher accuracy for detecting suspicious cases than it is possible via inspecting with naked eye. However, interpretation of dermoscopic images is time consuming and subjective, even for trained dermatologists. Therefore, there is currently a great interest in the development of computeraided diagnosis (CAD) systems for automated melanoma recognition. However, the majority of the CAD systems are still in the early development stage with lack of descriptive feature generation and benchmark evaluation in ground-truth datasets. This work is focusing on by addressing the various issues related to the development of such a CAD system with effective feature extraction from Non-Subsampled Contourlet Transform (NSCT) and Eig(Hess) histogram of oriented gradients (HOG) and lesion classification with efficient Extreme Learning Machine (ELM) due to its good generalization abilities and a high learning efficiency and evaluating its effectiveness in a benchmark data set of dermoscopic images towards the goal of realistic comparison and real clinical integration. The proposed research on melanoma recognition has huge potential for offering powerful services that would significantly benefit the present Biomedical Information Systems.

  20. Comprehensive Modeling of U-Tube Steam Generators Using Extreme Learning Machines

    Science.gov (United States)

    Beyhan, Selami; Kavaklioglu, Kadir

    2015-10-01

    This paper proposes artificial neural network and fuzzy system-based extreme learning machines (ELM) for offline and online modeling of U-tube steam generators (UTSG). Water level of UTSG systems is predicted in a one-step-ahead fashion using nonlinear autoregressive with exogenous input (NARX) topology. Modeling data are generated using a well-known and widely accepted dynamic model reported in the literature. Model performances are analyzed with different number of neurons for the neural network and with different number of rules for the fuzzy system. UTSG models are built at different reactor power levels as well as full range that corresponds to all reactor operating powers. A quantitative comparison of the models are made using the root-mean-squared error (RMSE) and the minimum-descriptive-length (MDL) criteria. Furthermore, conventional back propagation learning-based neural and fuzzy models are also designed for comparing ELMs to classical artificial models. The advantages and disadvantages of the designed models are discussed.

  1. A novel approach for lie detection based on F-score and extreme learning machine.

    Directory of Open Access Journals (Sweden)

    Junfeng Gao

    Full Text Available A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.

  2. Forecasting Computer Products Sales by Integrating Ensemble Empirical Mode Decomposition and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Chi-Jie Lu

    2012-01-01

    Full Text Available A hybrid forecasting model that integrates ensemble empirical model decomposition (EEMD, and extreme learning machine (ELM for computer products sales is proposed. The EEMD is a new piece of signal processing technology. It is based on the local characteristic time scales of a signal and could decompose the complicated signal into intrinsic mode functions (IMFs. The ELM is a novel learning algorithm for single-hidden-layer feedforward networks. In our proposed approach, the initial task is to apply the EEMD method to decompose the original sales data into a number of IMFs. The hidden useful information of the original data could be discovered in those IMFs. The IMFs are then integrated with the ELM method to develop an effective forecasting model for computer products sales. Experimental results from three real computer products sales data, including hard disk, display card, and notebook, showed that the proposed hybrid sales forecasting method outperforms the four comparative models and is an effective alternative for forecasting sales of computer products.

  3. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine

    Science.gov (United States)

    Mao, Wentao; He, Ling; Yan, Yunju; Wang, Jinwan

    2017-01-01

    Diagnosis of bearings generally plays an important role in fault diagnosis of mechanical system, and machine learning has been a promising tool in this field. In many real applications of bearings fault diagnosis, the data tend to be online imbalanced, which means, the number of fault data is much less than the normal data while they are all collected in online sequential way. Suffering from this problem, many traditional diagnosis methods will get low accuracy of fault data which acts as the minority class in the collected bearing data. To address this problem, an online sequential prediction method for imbalanced fault diagnosis problem is proposed based on extreme learning machine. This method introduces the principal curve and granulation division to simulate the flow distribution and overall distribution characteristics of fault data, respectively. Then a confident over-sampling and under-sampling process is proposed to establish the initial offline diagnosis model. In online stage, the obtained granules and principal curves are rebuilt on the bearing data which are arrived in sequence, and after the over-sampling and under-sampling process, the balanced sample set is formed to update the diagnosis model dynamically. A theoretical analysis is provided and proves that, even existing information loss, the proposed method has lower bound of the model reliability. Simulation experiments are conducted on IMS bearing data and CWRU bearing data. The comparative results demonstrate that the proposed method can improve the fault diagnosis accuracy with better effectiveness and robustness than other algorithms.

  4. A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

    Science.gov (United States)

    Gao, Junfeng; Wang, Zhao; Yang, Yong; Zhang, Wenjia; Tao, Chunyi; Guan, Jinan; Rao, Nini

    2013-01-01

    A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time. PMID:23755136

  5. Using Blood Indexes to Predict Overweight Statuses: An Extreme Learning Machine-Based Approach.

    Science.gov (United States)

    Chen, Huiling; Yang, Bo; Liu, Dayou; Liu, Wenbin; Liu, Yanlong; Zhang, Xiuhua; Hu, Lufeng

    2015-01-01

    The number of the overweight people continues to rise across the world. Studies have shown that being overweight can increase health risks, such as high blood pressure, diabetes mellitus, coronary heart disease, and certain forms of cancer. Therefore, identifying the overweight status in people is critical to prevent and decrease health risks. This study explores a new technique that uses blood and biochemical measurements to recognize the overweight condition. A new machine learning technique, an extreme learning machine, was developed to accurately detect the overweight status from a pool of 225 overweight and 251 healthy subjects. The group included 179 males and 297 females. The detection method was rigorously evaluated against the real-life dataset for accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC) curve) criterion. Additionally, the feature selection was investigated to identify correlating factors for the overweight status. The results demonstrate that there are significant differences in blood and biochemical indexes between healthy and overweight people (p-value < 0.01). According to the feature selection, the most important correlated indexes are creatinine, hemoglobin, hematokrit, uric Acid, red blood cells, high density lipoprotein, alanine transaminase, triglyceride, and γ-glutamyl transpeptidase. These are consistent with the results of Spearman test analysis. The proposed method holds promise as a new, accurate method for identifying the overweight status in subjects.

  6. Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Adnan O. M. Abuassba

    2017-01-01

    Full Text Available Extreme Learning Machine (ELM is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN. It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2, and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of training data selected through random resampling. The proposed AELM-Ensemble is evolved by employing an objective function of increasing diversity and accuracy among the final ensemble. Finally, the class label of unseen data is predicted using majority vote approach. Splitting the training data into subsets and incorporation of heterogeneous ELM classifiers result in higher prediction accuracy, better generalization, and a lower number of base classifiers, as compared to other models (Adaboost, Bagging, Dynamic ELM ensemble, data splitting ELM ensemble, and ELM ensemble. The validity of AELME is confirmed through classification on several real-world benchmark datasets.

  7. Estimation of Tsunami Bore Forces on a Coastal Bridge Using an Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Iman Mazinani

    2016-04-01

    Full Text Available This paper proposes a procedure to estimate tsunami wave forces on coastal bridges through a novel method based on Extreme Learning Machine (ELM and laboratory experiments. This research included three water depths, ten wave heights, and four bridge models with a variety of girders providing a total of 120 cases. The research was designed and adapted to estimate tsunami bore forces including horizontal force, vertical uplift and overturning moment on a coastal bridge. The experiments were carried out on 1:40 scaled concrete bridge models in a wave flume with dimensions of 24 m × 1.5 m × 2 m. Two six-axis load cells and four pressure sensors were installed to the base plate to measure forces. In the numerical procedure, estimation and prediction results of the ELM model were compared with Genetic Programming (GP and Artificial Neural Networks (ANNs models. The experimental results showed an improvement in predictive accuracy, and capability of generalization could be achieved by the ELM approach in comparison with GP and ANN. Moreover, results indicated that the ELM models developed could be used with confidence for further work on formulating novel model predictive strategy for tsunami bore forces on a coastal bridge. The experimental results indicated that the new algorithm could produce good generalization performance in most cases and could learn thousands of times faster than conventional popular learning algorithms. Therefore, it can be conclusively obtained that utilization of ELM is certainly developing as an alternative approach to estimate the tsunami bore forces on a coastal bridge.

  8. Online sparse Gaussian process based human motion intent learning for an electrically actuated lower extremity exoskeleton.

    Science.gov (United States)

    Long, Yi; Du, Zhi-Jiang; Chen, Chao-Feng; Dong, Wei; Wang, Wei-Dong

    2017-07-01

    The most important step for lower extremity exoskeleton is to infer human motion intent (HMI), which contributes to achieve human exoskeleton collaboration. Since the user is in the control loop, the relationship between human robot interaction (HRI) information and HMI is nonlinear and complicated, which is difficult to be modeled by using mathematical approaches. The nonlinear approximation can be learned by using machine learning approaches. Gaussian Process (GP) regression is suitable for high-dimensional and small-sample nonlinear regression problems. GP regression is restrictive for large data sets due to its computation complexity. In this paper, an online sparse GP algorithm is constructed to learn the HMI. The original training dataset is collected when the user wears the exoskeleton system with friction compensation to perform unconstrained movement as far as possible. The dataset has two kinds of data, i.e., (1) physical HRI, which is collected by torque sensors placed at the interaction cuffs for the active joints, i.e., knee joints; (2) joint angular position, which is measured by optical position sensors. To reduce the computation complexity of GP, grey relational analysis (GRA) is utilized to specify the original dataset and provide the final training dataset. Those hyper-parameters are optimized offline by maximizing marginal likelihood and will be applied into online GP regression algorithm. The HMI, i.e., angular position of human joints, will be regarded as the reference trajectory for the mechanical legs. To verify the effectiveness of the proposed algorithm, experiments are performed on a subject at a natural speed. The experimental results show the HMI can be obtained in real time, which can be extended and employed in the similar exoskeleton systems.

  9. An Urban Resilience to Extreme Weather Events Framework for Development of Post Event Learning and Transformative Adaptation in Cities

    Science.gov (United States)

    Solecki, W. D.; Friedman, E. S.; Breitzer, R.

    2016-12-01

    Increasingly frequent extreme weather events are becoming an immediate priority for urban coastal practitioners and stakeholders, adding complexity to decisions concerning risk management for short-term action and long-term needs of city climate stakeholders. The conflict between the prioritization of short versus long-term events by decision-makers creates disconnect between climate science and its applications. The Consortium for Climate Risk in the Urban Northeast (CCRUN), a NOAA RISA team, is developing a set of mechanisms to help bridge this gap. The mechanisms are designed to promote the application of climate science on extreme weather events and their aftermath. It is in the post event policy window where significant opportunities for science-policy linkages exist. In particular, CCRUN is interested in producing actionable and useful information for city managers to use in decision-making processes surrounding extreme weather events and climate change. These processes include a sector specific needs assessment survey instrument and two tools for urban coastal practitioners and stakeholders. The tools focus on post event learning and connections between resilience and transformative adaptation. Elements of the two tools are presented. Post extreme event learning supports urban coastal practitioners and decision-makers concerned about maximizing opportunities for knowledge transfer and assimilation, and policy initiation and development following an extreme weather event. For the urban U.S. Northeast, post event learning helps coastal stakeholders build the capacity to adapt to extreme weather events, and inform and develop their planning capacity through analysis of past actions and steps taken in response to Hurricane Sandy. Connecting resilience with transformative adaptation is intended to promote resilience in urban Northeast coastal settings to the long-term negative consequences of extreme weather events. This is done through a knowledge co

  10. Modified abdominoplasty for patients with the Prune Belly syndrome.

    Science.gov (United States)

    Dénes, Francisco Tibor; Lopes, Roberto Iglesias; Oliveira, Lorena Marçalo; Tavares, Alessandro; Srougi, Miguel

    2014-02-01

    To present the results of a new technique for abdominoplasty in patients with the Prune Belly syndrome (PBS). Since 1985, 46 children with PBS underwent surgical treatment that included urinary tract reconstruction (UTR), orchidopexy, and abdominoplasty. In 41 patients, we performed the abdominoplasty as follows: (1) fusiform longitudinal resection of the mid-abdominal skin and subcutaneous tissue, with preservation of the musculo-aponeurotic fascia (MAF) and umbilicus, (2) ellipsoid unilateral longitudinal incision of the MAF in the most weakened side of the abdomen, producing 2 flaps, with the umbilicus being kept intact in the widest flap, (3) after UTR and bilateral orchiopexy, suture fixation of the widest MAF layer to the inner side of the contralateral abdominal wall, creating an inner MAF layer, (4) lateral suture fixation of the other flap over the inner layer, creating an outer MAF layer with a buttonhole exposing the umbilicus, that is sutured to the outer layer, and (5) approximation of the skin edges with incorporation of the umbilicus in the suture. Skin coaptation was excellent in all patients, and no trimming was necessary in incision extremities. There was no dehiscence or skin necrosis and all patients presented immediate improvement of the abdominal tonus and appearance. Further improvement with growth was observed in all except 4 patients, 2 requiring secondary abdominoplasties. We conclude that this technique is applicable in all forms of weakened abdomen typical of PBS, even in asymmetrical cases, requiring only 1 MAF incision, with good cosmetic and functional results. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Extreme learning machine-based classification of ADHD using brain structural MRI data.

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    Xiaolong Peng

    Full Text Available BACKGROUND: Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1 Propose an ADHD classification model using the extreme learning machine (ELM algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2 Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM methods and analyze which brain segments are involved in ADHD. METHODS: High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc. were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. RESULTS: We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. CONCLUSION: Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

  12. Extreme learning machine-based classification of ADHD using brain structural MRI data.

    Science.gov (United States)

    Peng, Xiaolong; Lin, Pan; Zhang, Tongsheng; Wang, Jue

    2013-01-01

    Effective and accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) is currently of significant interest. ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc. The aims of this study were as follows: (1) Propose an ADHD classification model using the extreme learning machine (ELM) algorithm for automatic, efficient and objective clinical ADHD diagnosis. (2) Assess the computational efficiency and the effect of sample size on both ELM and support vector machine (SVM) methods and analyze which brain segments are involved in ADHD. High-resolution three-dimensional MR images were acquired from 55 ADHD subjects and 55 healthy controls. Multiple brain measures (cortical thickness, etc.) were calculated using a fully automated procedure in the FreeSurfer software package. In total, 340 cortical features were automatically extracted from 68 brain segments with 5 basic cortical features. F-score and SFS methods were adopted to select the optimal features for ADHD classification. Both ELM and SVM were evaluated for classification accuracy using leave-one-out cross-validation. We achieved ADHD prediction accuracies of 90.18% for ELM using eleven combined features, 84.73% for SVM-Linear and 86.55% for SVM-RBF. Our results show that ELM has better computational efficiency and is more robust as sample size changes than is SVM for ADHD classification. The most pronounced differences between ADHD and healthy subjects were observed in the frontal lobe, temporal lobe, occipital lobe and insular. Our ELM-based algorithm for ADHD diagnosis performs considerably better than the traditional SVM algorithm. This result suggests that ELM may be used for the clinical diagnosis of ADHD and the investigation of different brain diseases.

  13. Extreme Learning Machines for reverse engineering of gene regulatory networks from expression time series.

    Science.gov (United States)

    Rubiolo, M; Milone, D H; Stegmayer, G

    2017-11-22

    The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regulation among a group of genes and its reconstruction is today a challenging computational problem. Several methods were proposed, but most of them require different input sources to provide an acceptable prediction. Thus, it is a great challenge to reconstruct a GRN only from temporal gene-expression data. Extreme Learning Machine (ELM) is a new supervised neural model that has gained interest in the last years because of its higher learning rate and better performance than existing supervised models in terms of predictive power. This work proposes a novel approach for GRNs reconstruction in which ELMs are used for modeling the relationships between gene expression time series. Artificial datasets generated with the well-known benchmark tool used in DREAM competitions were used. Real datasets were used for validation of this novel proposal with well-known GRNs underlying the time series. The impact of increasing the size of GRNs was analyzed in detail for the compared methods. The results obtained confirm the superiority of the ELM approach against very recent state-of-the-art methods in the same experimental conditions. The webdemo can be found at http://sinc.unl.edu.ar/web-demo/elm-grnnminer/. The source code is available at: https://sourceforge.net/projects/sourcesinc/files/elm-grnnminer. mrubiolo@santafe-conicet.gov.ar. Supplementary materials are available at Bioinformatics online.

  14. MST-GEN: An Efficient Parameter Selection Method for One-Class Extreme Learning Machine.

    Science.gov (United States)

    Wang, Siqi; Liu, Qiang; Zhu, En; Yin, Jianping; Zhao, Wentao

    2017-10-01

    One-class classification (OCC) models a set of target data from one class to detect outliers. OCC approaches like one-class support vector machine (OCSVM) and support vector data description (SVDD) have wide practical applications. Recently, one-class extreme learning machine (OCELM), which inherits the fast learning speed of original ELM and achieves equivalent or higher data description performance than OCSVM and SVDD, is proposed as a promising alternative. However, OCELM faces the same thorny parameter selection problem as OCSVM and SVDD. It significantly affects the performance of OCELM and remains under-explored. This paper proposes minimal spanning tree (MST)-GEN, an automatic way to select proper parameters for OCELM. Specifically, we first build a n -round MST to model the structure and distribution of the given target set. With information from n -round MST, a controllable number of pseudo outliers are generated by edge pattern detection and a novel "repelling" process, which readily overcomes two fundamental problems in previous outlier generation methods: where and how many pseudo outliers should be generated. Unlike previous methods that only generate pseudo outliers, we further exploit n -round MST to generate pseudo target data, so as to avoid the time-consuming cross-validation process and accelerate the parameter selection. Extensive experiments on various datasets suggest that the proposed method can select parameters for OCELM in a highly efficient and accurate manner when compared with existing methods, which enables OCELM to achieve better OCC performance in OCC applications. Furthermore, our experiments show that MST-GEN can also be favorably applied to other prevalent OCC methods like OCSVM and SVDD.

  15. Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

    Directory of Open Access Journals (Sweden)

    Yuanfa Wang

    2017-01-01

    Full Text Available An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT and the nonlinear sparse extreme learning machine (SELM for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.

  16. [Research on concentration retrieval of gas FTIR spectra by interval extreme learning machine and genetic algorithm].

    Science.gov (United States)

    Chen, Yuan-Yuan; Wang, Zhi-Bin; Wang, Zhao-Ba; Li, Xiao

    2014-05-01

    This paper proposed a novel effective quantitative analysis method for FTIR spectroscopy of polluted gases, which select the best wavenumbers based on the idea of interval dividing. Meanwhile, genetic algorithm was adopted to optimize the connect weights and thresholds of the input layer and the hidden layer of extreme learning machine (ELM) because of its global search ability. Firstly, the whole spectrum region was divided into several subintervals; Secondly, the quantitative analysis model was established in each subinterval by using optimized GA-ELM; Thirdly, the best combination of subintervals was selected according to the generalized performance of each subinterval model by computing the parameters root mean square error (RMSE) and determined coefficients r. In this paper, the mixture of CO, CO2 and N2 O gases were selected as the research object and the whole spectrum range was from 2 140 to 2 220 cm-1. The experiment results showed that the RMSE of model established with the selected wavenumbers was 154. 996 3, the corresponding r can reach 0. 987 4, and the running time was just 0. 8 seconds, which indicated that the concentration retrieval model established with the proposed Interval-GA-ELM (iGELM) method can not only reduce the modeling time, but also can improve the stability and predict accuracy, especially under the condition of the exist of interferents, which providing an effective approach to the remote analysis of polluted gases.

  17. Transportation Mode Detection Based on Permutation Entropy and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Lei Zhang

    2015-01-01

    Full Text Available With the increasing prevalence of GPS devices and mobile phones, transportation mode detection based on GPS data has been a hot topic in GPS trajectory data analysis. Transportation modes such as walking, driving, bus, and taxi denote an important characteristic of the mobile user. Longitude, latitude, speed, acceleration, and direction are usually used as features in transportation mode detection. In this paper, first, we explore the possibility of using Permutation Entropy (PE of speed, a measure of complexity and uncertainty of GPS trajectory segment, as a feature for transportation mode detection. Second, we employ Extreme Learning Machine (ELM to distinguish GPS trajectory segments of different transportation. Finally, to evaluate the performance of the proposed method, we make experiments on GeoLife dataset. Experiments results show that we can get more than 50% accuracy when only using PE as a feature to characterize trajectory sequence. PE can indeed be effectively used to detect transportation mode from GPS trajectory. The proposed method has much better accuracy and faster running time than the methods based on the other features and SVM classifier.

  18. Assessing the suitability of extreme learning machines (ELM for groundwater level prediction

    Directory of Open Access Journals (Sweden)

    Yadav Basant

    2017-03-01

    Full Text Available Fluctuation of groundwater levels around the world is an important theme in hydrological research. Rising water demand, faulty irrigation practices, mismanagement of soil and uncontrolled exploitation of aquifers are some of the reasons why groundwater levels are fluctuating. In order to effectively manage groundwater resources, it is important to have accurate readings and forecasts of groundwater levels. Due to the uncertain and complex nature of groundwater systems, the development of soft computing techniques (data-driven models in the field of hydrology has significant potential. This study employs two soft computing techniques, namely, extreme learning machine (ELM and support vector machine (SVM to forecast groundwater levels at two observation wells located in Canada. A monthly data set of eight years from 2006 to 2014 consisting of both hydrological and meteorological parameters (rainfall, temperature, evapotranspiration and groundwater level was used for the comparative study of the models. These variables were used in various combinations for univariate and multivariate analysis of the models. The study demonstrates that the proposed ELM model has better forecasting ability compared to the SVM model for monthly groundwater level forecasting.

  19. Color Face Recognition Based on Steerable Pyramid Transform and Extreme Learning Machines

    Directory of Open Access Journals (Sweden)

    Ayşegül Uçar

    2014-01-01

    Full Text Available This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT to the local face regions. In this paper, the new three hybrid color spaces, YSCr, ZnSCr, and BnSCr, are firstly constructed using the Cb and Cr component images of the YCbCr color space, the S color component of the HSV color spaces, and the Zn and Bn color components of the normalized XYZ color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.

  20. A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

    Directory of Open Access Journals (Sweden)

    Yanjiao Li

    2017-08-01

    Full Text Available Gas utilization ratio (GUR is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs. In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF, depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

  1. Remaining Useful Life Prediction of Rolling Bearings Using PSR, JADE, and Extreme Learning Machine

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    Yongbin Liu

    2016-01-01

    Full Text Available Rolling bearings play a pivotal role in rotating machinery. The degradation assessment and remaining useful life (RUL prediction of bearings are critical to condition-based maintenance. However, sensitive feature extraction still remains a formidable challenge. In this paper, a novel feature extraction method is introduced to obtain the sensitive features through phase space reconstitution (PSR and joint with approximate diagonalization of Eigen-matrices (JADE. Firstly, the original features are extracted from bearing vibration signals in time and frequency domain. Secondly, the PSR is applied to embed the original features into high dimensional phase space. The between-class and within-class scatter (SS are calculated to evaluate the feature sensitivity through the phase point distribution of different degradation stages and then different weights are assigned to the corresponding features based on the calculated SS. Thirdly, the JADE is employed to fuse the weighted features to obtain the advanced features which can better reflect the bearing degradation process. Finally, the advanced features are input into the extreme learning machine (ELM to train the RUL prediction model. A set of experimental case studies are carried out to verify the effectiveness of the proposed method. The results show that the extracted advanced features can better reflect the degradation process compared to traditional features and could effectively predict the RUL of bearing.

  2. Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

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    Pak Kin Wong

    2015-01-01

    Full Text Available Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (ELM, to build a backup air-ratio model. With the prediction from the model, a limited air-ratio control performance can be maintained even when the lambda sensor does not work. Such strategy is realized as fault tolerance control. In order to verify the effectiveness of the proposed fault tolerance air-ratio control strategy, a model predictive control scheme is constructed based on the kernel ELM backup air-ratio model and implemented on a real engine. Experimental results show that the proposed controller can regulate the air-ratio to specific target values within a satisfactory tolerance under external disturbance and the absence of air-ratio feedback signal from the lambda sensor. This implies that the proposed fault tolerance air-ratio control is a promising scheme to maintain air-ratio control performance when the lambda sensor is under failure or warming up.

  3. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    Science.gov (United States)

    Li, Qiang; Zhao, Xuehua; Cai, ZhenNao; Tong, Changfei; Liu, Wenbin; Tian, Xin

    2017-01-01

    In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts. PMID:28246543

  4. A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines.

    Science.gov (United States)

    Sun, Zhan-Li; Au, Kin-Fan; Choi, Tsan-Ming

    2007-10-01

    This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.

  5. EEG-based emotion recognition with manifold regularized extreme learning machine.

    Science.gov (United States)

    Peng, Yong; Zhu, Jia-Yi; Zheng, Wei-Long; Lu, Bao-Liang

    2014-01-01

    EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously evoked by watching different types of movie clips. The MRELM can simultaneously consider the geometrical structure and discriminative information in EEG data. Using differential entropy features across whole five frequency bands, the average accuracy of MRELM is 81.01%, which is better than those obtained by GELM (80.25%) and SVM (76.62%). The accuracies obtained from high frequency band features (β, γ) are obviously superior to those of low frequency band features, which shows β and γ bands are more relevant to emotional states transition. Moreover, experiments are conducted to further evaluate the efficacy of MRELM, where the training and test sets are from different sessions. The results demonstrate that the proposed MRELM is a competitive model for EEG-based emotion recognition.

  6. A Pathological Brain Detection System based on Extreme Learning Machine Optimized by Bat Algorithm.

    Science.gov (United States)

    Lu, Siyuan; Qiu, Xin; Shi, Jianping; Li, Na; Lu, Zhi-Hai; Chen, Peng; Yang, Meng-Meng; Liu, Fang-Yuan; Jia, Wen-Juan; Zhang, Yudong

    2017-01-01

    It is beneficial to classify brain images as healthy or pathological automatically, because 3D brain images can generate so much information which is time consuming and tedious for manual analysis. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is the most suitable for brain, and it is now widely applied in hospitals, because it is helpful in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. There are automatic detection methods; however they suffer from low accuracy. Therefore, we proposed a novel approach which employed 2D discrete wavelet transform (DWT), and calculated the entropies of the subbands as features. Then, a bat algorithm optimized extreme learning machine (BA-ELM) was trained to identify pathological brains from healthy controls. A 10x10-fold cross validation was performed to evaluate the out-of-sample performance. The method achieved a sensitivity of 99.04%, a specificity of 93.89%, and an overall accuracy of 98.33% over 132 MR brain images. The experimental results suggest that the proposed approach is accurate and robust in pathological brain detection. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine

    Science.gov (United States)

    Adhi, H. A.; Wijaya, S. K.; Prawito; Badri, C.; Rezal, M.

    2017-03-01

    Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. The signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. The performance of detection was observed by the value of accuracy, sensitivity and specificity. The result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM.

  8. Reservoir computing and extreme learning machines for non-linear time-series data analysis.

    Science.gov (United States)

    Butcher, J B; Verstraeten, D; Schrauwen, B; Day, C R; Haycock, P W

    2013-02-01

    Random projection architectures such as Echo state networks (ESNs) and Extreme Learning Machines (ELMs) use a network containing a randomly connected hidden layer and train only the output weights, overcoming the problems associated with the complex and computationally demanding training algorithms traditionally used to train neural networks, particularly recurrent neural networks. In this study an ESN is shown to contain an antagonistic trade-off between the amount of non-linear mapping and short-term memory it can exhibit when applied to time-series data which are highly non-linear. To overcome this trade-off a new architecture, Reservoir with Random Static Projections (R(2)SP) is investigated, that is shown to offer a significant improvement in performance. A similar approach using an ELM whose input is presented through a time delay (TD-ELM) is shown to further enhance performance where it significantly outperformed the ESN and R(2)SP as well other architectures when applied to a novel task which allows the short-term memory and non-linearity to be varied. The hard-limiting memory of the TD-ELM appears to be best suited for the data investigated in this study, although ESN-based approaches may offer improved performance when processing data which require a longer fading memory. Copyright © 2012 Elsevier Ltd. All rights reserved.

  9. An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

    Directory of Open Access Journals (Sweden)

    Qiang Li

    2017-01-01

    Full Text Available In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO and kernel extreme learning machine (KELM, termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.

  10. Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Nibaldo Rodriguez

    2017-10-01

    Full Text Available The behavioural diagnostics of bearings play an essential role in the management of several rotation machine systems. However, current diagnostic methods do not deliver satisfactory results with respect to failures in variable speed rotational phenomena. In this paper, we consider the Shannon entropy as an important fault signature pattern. To compute the entropy, we propose combining stationary wavelet transform and singular value decomposition. The resulting feature extraction method, that we call stationary wavelet singular entropy (SWSE, aims to improve the accuracy of the diagnostics of bearing failure by finding a small number of high-quality fault signature patterns. The features extracted by the SWSE are then passed on to a kernel extreme learning machine (KELM classifier. The proposed SWSE-KELM algorithm is evaluated using two bearing vibration signal databases obtained from Case Western Reserve University. We compare our SWSE feature extraction method to other well-known methods in the literature such as stationary wavelet packet singular entropy (SWPSE and decimated wavelet packet singular entropy (DWPSE. The experimental results show that the SWSE-KELM consistently outperforms both the SWPSE-KELM and DWPSE-KELM methods. Further, our SWSE method requires fewer features than the other two evaluated methods, which makes our SWSE-KELM algorithm simpler and faster.

  11. A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces.

    Science.gov (United States)

    Li, Yanjiao; Zhang, Sen; Yin, Yixin; Xiao, Wendong; Zhang, Jie

    2017-08-10

    Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

  12. Extreme learning machine based optimal embedding location finder for image steganography.

    Directory of Open Access Journals (Sweden)

    Hayfaa Abdulzahra Atee

    Full Text Available In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM index, fusion matrices, and mean square error (MSE. The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

  13. Extreme learning machine based optimal embedding location finder for image steganography.

    Science.gov (United States)

    Atee, Hayfaa Abdulzahra; Ahmad, Robiah; Noor, Norliza Mohd; Rahma, Abdul Monem S; Aljeroudi, Yazan

    2017-01-01

    In image steganography, determining the optimum location for embedding the secret message precisely with minimum distortion of the host medium remains a challenging issue. Yet, an effective approach for the selection of the best embedding location with least deformation is far from being achieved. To attain this goal, we propose a novel approach for image steganography with high-performance, where extreme learning machine (ELM) algorithm is modified to create a supervised mathematical model. This ELM is first trained on a part of an image or any host medium before being tested in the regression mode. This allowed us to choose the optimal location for embedding the message with best values of the predicted evaluation metrics. Contrast, homogeneity, and other texture features are used for training on a new metric. Furthermore, the developed ELM is exploited for counter over-fitting while training. The performance of the proposed steganography approach is evaluated by computing the correlation, structural similarity (SSIM) index, fusion matrices, and mean square error (MSE). The modified ELM is found to outperform the existing approaches in terms of imperceptibility. Excellent features of the experimental results demonstrate that the proposed steganographic approach is greatly proficient for preserving the visual information of an image. An improvement in the imperceptibility as much as 28% is achieved compared to the existing state of the art methods.

  14. Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.

    Science.gov (United States)

    Zhu, Chengzhang; Zou, Beiji; Zhao, Rongchang; Cui, Jinkai; Duan, Xuanchu; Chen, Zailiang; Liang, Yixiong

    2017-01-01

    Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminative feature vectors, consisting of local features, morphological features, phase congruency, Hessian and divergence of vector fields, is extracted for each pixel of the fundus image. Then a matrix is constructed for pixel of the training set based on the feature vector and the manual labels, and acts as the input of the ELM classifier. The output of classifier is the binary retinal vascular segmentation. Finally, an optimization processing is implemented to remove the region less than 30 pixels which is isolated from the retinal vascilar. The experimental results testing on the public Digital Retinal Images for Vessel Extraction (DRIVE) database demonstrate that the proposed method is much faster than the other methods in segmenting the retinal vessels. Meanwhile the average accuracy, sensitivity, and specificity are 0.9607, 0.7140 and 0.9868, respectively. Moreover the proposed method exhibits high speed and robustness on a new Retinal Images for Screening (RIS) database. Therefore it has potential applications for real-time computer-aided diagnosis and disease screening. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wu Xiang

    2014-01-01

    Full Text Available It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM termed as WELM (wavelet based ELM for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.

  16. A Novel Gravity Compensation Method for High Precision Free-INS Based on "Extreme Learning Machine".

    Science.gov (United States)

    Zhou, Xiao; Yang, Gongliu; Cai, Qingzhong; Wang, Jing

    2016-11-29

    In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros), gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS), especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM) method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE) of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method.

  17. A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

    Science.gov (United States)

    Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

    2015-12-01

    Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

  18. Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

    Science.gov (United States)

    Ibrahim, Wisam; Abadeh, Mohammad Saniee

    2017-05-21

    Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. The Prediction of Yarn Elongation of Kenyan Ring-Spun Yarn using Extreme Learning Machines (ELM

    Directory of Open Access Journals (Sweden)

    Josphat Igadwa Mwasiagi

    2017-03-01

    Full Text Available The optimization of the manufacture of cotton yarns involves several processes, while the prediction of yarn quality parameters forms an important area of investigation. This research work concentrated on the prediction of cotton yarn elongation. Cotton lint and yarn samples were collected in textile factories in Kenya.The collected samples were tested under standard testing conditions. Cotton lint parameters, machine parameters and yarn elongation were used to design yarn elongation prediction models. The elongation prediction models used three network training algorithms, including backpropagation (BP, an extreme learning machine (ELM, and a hybrid of differential evolution (DE and an ELM referred to as DE-ELM. The prediction models recorded a mean squared error (mse value of 0.001 using 11, 43 and 2 neurons in the hidden layer for the BP, ELM and DE-ELM models respectively. The ELM models exhibited faster training speeds than the BP algorithms, but required more neurons in the hidden layer than other models. The DEELM hybrid algorithm was faster than the BP algorithm, but slower than the ELM algorithm.

  20. Antioxidant and sensorial properties of acacia honey supplemented with prunes

    Directory of Open Access Journals (Sweden)

    Tumbas Vesna T.

    2012-01-01

    Full Text Available The changes in total phenol and flavonoid content, as well as antioxidant activity was monitored in acacia honey supplemented with prunes in 20, 30 and 40% mass concentrations. The total phenolic content increased by 2.5 times (from 16.18 to 41.64 mg GAE/100 g with increasing concentration of prunes in honey, while the increase in flavonoid content was even higher, approximately 11.5-fold (from 2.65 to 30.86 mg RE/100 g. The addition of prunes also improved the antioxidant activity of acacia honey. The honey samples with highest content of prunes, 40%, exhibited the best antioxidant activity measured by hydroxyl radical sacvenging assay (EC50 •OH=4.56 mg/ml, 2,2-diphenyl-1-picrylhydrazyl (DPPH free radical scavenging assay (EC50 DPPH=16.48 mg/ml, and reducing power (EC50 RP=81.17 mg/ml. Judging from the high correlation coefficients, ranging from 0.771 to 0.947 for total phenolics, and from 0.862 to 0.993 for total flavonoids, it is obvious that these compounds were associated with the antioxidant mechanisms. On the other hand, sensorial properties of supplemented honeys were lower than that of pure acacia honey, where flavor of supplemented honey was the least affected. Our results indicate that the supplementation of honey with prunes improves antioxidant activity of honey by enriching the phenolic composition, with slight modifications in sensorial characteristics.

  1. Energy potential of fruit tree pruned biomass in Croatia

    Energy Technology Data Exchange (ETDEWEB)

    Bilandzija, N.; Voca, N.; Kricka, T.; Martin, A.; Jurisic, V.

    2012-11-01

    The world's most developed countries and the European Union (EU) deem that the renewable energy sources should partly substitute fossil fuels and become a bridge to the utilization of other energy sources of the future. This paper will present the possibility of using pruned biomass from fruit cultivars. It will also present the calculation of potential energy from the mentioned raw materials in order to determine the extent of replacement of non-renewable sources with these types of renewable energy. One of the results of the intensive fruit-growing process, in post pruning stage, is large amount of pruned biomass waste. Based on the calculated biomass (kg ha{sup 1}) from intensively grown woody fruit crops that are most grown in Croatia (apple, pear, apricots, peach and nectarine, sweet cherry, sour cherry, prune, walnut, hazelnut, almond, fig, grapevine, and olive) and the analysis of combustible (carbon 45.55-49.28%, hydrogen 5.91-6.83%, and sulphur 0.18-0.21%) and non-combustible matters (oxygen 43.34-46.6%, nitrogen 0.54-1.05%, moisture 3.65-8.83%, ashes 1.52-5.39%) with impact of lowering the biomass heating value (15.602-17.727 MJ kg{sup 1}), the energy potential of the pruned fruit biomass is calculated at 4.21 PJ. (Author) 31 refs.

  2. Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set.

    Science.gov (United States)

    Adler, Werner; Gefeller, Olaf; Gul, Asma; Horn, Folkert K; Khan, Zardad; Lausen, Berthold

    2016-12-07

    Random forests are successful classifier ensemble methods consisting of typically 100 to 1000 classification trees. Ensemble pruning techniques reduce the computational cost, especially the memory demand, of random forests by reducing the number of trees without relevant loss of performance or even with increased performance of the sub-ensemble. The application to the problem of an early detection of glaucoma, a severe eye disease with low prevalence, based on topographical measurements of the eye background faces specific challenges. We examine the performance of ensemble pruning strategies for glaucoma detection in an unbalanced data situation. The data set consists of 102 topographical features of the eye background of 254 healthy controls and 55 glaucoma patients. We compare the area under the receiver operating characteristic curve (AUC), and the Brier score on the total data set, in the majority class, and in the minority class of pruned random forest ensembles obtained with strategies based on the prediction accuracy of greedily grown sub-ensembles, the uncertainty weighted accuracy, and the similarity between single trees. To validate the findings and to examine the influence of the prevalence of glaucoma in the data set, we additionally perform a simulation study with lower prevalences of glaucoma. In glaucoma classification all three pruning strategies lead to improved AUC and smaller Brier scores on the total data set with sub-ensembles as small as 30 to 80 trees compared to the classification results obtained with the full ensemble consisting of 1000 trees. In the simulation study, we were able to show that the prevalence of glaucoma is a critical factor and lower prevalence decreases the performance of our pruning strategies. The memory demand for glaucoma classification in an unbalanced data situation based on random forests could effectively be reduced by the application of pruning strategies without loss of performance in a population with increased

  3. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    Science.gov (United States)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  4. Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.

    Science.gov (United States)

    Kuppili, Venkatanareshbabu; Biswas, Mainak; Sreekumar, Aswini; Suri, Harman S; Saba, Luca; Edla, Damodar Reddy; Marinhoe, Rui Tato; Sanches, J Miguel; Suri, Jasjit S

    2017-08-23

    Fatty Liver Disease (FLD) is caused by the deposition of fat in liver cells and leads to deadly diseases such as liver cancer. Several FLD detection and characterization systems using machine learning (ML) based on Support Vector Machines (SVM) have been applied. These ML systems utilize large number of ultrasonic grayscale features, pooling strategy for selecting the best features and several combinations of training/testing. As result, they are computationally intensive, slow and do not guarantee high performance due to mismatch between grayscale features and classifier type. This study proposes a reliable and fast Extreme Learning Machine (ELM)-based tissue characterization system (a class of Symtosis) for risk stratification of ultrasound liver images. ELM is used to train single layer feed forward neural network (SLFFNN). The input-to-hidden layer weights are randomly generated reducing computational cost. The only weights to be trained are hidden-to-output layer which is done in a single pass (without any iteration) making ELM faster than conventional ML methods. Adapting four types of K-fold cross-validation (K = 2, 3, 5 and 10) protocols on three kinds of data sizes: S0-original, S4-four splits, S8-sixty four splits (a total of 12 cases) and 46 types of grayscale features, we stratify the FLD US images using ELM and benchmark against SVM. Using the US liver database of 63 patients (27 normal/36 abnormal), our results demonstrate superior performance of ELM compared to SVM, for all cross-validation protocols (K2, K3, K5 and K10) and all types of US data sets (S0, S4, and S8) in terms of sensitivity, specificity, accuracy and area under the curve (AUC). Using the K10 cross-validation protocol on S8 data set, ELM showed an accuracy of 96.75% compared to 89.01% for SVM, and correspondingly, the AUC: 0.97 and 0.91, respectively. Further experiments also showed the mean reliability of 99% for ELM classifier, along with the mean speed improvement of 40% using

  5. Partial prune belly syndrome: A rare case report

    Directory of Open Access Journals (Sweden)

    Aditya Pratap Singh

    2017-01-01

    Full Text Available Prune belly syndrome (PBS is characterized by deficient development of abdominal muscles that causes the skin of the abdomen to wrinkle like a prune, bilateral cryptorchidism, abnormalities of the urinary tract. The etiology of PBS is unclear and possible familial genetic inheritance was reported in some of the studies. We are presenting here a case with the absence of the muscle in the right side of the abdomen as hernia, thinning of the muscle on left side with bilateral cryptorchidism, and abnormalities of the urinary tract. It is the partial presentation of the PBS.

  6. Black Walnut on Kansas Strip Mine Spoils: Some Observations 25 Years after Pruning

    Science.gov (United States)

    Alex L. Shigo; Nelson F. Rogers; E. Allen, Jr. McGinnes; David T. Funk

    1978-01-01

    Dissections of 14 slow-growing black walnut trees on a strip-mine site revealed that bands of discolored heartwood were associated with pruned and nonpruned branch stubs. Ring shakes were associated with a few pruned and nonpruned stubs, especially with groups of stubs at the same position on the stem. The advantage of early pruning was that even the defects that...

  7. Effect of Root Pruning and Irrigation Regimes on Yield and Physiology of Pear Trees

    DEFF Research Database (Denmark)

    Wang, Yufei

    Clara Frijs’ is the dominant pear (Pyrus communis L.) cultivar in Denmark. It is vigorous with long annual shoots, and therefore can be difficult to prune. Root pruning has been widely used to control the canopy size of fruit trees including pears. However, root pruned trees are more likely to su...

  8. The Hybrid of Classification Tree and Extreme Learning Machine for Permeability Prediction in Oil Reservoir

    KAUST Repository

    Prasetyo Utomo, Chandra

    2011-06-01

    Permeability is an important parameter connected with oil reservoir. Predicting the permeability could save millions of dollars. Unfortunately, petroleum engineers have faced numerous challenges arriving at cost-efficient predictions. Much work has been carried out to solve this problem. The main challenge is to handle the high range of permeability in each reservoir. For about a hundred year, mathematicians and engineers have tried to deliver best prediction models. However, none of them have produced satisfying results. In the last two decades, artificial intelligence models have been used. The current best prediction model in permeability prediction is extreme learning machine (ELM). It produces fairly good results but a clear explanation of the model is hard to come by because it is so complex. The aim of this research is to propose a way out of this complexity through the design of a hybrid intelligent model. In this proposal, the system combines classification and regression models to predict the permeability value. These are based on the well logs data. In order to handle the high range of the permeability value, a classification tree is utilized. A benefit of this innovation is that the tree represents knowledge in a clear and succinct fashion and thereby avoids the complexity of all previous models. Finally, it is important to note that the ELM is used as a final predictor. Results demonstrate that this proposed hybrid model performs better when compared with support vector machines (SVM) and ELM in term of correlation coefficient. Moreover, the classification tree model potentially leads to better communication among petroleum engineers concerning this important process and has wider implications for oil reservoir management efficiency.

  9. Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach.

    Science.gov (United States)

    Xia, Jianfu; Chen, Huiling; Li, Qiang; Zhou, Minda; Chen, Limin; Cai, Zhennao; Fang, Yang; Zhou, Hong

    2017-08-01

    It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features. There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study. The potential of extreme learning machine (ELM) has been explored for the first time to discriminate malignant and benign thyroid nodules based on the sonographic features in ultrasound images. The influence of two key parameters (the number of hidden neurons and type of activation function) on the performance of ELM was investigated. The relationship between feature subsets obtained by the feature selection method and the classification performance of ELM was also examined. A real-life dataset was used to evaluate the effectiveness of the proposed method in terms of classification accuracy, sensitivity, specificity, and area under the ROC (receiver operating characteristic) curve (AUC). The results demonstrate that there are significant differences between the malignant and benign thyroid nodules (p-value<0.01), the most discriminative features are echogenicity, calcification, margin, composition and shape. Compared with other methods, the proposed method not only has achieved very promising classification accuracy via 10-fold cross-validation (CV) scheme, but also greatly reduced the computational cost compared to other counterparts. The proposed ELM-based approach achieves 87.72% ACC, 0.8672 AUC, 78.89% sensitivity, and 94.55% specificity. Based on the empirical analysis, the proposed ELM-based approach for thyroid cancer detection has promising potential in clinical use, and it can be of assistance as an optional tool for the clinicians. Copyright © 2017 Elsevier B.V. All rights reserved.

  10. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine.

    Science.gov (United States)

    Song, Yuedong; Zhang, Jiaxiang

    2016-01-15

    Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. The objective of this research is to develop and present a novel classification framework that is utilised to discriminate interictal and preictal brain activities via quantitative analysis of electroencephalogram (EEG) recordings. Sample entropy-based features were extracted in parallel from 6 intracranial EEG channels, and then these features were fed to the extreme learning machine model for classification. Performance was evaluated on the basis of sensitivity and specificity and validation was performed using stratified cross-validation approach. The proposed method can correctly distinguish interictal and preictal EEGs with a sensitivity of 86.75% and a specificity of 83.80%, on average, across 21 patients and 6 epileptic seizure origins. Compared with traditional variance-based feature extraction, the proposed SampEn-based feature extraction method not only shows a significant improvement in the accuracy, but also has higher classification robustness and stability in terms of the much lower standard errors of classification accuracies across different evaluation periods. In addition, the proposed classification framework runs around 20 times faster than the support vector machine model during testing. The high accuracy and efficiency of the proposed method makes it feasible to extend it to the development of a real-time EEG-based brain monitoring system for epileptic seizure prediction. Copyright © 2015 Elsevier B.V. All rights reserved.

  11. Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines.

    Science.gov (United States)

    Savojardo, Castrense; Fariselli, Piero; Casadio, Rita

    2011-11-15

    Transmembrane β-barrels (TMBBs) are extremely important proteins that play key roles in several cell functions. They cross the lipid bilayer with β-barrel structures. TMBBs are presently found in the outer membranes of Gram-negative bacteria and of mitochondria and chloroplasts. Loop exposure outside the bacterial cell membranes makes TMBBs important targets for vaccine or drug therapies. In genomes, they are not highly represented and are difficult to identify with experimental approaches. Several computational methods have been developed to discriminate TMBBs from other types of proteins. However, the best performing approaches have a high fraction of false positive predictions. In this article, we introduce a new machine learning approach for TMBB detection based on N-to-1 Extreme Learning Machines that significantly outperforms previous methods achieving a Matthews correlation coefficient of 0.82, a probability of correct prediction of 0.92 and a sensitivity of 0.73.

  12. Motor imagery EEG classification with optimal subset of wavelet based common spatial pattern and kernel extreme learning machine.

    Science.gov (United States)

    Hyeong-Jun Park; Jongin Kim; Beomjun Min; Boreom Lee

    2017-07-01

    Performance of motor imagery based brain-computer interfaces (MI BCIs) greatly depends on how to extract the features. Various versions of filter-bank based common spatial pattern have been proposed and used in MI BCIs. Filter-bank based common spatial pattern has more number of features compared with original common spatial pattern. As the number of features increases, the MI BCIs using filter-bank based common spatial pattern can face overfitting problems. In this study, we used eigenvector centrality feature selection method, wavelet packet decomposition common spatial pattern, and kernel extreme learning machine to improve the performance of MI BCIs and avoid overfitting problems. Furthermore, the computational speed was improved by using kernel extreme learning machine.

  13. Parallel gene selection and dynamic ensemble pruning based on Affinity Propagation.

    Science.gov (United States)

    Meng, Jun; Zhang, Jing; Luan, Yu-Shi; He, Xin-Yu; Li, Li-Shuang; Zhu, Yuan-Feng

    2017-08-01

    Gene selection and sample classification based on gene expression data are important research areas in bioinformatics. Selecting important genes closely related to classification is a challenging task due to high dimensionality and small sample size of microarray data. Extended rough set based on neighborhood has been successfully applied to gene selection, as it can select attributes without redundancy and deal with numerical attributes directly. However, the computation of approximations in rough set is extremely time consuming. In this paper, in order to accelerate the process of gene selection, a parallel computation method is proposed to calculate approximations of intersection neighborhood rough set. Furthermore, a novel dynamic ensemble pruning approach based on Affinity Propagation clustering and dynamic pruning framework is proposed to reduce memory usage and computational cost. Experimental results on three Arabidopsis thaliana biotic and abiotic stress response datasets demonstrate that the proposed method can obtain better classification performance than ensemble method with gene pre-selection. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. Should Pruning be a Pre-Processor of any Linear System?

    Science.gov (United States)

    Sen, Syamal K.; Ramakrishnan, Suja; Agarwal, Ravi P.; Shaykhian, Gholam Ali

    2011-01-01

    measure a quantity with an accuracy greater that 0.005% or, equivalently with a relative error less than 0.005%. Hence measurement error is unavoidable in a numerical linear system when the quantities are continuous (or even discrete with extremely large number). Assumptions, though not desirable, are usually made when we find the problem sufficiently difficult to be solved within the available means/tools/resources and hence distort the PP and the corresponding MM. The . error thus introduced in the system could (not always necessarily though) make the system somewhat inconsistent. If the inconsistency (contradiction) is too much then one should definitely not proceed to solve the system in terms of getting a least-squares solution or the minimum-norm least-squares solution. All these solutions will be invariably of no real-world use. If, on the other hand, inconsistency is reasonably low, i.e. the system is near-consistent or, equivalently, has near-linearly-dependent rows, then the foregoing solutions are useful. Pruning in such a near-consistent system should be performed based on the desired accuracy and on the definition of near-linear dependence. In this article, we discuss pruning over various kinds of linear systems and strongly suggest its use as a pre-processor or as a part of an algorithm. Ideally pruning should (i) be a part of the solution process (algorithm) of the system, (ii) reduce both computational error and complexity of the process, and (iii) take into account the numerical zero defined in the context. These are precisely what we achieve through our proposed O(mn2) algorithm presented in Matlab, that uses a subprogram of solving a single linear equation and that has embedded in it the pruning.

  15. Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition

    OpenAIRE

    Zheng Chang; Xiaojuan Ban; Qing Shen; Jing Guo

    2015-01-01

    Based on the traditional machine vision recognition technology and traditional artificial neural networks about body movement trajectory, this paper finds out the shortcomings of the traditional recognition technology. By combining the invariant moments of the three-dimensional motion history image (computed as the eigenvector of body movements) and the extreme learning machine (constructed as the classification artificial neural network of body movements), the paper applies the method to the...

  16. Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

    OpenAIRE

    Nantian Huang; Chong Yuan; Guowei Cai; Enkai Xing

    2016-01-01

    Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD), the partial autocorrelation function (PACF), and weighted regularized extreme learning machine (WRELM), is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs). Second, the partial correlation of each IMF sequenc...

  17. Pathophysiologic and anesthetic correlations of the prune-belly syndrome.

    Science.gov (United States)

    Holder, J P

    1989-04-01

    "Prune-Belly" is the name given to the disease which is characterized by a congenital wrinkled appearance of the abdomen. Usually, a triad of congenital anomalies highlights the components of the prune-belly syndrome. This triad consists of undescended testicles, abdominal musculature deficiency and urinary tract abnormalities. The previously described triad of the syndrome is by no means the total spectrum of the disease. Prune-belly syndrome is also associated with diseases of the respiratory, cardiovascular, skeletal, gastrointestinal and central nervous systems. Associated diseases of major body systems which are caused by renal and cardiopulmonary anomalies, as well as the surgical procedure to be performed, influence the plan for the administration of anesthesia. The nature of the genitourinary diseases in the prune-belly baby may obviate the need for surgical intervention as early as the neonatal period of life. Although this congenital disease occurs with some rarity, the gravity of the syndrome demands an in-depth knowledge of its pathophysiology in order to assure uncomplicated anesthetic care. Astute surveillance during preanesthetic, anesthetic, and postanesthetic management is germane to the prevention of mishaps.

  18. On the use of a pruning prior for neural networks

    DEFF Research Database (Denmark)

    Goutte, Cyril

    1996-01-01

    We address the problem of using a regularization prior that prunes unnecessary weights in a neural network architecture. This prior provides a convenient alternative to traditional weight-decay. Two examples are studied to support this method and illustrate its use. First we use the sunspots...

  19. Prune-Belly Syndrome: A Case Report from Rwanda | Ngendahayo ...

    African Journals Online (AJOL)

    Background: Prune-Belly syndrome, Eagle-Barret syndrome and triad syndrome, all refer to congenital anomalies involving abdominal musculature, urinary tract and testicles. The syndrome consists of a triad of abdominal muscle aplasia, massive ureteral and bladder dilatation and cryptorchidism. Kidneys are often affected ...

  20. Effect of mycorrhiza and pruning regimes on seasonality of ...

    African Journals Online (AJOL)

    GRACE

    2006-07-16

    Jul 16, 2006 ... Effect of mycorrhiza and pruning regimes on seasonality of hedgerow tree mulch contribution to .... Mycorrhizae are symbiotic association between plant roots and certain soil fungi (Sieverding, 1991). ..... inoculum was put under the seeds in the polythene bags for inoculated hedgerow tree seedlings and ...

  1. Chemical root pruning of conifer seedlings in Mexico

    Science.gov (United States)

    Arnulfo Aldrete; John G. Mexal

    2002-01-01

    Many countries grow seedlings for reforestation in polybags where root spiraling and root egression can decrease seedling survival and growth following outplanting. The overall objectives of this study were to investigate the effect of chemical root pruning on root spiraling, root egression, and nursery performance of Pinus pseudostrobus, P...

  2. 7 CFR 993.150 - Disposition of prunes by handlers.

    Science.gov (United States)

    2010-01-01

    ... manifest or billing number; and (2) receive from the inspection service a DFA Form P-5 “Shipping Inspection...)(iii) of this section) the Committee's approval of his application to do so. (ii) Application for approval. The handler's application to ship or otherwise make final disposition of any such prunes shall be...

  3. influence of cassava planting patterns and pruning methods on crop ...

    African Journals Online (AJOL)

    2003-12-03

    Dec 3, 2003 ... Field experiments were conducted at the University of Ibadan, Ni geria on the effect ofcassava (Manihot esculenta. Crantz) planting pattern and pruning methods on eassava yield and yield ofassociated crops, namely, maize (Zea mays L. ), melon (Colocynthis vulgaris L.) and cowpea ( Vigna unguiculaia) in ...

  4. Growth responses of Eucalyptus globulus and E. nitens to pruning ...

    African Journals Online (AJOL)

    The responses of Eucalyptus globulus and E. nitens to pruning (removal of 0 or 60% of the green crown depth) in two lifts and nitrogen (N) fertiliser application (0 [N0], 100 [N1], 300 [N3] and 500 [N5] kg N ha–1) were compared at a site in south-east Tasmania under conditions where both species can be successfully grown.

  5. An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine.

    Science.gov (United States)

    Avci, Derya; Dogantekin, Akif

    2016-01-01

    Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

  6. The prune belly syndrome in a female foetus with urorectal septum malformation sequence: a case report on a rare entity with an unusual association.

    Science.gov (United States)

    Goswami, Dibyajyoti; Kusre, Giriraj; Dutta, Hemonta Kumar; Sarma, Adity

    2013-08-01

    The prune belly syndrome is a rare congenital anomaly which is characterized by the triad of an absent or a deficient development of the abdominal muscle, bilateral cryptorchidism and an anomalous urinary tract. In its full form, this condition occurs only in males. However, a similar condition occurs in females in the absence of cryptorchidism. On the other hand, the urorectal septum malformation sequence is a lethal congenital malformation which is characterized by the development of a phallus like structure, a smooth perineum and the absence of urethral, vaginal and anal openings. We are reporting a case of a female foetus with the prune belly syndrome, which was associated with a urorectal septum malformation sequence. A dead foetus with a protruded abdomen and ambiguous genitalia, was born at 32 weeks of pregnancy. On autopsy, it was found to have female internal genital organs. The left kidney, the urinary bladder and the rectum were absent. The sigmoid colon, the ureters and the fallopian tubes opened into a common cloacal sac. The histopathological examination of the ovary showed the presence of Leydig's cells. The occurrence of the female counterpart of the prune belly syndrome is extremely rare and only few of such cases were found to be discussed in the details in the indexed English literature so far. Hence, we hope that this case report will contribute to the existing knowledge on the prune belly syndrome.

  7. Learning from today's extreme weather events to increase our resilience to climate change

    Science.gov (United States)

    Ruin, I.; Lutoff, C.; Borga, M.; Creutin, J.-D.; Anquetin, S.; Gruntfest, E.; Scolobig, A.

    2009-04-01

    According to the IPCC, flooding is the most widespread serious potential impact of climate change on human settlement. Vulnerability to floods can be thought as a function of exposure and adaptive capacity, and all three entities have been increasing in many areas. Therefore, in order to inform decision-makers, it is crucial to better understand what are the vulnerability factors but also to what extend individuals and societies are capable to adapt their way of life to their changing environment. In this perspective, flash flood events offer a good example of the kind of extremes that our societies may have to face more often in the future. Characterized by their suddenness, fast and violent movement, rarity and small scale, they are particularly difficult to forecast accurately and leave very little lead-time for warnings. In this context, our interdisciplinary team conducts research focusing on individual and human organization responses to warning and crisis situations by using a comprehensive, coupled natural—human system approach over time and space scales. The objective is to understand i) what cognitive and situational factors help individuals and communities to shift from normal daily activities to adapted crisis response and ii) what is the dynamic of this process compared to the one of the natural phenomenon. In this regard, our research learned both from individual perception and behavioral intent survey ("what if" type of survey) than from actual behavioral data gathered in a context of post-event investigations. The review of the literature shows that behavioral intent surveys do not accurately predict warning and crisis response as well as behavioral data do. Knowing that, the difficulty is to obtain consistent and accurate spatio-temporal behavioral data. According to our experience, this is particularly difficult in the context of crisis situations. Behavioral verification requires real-time observations and data collection of indicators

  8. Unusual presentation of prune belly syndrome: a case report.

    Science.gov (United States)

    Demisse, Abayneh Girma; Berhanu, Ashenafi; Tadesse, Temesgen

    2017-12-04

    Prune belly syndrome is a rare congenital malformation of unknown etiology, with the following triad of findings: abdominal muscle wall weakness, undescended testes, and urinary tract abnormalities. In most cases, detection of prune belly syndrome occurs during neonatal or infancy period. In this case report, we describe a 12-year-old boy from Ethiopia with the triad of findings of prune belly syndrome along with skeletal malformations. We are unaware of any previous report of prune belly syndrome in Ethiopia. A 12-year-old Amhara boy from the Northwest Gondar Amhara regional state presented to our referral hospital with a complaint of swelling over his left flank for the past 3 months. Maternal pregnancy course and medical history were noncontributory, and he had an attended birth at a health center. He has seven siblings, none of whom had similar symptoms. On examination he had a distended abdomen, asymmetric with bulging left flank, visible horizontal line, upward umbilical slit, and absent rectus abdominis muscles. His abdomen was soft with a tender cystic, bimanually palpable mass on the left flank measuring 13 × 11 cm. Both testes were undescended and he also has developmental dysplasia of the hips. An abdominal ultrasound revealed a large cystic mass in his left kidney area with echo debris and a hip X-ray showed bilateral developmental dysplasia of the hip. Intraoperative findings were cystic left kidney, both testes were intraperitoneal, tortuous left renal vein, enlarged bladder reaching above umbilicus, and left megaureter. bilateral orchidectomy and left nephrectomy were done. He was given intravenously administered antibiotics for treatment of pyelonephritis and discharged home with an appointment for follow up and possible abdominoplasty. In the current report delayed presentation contributed to testicular atrophy and decision for orchidectomy. Furthermore, he will be at potential risk for sex hormone abnormality. Therefore, diagnosis of prune

  9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis.

    Science.gov (United States)

    You, Zhu-Hong; Lei, Ying-Ke; Zhu, Lin; Xia, Junfeng; Wang, Bing

    2013-01-01

    Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.

  10. Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

    Science.gov (United States)

    Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

    2018-01-01

    Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

  11. Estimation of soil coverage of chopped pruning residues in olive orchards by image analysis

    Directory of Open Access Journals (Sweden)

    F. Jiménez-Jiménez

    2013-07-01

    Full Text Available Residue chopping from orchard pruning is becoming a common practice in conservation agriculture after the establishment of eco-conditionality policies in the European Union. This type of residue is used to protect the soil from erosion and improve the water balance and fertility of soils by improving the organic matter content. However, no studies have evaluated the influence of pruning residues and size on soil coverage. This study examines the effect of different treatments on pruning residue soil coverage in an olive orchard (cv. Picual. Treatments consisted of two quantities of pruning residues, specifically, high (2.04 kg m-2 and low (1.02 kg m-2, and two chopping speeds, low (2.4 km h-1 and high (3.2 km h-1. The different treatments were evaluated by image analysis and pixel counting to determine the soil cover percentage, size, number and distribution of the pruning residues after chopping. After chopping, the soil cover percentage was 39% higher in the high quantity pruning residue treatments but was not significantly influenced by the chopping speed. The size and number of lignified residues was quantified via pixel counting. In the high quantity pruning residue treatments, the number of large lignified residues (> 6 cm2 was higher, and the number of pruning residues smaller than 2 cm2 was lower, when compared with low quantity pruning residue treatments. The high chopping speed treatments produced more smaller-sized pruning residues.

  12. Extreme Learning Machine to Analyze the Level of Default in Spanish Deposit Institutions || Análisis de la morosidad de las entidades financieras españolas mediante Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Becerra-Alonso, David

    2012-01-01

    Full Text Available The level of default in financial institutions is a key piece of information in the activity of these organizations and reveals their level of risk. This in turn explains the growing attention given to variables of this kind, during the crisis of these last years. This paper presents a method to estimate the default rate using the non-linear model defined by standard Multilayer Perceptron (MLP neural networks trained with a novel methodology called Extreme Learning Machine (ELM. The experimental results are promising, and show a good performance when comparing the MLP model trained with the Leverberg-Marquard algorithm. || La morosidad en las entidades financieras es un dato muy importante de la actividad de estas instituciones pues permite conocer el nivel de riesgo asumido por éstas. Esto a su vez explica la creciente atención otorgada a esta variable, especialmente en los últimos años de crisis. Este artículo presenta un método para estimar el nivel de la tasa de morosidad por medio de un modelo no lineal definido por la red neuronal Multilayer Perceptron (MLP entrenada con una nueva metodología llamada Extreme Learning Machine (ELM. Los resultados experimentales son prometedores, mostrando un buen resultado si se compara con el modelo MLP entrenado con el algoritmo de Leverberg-Marquard.

  13. Weight-supported training of the upper extremity in children with cerebral palsy: a motor learning study.

    Science.gov (United States)

    Keller, Jeffrey W; van Hedel, Hubertus J A

    2017-08-30

    Novel neurorehabilitation technologies build upon treatment principles derived from motor learning studies. However, few studies have investigated motor learning with assistive devices in children and adolescents with Cerebral Palsy (CP). The aim of this study was to investigate whether children with CP who trained with weight support in a playful, virtual environment would improve upper extremity task performance (i.e. skill acquisition), transfer, and retention, three aspects that indicate whether motor learning might have occurred or not. Eleven children with CP (mean age 13.3 years, standard deviation 3.4 years), who were mildly to moderately impaired, participated. They played in the Armeo® Spring the exergame Moorhuhn with their more affected arm during 3 days (70 min pure play time). For this within-subject design, kinematic assessments, the Box and Block Test, and five items of the Melbourne Assessment were administered twice during a baseline week (one week before the intervention), directly before and after the intervention, and one day after the training phase (retention). The average exergame score improved from 209.55 to 339.73 (p assessments and the Melbourne items did not change. Improvement in game score and Box and Bock Test persisted one day later (retention). We found evidence indicating the successful acquisition, transfer, and retention of upper extremity skills in children with CP. We therefore infer that motor learning occurred when children with CP trained their more affected arm with weight-support in a playful, virtual environment.

  14. [A case of Prune Belly Syndrome. Prenatal diagnosis].

    Science.gov (United States)

    Montoya, S; Palomo Góngora, E; García, V

    1995-09-01

    Prune Belly Syndrome is a rare and complicated condition affecting the genitourinary organs and abdominal wall, it was named after the aspect of the abdomen after the bladder has been drained. In its fully developed form presents with the triad: megalocyst, abdominal muscle deficiency and cryptorchidism. We present a case of a patient with 22 weeks of gestation with ultrasonographic diagnosis of a large thoracoabdominal cyst. The delivery was by cesarean operation. We analyzed the literature.

  15. Critical Dynamics of the k-Core Pruning Process

    Directory of Open Access Journals (Sweden)

    G. J. Baxter

    2015-08-01

    Full Text Available We present the theory of the k-core pruning process (progressive removal of nodes with degree less than k in uncorrelated random networks. We derive exact equations describing this process and the evolution of the network structure and solve them numerically and, in the critical regime of the process, analytically. We show that the pruning process exhibits three different behaviors depending on whether the mean degree ⟨q⟩ of the initial network is above, equal to, or below the threshold ⟨q⟩_{c} corresponding to the emergence of the giant k-core. We find that above the threshold the network relaxes exponentially to the k-core. The system manifests the phenomenon known as “critical slowing-down,” as the relaxation time diverges when ⟨q⟩ tends to ⟨q⟩_{c}. At the threshold, the dynamics become critical, characterized by a power-law relaxation (∝1/t^{2}. Below the threshold, a long-lasting transient process (a “plateau” stage occurs. This transient process ends with a collapse in which the entire network disappears completely. The duration of the process diverges when ⟨q⟩→⟨q⟩_{c}. We show that the critical dynamics of the pruning are determined by branching processes of spreading damage. Clusters of nodes of degree exactly k are the evolving substrate for these branching processes. Our theory completely describes this branching cascade of damage in uncorrelated networks by providing the time-dependent distribution function of branching. These theoretical results are supported by our simulations of the k-core pruning in Erdős-Rényi graphs.

  16. Detection of Illegal Traffic Pattern using Hybrid Improved CART and Multiple Extreme Learning Machine Approach

    National Research Council Canada - National Science Library

    J Lekha; Padmavathi Ganapathi

    2017-01-01

    .... Experiment is carried out using NSL -KDD dataset and performance of proposed approach is compared with traditional learning approaches in terms of training time, testing time, false positive ratio and detection ratio...

  17. An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures

    Directory of Open Access Journals (Sweden)

    Bin Li

    2014-01-01

    Full Text Available Robot execution failures prediction (classification in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm.

  18. An improved kernel based extreme learning machine for robot execution failures.

    Science.gov (United States)

    Li, Bin; Rong, Xuewen; Li, Yibin

    2014-01-01

    Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm.

  19. Extreme learning machine-based classification of ADHD using brain structural MRI data

    National Research Council Canada - National Science Library

    Peng, Xiaolong; Lin, Pan; Zhang, Tongsheng; Wang, Jue

    2013-01-01

    .... ADHD has been associated with multiple cortical features from structural MRI data. However, most existing learning algorithms for ADHD identification contain obvious defects, such as time-consuming training, parameters selection, etc...

  20. Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection

    National Research Council Canada - National Science Library

    Heng Liu; Caihong Zhang; Dongdong Huang

    2017-01-01

    .... Through learning from image samples, many features of industry objects, such as shapes, positions, and orientations angles, can be obtained and then can be well utilized to determine whether there is defect...

  1. An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures

    Science.gov (United States)

    Li, Bin; Rong, Xuewen; Li, Yibin

    2014-01-01

    Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm. PMID:24977234

  2. Partial polygon pruning of hydrographic features in automated generalization

    Science.gov (United States)

    Stum, Alexander K.; Buttenfield, Barbara P.; Stanislawski, Larry V.

    2017-01-01

    This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.

  3. On the use of inexact, pruned hardware in atmospheric modelling.

    Science.gov (United States)

    Düben, Peter D; Joven, Jaume; Lingamneni, Avinash; McNamara, Hugh; De Micheli, Giovanni; Palem, Krishna V; Palmer, T N

    2014-06-28

    Inexact hardware design, which advocates trading the accuracy of computations in exchange for significant savings in area, power and/or performance of computing hardware, has received increasing prominence in several error-tolerant application domains, particularly those involving perceptual or statistical end-users. In this paper, we evaluate inexact hardware for its applicability in weather and climate modelling. We expand previous studies on inexact techniques, in particular probabilistic pruning, to floating point arithmetic units and derive several simulated set-ups of pruned hardware with reasonable levels of error for applications in atmospheric modelling. The set-up is tested on the Lorenz '96 model, a toy model for atmospheric dynamics, using software emulation for the proposed hardware. The results show that large parts of the computation tolerate the use of pruned hardware blocks without major changes in the quality of short- and long-time diagnostics, such as forecast errors and probability density functions. This could open the door to significant savings in computational cost and to higher resolution simulations with weather and climate models.

  4. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Directory of Open Access Journals (Sweden)

    Qiang Shang

    Full Text Available Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS. Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM is proposed based on singular spectrum analysis (SSA and kernel extreme learning machine (KELM. SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA. Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  5. A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

    Science.gov (United States)

    Shang, Qiang; Lin, Ciyun; Yang, Zhaosheng; Bing, Qichun; Zhou, Xiyang

    2016-01-01

    Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.

  6. Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach

    Science.gov (United States)

    Yadav, Basant; Ch, Sudheer; Mathur, Shashi; Adamowski, Jan

    2016-12-01

    In-situ bioremediation is the most common groundwater remediation procedure used for treating organically contaminated sites. A simulation-optimization approach, which incorporates a simulation model for groundwaterflow and transport processes within an optimization program, could help engineers in designing a remediation system that best satisfies management objectives as well as regulatory constraints. In-situ bioremediation is a highly complex, non-linear process and the modelling of such a complex system requires significant computational exertion. Soft computing techniques have a flexible mathematical structure which can generalize complex nonlinear processes. In in-situ bioremediation management, a physically-based model is used for the simulation and the simulated data is utilized by the optimization model to optimize the remediation cost. The recalling of simulator to satisfy the constraints is an extremely tedious and time consuming process and thus there is need for a simulator which can reduce the computational burden. This study presents a simulation-optimization approach to achieve an accurate and cost effective in-situ bioremediation system design for groundwater contaminated with BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) compounds. In this study, the Extreme Learning Machine (ELM) is used as a proxy simulator to replace BIOPLUME III for the simulation. The selection of ELM is done by a comparative analysis with Artificial Neural Network (ANN) and Support Vector Machine (SVM) as they were successfully used in previous studies of in-situ bioremediation system design. Further, a single-objective optimization problem is solved by a coupled Extreme Learning Machine (ELM)-Particle Swarm Optimization (PSO) technique to achieve the minimum cost for the in-situ bioremediation system design. The results indicate that ELM is a faster and more accurate proxy simulator than ANN and SVM. The total cost obtained by the ELM-PSO approach is held to a minimum

  7. Síndrome de prune belly: presentación de caso Prune belly syndrome: a case report

    OpenAIRE

    María Elena Toledo Lamela; Orlando Rafael Expósito Reyes; Roberto E Segura Figueredo; Elsa M Díaz Suárez; Carlos Tornés Salgado

    2008-01-01

    El síndrome de prune belly es una rara enfermedad congénita de causa desconocida. Se presenta el caso de un recién nacido a término, del sexo masculino y de un día de nacido, que fue remitido al servicio de urología pediátrica por presentar ausencia de los músculos de la pared anterior del abdomen (rectos anteriores), criptorquidia bilateral y gran globo vesical. A partir de los hallazgos del examen físico se planteó el diagnóstico de síndrome de prune belly. Se encontraron anomalías asociada...

  8. PHENOLOGICAL CHARACTERISTICS OF GENOTYPES FROM CATTLEY GUAVA AND GUAVA TREES SUBMITTED TO FRUCTIFICATION PRUNING

    Directory of Open Access Journals (Sweden)

    CINTIA APARECIDA BREMENKAMP

    Full Text Available ABSTRACT Psidium cattleianum Sabine is a species from the Myrtaceae family that serves as an option for the native fruits cultivation, besides being considered a source of resistance to the Meloidogyne enterolobii nematode. Although cattley guava trees from this species produce flower buds in young branches, there are no reports of response to fructification pruning or phenological synchronism with the guava tree. The objective of this paper was the comparative evaluation of the genotype response of strawberry guava trees and guava cultivars to fructification pruning, thus, describing the phenology of both species under the same cultivation conditions. The experiment was conducted under an entirely randomized outline, in 7x2 factorial scheme, being evaluated seven genotypes (three from strawberry guava and four from guava trees, and with pruning performed in two seasons (May 2012 and March 2013, with three repetitions. Fructification pruning was executed by a lopping on all mature branches, from the last growth flow in the woody branch region. Were evaluated budding characteristics and fruit harvesting, as well as number of days from pruning to the observation of the phenological event. Cattley guava tree pruning stimulated fructification of all three genotypes after pruning done on May and two genotypes after the March’s pruning. There has been a sync between the guava cultivars’ flowering and both strawberry guava trees genotypes, when those were pruned on May.

  9. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  10. Online Sequential Prediction for Nonstationary Time Series with New Weight-Setting Strategy Using Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wentao Mao

    2015-01-01

    Full Text Available Accurate and fast prediction of nonstationary time series is challenging and of great interest in both practical and academic areas. In this paper, an online sequential extreme learning machine with new weighted strategy is proposed for nonstationary time series prediction. First, a new leave-one-out (LOO cross-validation error estimation for online sequential data is proposed based on inversion of block matrix. Second, a new weighted strategy based on the proposed LOO error estimation is proposed. This strategy ranks the samples’ importance by means of the LOO error of each new added sample and then assigns various weights. Performance comparisons of the proposed method with other existing algorithms are presented based on chaotic and real-world nonstationary time series data. The results show that the proposed method outperforms the classical ELM and OS-ELM in terms of generalization performance and numerical stability.

  11. Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction

    Directory of Open Access Journals (Sweden)

    Qisheng Yan

    2014-01-01

    Full Text Available A hybrid forecasting approach combining empirical mode decomposition (EMD, phase space reconstruction (PSR, and extreme learning machine (ELM for international uranium resource prices is proposed. In the first stage, the original uranium resource price series are first decomposed into a finite number of independent intrinsic mode functions (IMFs, with different frequencies. In the second stage, the IMFs are composed into three subseries based on the fine-to-coarse reconstruction rule. In the third stage, based on phase space reconstruction, different ELM models are used to model and forecast the three subseries, respectively, according to the intrinsic characteristic time scales. Finally, in the foruth stage, these forecasting results are combined to output the ultimate forecasting result. Experimental results from real uranium resource price data demonstrate that the proposed hybrid forecasting method outperforms RBF neural network (RBFNN and single ELM in terms of RMSE, MAE, and DS.

  12. Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Nantian Huang

    2016-11-01

    Full Text Available Accurate wind speed forecasting is a fundamental element of wind power prediction. Thus, a new hybrid wind speed forecasting model, using variational mode decomposition (VMD, the partial autocorrelation function (PACF, and weighted regularized extreme learning machine (WRELM, is proposed to improve the accuracy of wind speed forecasting. First, the historic wind speed time series is decomposed into several intrinsic mode functions (IMFs. Second, the partial correlation of each IMF sequence is analyzed using PACF to select the optimal subfeature set for particular predictors of each IMF. Then, the predictors of each IMF are constructed in order to enhance its strength using WRELM. Finally, wind speed is obtained by adding up all the predictors. The experiment, using real wind speed data, verified the effectiveness and advancement of the new approach.

  13. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea

    Science.gov (United States)

    Vasu, Nikhil N.; Lee, Seung-Rae

    2016-06-01

    An ever-increasing trend of extreme rainfall events in South Korea owing to climate change is causing shallow landslides and debris flows in mountains that cover 70% of the total land area of the nation. These catastrophic, gravity-driven processes cost the government several billion KRW (South Korean Won) in losses in addition to fatalities every year. The most common type of landslide observed is the shallow landslide, which occurs at 1-3 m depth, and may mobilize into more catastrophic flow-type landslides. Hence, to predict potential landslide areas, susceptibility maps are developed in a geographical information system (GIS) environment utilizing available morphological, hydrological, geotechnical, and geological data. Landslide susceptibility models were developed using 163 landslide points and an equal number of nonlandslide points in Mt. Woomyeon, Seoul, and 23 landslide conditioning factors. However, because not all of the factors contribute to the determination of the spatial probability for landslide initiation, and a simple filter or wrapper-based approach is not efficient in identifying all of the relevant features, a feedback-loop-based hybrid algorithm was implemented in conjunction with a learning scheme called an extreme learning machine, which is based on a single-layer, feed-forward network. Validation of the constructed susceptibility model was conducted using a testing set of landslide inventory data through a prediction rate curve. The model selected 13 relevant conditioning factors out of the initial 23; and the resulting susceptibility map shows a success rate of 85% and a prediction rate of 89.45%, indicating a good performance, in contrast to the low success and prediction rate of 69.19% and 56.19%, respectively, as obtained using a wrapper technique.

  14. Abdominal wall reconstruction in the prune belly syndrome.

    Science.gov (United States)

    Randolph, J; Cavett, C; Eng, G

    1981-12-01

    In our institution, 12 patients have been encountered with prune belly syndrome. Eight children have undergone evaluation of their abdominal musculature by electromyography. Results of their studies show that major functioning or recoverable muscle exists in the lateral and upper sector of the abdomen, but that little or no muscle exists in the lower central abdomen. Based in part on these findings, an operation has been devised which spares all potentially functioning musculature and corresponding motor nerves, and disposes of nonfunctioning and nonrecoverable muscle. In terms of cosmetic appearance and gross motor testing, these growing boys show significant improvement.

  15. REDUCING COMPETITION IN AGROFORESTRY BY PRUNING NATIVE TREES

    OpenAIRE

    Nicodemo,Maria Luiza Franceschi; Castiglioni, Paula Priscila; Pezzopane,José Ricardo Macedo; Tholon, Patrícia; Carpanezzi, Antônio Aparecido

    2016-01-01

    ABSTRACT The degree to which pruning helps reestablish balance in agroforestry was assessed in a system established in São Carlos, São Paulo, Brazil, in 2008. Seven native tree species were planted at a density of 600 trees/ha in five strips of three rows each, and annual crops were cultivated in the 17-m crop strips between the tree strips. Competition was established after 35 months, decreasing the aboveground biomass production of corn planted close to the trees. An assessment of black oat...

  16. Influence of plant prunings on soil properties and yield of yam minisett

    African Journals Online (AJOL)

    The importance of the application of agroforestry prunings on improvement of the soil organic matter cannot be overemphasized. The study focused on the influence of plant prunings on soil properties and yield of yam minisett. There is no doubt that effective production of seed yam through minisett technique requires ...

  17. Prune belly syndrome associated with bilateral multicystic dysplastic kidneys and urethral obstruction: A case report

    Directory of Open Access Journals (Sweden)

    Arzu Akdag

    2015-06-01

    Full Text Available Prune belly syndrome is a rare congenital disorder defined by a characteristic clinical triad: Abdominal muscle deficiency, severe urinary tract abnormalities, and bilateral cryptorchidism. We describe a preterm neonate of Prune Belly syndrome who had abdominal muscle deficiency, multicystic dysplastic kidney, urethral hypoplasia and pulmonary hypoplasia. We presented this rare case with the data gathered from the literatüre.

  18. Source-Sink Relations in Fruits VII. Effects of Pruning in Sour Cherry and Plum

    DEFF Research Database (Denmark)

    Toldam-Andersen, Torben Bo; Hansen, P.

    1993-01-01

    Sour cherries cv. 'Stevnsbær' and plums cv. 'Victoria' were heavily pruned in 1987. Fruit samples were collected during the growing season and concentrations of different quality components were determined. Pruning resulted in a small increase in fruit size, the effect being greater on the older...

  19. Response of smaller European elm bark beetles to pruning wounds on American elm

    Science.gov (United States)

    Jack H. Barger; William N. Cannon

    1987-01-01

    From 1982 to 1984, inflight smaller European elm bark beetles, Scolytus multistriatus, were captured on American elms, Ulmus americana, that were therapeutically pruned for Dutch elm disease control. Pruning wounds were treated with wound dressing or left untreated to determine effects of the treatments on beetle attraction....

  20. EVALUATION OF THE CUTTING FORCE ON VINE BRANCHES IN WINTER PRUNING

    Directory of Open Access Journals (Sweden)

    Fabio Pezzi

    2009-03-01

    Full Text Available Mechanical pruning, usually integrated with manual finishing, still excites some doubts regarding the quality of the cut which appears not to be the best as precision and cleanliness. This aspect, probably conditioned by the model and the application of pruning machines, might also be affected by the characteristics of the vine branches in particular by their cutting force. In order to evaluate the cutting force on different vine type, laboratory and field trails were carried out. The experiments were set in order to evaluate this parameter during winter pruning in relation to three variables: vine type; branch size; pruning time. The results show how the cutting resistance of vine branches during vegetative rest depends on vine variety (Trebbiano 675 N Cabernet 1175 N, on the diameter of the canes (increase of force from 56 % to 86 % between the classes of diameter and on the pruning date (26% decrease of the cutting force in the latest period for Trebbiano and Sangiovese, and 32 % for Cabernet Sauvignon. Such variations, which can be considerable, affect directly the pruning results. High values of cutting force can generate tiring of operator in manual pruning or cutting damages on the vegetation during mechanical pruning.

  1. Prediction of hot spots in protein interfaces using extreme learning machines with the information of spatial neighbour residues.

    Science.gov (United States)

    Wang, Lin; Zhang, Wenjuan; Gao, Qiang; Xiong, Congcong

    2014-08-01

    The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming increasingly important for the research on protein-protein interaction and drug design. For each interface residue or target residue to be predicted, the authors extract hybrid features which incorporate a wide range of information of the target residue and its spatial neighbor residues, that is, the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). Here, feature selection is performed using random forests to avoid over-fitting. Thereafter, the extreme learning machine is employed to effectively integrate these hybrid features for predicting hot spots in protein interfaces. By the 5-fold cross validation in the training set, their method can achieve accuracy (ACC) of 82.1% and Matthew's correlation coefficient (MCC) of 0.459, and outperforms some alternative machine learning methods in the comparison study. Furthermore, their method achieves ACC of 76.8% and MCC of 0.401 in the independent test set, and is more effective than the major existing hot spot predictors. Their prediction method offers a powerful tool for uncovering candidate residues in the studies of alanine scanning mutagenesis for functional protein interaction sites.

  2. Illumination correction of dyed fabrics approach using Bagging-based ensemble particle swarm optimization-extreme learning machine

    Science.gov (United States)

    Zhou, Zhiyu; Xu, Rui; Wu, Dichong; Zhu, Zefei; Wang, Haiyan

    2016-09-01

    Changes in illumination will result in serious color difference evaluation errors during the dyeing process. A Bagging-based ensemble extreme learning machine (ELM) mechanism hybridized with particle swarm optimization (PSO), namely Bagging-PSO-ELM, is proposed to develop an accurate illumination correction model for dyed fabrics. The model adopts PSO algorithm to optimize the input weights and hidden biases for the ELM neural network called PSO-ELM, which enhances the performance of ELM. Meanwhile, to further increase the prediction accuracy, a Bagging ensemble scheme is used to construct an independent PSO-ELM learning machine by taking bootstrap replicates of the training set. Then, the obtained multiple different PSO-ELM learners are aggregated to establish the prediction model. The proposed prediction model is evaluated with real dyed fabric images and discussed in comparison with several related methods. Experimental results show that the ensemble color constancy method is able to generate a more robust illuminant estimation model with better generalization performance.

  3. An Improved Pathological Brain Detection System Based on Two-Dimensional PCA and Evolutionary Extreme Learning Machine.

    Science.gov (United States)

    Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

    2017-12-07

    Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images is proposed that markedly improves the recent results. The proposed system makes use of contrast limited adaptive histogram equalization (CLAHE) to enhance the quality of the input MR images. Thereafter, two-dimensional PCA (2DPCA) strategy is employed to extract the features and subsequently, a PCA+LDA approach is used to generate a compact and discriminative feature set. Finally, a new learning algorithm called MDE-ELM is suggested that combines modified differential evolution (MDE) and extreme learning machine (ELM) for segregation of MR images as pathological or healthy. The MDE is utilized to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks (SLFN), whereas an analytical method is used for determining the output weights. The proposed algorithm performs optimization based on both the root mean squared error (RMSE) and norm of the output weights of SLFNs. The suggested scheme is benchmarked on three standard datasets and the results are compared against other competent schemes. The experimental outcomes show that the proposed scheme offers superior results compared to its counterparts. Further, it has been noticed that the proposed MDE-ELM classifier obtains better accuracy with compact network architecture than conventional algorithms.

  4. Adaptive myoelectric pattern recognition for arm movement in different positions using advanced online sequential extreme learning machine.

    Science.gov (United States)

    Anam, Khairul; Al-Jumaily, Adel

    2016-08-01

    The performance of the myoelectric pattern recognition system sharply decreases when working in various limb positions. The issue can be solved by cumbersome training procedure that can anticipate all possible future situations. However, this procedure will sacrifice the comfort of the user. In addition, many unpredictable scenarios may be met in the future. This paper proposed a new adaptive myoelectric pattern recognition using advance online sequential extreme learning (AOS-ELM) for classification of the hand movements to five different positions. AOS-ELM is an improvement of OS-ELM that can verify the adaptation validity using entropy. The proposed adaptive MPR was able to classify eight different classes from eleven subjects by accuracy of 95.42 % using data from one position. After learning the data from whole positions, the performance of the proposed system is 86.13 %. This performance was better than the MPR that employed original OS-ELM, but it was worse than the MPR that utilized the batch classifiers. Nevertheless, the adaptation mechanism of AOS-ELM is preferred in the real-time application.

  5. A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Qing Ye

    2015-01-01

    Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

  6. Evaluation of soil carbon pools after the addition of prunings in subtropical orchards placed in terraces

    Science.gov (United States)

    Márquez San Emeterio, Layla; Martín Reyes, Marino Pedro; Ortiz Bernad, Irene; Fernández Ondoño, Emilia; Sierra Aragón, Manuel

    2017-04-01

    The amount of carbon that can be stored in a soil depends on many factors, such as the type of soil, the chemical composition of plant rests and the climate, and is also highly affected by land use and soil management. Agricultural ecosystems are proved to absorb a large amount of CO2 from the atmosphere through several sustainable management practices. In addition, organic materials such as leaves, grass, prunings, etc., comprise a significant type of agricultural practices as a result of waste recycling. The aim of this research was to evaluate the effects of the addition of different organic prunings on the potential for carbon sequestration in agricultural soils placed in terraces. Three subtropical orchards were sampled in Almuñécar (Granada, S Spain): mango (Mangifera indica L.), avocado (Persea americana Mill.) and cherimoya (Annonacherimola Mill.). The predominant climate is Subtropical Mediterranean and the soil is an Eutric Anthrosol. The experimental design consisted in the application of prunings from avocado, cherimoya and mango trees, placed on the surface soil underneath their correspondent trees, as well as garden prunings from the green areas surrounding the town center on the surface soils under the three orchard trees. Control experiences without the addition of prunings were also evaluated. These experiences were followed for three years. Soil samples were taken at4 cm depth. They were dried for 3-4 days and then sieved (avocado prunings and their control soil, and between soils under garden prunings with cherimoya and their control soil. Regarding the water-soluble soil organic carbon, low differences were shown. Differences in mineral-associated and non-oxidable organic carbon fractions were also statistically significant between soils under avocado prunings and their control soil, and between soils under garden prunings with cherimoya and their control soil. No significant differences in any organic carbon pool were founded for the soils

  7. Phenotype profiling and multivariate statistical analysis of Spur-pruning type Grapevine in National Clonal Germplasm Repository (NCGR, Davis)

    Science.gov (United States)

    Most Korean vineyards employed spur-pruning type modified-T trellis system. This produce system is suitable to spur-pruning type cultivars. But most European table grape is not adaptable to this produce system because their fruitfulness is sufficient to cane-pruning type system. Total 20 of fruit ch...

  8. Síndrome de prune belly: presentación de caso Prune belly syndrome: a case report

    Directory of Open Access Journals (Sweden)

    María Elena Toledo Lamela

    2008-03-01

    Full Text Available El síndrome de prune belly es una rara enfermedad congénita de causa desconocida. Se presenta el caso de un recién nacido a término, del sexo masculino y de un día de nacido, que fue remitido al servicio de urología pediátrica por presentar ausencia de los músculos de la pared anterior del abdomen (rectos anteriores, criptorquidia bilateral y gran globo vesical. A partir de los hallazgos del examen físico se planteó el diagnóstico de síndrome de prune belly. Se encontraron anomalías asociadas como escoliosis y agenesia del pie derecho. En el estudio radiológico del tracto urinario se confirmaron malformaciones congénitas como valva de uretra posterior y megavejiga con uretero-hidronefrosis bilateral. Los análisis de laboratorio confirmaron la afectación de la función renal y una infección urinaria asociada. Se practicó una cistostomía a cielo abierto. El paciente falleció a los 10 días a causa de las complicaciones de la insuficiencia renalThe prune belly syndrome is a congenital rare disease of unknown origin. The case of a one-day-old full- term male newborn infant that was referred to the pediatric urology service for presenting absence of the muscles of the anterior abdomen wall (anterior rectus muscle, bilateral cryptochordism and big vesical globe, was presented. Starting from the findings of the physical examination, the prune belly syndrome was diagnosed. Associated abnormalities such as scoliosis and agenesis of the right leg were found. In the radiological study of the urinary tract, congenital malformations as posterior urethra valve and megabladder with bilateral ureterohydronephrosis were confirmed. The lab tests corroborated the affectation of the renal function and an associated urinary infection. The patient died at 10 days as a result of the complications of renal failure

  9. Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

    Science.gov (United States)

    Deo, Ravinesh C.; Şahin, Mehmet

    2015-02-01

    The prediction of future drought is an effective mitigation tool for assessing adverse consequences of drought events on vital water resources, agriculture, ecosystems and hydrology. Data-driven model predictions using machine learning algorithms are promising tenets for these purposes as they require less developmental time, minimal inputs and are relatively less complex than the dynamic or physical model. This paper authenticates a computationally simple, fast and efficient non-linear algorithm known as extreme learning machine (ELM) for the prediction of Effective Drought Index (EDI) in eastern Australia using input data trained from 1957-2008 and the monthly EDI predicted over the period 2009-2011. The predictive variables for the ELM model were the rainfall and mean, minimum and maximum air temperatures, supplemented by the large-scale climate mode indices of interest as regression covariates, namely the Southern Oscillation Index, Pacific Decadal Oscillation, Southern Annular Mode and the Indian Ocean Dipole moment. To demonstrate the effectiveness of the proposed data-driven model a performance comparison in terms of the prediction capabilities and learning speeds was conducted between the proposed ELM algorithm and the conventional artificial neural network (ANN) algorithm trained with Levenberg-Marquardt back propagation. The prediction metrics certified an excellent performance of the ELM over the ANN model for the overall test sites, thus yielding Mean Absolute Errors, Root-Mean Square Errors, Coefficients of Determination and Willmott's Indices of Agreement of 0.277, 0.008, 0.892 and 0.93 (for ELM) and 0.602, 0.172, 0.578 and 0.92 (for ANN) models. Moreover, the ELM model was executed with learning speed 32 times faster and training speed 6.1 times faster than the ANN model. An improvement in the prediction capability of the drought duration and severity by the ELM model was achieved. Based on these results we aver that out of the two machine learning

  10. Lower extremity compartment syndrome in the acute care surgery paradigm: safety lessons learned

    Directory of Open Access Journals (Sweden)

    Cothren Clay C

    2009-06-01

    Full Text Available Abstract Background Prompt diagnosis and decompression of acute lower extremity compartment syndrome (LECS in the multisystem injured patient is essential to avoid the devastating complications of progressive tissue necrosis and amputation. Despite collaborative trauma and orthopedic management of these difficult cases, significant delays in diagnosis and treatment occur. Periodic system review of our trauma and orthopedic data for complications of LECS led us to hypothesize that delayed diagnosis and limb loss were potentially preventable events in our trauma center. Setting Academic level 1 trauma center. Methods We performed a prospective review of our trauma registry for all cases of LECS over a 7 year period (2/98–10/2005. Variables reviewed included demographics, injury patterns, tissue necrosis, amputation and mortality. Results Eighty-three (10 female, 73 male cases were reviewed. Mean age = 33.3 years (range 1–78. Mean ISS = 19.4, GCS = 12.5. Five (6.0% had amputations; 7 (8.4% died. Fractures occurred in 68.7% (n = 57, and vascular injuries were present in 38.6% (n = 32. In 7 patients (8.4%, a delayed compartment release resulted in muscle necrosis requiring multiple debridements, subsequent wound closure problems, and long term disability. Of note, none of these patients had prior compartment pressure measurements. Furthermore, 6 patients (7% had superficial peroneal nerve transections as complications of their fasciotomy. Conclusion In the multisystem injured patient, LECS remains a major diagnostic and treatment challenge with significant risks of limb loss as well as complications from decompressive fasciotomy. These data underscore the importance of routine surveillance for LECS. In addition, a thorough knowledge of regional anatomy is essential to avoid technical morbidity.

  11. Pruning method for a cluster-based neural network

    Science.gov (United States)

    Ranney, Kenneth I.; Khatri, Hiralal; Nguyen, Lam H.; Sichina, Jeffrey

    2000-08-01

    Many radar automatic target detection (ATD) algorithms operate on a set of data statistics or features rather than on the raw radar sensor data. These features are selected based on their ability to separate target data samples from background clutter samples. The ATD algorithms often operate on the features through a set of parameters that must be determined from a set of training data that are statistically similar to the data set to be encountered in practice. The designer usually attempts to minimize the number of features used by the algorithm -- a process commonly referred to as pruning. This not only reduces the computational demands of the algorithm, but it also prevents overspecialization to the samples from the training data set. Thus, the algorithm will perform better on a set of test data samples it has not encountered during training. The Optimal Brain Surgeon (OBS) and Divergence Method provide two different approaches to pruning. We apply the two methods to a set of radar data features to determine a new, reduced set of features. We then evaluate the resulting feature sets and discuss the differences between the two methods.

  12. A Novel Hybrid Model Based on Extreme Learning Machine, k-Nearest Neighbor Regression and Wavelet Denoising Applied to Short-Term Electric Load Forecasting

    Directory of Open Access Journals (Sweden)

    Weide Li

    2017-05-01

    Full Text Available Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM, which combines k-Nearest Neighbor (KNN and Extreme Learning Machine (ELM based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM, Wavelet Denoising-Extreme Learning Machine (WKM and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM, the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning.

  13. First steps in translating human cognitive processes of cane pruning grapevines into AI rules for automated robotic pruning

    Directory of Open Access Journals (Sweden)

    Saxton Valerie

    2014-01-01

    Full Text Available Cane pruning of grapevines is a skilled task for which, internationally, there is a dire shortage of human pruners. As part of a larger project developing an automated robotic pruner, we have used artificial intelligence (AI algorithms to create an expert system for selecting new canes and cutting off unwanted canes. A domain and ontology has been created for AI, which reflects the expertise of expert human pruners. The first step in the creation of an expert system was to generate virtual vines, which were then ‘pruned’ by human pruners and also by the expert system in its infancy. Here we examined the decisions of 12 human pruners, for consistency of decision, on 60 virtual vines. 96.7% of the 12 pruners agreed on at least one cane choice after which there was diminishing agreement on which further canes to select for laying. Our results indicate that techniques developed in computational intelligence can be used to co-ordinate and synthesise the expertise of human pruners into a best practice format. This paper describes first steps in this knowledge elicitation process, and discusses the fit between cane pruning expertise and the expertise that can be elicited using AI based expert system techniques.

  14. Effects of pruning in Monterey pine plantations affected by Fusarium circinatum

    Energy Technology Data Exchange (ETDEWEB)

    Bezos, D.; Lomba, J. M.; Martinez-Alvarez, P.; Fernandez, M.; Diez, J. J.

    2012-07-01

    Fusarium circinatum Nirenberg and O'Donnell (1998) is the causal agent of Pitch Canker Disease (PCD) in Pinus species, producing damage to the main trunk and lateral branches as well as causing branch dieback. The disease has been detected recently in northern Spain in Pinus spp. seedlings at nurseries and in Pinus radiata D. Don adult trees in plantations. Fusarium circinatum seems to require a wound to enter the tree, not only that as caused by insects but also that resulting from damage by humans, i.e. mechanical wounds. However, the effects of pruning on the infection process have yet to be studied. The aim of the present study was to know how the presence of mechanical damage caused by pruning affects PCD occurrence and severity in P. radiata plantations. Fifty P. radiata plots (pruned and unpruned) distributed throughout 16 sites affected by F. circinatum in the Cantabria region (northern Spain) were studied. Symptoms of PCD presence, such as dieback, oozing cankers and trunk deformation were evaluated in 25 trees per plot and related to pruning effect. A significant relationship between pruning and the number of cankers per tree was observed, concluding that wounds caused by pruning increase the chance of pathogen infection. Other trunk symptoms, such as the presence of resin outside the cankers, were also higher in pruned plots. These results should be taken into account for future management of Monterey Pine plantations. (Author) 36 refs.

  15. Engulfing action of glial cells is required for programmed axon pruning during Drosophila metamorphosis.

    Science.gov (United States)

    Awasaki, Takeshi; Ito, Kei

    2004-04-20

    Axon pruning is involved in establishment and maintenance of functional neural circuits. During metamorphosis of Drosophila, selective pruning of larval axons is developmentally regulated by ecdysone and caused by local axon degeneration. Previous studies have revealed intrinsic molecular and cellular mechanisms that trigger this pruning process, but how pruning is accomplished remains essentially unknown. Detailed analysis of morphological changes in the axon branches of Drosophila mushroom body (MB) neurons revealed that during early pupal stages, clusters of neighboring varicosities, each of which belongs to different axons, disappear simultaneously shortly before the onset of local axon degeneration. At this stage, bundles of axon branches are infiltrated by the processes of surrounding glia. These processes engulf clusters of varicosities and accumulate intracellular degradative compartments. Selective inhibition of cellular functions, including endocytosis, in glial cells via the temperature-sensitive allele of shibire both suppresses glial infiltration and varicosity elimination and induces a severe delay in axon pruning. Selective inhibition of ecdysone receptors in the MB neurons severely suppressed not only axon pruning but also the infiltration and engulfing action of the surrounding glia. These findings strongly suggest that glial cells are extrinsically activated by ecdysone-stimulated MB neurons. These glial cells infiltrate the mass of axon branches to eliminate varicosities and break down axon branches actively rather than just scavenging already-degraded debris. We therefore propose that neuron-glia interaction is essential for the precisely coordinated axon-pruning process during Drosophila metamorphosis.

  16. Rootstock on vine performance and wine quality of ‘Syrah’ under double pruning management

    Directory of Open Access Journals (Sweden)

    Frederico Alcântara Novelli Dias

    Full Text Available ABSTRACT In the Brazilian Southeast, the production of high quality wines is attained by a new management approach called double pruning. This management changes the harvesting of wine grape (Vitis vinifera L. from wet summer to dry winter through a two pruning procedures carried out during the year. The first pruning is done during the winter to induce a vegetative cycle (all clusters are removed and a second pruning is done during the summer to induce the reproductive cycle. In this study, ten different rootstocks were compared in order to optimize yield and wine quality of Syrah vines conducted under autumn-winter season by double pruning approach. Syrah grapevines grafted onto ‘Rupestris du Lot’ and ‘IAC 766’ showed the highest pruning weight, while ‘110 Richter’ and ‘161-49 Courdec’ induced the lowest cane vigor. The average production of two seasons identified ‘IAC 766’, ‘Kober 5BB’ and ‘Rupestris du Lot’ as the most productive rootstocks. In both seasons, the grape quality was more influenced by the plant development status than by rootstocks. ‘Syrah’ wine from vigorous and high yielding rootstocks, ‘IAC 766’ and ‘Rupestris du Lot’, showed satisfactory wine phenolic composition and alcohol/acidity balance. This study showed that vigorous rootstock increased yield without compromising grape and winter wine quality of Syrah grapevines subjected to double pruning management in the Brazilian Southeast.

  17. Effect of root pruning and irrigation regimes on leaf water relations and xylem ABA and ionic concentrations in pear trees

    DEFF Research Database (Denmark)

    Wang, Yufei; Bertelsen, Marianne G.; Petersen, Karen Koefoed

    2014-01-01

    pruning caused water deficit stress in pear trees. Further RP trees had significantly lower concentrations of total cations and anions and the sum of cations and anions than the NP trees implying root pruning decreased acquisition of nutrients from the soil. In the root pruned trees, the leaf water......Root pruning is an effective approach for controlling vegetative growth of pear trees (Pyrus communis L.), yet the underlying mechanisms for such effect remain largely elusive. A two-year field experiment was conducted to investigate the effect of root pruning and irrigation regimes on leaf water...... relation characteristics, stomatal conductance and xylem sap abscisic acid (ABA) and ionic concentrations. Results showed that leaf water potential, leaf turgor and stomatal conductance of root pruning (RP) treatment was significantly lower than those of non-root pruning (NP) treatment indicating that root...

  18. Post-pruning shoot growth increases fruit abscission and reduces stem carbohydrates and yield in macadamia.

    Science.gov (United States)

    McFadyen, Lisa M; Robertson, David; Sedgley, Margaret; Kristiansen, Paul; Olesen, Trevor

    2011-05-01

    There is good evidence for deciduous trees that competition for carbohydrates from shoot growth accentuates early fruit abscission and reduces yield but the effect for evergreen trees is not well defined. Here, whole-tree tip-pruning at anthesis is used to examine the effect of post-pruning shoot development on fruit abscission in the evergreen subtropical tree macadamia (Macadamia integrifolia, M. integrifolia × tetraphylla). Partial-tree tip-pruning is also used to test the localization of the effect. In the first experiment (2005/2006), all branches on trees were tip-pruned at anthesis, some trees were allowed to re-shoot (R treatment) and shoots were removed from others (NR treatment). Fruit set and stem total non-structural carbohydrates (TNSC) over time, and yield were measured. In the second experiment (2006/2007), upper branches of trees were tip-pruned at anthesis, some trees were allowed to re-shoot (R) and shoots were removed from others (NR). Fruit set and yield were measured separately for upper (pruned) and lower (unpruned) branches. In the first experiment, R trees set far fewer fruit and had lower yield than NR trees. TNSC fell and rose in all treatments but the decline in R trees occurred earlier than in NR trees and coincided with early shoot growth and the increase in fruit abscission relative to the other treatments. In the second experiment, fruit abscission on upper branches of R trees increased relative to the other treatments but there was little difference in fruit abscission between treatments on lower branches. This study is the first to demonstrate an increase in fruit abscission in an evergreen tree in response to pruning. The effect appeared to be related to competition for carbohydrates between post-pruning shoot growth and fruit development and was local, with shoot growth on pruned branches having no effect on fruit abscission on unpruned branches.

  19. Growing degree-days for the `Niagara Rosada' grapevine pruned in different seasons

    Science.gov (United States)

    Scarpare, Fábio Vale; Scarpare Filho, João Alexio; Rodrigues, Alessandro; Reichardt, Klaus; Angelocci, Luiz Roberto

    2012-09-01

    Plant growth and development are proportional to biological time, or the thermal time of the species, which can be defined as the integral of the temperature over time between the lower and upper temperature developmental thresholds. The objective of this study was to investigate the efficiency of the growing degree-day (GDD) approach for vines of the `Niagara Rosada' cultivar pruned in winter and summer seasons, and physiological phases (mobilisation and reserve accumulation) in a humid subtropical region. The experiment was carried out on 13-year-old plants in Piracicaba, São Paulo State-Brazil, evaluating 24 production cycles, 12 from the winter pruning, and 12 from the summer pruning. The statistical design was comprised of randomised blocks, using the pruning dates as treatment: 20 July, 4 August, 19 August, and 3 September (winter); 1 February, 15 February, 2 March, and 16 March (summer). Comparison of the mean values of GDD among pruning dates was evaluated by the Tukey test, and comparison between pruning seasons was made by the F test for orthogonal contrasts, both at the 5% probability level. The results showed good agreement between the values of GDD required to complete the cycle from the winter pruning until harvest when compared with other studies performed with the same cultivar grown in the Southern and Southeastern regions of Brazil. However, there was a consistent statistical difference between GDD computed for winter and summer pruning, which allowed us to conclude that this bio-meteorological index is not sufficient to distinguish vines pruned in different seasons and physiological phases applied in humid subtropical climates.

  20. Growing degree-days for the 'Niagara Rosada' grapevine pruned in different seasons.

    Science.gov (United States)

    Scarpare, Fábio Vale; Scarpare Filho, João Alexio; Rodrigues, Alessandro; Reichardt, Klaus; Angelocci, Luiz Roberto

    2012-09-01

    Plant growth and development are proportional to biological time, or the thermal time of the species, which can be defined as the integral of the temperature over time between the lower and upper temperature developmental thresholds. The objective of this study was to investigate the efficiency of the growing degree-day (GDD) approach for vines of the 'Niagara Rosada' cultivar pruned in winter and summer seasons, and physiological phases (mobilisation and reserve accumulation) in a humid subtropical region. The experiment was carried out on 13-year-old plants in Piracicaba, São Paulo State-Brazil, evaluating 24 production cycles, 12 from the winter pruning, and 12 from the summer pruning. The statistical design was comprised of randomised blocks, using the pruning dates as treatment: 20 July, 4 August, 19 August, and 3 September (winter); 1 February, 15 February, 2 March, and 16 March (summer). Comparison of the mean values of GDD among pruning dates was evaluated by the Tukey test, and comparison between pruning seasons was made by the F test for orthogonal contrasts, both at the 5% probability level. The results showed good agreement between the values of GDD required to complete the cycle from the winter pruning until harvest when compared with other studies performed with the same cultivar grown in the Southern and Southeastern regions of Brazil. However, there was a consistent statistical difference between GDD computed for winter and summer pruning, which allowed us to conclude that this bio-meteorological index is not sufficient to distinguish vines pruned in different seasons and physiological phases applied in humid subtropical climates.

  1. Ability of chestnut oak to tolerate acorn pruning by rodents: The role of the cotyledonary petiole.

    Science.gov (United States)

    Yi, Xianfeng; Curtis, Rachel; Bartlow, Andrew W; Agosta, Salvatore J; Steele, Michael A

    2013-01-01

    Acorns of many white oak species germinate soon after autumn seed fall, a characteristic widely interpreted as a general adaptation to escape predation by small rodents. However, the mechanism by which early, rapid germination allows escape and/or tolerance of seed damage remains unclear. Here we reported how specific germination traits of chestnut oak (Quercus montana) acorns, and those of other white oak species, allow successful escape from acorn pruning by rodents. During germination, chestnut oak acorns develop elongated cotyledonary petioles, which extend beyond the distal end of the acorn (1-2 cm) to the point at which the epicotyl and radicle diverge. However, granivorous rodents often prune the taproots above or below the plumule when eating or caching these germinated acorns in autumn. Hence, we hypothesized elongation of cotyledonary petioles allows chestnut oaks to escape acorn pruning by rodents. We simulated pruning by rodents by cutting the taproot at different stages of germination (radicle length) to evaluate the regeneration capacity of four resulting seedling remnants following taproot pruning: acorns with the plumule (remnant I), acorns without the plumule (remnant II), and pruned taproots with (remnant III) or without the plumule (remnant IV). Our results showed that remnant I germinated into seedlings regardless of the length of the taproot previously pruned and removed. Remnant III successfully germinated and survived provided that taproots were ≥6 cm in length, whereas remnant IV was unable to produce seedlings. Remnant II only developed adventitious roots near the severed ends of the cotyledonary petioles. Field experiments also showed that pruned taproots with the plumule successfully regenerated into seedlings. We suggest that the elongated cotyledonary petioles, typical of most white oak species in North America, represent a key adaptation that allows frequent escape from rodent damage and predation. The ability of pruned taproots to

  2. Ability of chestnut oak to tolerate acorn pruning by rodents. The role of the cotyledonary petiole

    Science.gov (United States)

    Yi, Xianfeng; Curtis, Rachel; Bartlow, Andrew W.; Agosta, Salvatore J.; Steele, Michael A.

    2013-01-01

    Acorns of many white oak species germinate soon after autumn seed fall, a characteristic widely interpreted as a general adaptation to escape predation by small rodents. However, the mechanism by which early, rapid germination allows escape and/or tolerance of seed damage remains unclear. Here we reported how specific germination traits of chestnut oak ( Quercus montana) acorns, and those of other white oak species, allow successful escape from acorn pruning by rodents. During germination, chestnut oak acorns develop elongated cotyledonary petioles, which extend beyond the distal end of the acorn (1-2 cm) to the point at which the epicotyl and radicle diverge. However, granivorous rodents often prune the taproots above or below the plumule when eating or caching these germinated acorns in autumn. Hence, we hypothesized elongation of cotyledonary petioles allows chestnut oaks to escape acorn pruning by rodents. We simulated pruning by rodents by cutting the taproot at different stages of germination (radicle length) to evaluate the regeneration capacity of four resulting seedling remnants following taproot pruning: acorns with the plumule (remnant I), acorns without the plumule (remnant II), and pruned taproots with (remnant III) or without the plumule (remnant IV). Our results showed that remnant I germinated into seedlings regardless of the length of the taproot previously pruned and removed. Remnant III successfully germinated and survived provided that taproots were ≥6 cm in length, whereas remnant IV was unable to produce seedlings. Remnant II only developed adventitious roots near the severed ends of the cotyledonary petioles. Field experiments also showed that pruned taproots with the plumule successfully regenerated into seedlings. We suggest that the elongated cotyledonary petioles, typical of most white oak species in North America, represent a key adaptation that allows frequent escape from rodent damage and predation. The ability of pruned taproots to

  3. EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine

    Science.gov (United States)

    Gao, Lin; Cheng, Wei; Zhang, Jinhua; Wang, Jue

    2016-08-01

    Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left hand motor imagery, right hand motor imagery, and resting state for ten healthy human subjects. We proposed using the Kolmogorov complexity (Kc) for feature extraction and a multi-class Adaboost classifier with extreme learning machine as base classifier for classification, in order to classify the three-class EEG samples. An average classification accuracy of 79.5% was obtained for ten subjects, which greatly outperformed commonly used approaches. Thus, it is concluded that the proposed method could improve the performance for classification of motor imagery tasks for multi-class samples. It could be applied in further studies to generate the control commands to initiate the movement of a robotic exoskeleton or orthosis, which finally facilitates the rehabilitation of disabled people.

  4. A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

    Directory of Open Access Journals (Sweden)

    Xiao Zhou

    2016-11-01

    Full Text Available In recent years, with the emergency of high precision inertial sensors (accelerometers and gyros, gravity compensation has become a major source influencing the navigation accuracy in inertial navigation systems (INS, especially for high-precision INS. This paper presents preliminary results concerning the effect of gravity disturbance on INS. Meanwhile, this paper proposes a novel gravity compensation method for high-precision INS, which estimates the gravity disturbance on the track using the extreme learning machine (ELM method based on measured gravity data on the geoid and processes the gravity disturbance to the height where INS has an upward continuation, then compensates the obtained gravity disturbance into the error equations of INS to restrain the INS error propagation. The estimation accuracy of the gravity disturbance data is verified by numerical tests. The root mean square error (RMSE of the ELM estimation method can be improved by 23% and 44% compared with the bilinear interpolation method in plain and mountain areas, respectively. To further validate the proposed gravity compensation method, field experiments with an experimental vehicle were carried out in two regions. Test 1 was carried out in a plain area and Test 2 in a mountain area. The field experiment results also prove that the proposed gravity compensation method can significantly improve the positioning accuracy. During the 2-h field experiments, the positioning accuracy can be improved by 13% and 29% respectively, in Tests 1 and 2, when the navigation scheme is compensated by the proposed gravity compensation method.

  5. Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features

    Directory of Open Access Journals (Sweden)

    Ramesh Kumar Lama

    2017-01-01

    Full Text Available Alzheimer’s disease (AD is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR images to discriminate AD, mild cognitive impairment (MCI, and healthy control (HC subjects using a support vector machine (SVM, an import vector machine (IVM, and a regularized extreme learning machine (RELM. The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  6. Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran

    Science.gov (United States)

    Shamshirband, Shahaboddin; Mohammadi, Kasra; Chen, Hui-Ling; Narayana Samy, Ganthan; Petković, Dalibor; Ma, Chao

    2015-11-01

    Lately, the kernel extreme learning machine (KELM) has gained considerable importance in the scientific area due to its great efficiency, easy implementation and fast training speed. In this paper, for the first time the potential of KELM to predict the daily horizontal global solar radiation from the maximum and minimum air temperatures (Tmax and Tmin) is appraised. The effectiveness of the proposed KELM method is evaluated against the grid search based support vector regression (SVR), as a robust methodology. Three KELM and SVR models are developed using different input attributes including: (1) Tmin and Tmax, (2) Tmin and Tmax-Tmin, and (3) Tmax and Tmax-Tmin. The achieved results reveal that the best predictions precision is achieved by models (3). The achieved results demonstrate that KELM offers favorable predictions and outperforms the SVR. For the KELM (3) model, the obtained statistical parameters of mean absolute bias error, root mean square error, relative root mean square error and correlation coefficient are 1.3445 MJ/m2, 2.0164 MJ/m2, 11.2464% and 0.9057%, respectively for the testing data. As further examination, a month-by-month evaluation is conducted and found that in six months from May to October the KELM (3) model provides further accuracy than overall accuracy. Based upon the relative root mean square error, the KELM (3) model shows excellent capability in the period of April to October while in the remaining months represents good performance.

  7. Acoustic Log Prediction on the Basis of Kernel Extreme Learning Machine for Wells in GJH Survey, Erdos Basin

    Directory of Open Access Journals (Sweden)

    Jianhua Cao

    2017-01-01

    Full Text Available In petroleum exploration, the acoustic log (DT is popularly used as an estimator to calculate formation porosity, to carry out petrophysical studies, or to participate in geological analysis and research (e.g., to map abnormal pore-fluid pressure. But sometime it does not exist in those old wells drilled 20 years ago, either because of data loss or because of just being not recorded at that time. Thus synthesizing the DT log becomes the necessary task for the researchers. In this paper we propose using kernel extreme learning machine (KELM to predict missing sonic (DT logs when only common logs (e.g., natural gamma ray: GR, deep resistivity: REID, and bulk density: DEN are available. The common logs are set as predictors and the DT log is the target. By using KELM, a prediction model is firstly created based on the experimental data and then confirmed and validated by blind-testing the results in wells containing both the predictors and the target (DT values used in the supervised training. Finally the optimal model is set up as a predictor. A case study for wells in GJH survey from the Erdos Basin, about velocity inversion using the KELM-estimated DT values, is presented. The results are promising and encouraging.

  8. Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Dongrae Cho

    2017-10-01

    Full Text Available Virtual reality (VR is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG, electrodermal activity (EDA, and skin temperature (SKT. In general, stress is an important factor that can influence the autonomic nervous system (ANS. Heart-rate variability (HRV is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT. In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

  9. Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.

    Science.gov (United States)

    Cho, Dongrae; Ham, Jinsil; Oh, Jooyoung; Park, Jeanho; Kim, Sayup; Lee, Nak-Kyu; Lee, Boreom

    2017-10-24

    Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

  10. Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

    Energy Technology Data Exchange (ETDEWEB)

    Jiang, Huaiguang; Zhang, Yingchen

    2016-11-14

    This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vector regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.

  11. [Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

    Science.gov (United States)

    Wang, Yue; Yu, Lei; Fu, Jianming; Fang, Qiang

    2014-04-01

    In order to realize an individualized and specialized rehabilitation assessment of remoteness and intelligence, we set up a remote intelligent assessment system of upper limb movement function of post-stroke patients during rehabilitation. By using the remote rehabilitation training sensors and client data sampling software, we collected and uploaded the gesture data from a patient's forearm and upper arm during rehabilitation training to database of the server. Then a remote intelligent assessment system, which had been developed based on the extreme learning machine (ELM) algorithm and Brunnstrom stage assessment standard, was used to evaluate the gesture data. To evaluate the reliability of the proposed method, a group of 23 stroke patients, whose upper limb movement functions were in different recovery stages, and 4 healthy people, whose upper limb movement functions were normal, were recruited to finish the same training task. The results showed that, compared to that of the experienced rehabilitation expert who used the Brunnstrom stage standard table, the accuracy of the proposed remote Brunnstrom intelligent assessment system can reach a higher level, as 92.1%. The practical effects of surgery have proved that the proposed system could realize the intelligent assessment of upper limb movement function of post-stroke patients remotely, and it could also make the rehabilitation of the post-stroke patients at home or in a community care center possible.

  12. Diagnosis of Alzheimer's Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features.

    Science.gov (United States)

    Lama, Ramesh Kumar; Gwak, Jeonghwan; Park, Jeong-Seon; Lee, Sang-Woong

    2017-01-01

    Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.

  13. Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

    2017-06-08

    Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

  14. Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

    Science.gov (United States)

    Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

    2016-01-01

    Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37-52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model.

  15. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

    Science.gov (United States)

    Chen, Lili; Hao, Yaru

    2017-01-01

    Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  16. OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features.

    Science.gov (United States)

    Pian, Cong; Zhang, Jin; Chen, Yuan-Yuan; Chen, Zhi; Li, Qin; Li, Qiang; Zhang, Liang-Yun

    2016-02-01

    MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. Therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). These features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). The results show that the specificity ([Formula: see text])and sensitivity ([Formula: see text]) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time.

  17. Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Lili Chen

    2017-01-01

    Full Text Available Preterm birth (PTB is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT and extreme learning machine (ELM. For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs using empirical mode decomposition (EMD. Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.

  18. Feticismo e desiderio in Poulet aux prunes di Marjane Satrapi

    Directory of Open Access Journals (Sweden)

    Stefania Rimini

    2013-05-01

    Full Text Available Il saggio si propone di individuare e descrivere le sfumature erotiche e sentimentali del film Poulet aux prunes di Marjane Satrapi, felice adattamento dell’omonima graphic novel. Il racconto procede per scarti, ellissi, anticipazioni e flasback, dando luogo a una narrazione labirintica dalla singolare trama arabescante. Le sottili dinamiche seduttive messe in campo dal complesso stile di regia di Satrapi-Paronnaud vengono analizzate alla luce delle categorie feticistiche individuate da Massimo Fusillo, che offrono interessanti modelli di interazione fra desiderio e creatività. L’esito di tale indagine consente di ampliare il dibattito relativo al rapporto di scambio e interferenza fra oggetti, pulsioni e sguardi nel cinema contemporaneo.

  19. Generalized k-core pruning process on directed networks

    Science.gov (United States)

    Zhao, Jin-Hua

    2017-06-01

    The resilience of a complex interconnected system concerns the size of the macroscopic functioning node clusters after external perturbations based on a random or designed scheme. For a representation of interconnected systems with directional or asymmetrical interactions among constituents, the directed network is a convenient choice. Yet, how the interaction directions affect the network resilience still lacks a thorough exploration. Here, we study the resilience of directed networks with a generalized k-core pruning process as a simple failure procedure based on both the in- and out-degrees of nodes, in which any node with an in-degree  networks more vulnerable against perturbations based on in- and out-degrees separately.

  20. Associated rare anomalies in prune belly syndrome: A case report

    Directory of Open Access Journals (Sweden)

    Andreas Fette

    2015-02-01

    Full Text Available The triad of deficient abdominal wall musculature, undescended testes and urinary tract anomalies characterizes the Prune Belly Syndrome (PBS. PBS can be associated with other comorbid urological and non urological conditions. But the full pathogenesis and best treatment is still a matter of debate. A term newborn with a classical PBS (Woodhouse Group 2, Smith and Woodard Group 2 plus lung hypoplasia and funnel chest deformity, a megapenis with a tight phimosis and an obturated anterior urethra is presented. Unfortunately, the baby died in urosepsis and renal failure in his 3rd week of life, despite urine drainage surgery and peritoneal dialysis undertaken. According to the best of our knowledge, this is an unique combination of rare anomalies in PBS patients.

  1. Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines.

    Science.gov (United States)

    Antuvan, Chris Wilson; Bisio, Federica; Marini, Francesca; Yen, Shih-Cheng; Cambria, Erik; Masia, Lorenzo

    2016-08-15

    Myoelectric signals offer significant insights in interpreting the motion intention and extent of effort involved in performing a movement, with application in prostheses, orthosis and exoskeletons. Feature extraction plays a vital role, and follows two approaches: EMG and synergy features. More recently, muscle synergy based features are being increasingly explored, since it simplifies dimensionality of control, and are considered to be more robust to signal variations. Another important aspect in a myoelectrically controlled devices is the learning capability and speed of performance for online decoding. Extreme learning machine (ELM) is a relatively new neural-network based learning algorithm: its performance hasn't been explored in the context of online control, which is a more reliable measure compared to offline analysis. To this purpose we aim at focusing our investigation on a myoelectric-based interface which is able to identify and online classify, upper limb motions involving shoulder and elbow. The main objective is to compare the performance of the decoder trained using ELM, for two different features: EMG and synergy features. The experiments are broadly divided in two phases training/calibration and testing respectively. ELM is used to train the decoder using data acquired during the calibration phase. The performance of the decoder is then tested in online motion control by using a simulated graphical user interface replicating the human limb: subjects are requested to control a virtual arm by using their muscular activity. The decoder performance is quantified using ad-hoc metrics based on the following indicators: motion selection time, motion completion time, and classification accuracy. Performance has been evaluated for both offline and online contexts. The offline classification results indicated better performance in the case of EMG features, whereas a better classification accuracy for synergy feature was observed for online decoding. Also

  2. Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling.

    Science.gov (United States)

    Jiménez-Brenes, F M; López-Granados, F; de Castro, A I; Torres-Sánchez, J; Serrano, N; Peña, J M

    2017-01-01

    Tree pruning is a costly practice with important implications for crop harvest and nutrition, pest and disease control, soil protection and irrigation strategies. Investigations on tree pruning usually involve tedious on-ground measurements of the primary tree crown dimensions, which also might generate inconsistent results due to the irregular geometry of the trees. As an alternative to intensive field-work, this study shows a innovative procedure based on combining unmanned aerial vehicle (UAV) technology and advanced object-based image analysis (OBIA) methodology for multi-temporal three-dimensional (3D) monitoring of hundreds of olive trees that were pruned with three different strategies (traditional, adapted and mechanical pruning). The UAV images were collected before pruning, after pruning and a year after pruning, and the impacts of each pruning treatment on the projected canopy area, tree height and crown volume of every tree were quantified and analyzed over time. The full procedure described here automatically identified every olive tree on the orchard and computed their primary 3D dimensions on the three study dates with high accuracy in the most cases. Adapted pruning was generally the most aggressive treatment in terms of the area and volume (the trees decreased by 38.95 and 42.05% on average, respectively), followed by trees under traditional pruning (33.02 and 35.72% on average, respectively). Regarding the tree heights, mechanical pruning produced a greater decrease (12.15%), and these values were minimal for the other two treatments. The tree growth over one year was affected by the pruning severity and by the type of pruning treatment, i.e., the adapted-pruning trees experienced higher growth than the trees from the other two treatments when pruning intensity was low (<10%), similar to the traditionally pruned trees at moderate intensity (10-30%), and lower than the other trees when the pruning intensity was higher than 30% of the crown volume

  3. Supertrees Based on the Subtree Prune-and-Regraft Distance.

    Science.gov (United States)

    Whidden, Christopher; Zeh, Norbert; Beiko, Robert G

    2014-07-01

    Supertree methods reconcile a set of phylogenetic trees into a single structure that is often interpreted as a branching history of species. A key challenge is combining conflicting evolutionary histories that are due to artifacts of phylogenetic reconstruction and phenomena such as lateral gene transfer (LGT). Many supertree approaches use optimality criteria that do not reflect underlying processes, have known biases, and may be unduly influenced by LGT. We present the first method to construct supertrees by using the subtree prune-and-regraft (SPR) distance as an optimality criterion. Although calculating the rooted SPR distance between a pair of trees is NP-hard, our new maximum agreement forest-based methods can reconcile trees with hundreds of taxa and>50 transfers in fractions of a second, which enables repeated calculations during the course of an iterative search. Our approach can accommodate trees in which uncertain relationships have been collapsed to multifurcating nodes. Using a series of benchmark datasets simulated under plausible rates of LGT, we show that SPR supertrees are more similar to correct species histories than supertrees based on parsimony or Robinson-Foulds distance criteria. We successfully constructed an SPR supertree from a phylogenomic dataset of 40,631 gene trees that covered 244 genomes representing several major bacterial phyla. Our SPR-based approach also allowed direct inference of highways of gene transfer between bacterial classes and genera. A Small number of these highways connect genera in different phyla and can highlight specific genes implicated in long-distance LGT. [Lateral gene transfer; matrix representation with parsimony; phylogenomics; prokaryotic phylogeny; Robinson-Foulds; subtree prune-and-regraft; supertrees.]. © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.

  4. Síndrome de Prune Belly: Presentación de un caso y revisión de la literatura Prune Belly Syndrome: Case report and review

    Directory of Open Access Journals (Sweden)

    Albert Franz Guerrero

    2010-04-01

    Full Text Available Introducción: El síndrome de Prune Belly (SPB, también conocido como el síndrome de Eagle Barrett, se caracteriza por una triada de anomalías que incluye grados variables de hipoplasia de la musculatura abdominal, anomalías del tracto urinario y criptorquidia bilateral. Objetivo: Se describe el caso de un paciente masculino con Síndrome de Prune Belly y se realiza una revisión de la literatura sobre esta rara enfermedad. Conclusión: La característica arrugada del abdomen similar a una ciruela pasa, le da el nombre al síndrome. Además, puede estar asociado a alteraciones cardiovasculares, respiratorias, ortopédicas y gastrointestinales. Salud UIS 2010; 42: 78-85Introduction: Prune-belly syndrome, also known as Eagle-Barrett syndrome is characterized by a triad of anomalies that include varying degrees of abdominal musculature hypoplasia, urinary tract anomalies, and bilateral cryptorchidism. Objective: We describe the case of a male patient with Prune Belly Syndrome and we review the literature on this rare disease. Conclusions: The characteristic wrinkled, prune-like abdomen, gives the name to the syndrome. Can also be associated with cardiovascular, respiratory, orthopedic and gastrointestinal anomalies. Salud UIS 2010; 42: 78-85.

  5. EVALUATION OF TEMPORALVARIATIONS IN MOISTURE AND CALORIFIC VALUE OF VINE AND OLIVE PRUNING

    National Research Council Canada - National Science Library

    Porceddu, Pier Riccardo; Rosati, Laura; Dionigi, Marco

    2012-01-01

    .... The calorific value is significantly influenced by the moisture content of wood. This work has evaluated the changes in moisture content and calorific value with time for different harvesting and storage systems of vine and olive pruning...

  6. [On breeding burst-resistant self-pruning tomatoes : Preliminary communication].

    Science.gov (United States)

    Paschke, M; Schwanitz, F

    1968-01-01

    From crossings of a self-pruning German commercial tomato variety with medium sized round fruits, relative resistance against bursting in rain with a self-pruning Central American form with medium sized pruneshaped and never bursting fruits a selfpruning F1 was obtained whose round fruits of medium size never burst. In the F2 a greater number of self-pruning types with round fruits completely burst-resistant and of medium size was found. One self-pruning and burst-resistant plant had remarkably large fruits. Furthermore, two dwarfs and a great number of "cherry"-tomatoes were found. The value of these findings for plant breeding and evolution is discussed.

  7. Regulating mineralization rates of Tithonia diversifolia and Lantana camara prunings to improve phosphorus availability in calcareous soils

    Directory of Open Access Journals (Sweden)

    Y. Nuraini

    2014-01-01

    Full Text Available The effect of mixing of Tithonia diversifolia and Lantana camara prunings to improve synchronization between P released from the prunings with crop demand for P was studied in a laboratory and in a glasshouse. Tithonia diversifolia prunings (Td, Lantana camara prunings (Lc, and farmyard manure (Pk were thoroughly mixed with the proportion (% of dry weight of; 25Td +75 Lc ; 50Td +50 Lc ; 75Td +25 Lc ; 90Lc +10 Pk ; 45Td +45 +10 Lc Pk ; 100Td and 100Lc, and then mixed with 100 g of air-dried soil with a rate equivalent to 100 kg P / ha. Results of the study showed that the pruning mixtures decomposed and mineralized faster than that of Lantana camara pruning only, but slower than that of Tithonia diversifolia pruning only. The amount of P released from the pruning mixtures increased with increasing proportion of Tithonia diversifolia pruning in the mixtures. Increasing proportion of Tithonia diversifolia pruning in the mixture applied to the soil increased the amount of P taken up by maize.

  8. Factors affecting branch wound occlusion and associated decay following pruning – a case study with wild cherry (Prunus avium L.

    Directory of Open Access Journals (Sweden)

    Jonathan Sheppard

    2016-11-01

    Full Text Available Pruning wild cherry (Prunus avium L. is a common silvicultural practice carried out to produce valuable timber at a veneer wood quality. Sub-optimal pruning treatments can permit un-occluded pruning wounds to develop devaluing decay. The aim of this study is to determine relevant branch, tree and pruning characteristics affecting the occlusion process of pruning wounds. Important factors influencing occlusion time for an optimised pruning treatment for valuable timber production utilising wild cherry are derived. 85 artificially pruned branches originating from ten wild cherry trees were retrospectively analysed. Branch stub length, branch diameter and radial stem increment during occlusion were found to be significant predictors for occlusion time. From the results it could be concluded that for the long term success of artificial pruning of wild cherry it is crucial to (i keep branch stubs short (while avoiding damage to the branch collar, (ii to enable the tree to maintain significant radial growth after pruning, (iii to avoid large pruning wounds (>2.5 cm by removing steeply angled and fast growing branches at an early stage.

  9. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Directory of Open Access Journals (Sweden)

    Mark D McDonnell

    Full Text Available Recent advances in training deep (multi-layer architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM approach, which also enables a very rapid training time (∼ 10 minutes. Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  10. Short-Term Electricity-Load Forecasting Using a TSK-Based Extreme Learning Machine with Knowledge Representation

    Directory of Open Access Journals (Sweden)

    Chan-Uk Yeom

    2017-10-01

    Full Text Available This paper discusses short-term electricity-load forecasting using an extreme learning machine (ELM with automatic knowledge representation from a given input-output data set. For this purpose, we use a Takagi-Sugeno-Kang (TSK-based ELM to develop a systematic approach to generating if-then rules, while the conventional ELM operates without knowledge information. The TSK-ELM design includes a two-phase development. First, we generate an initial random-partition matrix and estimate cluster centers for random clustering. The obtained cluster centers are used to determine the premise parameters of fuzzy if-then rules. Next, the linear weights of the TSK fuzzy type are estimated using the least squares estimate (LSE method. These linear weights are used as the consequent parameters in the TSK-ELM design. The experiments were performed on short-term electricity-load data for forecasting. The electricity-load data were used to forecast hourly day-ahead loads given temperature forecasts; holiday information; and historical loads from the New England ISO. In order to quantify the performance of the forecaster, we use metrics and statistical characteristics such as root mean squared error (RMSE as well as mean absolute error (MAE, mean absolute percent error (MAPE, and R-squared, respectively. The experimental results revealed that the proposed method showed good performance when compared with a conventional ELM with four activation functions such sigmoid, sine, radial basis function, and rectified linear unit (ReLU. It possessed superior prediction performance and knowledge information and a small number of rules.

  11. Advancing of Land Surface Temperature Retrieval Using Extreme Learning Machine and Spatio-Temporal Adaptive Data Fusion Algorithm

    Directory of Open Access Journals (Sweden)

    Yang Bai

    2015-04-01

    Full Text Available As a critical variable to characterize the biophysical processes in ecological environment, and as a key indicator in the surface energy balance, evapotranspiration and urban heat islands, Land Surface Temperature (LST retrieved from Thermal Infra-Red (TIR images at both high temporal and spatial resolution is in urgent need. However, due to the limitations of the existing satellite sensors, there is no earth observation which can obtain TIR at detailed spatial- and temporal-resolution simultaneously. Thus, several attempts of image fusion by blending the TIR data from high temporal resolution sensor with data from high spatial resolution sensor have been studied. This paper presents a novel data fusion method by integrating image fusion and spatio-temporal fusion techniques, for deriving LST datasets at 30 m spatial resolution from daily MODIS image and Landsat ETM+ images. The Landsat ETM+ TIR data were firstly enhanced based on extreme learning machine (ELM algorithm using neural network regression model, from 60 m to 30 m resolution. Then, the MODIS LST and enhanced Landsat ETM+ TIR data were fused by Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT in order to derive high resolution synthetic data. The synthetic images were evaluated for both testing and simulated satellite images. The average difference (AD and absolute average difference (AAD are smaller than 1.7 K, where the correlation coefficient (CC and root-mean-square error (RMSE are 0.755 and 1.824, respectively, showing that the proposed method enhances the spatial resolution of the predicted LST images and preserves the spectral information at the same time.

  12. Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm.

    Science.gov (United States)

    McDonnell, Mark D; Tissera, Migel D; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

    2015-01-01

    Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

  13. Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

    Science.gov (United States)

    An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

    2017-08-18

    Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

  14. Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: a case study

    Science.gov (United States)

    Wang, Xin; Li, Yan; Chen, Tongjun; Yan, Qiuyan; Ma, Li

    2017-04-01

    The thickness of tectonically deformed coal (TDC) has positive correlation associations with gas outbursts. In order to predict the TDC thickness of coal beds, we propose a new quantitative predicting method using an extreme learning machine (ELM) algorithm, a principal component analysis (PCA) algorithm, and seismic attributes. At first, we build an ELM prediction model using the PCA attributes of a synthetic seismic section. The results suggest that the ELM model can produce a reliable and accurate prediction of the TDC thickness for synthetic data, preferring Sigmoid activation function and 20 hidden nodes. Then, we analyze the applicability of the ELM model on the thickness prediction of the TDC with real application data. Through the cross validation of near-well traces, the results suggest that the ELM model can produce a reliable and accurate prediction of the TDC. After that, we use 250 near-well traces from 10 wells to build an ELM predicting model and use the model to forecast the TDC thickness of the No. 15 coal in the study area using the PCA attributes as the inputs. Comparing the predicted results, it is noted that the trained ELM model with two selected PCA attributes yields better predication results than those from the other combinations of the attributes. Finally, the trained ELM model with real seismic data have a different number of hidden nodes (10) than the trained ELM model with synthetic seismic data. In summary, it is feasible to use an ELM model to predict the TDC thickness using the calculated PCA attributes as the inputs. However, the input attributes, the activation function and the number of hidden nodes in the ELM model should be selected and tested carefully based on individual application.

  15. Congenital megalourethra in 2 weeks old boy associated with Prune-Belly syndrome

    Directory of Open Access Journals (Sweden)

    Lawal Barau Abdullahi

    2015-02-01

    Full Text Available The megalourethra is a rare congenital anomaly of the penile urethra. It is characterized by the congenital absence of the corpus spongiosum and/or corpus cavernosum. It is especially common associated with Prune-Belly syndrome, and with upper tract abnormalities. We present a 2 weeks old boy with congenital megalourethra because of its association with the Prune-Belly syndrome.

  16. Pseudo Prune Belly Syndrome: Diagnosis Revealed by Imaging ? A Case Report and Brief Review

    OpenAIRE

    Grover, Hemal; Sethi, Sanjay; Garg, Jatin; Ahluwalia, Amrit Pal

    2017-01-01

    Summary Background Prune Belly Syndrome (PBS) is a rare entity, usually found in male neonates. It comprises complex urinary tract anomalies, bilateral undescended testis and absence of anterior abdominal wall muscles. Patients with unilateral abdominal wall deficiency, unilateral undescended testis and female neonates with abdominal wall laxity are classified as Pseudo Prune Belly syndrome (PPBS). Reports on PPBS do not highlight the radiological and imaging characteristics of this syndrome ...

  17. Prune belly syndrome in an Egyptian infant with Down syndrome: A case report

    OpenAIRE

    Metwalley Kotb A; Farghalley Hekma S; Abd-Elsayed Alaa A

    2008-01-01

    Abstract Introduction Prune belly syndrome is a rare congenital anomaly of uncertain aetiology almost exclusive to males. The association between prune belly syndrome and Down syndrome is very rare. Case presentation A 4-month-old Egyptian boy was admitted to our institute for management of acute bronchiolitis. He was born at full term by normal vaginal delivery. His mother, a 42-year-Egyptian villager with six other children, had no antenatal or prenatal care. On examination, the boy was fou...

  18. Drosophila fragile X mental retardation protein developmentally regulates activity-dependent axon pruning.

    Science.gov (United States)

    Tessier, Charles R; Broadie, Kendal

    2008-04-01

    Fragile X Syndrome (FraX) is a broad-spectrum neurological disorder with symptoms ranging from hyperexcitability to mental retardation and autism. Loss of the fragile X mental retardation 1 (fmr1) gene product, the mRNA-binding translational regulator FMRP, causes structural over-elaboration of dendritic and axonal processes, as well as functional alterations in synaptic plasticity at maturity. It is unclear, however, whether FraX is primarily a disease of development, a disease of plasticity or both: a distinction that is vital for engineering intervention strategies. To address this crucial issue, we have used the Drosophila FraX model to investigate the developmental function of Drosophila FMRP (dFMRP). dFMRP expression and regulation of chickadee/profilin coincides with a transient window of late brain development. During this time, dFMRP is positively regulated by sensory input activity, and is required to limit axon growth and for efficient activity-dependent pruning of axon branches in the Mushroom Body learning/memory center. These results demonstrate that dFMRP has a primary role in activity-dependent neural circuit refinement during late brain development.

  19. PRUNE is crucial for normal brain development and mutated in microcephaly with neurodevelopmental impairment.

    Science.gov (United States)

    Zollo, Massimo; Ahmed, Mustafa; Ferrucci, Veronica; Salpietro, Vincenzo; Asadzadeh, Fatemeh; Carotenuto, Marianeve; Maroofian, Reza; Al-Amri, Ahmed; Singh, Royana; Scognamiglio, Iolanda; Mojarrad, Majid; Musella, Luca; Duilio, Angela; Di Somma, Angela; Karaca, Ender; Rajab, Anna; Al-Khayat, Aisha; Mohan Mohapatra, Tribhuvan; Eslahi, Atieh; Ashrafzadeh, Farah; Rawlins, Lettie E; Prasad, Rajniti; Gupta, Rashmi; Kumari, Preeti; Srivastava, Mona; Cozzolino, Flora; Kumar Rai, Sunil; Monti, Maria; Harlalka, Gaurav V; Simpson, Michael A; Rich, Philip; Al-Salmi, Fatema; Patton, Michael A; Chioza, Barry A; Efthymiou, Stephanie; Granata, Francesca; Di Rosa, Gabriella; Wiethoff, Sarah; Borgione, Eugenia; Scuderi, Carmela; Mankad, Kshitij; Hanna, Michael G; Pucci, Piero; Houlden, Henry; Lupski, James R; Crosby, Andrew H; Baple, Emma L

    2017-04-01

    PRUNE is a member of the DHH (Asp-His-His) phosphoesterase protein superfamily of molecules important for cell motility, and implicated in cancer progression. Here we investigated multiple families from Oman, India, Iran and Italy with individuals affected by a new autosomal recessive neurodevelopmental and degenerative disorder in which the cardinal features include primary microcephaly and profound global developmental delay. Our genetic studies identified biallelic mutations of PRUNE1 as responsible. Our functional assays of disease-associated variant alleles revealed impaired microtubule polymerization, as well as cell migration and proliferation properties, of mutant PRUNE. Additionally, our studies also highlight a potential new role for PRUNE during microtubule polymerization, which is essential for the cytoskeletal rearrangements that occur during cellular division and proliferation. Together these studies define PRUNE as a molecule fundamental for normal human cortical development and define cellular and clinical consequences associated with PRUNE mutation. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain.

  20. Síndrome de Prune Belly: Presentación de un caso y revisión de la literatura Prune Belly Syndrome: Case report and review

    OpenAIRE

    Albert Franz Guerrero; Carlos Augusto Cuadros; Diana Carolina Archila; Sandra Milena Beltrán; Gustavo Adolfo Cuadros

    2010-01-01

    Introducción: El síndrome de Prune Belly (SPB), también conocido como el síndrome de Eagle Barrett, se caracteriza por una triada de anomalías que incluye grados variables de hipoplasia de la musculatura abdominal, anomalías del tracto urinario y criptorquidia bilateral. Objetivo: Se describe el caso de un paciente masculino con Síndrome de Prune Belly y se realiza una revisión de la literatura sobre esta rara enfermedad. Conclusión: La característica arrugada del abdomen similar a una ciruel...

  1. Adaptive Control Using Fully Online Sequential-Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation

    Directory of Open Access Journals (Sweden)

    Pak Kin Wong

    2014-01-01

    Full Text Available Most adaptive neural control schemes are based on stochastic gradient-descent backpropagation (SGBP, which suffers from local minima problem. Although the recently proposed regularized online sequential-extreme learning machine (ReOS-ELM can overcome this issue, it requires a batch of representative initial training data to construct a base model before online learning. The initial data is usually difficult to collect in adaptive control applications. Therefore, this paper proposes an improved version of ReOS-ELM, entitled fully online sequential-extreme learning machine (FOS-ELM. While retaining the advantages of ReOS-ELM, FOS-ELM discards the initial training phase, and hence becomes suitable for adaptive control applications. To demonstrate its effectiveness, FOS-ELM was applied to the adaptive control of engine air-fuel ratio based on a simulated engine model. Besides, controller parameters were also analyzed, in which it is found that large hidden node number with small regularization parameter leads to the best performance. A comparison among FOS-ELM and SGBP was also conducted. The result indicates that FOS-ELM achieves better tracking and convergence performance than SGBP, since FOS-ELM tends to learn the unknown engine model globally whereas SGBP tends to “forget” what it has learnt. This implies that FOS-ELM is more preferable for adaptive control applications.

  2. Dictionary Pruning with Visual Word Significance for Medical Image Retrieval

    Science.gov (United States)

    Zhang, Fan; Song, Yang; Cai, Weidong; Hauptmann, Alexander G.; Liu, Sidong; Pujol, Sonia; Kikinis, Ron; Fulham, Michael J; Feng, David Dagan; Chen, Mei

    2016-01-01

    Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic. The latent topics are learnt, based on the relationship between the images and words, and are employed to bridge the gap between low-level visual features and high-level semantics. These latent topics describe the images and words semantically and can thus facilitate more meaningful comparisons between the words. Second, we compute an overall-word significance value to evaluate the significance of a visual word within the entire dictionary. We designed an iterative ranking method to measure overall-word significance by considering the relationship between all latent topics and words. The words with higher values are considered meaningful with more significant discriminative power in differentiating medical images. We evaluated our method on two public medical imaging datasets and it showed improved retrieval accuracy and efficiency. PMID:27688597

  3. Randomised clinical trial: dried plums (prunes) vs. psyllium for constipation.

    Science.gov (United States)

    Attaluri, A; Donahoe, R; Valestin, J; Brown, K; Rao, S S C

    2011-04-01

    Treatment of chronic constipation remains challenging with 50% of patients dissatisfied with current therapy. There is an unmet need for natural and safe alternatives. Dried plums (prunes) have been used traditionally for constipation but their efficacy is not known. Aim To assess and compare the effects of dried plums and psyllium in patients with chronic constipation. Subjects were enrolled in an 8-week, single-blind, randomised cross-over study. Subjects received either dried plums (50 g b.d., fibre=6 gm/day) or psyllium (11 g b.d., fibre=6 gm/day) for 3 weeks each, in a crossover trial with a 1-week washout period. Subjects maintained a daily symptom and stool diary. Assessments included number of complete spontaneous bowel movements per week, global relief of constipation, stool consistency, straining, tolerability and taste. Forty constipated subjects (m/f=3/37, mean age=38 years) participated. The number of complete spontaneous bowel movements per week (primary outcome measure) and stool consistency scores improved significantly (Ppsyllium. Straining and global constipation symptoms did not differ significantly between treatments (P=N.S.). Dried plums and psyllium were rated as equally palatable and both were safe and well tolerated. Dried plums are safe, palatable and more effective than psyllium for the treatment of mild to moderate constipation, and should be considered as a first line therapy. © 2011 Blackwell Publishing Ltd.

  4. Prune belly syndrome, splenic torsion, and malrotation: a case report.

    Science.gov (United States)

    Tran, Sifrance; Grossman, Eric; Barsness, Katherine A

    2013-02-01

    An 18 year old male with a history of prune belly syndrome (PBS) presented with acute abdominal pain and palpable left upper quadrant mass. Computed tomography (CT) of the abdomen revealed a medialized spleen with a "whirl sign" in the splenic vessels, consistent with splenic torsion. Coincidentally, the small bowel was also noted to be on the right side of the abdomen, while the colon was located on the left, indicative of malrotation. Emergent diagnostic laparoscopy confirmed splenic torsion and intestinal malrotation. Successful laparoscopic reduction of the splenic torsion was achieved, however, conversion to an open procedure by a vertical midline incision was necessary owing to the patient's unique anatomy. Open splenopexy with a mesh sling and Ladd's procedure were subsequently performed. Malrotation and wandering spleen are known, rare associated anomalies in PBS; however, both have not been reported concurrently in a patient with PBS in the literature. In patients with PBS, acute abdominal pain, and an abdominal mass, high clinical suspicion for gastrointestinal malformations and prompt attention can result in spleen preservation and appropriate malrotation management. We present a case of a teenager who presented with a history of PBS, acute abdominal pain, and a palpable abdominal mass. The patient was found to have splenic torsion and intestinal malrotation. The clinical findings, diagnostic imaging, and surgical treatment options of splenic torsion are reviewed. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

    Science.gov (United States)

    Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

    2016-11-01

    Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this

  6. Formation of acrylamide at temperatures lower than 100°C: the case of prunes and a model study

    Science.gov (United States)

    Becalski, A.; Brady, B.; Feng, S.; Gauthier, B.R.; Zhao, T.

    2011-01-01

    Acrylamide concentrations in prune products – baby strained prunes (range = 75–265 μg kg−−1), baby apple/prune juice (33–61 μg kg−−1), prune juice (186–916 μg kg−−1) and prunes (58–332 μg kg−−1) – on the Canadian market were determined. The formation of acrylamide in a simulated plum juice was also investigated under ‘drying conditions’ in an open vessel at temperatures Acrylamide was produced in a simulated plum juice under ‘drying conditions’ in amounts comparable with those found in prunes and prune juices. Acrylamide was not produced in simulated plum juice under ‘wet conditions’ in a closed vessel at temperature of 120°C for 1 h, but under the same condition an authentic prune juice doubled its acrylamide concentration. Formation of acrylamide in prune products was attributed to the presence of asparagine and sugars in the starting materials. PMID:21623495

  7. Yield and crop cycle time of peaches cultivated in subtropical climates and subjected to different pruning times

    Directory of Open Access Journals (Sweden)

    Rafael Augusto Ferraz

    2015-12-01

    Full Text Available The cultivation of peaches in regions of subtropical and tropical climate is currently achieved through a set of practices such as using less demanding cultivars in cold conditions, applying plant growth regulators to break dormancy, and performing specific pruning, like production and renewal pruning. Research on the climate adaptation of cultivars is of great importance in establishing a crop in a given region. Therefore, the objective of this study was to evaluate the agronomic performance of three cultivars subjected to different production pruning times in Botucatu/SP, where 2-year old peach trees were evaluated, grown at a spacing of 6.0 x 4.0 meters. The experimental design was a split plot design with four blocks, using the cultivars Douradão, BRS Kampai and BRS Rubimel, and the subplots corresponded to pruning times in May, June, July and August. Ten plants were used per plot, with the four central plants considered useful and the remaining considered as margins. Pruning in June and July showed the best results in terms of percentage of fruit set and production. The cultivar BRS Rubimel showed the best percentage of fruit set when pruned in June (44.96%, and best fruit production when pruned in July (18.7 kg plant-1. Pruning in May anticipated the harvest of cultivar BRS Rubimel by 13 days whereas pruning carried out in July and August provided late harvests for cultivars Douradão and BRS Kampai.

  8. An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland.

    Science.gov (United States)

    Deo, Ravinesh C; Şahin, Mehmet

    2016-02-01

    A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model was utilized to simulate the mean streamflow water level (Q WL) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert, and Mary River). The performance of the ELM model was benchmarked with the artificial neural network (ANN) model. The ELM model was a fast computational method using single-layer feedforward neural networks and randomly determined hidden neurons that learns the historical patterns embedded in the input variables. A set of nine predictors with the month (to consider the seasonality of Q WL); rainfall; Southern Oscillation Index; Pacific Decadal Oscillation Index; ENSO Modoki Index; Indian Ocean Dipole Index; and Nino 3.0, Nino 3.4, and Nino 4.0 sea surface temperatures (SSTs) were utilized. A selection of variables was performed using cross correlation with Q WL, yielding the best inputs defined by (month; P; Nino 3.0 SST; Nino 4.0 SST; Southern Oscillation Index (SOI); ENSO Modoki Index (EMI)) for Gowrie Creek, (month; P; SOI; Pacific Decadal Oscillation (PDO); Indian Ocean Dipole (IOD); EMI) for Albert River, and by (month; P; Nino 3.4 SST; Nino 4.0 SST; SOI; EMI) for Mary River site. A three-layer neuronal structure trialed with activation equations defined by sigmoid, logarithmic, tangent sigmoid, sine, hardlim, triangular, and radial basis was utilized, resulting in optimum ELM model with hard-limit function and architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River), and 6-146-1 (Mary River). The alternative ELM and ANN models with two inputs (month and rainfall) and the ELM model with all nine inputs were also developed. The performance was evaluated using the mean absolute error (MAE), coefficient of determination (r (2)), Willmott's Index (d), peak deviation (P dv), and Nash

  9. Cover crops and pruning in Bobal and Tempranillo vineyards have little influence on grapevine nutrition

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    Pedro Pérez-Bermúdez

    2016-06-01

    Full Text Available ABSTRACT Cover crops may improve vineyard soil properties, grapevine nutrient status and berry composition, however, factors such as cover crop type, annual rainfall, climate and irrigation may change their effects on vineyards. From 2008 to 2011, the effects of a non-permanent cover crop and two pruning techniques on soil as well as vine nutrients and grapevine performance of two vineyards (cv. Tempranillo and cv. Bobal were evaluated. For that purpose, two legumes were sown in inter-rows of hand-pruned vines in February and were tilled at flowering. Soil tillage, or cover cropping, was combined with either light pruning or severe pruning to study foliar nutrient variations. Soil N, P, K and total organic carbon (TOC were determined in samples taken from the Ap1 horizon in January prior to vine pruning. Foliar N, P, K contents were measured in leaves sampled upon grape veraison. The differences between vineyards with cover cropping and bare soils suggest that legumes positively affected soil N (1.55 vs. 1.68 g kg−1 and 1.49 vs. 1.76 g kg−1 in Bobal and Tempranillo vineyards, respectively and soil organic matter (SOM (12.5 vs. 15.5 g kg−1 and 12.9 vs. 17.2 g kg−1 in Bobal and Tempranillo vineyards, respectively. The use of cover crops did not affect grapevine yields nor quality of Bobal and Tempranillo berry . Cover crops, or light pruning, did not alter the foliar N, P, K contents of both cultivars since their concentrations were similar to those found in the leaves from vineyards with soil tillage or severe pruning.

  10. EVALUATION OF TEMPORALVARIATIONS IN MOISTURE AND CALORIFIC VALUE OF VINE AND OLIVE PRUNING

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    Pier Riccardo Porceddu

    2012-06-01

    Full Text Available In Italy arboreal crops, in particular vine and olive, cover a surface area of around 19.6×109 m2 from which about 4.6×109 kg of pruning are cut. These by-products are currently ploughed into the soil or else harvested and burned in open fields. On the other hand such materials would be more useful as an energy source. If these materials are to be used as fuel, it is important to know their calorific value. The calorific value is significantly influenced by the moisture content of wood. This work has evaluated the changes in moisture content and calorific value with time for different harvesting and storage systems of vine and olive pruning. The observed decrease in the moisture content of the vine and olive pruning depended on the storage system utilized, in particular on the product compression ratio and air circulation. Some differences were observed between the results obtained for vine and olive pruning. The time required for these materials to obtain their best energetic performance was identified at 32 weeks from their harvesting. Harvesting with balers and forwarding costs are about 6.21×10-2 €/kg for vine pruning and 4.64×10-2 €/kg for olive pruning. They are very similar to the price currently offered for energy biomass in Italy (5.00×10-2 €/kg. While the cost actually paid to plough pruning into the soil amounts to about 2.50×10-2 €/kg. Therefore the energy chain encourages a cost-and-benefit analysis.

  11. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

    Science.gov (United States)

    Acharya, Nachiketa; Shrivastava, Nitin Anand; Panigrahi, B. K.; Mohanty, U. C.

    2014-09-01

    The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982-2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982-2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson's and Kendal's correlation coefficient and Wilmort's index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.

  12. Olive Crown Porosity Measurement Based on Radiation Transmittance: An Assessment of Pruning Effect

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    Francisco J. Castillo-Ruiz

    2016-05-01

    Full Text Available Crown porosity influences radiation interception, air movement through the fruit orchard, spray penetration, and harvesting operation in fruit crops. The aim of the present study was to develop an accurate and reliable methodology based on transmitted radiation measurements to assess the porosity of traditional olive trees under different pruning treatments. Transmitted radiation was employed as an indirect method to measure crown porosity in two olive orchards of the Picual and Hojiblanca cultivars. Additionally, three different pruning treatments were considered to determine if the pruning system influences crown porosity. This study evaluated the accuracy and repeatability of four algorithms in measuring crown porosity under different solar zenith angles. From a 14° to 30° solar zenith angle, the selected algorithm produced an absolute error of less than 5% and a repeatability higher than 0.9. The described method and selected algorithm proved satisfactory in field results, making it possible to measure crown porosity at different solar zenith angles. However, pruning fresh weight did not show any relationship with crown porosity due to the great differences between removed branches. A robust and accurate algorithm was selected for crown porosity measurements in traditional olive trees, making it possible to discern between different pruning treatments.

  13. Density based pruning for identification of differentially expressed genes from microarray data

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    Xu Jia

    2010-11-01

    Full Text Available Abstract Motivation Identification of differentially expressed genes from microarray datasets is one of the most important analyses for microarray data mining. Popular algorithms such as statistical t-test rank genes based on a single statistics. The false positive rate of these methods can be improved by considering other features of differentially expressed genes. Results We proposed a pattern recognition strategy for identifying differentially expressed genes. Genes are mapped to a two dimension feature space composed of average difference of gene expression and average expression levels. A density based pruning algorithm (DB Pruning is developed to screen out potential differentially expressed genes usually located in the sparse boundary region. Biases of popular algorithms for identifying differentially expressed genes are visually characterized. Experiments on 17 datasets from Gene Omnibus Database (GEO with experimentally verified differentially expressed genes showed that DB pruning can significantly improve the prediction accuracy of popular identification algorithms such as t-test, rank product, and fold change. Conclusions Density based pruning of non-differentially expressed genes is an effective method for enhancing statistical testing based algorithms for identifying differentially expressed genes. It improves t-test, rank product, and fold change by 11% to 50% in the numbers of identified true differentially expressed genes. The source code of DB pruning is freely available on our website http://mleg.cse.sc.edu/degprune

  14. Pruning management of Chardonnay grapevines at high altitude in Brazilian southeast

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    Tania dos Reis Mendonça

    2016-03-01

    Full Text Available ABSTRACT The agronomical responses of Chardonnay, a variety indicated for sparkling wine production, is influenced by the vineyard management and the edaphoclimatic conditions of the region. The objective of this study was to evaluate the effects of two pruning types (Royat and double Guyot on vegetative and reproductive development of Chardonnay vine growing at high altitude in the Brazilian southeastern region. The experiment was carried out in a commercial vineyard located at 1,280 m of altitude in Divinolândia, São Paulo State, Brazil. The Chardonnay vines (clone 96, grafted onto 1103 Paulsen rootstock and trained in a vertical shoot positioning trellis system, were assessed. Vegetative vigor, bud fruitfulness, production and physicochemical composition of grapes were evaluated during 2014 and 2015 growing seasons. The Royat pruning induced higher vegetative vigor and increased the bud fruitfulness, the cluster number and the productivity of Chardonnay vine when compared to Guyot pruning. Even though the increase on yield was observed, there was no effect of pruning type on grape final quality. Therefore, the choice of pruning method in function of variety genetic characteristics and their interaction with environment can optimize the vineyard profitability. In the Brazilian southeast, the Royat system is the most suitable one to grow Chardonnay for sparkling wines production.

  15. A robust hybrid model integrating enhanced inputs based extreme learning machine with PLSR (PLSR-EIELM) and its application to intelligent measurement.

    Science.gov (United States)

    He, Yan-Lin; Geng, Zhi-Qiang; Xu, Yuan; Zhu, Qun-Xiong

    2015-09-01

    In this paper, a robust hybrid model integrating an enhanced inputs based extreme learning machine with the partial least square regression (PLSR-EIELM) was proposed. The proposed PLSR-EIELM model can overcome two main flaws in the extreme learning machine (ELM), i.e. the intractable problem in determining the optimal number of the hidden layer neurons and the over-fitting phenomenon. First, a traditional extreme learning machine (ELM) is selected. Second, a method of randomly assigning is applied to the weights between the input layer and the hidden layer, and then the nonlinear transformation for independent variables can be obtained from the output of the hidden layer neurons. Especially, the original input variables are regarded as enhanced inputs; then the enhanced inputs and the nonlinear transformed variables are tied together as the whole independent variables. In this way, the PLSR can be carried out to identify the PLS components not only from the nonlinear transformed variables but also from the original input variables, which can remove the correlation among the whole independent variables and the expected outputs. Finally, the optimal relationship model of the whole independent variables with the expected outputs can be achieved by using PLSR. Thus, the PLSR-EIELM model is developed. Then the PLSR-EIELM model served as an intelligent measurement tool for the key variables of the Purified Terephthalic Acid (PTA) process and the High Density Polyethylene (HDPE) process. The experimental results show that the predictive accuracy of PLSR-EIELM is stable, which indicate that PLSR-EIELM has good robust character. Moreover, compared with ELM, PLSR, hierarchical ELM (HELM), and PLSR-ELM, PLSR-EIELM can achieve much smaller predicted relative errors in these two applications. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  16. The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine.

    Science.gov (United States)

    Han, Fei; Huang, De-Shuang; Zhu, Zhi-Hua; Rong, Tie-Hua

    2006-02-01

    In this paper, a new effective model is proposed to forecast how long the postoperative patients suffered from non-small cell lung cancer will survive. The new effective model which is based on the extreme learning machine (ELM) and principal component analysis (PCA) can forecast successfully the postoperative patients' survival time. The new model obtains better prediction accuracy and faster convergence rate which the model using backpropagation (BP) algorithm and the Levenberg-Marquardt (LM) algorithm to forecast the postoperative patients' survival time can not achieve. Finally, simulation results are given to verify the efficiency and effectiveness of our proposed new model.

  17. Urethral obstruction malformation complex: a cause of abdominal muscle deficiency and the "prune belly".

    Science.gov (United States)

    Pagon, R A; Smith, D W; Shepard, T H

    1979-06-01

    Abdominal muscle deficiency with a "prune belly" abdomen as been a major feature of the so-called prune belly syndrome, which has been regarded as a specific entity, although the etiology and developmental pathology are not understood. We present evidence that abdominal muscle deficiency is an etiologically nonspecific anatomic defect which is secondary to fetal abdominal distention of various causes. One of the more common causes is urethral obstruction with consequent early bladder distention, causing abdominal distention and other anomalies, a constellation of findings which we have termed the urethral obstruction malformation complex. This interpretation of the etiology of most cases of prune belly syndrome accounts for the male predominance, the observed variability in severity, and the lack of a defined mode of inheritance. Recurrence risk figures need to be redefined for each specific obstructing lesion of the urethra. The possibility of early prenatal diagnosis and management of fetuses with urethral obstruction needs further study.

  18. An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine

    Directory of Open Access Journals (Sweden)

    Ali Hashemi

    2014-06-01

    Full Text Available Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.

  19. Longleaf Pine Root System Development and Seedling Quality in Response to Copper Root Pruning and Cavity Size

    Science.gov (United States)

    Mary Anne Sword Sayer; Shi-Jean Susana Sung; James D. Haywood

    2011-01-01

    Cultural practices that modify root system structure in the plug of container-grown seedlings have the potential to improve root system function after planting. Our objective was to assess how copper root pruning affects the quality and root system development of longleaf pine seedlings grown in three cavity sizes in a greenhouse. Copper root pruning increased seedling...

  20. Effects of leader topping and branch pruning on efficiency of Douglas-fir cone harvesting with a tree shaker.

    Science.gov (United States)

    D.L. Copes

    1985-01-01

    In 1983, a study was conducted to evaluate the effects of leader topping and branch pruning on the efficiency to tree shaking to remove Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) cones. Removal efficiency for three topping and pruning treatments averaged 69 percent, whereas for the uncut control treatment it was 62 percent. The treatment...

  1. Pruning for crop regulation in high density guava (Psidium guajava L.) plantation

    Energy Technology Data Exchange (ETDEWEB)

    Thakre, M.; Lal, S.; Uniyal, S.; Goswami, A.K. Prakash. P.

    2016-11-01

    High density management and crop regulation are two important aspects in guava (Psidium guajava L.) production. Therefore, to find out the economic way of managing high density planting and crop regulation, the present work was carried out on 6-year-old guava trees of cv. Pant Prabhat under double-hedge row system of planting during 2009-10 and 2010-11. Seven different forms of pruning [FBT: flower bud thinning by hand, FBTT: flower bud thinning by hand followed by removal of terminal one leaf pair, RLFO: removal of leaves and flower buds by hand, retaining one leaf pair at the top, RLF: removal of all leaves and flowers by hand, OLPS: one leaf pair shoot pruning, FSP: full shoot pruning, OLPF: one leaf pair pruning of fruited shoots only] were studied along with control (C).Minimum annual increase in tree volume (6.764 m3) was recorded with the treatment OLPF, which was 2.31 times less than the control (15.682 m3). Highest yield during winter season (55.30 kg/tree) and total yield (59.87 kg/tree) was obtained from treatment OLPF. One leaf pair pruning of fruited shoots only (OLPF) was also found profitable among other treatments by recording cost:benefit ratio of 1:2.96. This treatment also recorded the highest return distributed in rainy as well as in winter season. On the basis of findings it can be concluded that one leaf pair pruning of fruited shoots only is suitable for profitable high density management as well as crop regulation of guava in farmer friendly manner. (Author)

  2. An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-Tracking Data

    Science.gov (United States)

    Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre

    2017-01-01

    The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in statistics, called extreme value theory (EVT). EVT claims that the bulk of normal distribution is comprised mainly of uninteresting variations while the most…

  3. Case Report: Prune perineum syndrome: a rare case with an unfavourable outcome

    OpenAIRE

    Lopes, Roberto I.; D?nes, Francisco T.; Messi, Gustavo B.; Machado, Marcos G.

    2016-01-01

    Prune perineum syndrome (PPS) is a rare anomaly, with only two previous case reports, both dying in the perinatal period. We report the first case of PPS that reached childhood. The patient presented with a hypoplastic genitalia and bilateral cryptorchidism. There was no evidence of an anal orifice. A significant prune-like mass was observed, extending from the perineum to both gluteal regions and to a cephalic mid-line bony prominence, with a 1cm central orifice that discharged urine. MRI co...

  4. Cane pruning on Chardonnay grapevine in the high-altitude regions of Southern Brazil

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    Filho José Luiz Marcon

    2016-01-01

    Full Text Available High-altitude regions of southern Brazil, located above 900 m above sea level, the cordon training with spur pruning is widely used because of easier application. In these regions, Chardonnay wine grape shows potential to produce quality wines, however, in commercial vineyards, the training system used has not provided productivities that makes economically viable the cultivation of this variety. Given this, the present study aimed to evaluate the effect of different cane-pruning systems on the vegetative, productive and enological potential of Chardonnay grapevines grown in the high-altitude region of Southern Brazil. The experiment was conducted in a commercial Chardonnay vineyard, located in São Joaquim – Santa Catarina State (28o17 ′39”S and 49∘ 55′56” W, to 1230 m a.s.l during 2015 and 2016 vintages. Chardonnay vines (grafted on 1103 Paulsen were planted in 2010, with a 3.0 m (row × 1.0 m (vine spacing. The treatments consisted of different cane-pruning systems: Cordon spur-pruning (control; Sylvoz; Cazenave; Capovolto; single Guyot and double Guyot. Pruning was performed in August of each year when the buds were in the green tip developmental stage. Data was analyzed by Scott Knott test (p < 0.05 following a randomized block design with four replicates, each consisting of 12 vines per plot. We observed higher yield in the Cazenave and double Guyot training system with three and two more tons of grapes than spur-pruning respectively. The bud fertility was higher in plants trained in double Guyot. Vines spur-pruned showed higher relation of leaf area: production, with values above 100 cm2 g−1 grape at 2016 vintage. Commercial maturity of grapes (soluble solids, acidity and polyphenols did not differ among training systems studied. The results suggest that cane-pruning systems could be an alternative to increase production efficiency of Chardonnay in high-altitude region of southern Brazil.

  5. Grapevine cultivar variation to pruning wound protection by Trichoderma species against trunk pathogens

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    Cheusi MUTAWILA

    2011-12-01

    Full Text Available Using Trichoderma species to protect grapevine pruning wounds from trunk disease pathogensis one of the options available for managing grapevine trunk diseases. The growth and persistence of Trichoderma species in the pruning wound and the resulting control effect may depend on intrinsic wound factors and hence may vary between cultivars. Cultivar variability to pruning wound protection by Trichoderma species was evaluated in eight wine grape (Cabernet Sauvignon, Chardonnay, Chenin blanc, Colombar, Merlot,Pinotage, Sauvignon blanc and Shiraz and four table grape (Prime, Red Globe, Thompson Seedless andVictoria cultivars. Two strains of Trichoderma atroviride (USPP-T1 and USPP-T2 separately or in combinationand Eco 77® a registered pruning wound biocontrol agent based on T. harzianum, were applied to fresh pruning wounds of spur-pruned wine grapevines and cane-pruned table grapevines. Trichoderma spp. and a variety of trunk pathogens, Phaeomoniella chlamydospora and species of Phaeoacremonium, Phomopsis, Botryosphaeriaceae and Diatrypaceae, were isolated from the pruning wounds eight months after treatment. Significant treatment × cultivar interactions (P<0.01 were found in the incidence of Trichoderma spp. in table and wine grapes. Trichoderma incidence varied greatly between cultivars and was less between Trichoderma treatments within the same cultivar. The highest Trichoderma incidence in wine grapes was found in Chenin blanc (71.4–82.5% and in table grapes in Thompson Seedless (43.5–76.7%. In the remaining winegrape cultivars Trichoderma incidence varied between 20–50% while with all treatments in Chardonnay Trichoderma incidence was less than 24%. In table grapes Trichoderma incidence varied from 20-67% in the other cultivars. Trichoderma reduced the pathogen by between 10.3% in Chardonnay to 66.7% in Cheninblanc. Trichoderma incidence and pathogen reduction were significantly correlated in most cultivars (r> 0.50;P<0.05 though not

  6. Dried Plums, Prunes and Bone Health: A Comprehensive Review

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    Taylor C. Wallace

    2017-04-01

    Full Text Available The 2015–2020 Dietary Guidelines for Americans advocate for increasing fruit intake and replacing energy-dense foods with those that are nutrient-dense. Nutrition across the lifespan is pivotal for the healthy development and maintenance of bone. The National Osteoporosis Foundation estimates that over half of Americans age 50+ have either osteoporosis or low bone mass. Dried plums, also commonly referred to as prunes, have a unique nutrient and dietary bioactive profile and are suggested to exert beneficial effects on bone. To further elucidate and summarize the potential mechanisms and effects of dried plums on bone health, a comprehensive review of the scientific literature was conducted. The PubMed database was searched through 24 January 2017 for all cell, animal, population and clinical studies that examined the effects of dried plums and/or extracts of the former on markers of bone health. Twenty-four studies were included in the review and summarized in table form. The beneficial effects of dried plums on bone health may be in part due to the variety of phenolics present in the fruit. Animal and cell studies suggest that dried plums and/or their extracts enhance bone formation and inhibit bone resorption through their actions on cell signaling pathways that influence osteoblast and osteoclast differentiation. These studies are consistent with clinical studies that show that dried plums may exert beneficial effects on bone mineral density (BMD. Long-term prospective cohort studies using fractures and BMD as primary endpoints are needed to confirm the effects of smaller clinical, animal and mechanistic studies. Clinical and prospective cohort studies in men are also needed, since they represent roughly 29% of fractures, and likewise, diverse race and ethnic groups. No adverse effects were noted among any of the studies included in this comprehensive review. While the data are not completely consistent, this review suggests that

  7. Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

    Directory of Open Access Journals (Sweden)

    Muhammad Naveed Iqbal Qureshi

    Full Text Available The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM classifier. We compared the performance of this classifier with that of a support vector machine (SVM and basic extreme learning machine (ELM for cortical MRI data from attention deficit/hyperactivity disorder (ADHD patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC, ADHD-inattentive (ADHD-I, and ADHD-combined (ADHD-C. We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM that enabled us to achieve good classification accuracy (60.78%. In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

  8. Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

    Science.gov (United States)

    Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

    2017-04-19

    As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

  9. Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

    Science.gov (United States)

    Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

    2016-01-01

    The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

  10. On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

    DEFF Research Database (Denmark)

    Alizadeh, Tohid

    2008-01-01

    This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP-RBF neu...

  11. Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix

    DEFF Research Database (Denmark)

    Havgaard, Jakob Hull; Torarinsson, Elfar; Gorodkin, Jan

    2007-01-01

    not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving...

  12. Pruning affects the vegetative balance of the wine grape (Vitis vinifera L.

    Directory of Open Access Journals (Sweden)

    Pedro José Almanza-Merchán

    2014-08-01

    Full Text Available Grape cultivation for wine production at altitudes between 2,200 and 2,600 m a.s.l. started in the department of Boyaca in 1982. Quality wines are produced by the AinKarim Vineyard in Ricaurte High. Wine grapes have to possess suitable organoleptic compounds at harvest in order to guarantee quality grape must that can be converted into wine. Therefore, it is necessary to maintain a suitable ratio the sources and the sinks and to guarantee production, quality and vegetative sustainability over time, conserving the equilibrium and benefiting the productive potential of the vineyard. The aim of this study was to evaluate the productive and vegetative balance effect in the wine grape varieties Cabernet Sauvignon and Sauvignon Blanc in Sutamarchan-Boyaca, considering different pruning types (short, long, and mixed. A bifactorial, completely random statistical design was used. At the time of harvest, the fruit production and pruned wood were evaluated. The long-pruned vines showed the best behavior and the most balanced source/sink relationship,, while Sauvignon Blanc demonstrated a better productive yield. Meanwhile, the short and mixed prunings had the better values for the Ravaz index (balance between fruit production and vegetative growth, indicating that they are more suitable for the conditions of the region, allowing for sustainability during the productive cycles of the wine grapes.

  13. Sensitivity of directed networks to the addition and pruning of edges and vertices

    Science.gov (United States)

    Goltsev, A. V.; Timár, G.; Mendes, J. F. F.

    2017-08-01

    Directed networks have various topologically different extensive components, in contrast to a single giant component in undirected networks. We study the sensitivity (response) of the sizes of these extensive components in directed complex networks to the addition and pruning of edges and vertices. We introduce the susceptibility, which quantifies this sensitivity. We show that topologically different parts of a directed network have different sensitivity to the addition and pruning of edges and vertices and, therefore, they are characterized by different susceptibilities. These susceptibilities diverge at the critical point of the directed percolation transition, signaling the appearance (or disappearance) of the giant strongly connected component in the infinite size limit. We demonstrate this behavior in randomly damaged real and synthetic directed complex networks, such as the World Wide Web, Twitter, the Caenorhabditis elegans neural network, directed Erdős-Rényi graphs, and others. We reveal a nonmonotonic dependence of the sensitivity to random pruning of edges or vertices in the case of C. elegans and Twitter that manifests specific structural peculiarities of these networks. We propose the measurements of the susceptibilities during the addition or pruning of edges and vertices as a new method for studying structural peculiarities of directed networks.

  14. 7 CFR 944.350 - Safeguard procedures for avocados, grapefruit, kiwifruit, olives, oranges, prune variety plums...

    Science.gov (United States)

    2010-01-01

    ... Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements and Orders; Fruits, Vegetables... plums (fresh prunes), and table grapes for processing; (3) Olives for processing into oil; (4) Grapefruit for animal feed; or (5) Avocados for seed shall obtain an “Importer's Exempt Commodity Form” (FV-6...

  15. Pruning dwarf mistletoe brooms reduces stress on Jeffrey pines, Cleveland National Forest, California

    Science.gov (United States)

    Robert F. Scharpf; Richard S. Smith; Detlev Vogler

    1987-01-01

    Western dwarf mistletoe (Arceuthobium campylopodum) is a damaging parasite of Jeffrey pines (Pinus jeffreyi) in southern California. Infected branches that develop into brooms are believed to reduce tlee vigor and increase mortality. Brooms were pruned from Jeffrey pines with varying levels of dwarf mistletoe infection and live...

  16. Prune belly syndrome in an Egyptian infant with Down syndrome: a case report.

    Science.gov (United States)

    Metwalley, Kotb A; Farghalley, Hekma S; Abd-Elsayed, Alaa A

    2008-10-02

    Prune belly syndrome is a rare congenital anomaly of uncertain aetiology almost exclusive to males. The association between prune belly syndrome and Down syndrome is very rare. A 4-month-old Egyptian boy was admitted to our institute for management of acute bronchiolitis. He was born at full term by normal vaginal delivery. His mother, a 42-year-Egyptian villager with six other children, had no antenatal or prenatal care. On examination, the boy was found to be hypotonic. In addition to features of Down syndrome, karyotyping confirmed the diagnosis of trisomy 21. Ultrasound examination of the abdomen showed bilateral gross hydronephrosis with megaureter. Micturating cystourethrography showed grade V vesicoureteric reflux bilaterally with no urethral obstruction. Serum creatinine concentration was 90 mumol/litre, serum sodium was 132 mmol/litre and serum potassium was 5.9 mmol/litre. We report an Egyptian infant with Down syndrome and prune belly syndrome. The incidence of this association is unknown. Routine antenatal ultrasonography will help in discovering renal anomalies which can be followed postnatally. Postnatal detection of prune belly syndrome necessitates full radiological investigation to detect any renal anomalies. Early diagnosis of this syndrome and determining its optimal treatment are very important in helping to avoid its fatal course.

  17. Prune belly syndrome in an Egyptian infant with Down syndrome: A case report

    Directory of Open Access Journals (Sweden)

    Metwalley Kotb A

    2008-10-01

    Full Text Available Abstract Introduction Prune belly syndrome is a rare congenital anomaly of uncertain aetiology almost exclusive to males. The association between prune belly syndrome and Down syndrome is very rare. Case presentation A 4-month-old Egyptian boy was admitted to our institute for management of acute bronchiolitis. He was born at full term by normal vaginal delivery. His mother, a 42-year-Egyptian villager with six other children, had no antenatal or prenatal care. On examination, the boy was found to be hypotonic. In addition to features of Down syndrome, karyotyping confirmed the diagnosis of trisomy 21. Ultrasound examination of the abdomen showed bilateral gross hydronephrosis with megaureter. Micturating cystourethrography showed grade V vesicoureteric reflux bilaterally with no urethral obstruction. Serum creatinine concentration was 90 μmol/litre, serum sodium was 132 mmol/litre and serum potassium was 5.9 mmol/litre. Conclusion We report an Egyptian infant with Down syndrome and prune belly syndrome. The incidence of this association is unknown. Routine antenatal ultrasonography will help in discovering renal anomalies which can be followed postnatally. Postnatal detection of prune belly syndrome necessitates full radiological investigation to detect any renal anomalies. Early diagnosis of this syndrome and determining its optimal treatment are very important in helping to avoid its fatal course.

  18. 75 FR 1269 - Dried Prunes Produced in California; Decreased Assessment Rate

    Science.gov (United States)

    2010-01-11

    ..., Regional Manager, California Marketing Field Office, Marketing Order Administration Branch, Fruit and... Agricultural Marketing Service 7 CFR Part 993 Dried Prunes Produced in California; Decreased Assessment Rate AGENCY: Agricultural Marketing Service, USDA. ACTION: Affirmation of interim final rule as final rule...

  19. Growing Up With Prune Belly Syndrome In A Resource Poor Setting ...

    African Journals Online (AJOL)

    The challenges here are for the care givers as well as the physicians who are confronted with the enormous task of taking care of a child with congenital abnormality against a backdrop of lack of resources and a cultural environment that is almost intolerant of dysmorphology. Keywords : Dysmorphology, Prune belly ...

  20. Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

    Directory of Open Access Journals (Sweden)

    Saket Navlakha

    2015-07-01

    Full Text Available Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

  1. Prune belly syndrome in a set of twins, a family tragedy: Case report ...

    African Journals Online (AJOL)

    We report prune belly syndrome, a rare congenital malformation, in a set of twins delivered to a young couple with a history of three previous first trimester spontaneous abortions, discordant HIV seropositivity and antenatal ultrasound report that indicated renal abnormalities in only one of the twins. The challenges of ...

  2. On failure of the pruning technique in "error repair in shift-reduce parsers"

    NARCIS (Netherlands)

    Bertsch, E; Nederhof, MJ

    A previous article presented a technique to compute the least-cost error repair by incrementally generating configurations that result from inserting and deleting tokens in a syntactically incorrect input. An additional mechanism to improve the run-time efficiency of this algorithm by pruning some

  3. 76 FR 75805 - Common Crop Insurance Regulations; Prune Crop Insurance Provisions

    Science.gov (United States)

    2011-12-05

    ... Federal Crop Insurance Corporation 7 CFR Part 457 RIN 0563-AC36 Common Crop Insurance Regulations; Prune Crop Insurance Provisions AGENCY: Federal Crop Insurance Corporation, USDA. ACTION: Proposed rule. SUMMARY: The Federal Crop Insurance Corporation (FCIC) proposes to amend the Common Crop Insurance...

  4. Development of epicormic sprouts in Sitka spruce following thinning and pruning in south-east Alaska.

    Science.gov (United States)

    Robert L. Deal; R. James Barbour; Michael H. McClellan; Dean L. Parry

    2003-01-01

    The frequency and size of epicormic sprouts in Sitka spruce (Picea sitchensis (Bong.) Carr.) were assessed in five 23-29 year-old mixed Sitka spruce-western hemlock (Tsuga heterophylla (Raf.) Sarg.) stands that were uniformly thinned and pruned to 2.4, 3.7 and 5.2 m lift heights. Six to nine years after treatment sprouts were...

  5. Can early thinning and pruning lessen the impact of pine plantations ...

    African Journals Online (AJOL)

    dwelling insects found in pine tree plantations in Patagonia. We compared the abundance, species richness and composition of the beetle and ant assemblages within 16-year-old pine stands (n = 10) subjected to early pruning and thinning (i.e. ...

  6. Competition in apple, as influenced by Alar sprays, fruiting, pruning and tree spacing

    NARCIS (Netherlands)

    Verheij, E.W.M.

    1972-01-01

    In the spring of 1965 a trial was planted with Golden Delicious IX and James Grieve 'aimed' VII, in which tree spacing, deblossoming, Alar sprays and pruning were variable factors, Results are presented over the period 1966-1969.

    At the end of 1969, the 5th year from planting, 400

  7. Effect of Staking and Pruning on the Growth and Yield of Cucumber ...

    African Journals Online (AJOL)

    A field trial was conducted to evaluate the effect of pruning and staking on the vegetative growth and yield of cucumber ( Cucumis sativus L.). The experiment was a 3 x 2 factorial laid out in Randomized Complete Block Design (RCBD) with five replications. The results showed that vine length, number of flowers, total ...

  8. Effect of Time and Level of Pruning on Vegetative Growth, Flowering, Yield, and Quality of Guava

    DEFF Research Database (Denmark)

    Adhikari, Shiva; Kandel, Tanka Prasad

    2015-01-01

    Poor quality fruit production in the rainy season and failure to manipulate production periods are common problems for guava production in India and Nepal. As a possible management to overcome these problems, a field experiment was conducted to understand the effect of time and level of pruning o...

  9. Evaluation of fungicides as potential grapevine pruning wound protectants against Botryosphaeria species

    NARCIS (Netherlands)

    Bester, W.; Crous, P.W.; Fourie, P.H.

    2007-01-01

    Protection of wounds against infection by trunk disease pathogens is the most efficient and cost-effective means to prevent grapevine trunk diseases. Studies done to determine the effectiveness of chemical pruning wound protectants have mostly focused on the control of Eutypa lata. However, other

  10. Effect of electrical conductivity, fruit pruning, and truss position on quality in greenhouse tomato fruit

    NARCIS (Netherlands)

    Fanasca, S.; Martino, A.; Heuvelink, E.; Stanghellini, C.

    2007-01-01

    The combined effects of electrical conductivity (an EC of 2.5 dS m-1 or 8 dS m-1 in the root zone) and fruit pruning (three or six fruit per truss) on tomato fruit quality were studied in a greenhouse experiment, planted in January 2005. Taste-related attributes [dry matter content (DM), total

  11. Effect of edge pruning on structural controllability and observability of complex networks

    Science.gov (United States)

    Mengiste, Simachew Abebe; Aertsen, Ad; Kumar, Arvind

    2015-12-01

    Controllability and observability of complex systems are vital concepts in many fields of science. The network structure of the system plays a crucial role in determining its controllability and observability. Because most naturally occurring complex systems show dynamic changes in their network connectivity, it is important to understand how perturbations in the connectivity affect the controllability of the system. To this end, we studied the control structure of different types of artificial, social and biological neuronal networks (BNN) as their connections were progressively pruned using four different pruning strategies. We show that the BNNs are more similar to scale-free networks than to small-world networks, when comparing the robustness of their control structure to structural perturbations. We introduce a new graph descriptor, ‘the cardinality curve’, to quantify the robustness of the control structure of a network to progressive edge pruning. Knowing the susceptibility of control structures to different pruning methods could help design strategies to destroy the control structures of dangerous networks such as epidemic networks. On the other hand, it could help make useful networks more resistant to edge attacks.

  12. 78 FR 63128 - Dried Prunes Produced in California; Increased Assessment Rate

    Science.gov (United States)

    2013-10-23

    ...; ] DEPARTMENT OF AGRICULTURE Agricultural Marketing Service 7 CFR Part 993 Dried Prunes Produced in California; Increased Assessment Rate AGENCY: Agricultural Marketing Service, USDA. ACTION: Proposed rule. SUMMARY: This... the Agricultural Marketing Agreement Act of 1937, as amended (7 U.S.C. 601-674), hereinafter referred...

  13. Mineralization and N-use efficiency of tree legume prunings from ...

    African Journals Online (AJOL)

    The treatment combinations were laid out as a randomized complete blocks design. Mixtures of tree prunings with 2.5 t ha-1 maize stover increased maize N uptake and grain yield whereas 5 t ha-1 maize stover reduced maize N uptake and grain yield during the wetter season. Mixtures of Pea-R, Stover-1 or Stover-2 with ...

  14. Estimating the fruit quality after application the pruning of blue honeysuckle bushes

    Directory of Open Access Journals (Sweden)

    Iwona Szot

    2013-04-01

    Full Text Available The study was performed at University of Life Science in the eighth year after plant’s establishment in 2001. In 2008 the “crown thinning” of blue honeysuckle cv. ‘Atut’ and‘Duet’, to improve light penetration and air circulation, was done. In the next year during the “crown reduction” the shortening branches to 5 cm height from ground surface was applied. Effect of treatments on fruit quality in 2010 was evaluated. Berries from pruned bushes characterized significantly by bigger mass of individual fruit and length of fruit, however the diameters of fruit were similar to that from non-pruned bushes Berries from non-pruned bushes had significantly higher content of soluble solids, sugar and acidity and lower content of anthocyanins as compared to fruits from pruned bushes. Significantly bigger fruit with regard to their weight, length and diameter stated in the 2 nd harvest. Fruits from 2 nd harvest had much more soluble solids, sugar, anthocyanins and acidy versus that harvested in the 1 st term.

  15. Wood quality for longleaf pines: a spacing, thinning and pruning study on the Kisatchie National Forest

    Science.gov (United States)

    Chi-Leung So; Thomas L. Eberhardt; Daniel J. Leduc; Leslie H. Groom; Jeffery C. G. Goelz

    2010-01-01

    Twenty 70-year-old longleaf pine (Pinus palustris Mill.) trees were harvested from a spacing, thinning, and pruning study on the Kisatchie National Forest, LA. Tree property mapping was used to show the property variation within and between three of the trees. The construction of such maps is both time consuming and cost prohibitive using traditional...

  16. Delayed axonal pruning in the ant brain: a study of developmental trajectories.

    Science.gov (United States)

    Seid, Marc A; Wehner, Rüdiger

    2009-05-01

    The coordination of neuronal maturation and behavioral development is a vital component of survival. The degradation of excessive axonal processes and neuronal networks is a ubiquitous developmental process. In Drosophila, a great portion of axonal pruning occurs during metamorphosis and transpires within hours after pupation. In contrast, we show, using EM-serial sectioning and 3D-reconstructions, that axonal pruning occurs after eclosion and over the course of 60 days in Cataglyphis albicans. Using the mushroom bodies of the brains of Cataglyphis, which have well-developed lip (olfactory integrator) and collar (visual integrator) regions, we show that axonal pruning is dependent upon the differences in the developmental trajectory of the lip and the collar brain regions and happens after eclosion. The elimination of the axonal boutons is most delayed in the collar region, where it is postponed until the ant has had extensive visual experience. We found that individual brain components within a single neuropil can develop at different rates that correlate with the behavioral ecology of these ants and suggest that glia may be mediating the axonal pruning. Our study provides evidence that adult ants may have relatively neotenous brains, and thus more flexibility, allowing them to neuronally adapt to the environment. This neoteny may, in part, explain the neural basis for age-dependent division of labor and the amazing behavioral flexibility exhibited by ants.

  17. Time series modeling with pruned multi-layer perceptron and 2-stage damped least-squares method

    Science.gov (United States)

    Voyant, Cyril; Tamas, Wani; Paoli, Christophe; Balu, Aurélia; Muselli, Marc; Nivet, Marie-Laure; Notton, Gilles

    2014-03-01

    A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its "black box" aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where "all" configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA.

  18. The potential of legume tree prunings as organic matters for improving phosphorus availability in an acid soil

    Directory of Open Access Journals (Sweden)

    I Wahyudi

    2015-01-01

    Full Text Available A study that was aimed to elucidate roles of Gliricidia sepium and Tithonia diversifolia prunings and their extracted humic and fulvic acids on improving phosphorus availability and decreasing aluminum concentration in an Ultisol was conducted in a glasshouse. Thirteen treatments consisting of two prunings, six rates of pruning application (5, 7.5, 10, 20, 40 and 80 t/ha and one control (no added prunings were arranged in a randomized block design with four replicates. Each mixture of prunings and soil was placed in a pot containing 8 kg of soil and maize of Srikandi cultivar was grown on it for 45 days. At harvest, soil pH, P content and aluminium concentration were measured. Results of the glasshouse experiment showed that application of Gliricidia and Tithonia prunings significantly increased soil pH, reduced Alo concentration, increased Alp content, increased P availability, and increased P taken up by maize grown for 45 days. The optimum rate of both Gliricidia and Tithonia pruning should be 40 t/ha. However, at the same rate, optimum production gained by Tithonia would be higher than that of Gliricidia.

  19. Effect of pruning height on the architecture of plants of camu camu (Myrciaria dubia HBK Mc Vaugh in the experimental station of IIAP, Ucayali, Peru.

    Directory of Open Access Journals (Sweden)

    Carlos Abanto

    2011-06-01

    Full Text Available In order to evaluate the response camu camu’s plants in plantations initial undergoing training pruning, an experiment was installed in EE - IIAP-Ucayali, under a design randomized complete block, with 3 replications, making use of 20 plants per experimental unit. The pruning treatments were performing at different heights from the base of the stem, it was considered T0 [witness without pruning], T1 [pruning to 10 cm from the base], T2 [pruning to 20 cm from the base] and T3 [pruning to 40 cm from the base] in plants of known provenance. The investigation was directed to evaluate the number of shoots, shoot growth, plant height, basal diameter, crown diameter and number of branches. After of 9 months of evaluation was found significant differences between the variables except in top diameter. In the variable Height, treatment T0 [witness without pruning] outstanding because they were not pruned, among the remaining treatments has similarly behaved, surpassing the control treatment by 267 %. In addition to this, the basal diameter was found that T1 [pruning to 10 cm from the base] has a better performed with an average value of 1.96 cm compared with the control (1.7cm. The pruning had a positive influence in the number of branches, with an average of 13.4 branches compared with T0 that get 3.1 branches on average, its shows that the pruning can increased up the production of branches to 432.3%.

  20. The study of using an extreme learning machine for rapid concentration estimation in multi-component gas mixtures

    Science.gov (United States)

    Zhao, Lin; Qi, Jinqing; Wang, Jing; Yao, Pengjun

    2012-08-01

    Cross-sensitivity is one of the major unpleasant characteristics of metal oxide gas sensors. In order to solve this challenging problem, artificial neural networks have proved to be very powerful tools, among which back propagation (BP) and radial basis function (RBF) neural networks are the two most commonly used ones in data analysis of metal oxide gas sensors and arrays. However, relatively long training time is the major disadvantage for the BP and RBF neural networks. In order to solve this problem, an extreme leaning machine (ELM) is introduced and studied in this paper. Experimental results show that ELM networks can achieve 466 and 333 times faster training speed than the BP and RBF neural networks, respectively. In addition, ELM networks can achieve comparable concentration prediction accuracy to RBF networks which is much better than BP networks.

  1. Impact of wood pruning to greenhouse gas emissions in three orchards and a vineyard

    Science.gov (United States)

    Germer, Sonja; Schleicher, Sarah; Bischoff, Wolf-Anno; Gomez Palermo, Maider; Kern, Jürgen

    2015-04-01

    Pruning of orchards and vineyards is usually burned or left on the soil for nutrient and organic carbon recycling. Recently the interest rose to extract pruning for energetic use. Very few studies exist that analyzed the effects of pruning removal on soil physical and chemical characteristics. This is linked to the fact that changes are expected rather in the long term, but project funding is typically restricted to 2 or 3 years. Some soil characteristics, however, as organic carbon content and greenhouse gas emissions might also change on the short term as our literature review reveals. The main objective of this research is to determine if pruning extraction from orchards and vineyards impact greenhouse gas emissions (N2O, CH4, and CO2) from soil to the atmosphere, change soil nitrogen and carbon content or effect nitrogen leaching. Results from our study and from the literature will be compiled to formulate best management practices for sustainable pruning utilization from orchards and vineyards. Here we compare four different study sites in a block design over two rows each with two parcels where we extracted pruning and two parcels where pruning was chipped and left on the soil (n=4). Comparisons were made for initial soil chemistry and greenhouse gas emissions in a cherry orchard without irrigation in Germany, a vineyard without irrigation in France, an almond orchard with drip irrigation in Spain and a peach orchard with flood irrigation in Spain. Soil greenhouse gas emissions depend on soil chemistry and soil moisture. These characteristics can be expected to vary between the tree rows and inter-rows of orchards. Therefore we took soil samples from row and inter-row positions of each study site and analyzed them for chemical parameters (pH, total C, N, S, and H, and available PO4, NH4, NO3, K, Mg, Ca). Additionally soil moisture and temperature data have been recorded for tree rows and inter-rows in the cherry orchard and the vineyard. Gas samples were

  2. Extreme cosmos

    CERN Document Server

    Gaensler, Bryan

    2011-01-01

    The universe is all about extremes. Space has a temperature 270°C below freezing. Stars die in catastrophic supernova explosions a billion times brighter than the Sun. A black hole can generate 10 million trillion volts of electricity. And hypergiants are stars 2 billion kilometres across, larger than the orbit of Jupiter. Extreme Cosmos provides a stunning new view of the way the Universe works, seen through the lens of extremes: the fastest, hottest, heaviest, brightest, oldest, densest and even the loudest. This is an astronomy book that not only offers amazing facts and figures but also re

  3. A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection

    Science.gov (United States)

    Liu, Tong; Hu, Liang; Ma, Chao; Wang, Zhi-Yan; Chen, Hui-Ling

    2015-04-01

    In this paper, a novel hybrid method, which integrates an effective filter maximum relevance minimum redundancy (MRMR) and a fast classifier extreme learning machine (ELM), has been introduced for diagnosing erythemato-squamous (ES) diseases. In the proposed method, MRMR is employed as a feature selection tool for dimensionality reduction in order to further improve the diagnostic accuracy of the ELM classifier. The impact of the type of activation functions, the number of hidden neurons and the size of the feature subsets on the performance of ELM have been investigated in detail. The effectiveness of the proposed method has been rigorously evaluated against the ES disease dataset, a benchmark dataset, from UCI machine learning database in terms of classification accuracy. Experimental results have demonstrated that our method has achieved the best classification accuracy of 98.89% and an average accuracy of 98.55% via 10-fold cross-validation technique. The proposed method might serve as a new candidate of powerful methods for diagnosing ES diseases.

  4. Classification of breast masses in ultrasound images using self-adaptive differential evolution extreme learning machine and rough set feature selection.

    Science.gov (United States)

    Prabusankarlal, Kadayanallur Mahadevan; Thirumoorthy, Palanisamy; Manavalan, Radhakrishnan

    2017-04-01

    A method using rough set feature selection and extreme learning machine (ELM) whose learning strategy and hidden node parameters are optimized by self-adaptive differential evolution (SaDE) algorithm for classification of breast masses is investigated. A pathologically proven database of 140 breast ultrasound images, including 80 benign and 60 malignant, is used for this study. A fast nonlocal means algorithm is applied for speckle noise removal, and multiresolution analysis of undecimated discrete wavelet transform is used for accurate segmentation of breast lesions. A total of 34 features, including 29 textural and five morphological, are applied to a [Formula: see text]-fold cross-validation scheme, in which more relevant features are selected by quick-reduct algorithm, and the breast masses are discriminated into benign or malignant using SaDE-ELM classifier. The diagnosis accuracy of the system is assessed using parameters, such as accuracy (Ac), sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), Matthew's correlation coefficient (MCC), and area ([Formula: see text]) under receiver operating characteristics curve. The performance of the proposed system is also compared with other classifiers, such as support vector machine and ELM. The results indicated that the proposed SaDE algorithm has superior performance with [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] compared to other classifiers.

  5. Incipient loose detection of hoops for pipeline based on ensemble empirical mode decomposition and multi-scale entropy and extreme learning machine

    Science.gov (United States)

    Li, Xiaowei; Wei, Qin; Qu, Yongzhi; Cai, Lin

    2017-06-01

    Hoops are very important fittings in hydraulic pipeline, incipient loose detection of hoops will help to prevent hydraulic piping system from breaking down. Since the vibration signals of fluid pipe are non-stationary and of great complexity, multi-scale entropy(MSE), a method characterized by evaluating complexity and irregularity of time series on multiple scales, is used for extracting feature vectors from the vibration signals. In order to obtain components related to system characteristics, ensemble empirical mode decomposition(EEMD) is applied to reconstruct the original signals before the procedure of MSE. Extreme learning machine(ELM) is a new machine learning algorithm characterized by high accuracy and efficiency. In this paper, ELM is introduced as a classifier to identify the different conditions of hoops according to feature vectors extracted by EEMD and MSE algorithms. Thus a novel loose detection method combining with EEMD-MSE and ELM is put forward. The analysis and experimental results demonstrate that the proposed loose detection and feature extraction method for hydraulic pipeline is effective with high performance.

  6. Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters.

    Science.gov (United States)

    Liu, Zhijian; Li, Hao; Tang, Xindong; Zhang, Xinyu; Lin, Fan; Cheng, Kewei

    2016-01-01

    Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower. To address this problem, we previously used artificial neural networks and support vector machine to develop precise knowledge-based models for predicting the heat collection rates and heat loss coefficients of water-in-glass evacuated tube solar water heaters, setting the properties measured by "portable test instruments" as the independent variables. A robust software for determination was also developed. However, in previous results, the prediction accuracy of heat loss coefficients can still be improved compared to those of heat collection rates. Also, in practical applications, even a small reduction in root mean square errors (RMSEs) can sometimes significantly improve the evaluation and business processes. As a further study, in this short report, we show that using a novel and fast machine learning algorithm-extreme learning machine can generate better predicted results for heat loss coefficient, which reduces the average RMSEs to 0.67 in testing.

  7. Tree growth and management in Ugandan agroforestry systems: effects of root pruning on tree growth and crop yield.

    Science.gov (United States)

    Wajja-Musukwe, Tellie-Nelson; Wilson, Julia; Sprent, Janet I; Ong, Chin K; Deans, J Douglas; Okorio, John

    2008-02-01

    Tree root pruning is a potential tool for managing belowground competition when trees and crops are grown together in agroforestry systems. We investigated the effects of tree root pruning on shoot growth and root distribution of Alnus acuminata (H.B. & K.), Casuarina equisetifolia L., Grevillea robusta A. Cunn. ex R. Br., Maesopsis eminii Engl. and Markhamia lutea (Benth.) K. Schum. and on yield of adjacent crops in sub-humid Uganda. The trees were 3 years old at the commencement of the study, and most species were competing strongly with crops. Tree roots were pruned 41 months after planting by cutting and back-filling a trench to a depth of 0.3 m, at a distance of 0.3 m from the trees, on one side of the tree row. The trench was reopened and roots recut at 50 and 62 months after planting. We assessed the effects on tree growth and root distribution over a 3 year period, and crop yield after the third root pruning at 62 months. Overall, root pruning had only a slight effect on aboveground tree growth: height growth was unaffected and diameter growth was reduced by only 4%. A substantial amount of root regrowth was observed by 11 months after pruning. Tree species varied in the number and distribution of roots, and C. equisetifolia and M. lutea had considerably more roots per unit of trunk volume than the other species, especially in the surface soil layers. Casuarina equisetifolia and M. eminii were the tree species most competitive with crops and G. robusta and M. lutea the least competitive. Crop yield data provided strong evidence of the redistribution of root activity following root pruning, with competition increasing on the unpruned side of tree rows. Thus, one-sided root pruning will be useful in only a few circumstances.

  8. Integrating olive grove maintenance and energy biomass recovery with a single-pass pruning and harvesting machine

    Energy Technology Data Exchange (ETDEWEB)

    Spinelli, Raffaele; Nati, Carla; Picchi, Gianni [CNR-IVALSA, Via Madonna del Piano 10, I 50019 Sesto Fiorentino, FI (Italy); Magagnotti, Natascia [DEIAGRA, University of Bologna, Via Fanin 50, Bologna (Italy); Cantini, Claudio; Sani, Graziano [CNR-IVALSA, Azienda S. Paolina, Follonica, GR (Italy); Biocca, Marcello [CRA-ISMA, Via della Pascolare 16, Monterotondo, Roma (Italy)

    2011-02-15

    In Italy, olive tree groves may offer up to a million tonnes of dry biomass per year as pruning residue. Searching for a cost-effective way to tap this potential, the authors tested a new machine, capable of recovering pruning residue at the same time as pruning. The pre-commercial prototype was tested on four different plots and compared to a simpler tractor-base mechanical pruning unit. The authors conducted detailed time-studies in order to determine machine productivity and residue recovery cost. The integrated machine can treat between 0.2 and 0.6 ha h{sup -1}, producing between 0.33 and 1.03 tonnes of fresh residue hour{sup -1}. Its integrated residue recovery function does not slow the pruning, which actually proceeds faster than with the tractor-base unit, due to the more efficient multiple-disc cutting bar. The marginal cost of residue recovery hovers around 40-45 EUR fresh tonne{sup -1}. However, the new machine must not be considered just as a biomass harvester, but rather as a mechanical pruning unit with an integrated biomass recovery function. Its main benefit derives from the capacity of performing a very effective mechanical pruning, and the residue recovery function is a secondary benefit yet unavailable on standard pruning machines. Its deployment must be seen in the context of a general effort to modernize olive grove management and to develop an integrated biomass production system, rather than as a further attempt to build a specialised biomass supply chain. (author)

  9. Sec71 functions as a GEF for the small GTPase Arf1 to govern dendrite pruning ofDrosophilasensory neurons.

    Science.gov (United States)

    Wang, Yan; Zhang, Heng; Shi, Meng; Liou, Yih-Cherng; Lu, Lei; Yu, Fengwei

    2017-05-15

    Pruning, whereby neurons eliminate their excess neurites, is central for the maturation of the nervous system. In Drosophila , sensory neurons, ddaCs, selectively prune their larval dendrites without affecting their axons during metamorphosis. However, it is unknown whether the secretory pathway plays a role in dendrite pruning. Here, we show that the small GTPase Arf1, an important regulator of the secretory pathway, is specifically required for dendrite pruning of ddaC/D/E sensory neurons but dispensable for apoptosis of ddaF neurons. Analyses of the GTP- and GDP-locked forms of Arf1 indicate that the cycling of Arf1 between GDP-bound and GTP-bound forms is essential for dendrite pruning. We further identified Sec71 as a guanine nucleotide exchange factor for Arf1 that preferentially interacts with its GDP-bound form. Like Arf1, Sec71 is also important for dendrite pruning, but not for apoptosis, of sensory neurons. Arf1 and Sec71 are interdependent for their localizations on Golgi. Finally, we show that the Sec71/Arf1-mediated trafficking process is a prerequisite for Rab5-dependent endocytosis to facilitate endocytosis and degradation of the cell-adhesion molecule Neuroglian (Nrg). © 2017. Published by The Company of Biologists Ltd.

  10. Influence of rootstocks and pruning times on yield and on nutrient content and extraction in 'Niagara Rosada' grapevine

    Directory of Open Access Journals (Sweden)

    Marco Antonio Tecchio

    2014-05-01

    Full Text Available The objective of this work was to evaluate the influence of rootstocks and pruning times on yield and on nutrient content and extraction by pruned branches and harvested bunches of 'Niagara Rosada' grapevine in subtropical climate. The rootstocks 'IAC 766', 'IAC 572', 'IAC 313', 'IAC 571-6', and '106-8 Mgt' were evaluated. Treatments consisted of a combination between five rootstocks and three pruning times. At pruning, fresh and dry matter mass of branches were evaluated to estimate biomass accumulation. At harvest, yield was estimated by weighing of bunches per plant. Branches and bunches were sampled at pruning and at harvest, respectively, for nutrient content analysis. Nutrient content and dry matter mass of branches and bunches were used to estimate total nutrient extraction. 'Niagara Rosada' grapevine grafted onto the 'IAC 572' rootstock had the highest yield and dry matter mass of bunches, which were significantly different from the ones observed in 'Niagara Rosada'/'IAC 313'. 'Niagara Rosada' grafted onto the 'IAC 572' rootstock extracted the largest quantity of K, P, Mg, S, Cu, and Fe, differing from 'IAC 313' and 'IAC 766' in K and P extraction, and from '106-8 Mgt' in Mg and S extraction. Winter pruning results in higher yield, dry matter accumulation by branches, and total nutrient content and extraction.

  11. Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements

    Directory of Open Access Journals (Sweden)

    Jaeseok Lee

    2017-01-01

    Full Text Available Due to the necessity of the low-power implementation of newly-developed electrocardiogram (ECG sensors, exact ECG data reconstruction from the compressed measurements has received much attention in recent years. Our interest lies in improving the compression ratio (CR, as well as the ECG reconstruction performance of the sparse signal recovery. To this end, we propose a sparse signal reconstruction method by pruning-based tree search, which attempts to choose the globally-optimal solution by minimizing the cost function. In order to achieve low complexity for the real-time implementation, we employ a novel pruning strategy to avoid exhaustive tree search. Through the restricted isometry property (RIP-based analysis, we show that the exact recovery condition of our approach is more relaxed than any of the existing methods. Through the simulations, we demonstrate that the proposed approach outperforms the existing sparse recovery methods for ECG reconstruction.

  12. A novel technique for reconstruction of the abdominal wall in the prune belly syndrome.

    Science.gov (United States)

    Monfort, G; Guys, J M; Bocciardi, A; Coquet, M; Chevallier, D

    1991-08-01

    There is currently widespread enthusiasm for abdominal wall reconstruction in patients with the prune belly syndrome. We have devised an operation that appears to offer some advantages over those proposed by Ehrlich and Randolph. The technique preserves the umbilicus, and thickens and strengthens the anterior abdominal wall. By narrowing the waist, it also produces a better cosmetic appearance. After full thickness resection of a varying amount of skin from the central abdomen, the anterior wall is sutured in double-breasted fashion, thus, preserving all vascularization and the umbilicus. Since 1969 we have successfully performed this procedure on 9 prune belly patients including 1 girl. The results were excellent in terms of duration and cosmetic appearance.

  13. Brief clinical report: prune belly syndrome: observations supporting the hypothesis of abdominal overdistention.

    Science.gov (United States)

    Burton, B K; Dillard, R G

    1984-03-01

    We report on an infant with the prune belly syndrome who was unusual in that the typical manifestations of the disorder were accompanied by an anterior abdominal wall defect. We speculate that this defect may have occurred as a result of splitting of the abdominal wall secondary to massive bladder dilatation and stretching of the abdominal muscles. An alternative explanation is that the defect may have been the result of secondary pressure necrosis from stretching forces or from contact with another structure, such as the cervix. This case lends further support to the hypothesis that bladder distention with overdistention of the abdomen may be the primary event leading to the findings observed in the prune belly syndrome.

  14. Degree Associated Edge Reconstruction Number of Graphs with Regular Pruned Graph

    Directory of Open Access Journals (Sweden)

    P. Anusha Devi

    2015-10-01

    Full Text Available An ecard of a graph $G$ is a subgraph formed by deleting an edge. A da-ecard specifies the degree of the deleted edge along with the ecard. The degree associated edge reconstruction number of a graph $G,~dern(G,$ is the minimum number of da-ecards that uniquely determines $G.$  The adversary degree associated edge reconstruction number of a graph $G, adern(G,$ is the minimum number $k$ such that every collection of $k$ da-ecards of $G$ uniquely determines $G.$ The maximal subgraph without end vertices of a graph $G$ which is not a tree is the pruned graph of $G.$ It is shown that $dern$ of complete multipartite graphs and some connected graphs with regular pruned graph is $1$ or $2.$ We also determine $dern$ and $adern$ of corona product of standard graphs.

  15. Combining soft decision algorithms and scale-sequential hypotheses pruning for object recognition

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, V.P.; Manolakos, E.S. [Northeastern Univ., Boston, MA (United States)

    1996-12-31

    This paper describes a system that exploits the synergy of Hierarchical Mixture Density (HMD) estimation with multiresolution decomposition based hypothesis pruning to perform efficiently joint segmentation and labeling of partially occluded objects in images. First we present the overall structure of the HMD estimation algorithm in the form of a recurrent neural network which generates the posterior probabilities of the various hypotheses associated with the image. Then in order to reduce the large memory and computation requirement we propose a hypothesis pruning scheme making use of the orthonormal discrete wavelet transform for dimensionality reduction. We provide an intuitive justification for the validity of this scheme and present experimental results and performance analysis on real and synthetic images to verify our claims.

  16. Improvement of growth parameters of prune callus cultures destined to initiate celi suspensions

    Directory of Open Access Journals (Sweden)

    Ewa Hanus-Fajerska

    2011-01-01

    Full Text Available Callus was inducted on wounded leaf explants from shoot tips of a particular Prunus domestica 'Węgierka Zwykła' clone cultivated in vitro. The improvement of Sweet Common Prune stock callus tissue parameters has been approached by experiments on culture protocols. Either for the induction or maintenance of tissue modified Murashige and Skoog medium, supplemented with different auxins and cytokinins at varying concentrations, was used. The goal was to obtain the highiest possible proliferative capacity of friable tissue without any signs of cell redifferentiation for about 10 weeks. The choice of auxin was an important factor regulating the rate and kind of tissue growth, and for the examined prune clone auxin alone brought a relatively small proportion of cells into division, so advantageous was to combine it with oxygenated cytokinin. Friable tissue was obtained on media supplemented with dicamba or with picloram, but not with 2.4-D neither alone nor combinated with IBA.

  17. Growth following pruning of young loblolly pine trees: some early results

    Science.gov (United States)

    Ralph L. Amateis; Harold E. Burkhart

    2006-01-01

    In the spring of 2000, a designed experiment was established to study the effects of pruning on juvenile loblolly pine (Pinus taeda L.) tree growth and the subsequent formation of mature wood. Trees were planted at a 3 m x 3 m square spacing in plots of 6 rows with 6 trees per row, with the inner 16 trees constituting the measurement plot. Among the...

  18. Thinning and Pruning Influence Glaze Damage in a Loblolly Pine Plantation

    Science.gov (United States)

    James D. Burton

    1981-01-01

    An old-field plantation was thinned and pruned at age 11 and again at age 14 to 4 basal area levels and 3 crown percent levels. A survey was made to determine how damage by an ice storm at age 15 was influenced by treatment. Severe damage was heaviest in the densest stands and in stands with the shortest crowns, while the percent of stand destroyed was least under the...

  19. Using State Merging and State Pruning to Address the Path Explosion Problem Faced by Symbolic Execution

    Science.gov (United States)

    2014-06-19

    Forward and Pruned States. . . . . . . . . . . . . . . 56 xii List of Acronyms Acronym Definition ASLR address space layout randomization CFG control flow...Decomposition LLVM low-level virtual machine SUT system under test CUT component under test CFG control flow graph KLOC thousands (kilo) of lines of code LOC...State merging can be done either statically or dynamically. Static state merging requires the building and traversal of a control flow graph ( CFG ) [20

  20. Mycotoxin risks and toxigenic fungi in date, prune and dried apricot among Mediterranean crops

    Directory of Open Access Journals (Sweden)

    Hayrettin OZER

    2012-05-01

    Full Text Available Dried fruit is fruit that is preserved by removing the original water content naturally, through sun drying or artificially, by the use of specialized dryers or dehydrators. Dried fruit has a long tradition of use dating back to the fourth millennium BC in Mesopotamia and is prized because of its sweet taste, nutritive value and long shelf life. Traditional dried fruits such as raisins, figs, dates, apricots and prunes have been a staple of Mediterranean diets for millennia. The Mediterranean region is very favourable for production of dried fruits, not only with its climatic conditions, but also its exceptional fertile lands. Additionally, proximity to trade routes historically has allowed Mediterranean countries more access to dried fruits than landlocked countries. Today, dried fruit consumption is widespread. Nearly half of the dried fruits sold throughout the world are raisins, followed by dates, prunes (dried plums, figs, apricots, peaches, apples and pears. Dates, prunes, apricots, figs and raisins are the major dried fruits produced in the Mediterranean area. Dried fruits are not perishable but can support mold growth, some of which can produce mycotoxins. Occurence of toxigenic molds and mycotoxins on these dried fruits can be a problem in the Mediterranean basin, as in the other parts of the world, being a health hazard to the population as well as a trade issue for the export of local products. Although the most important mycotoxins occuring in Mediterranean crops are aflatoxins (B1, B2, G1 and G2 and ochratoxin A, the type and level of mycotoxins and toxigenic molds vary by crop and also by country and in some cases geographic location within a country. In this review mycotoxin risks and toxigenic fungi in date, prune and dried apricot among Mediterranean crops are reported and discussed.

  1. Visual versus chemical evaluation: Effects of pruning wood decomposition on soil quality in a cherry orchard (Northeast Germany).

    Science.gov (United States)

    van Dongen, Renee; Germer, Sonja; Kern, Jürgen; Stoorvogel, Jetse

    2016-04-01

    Returning crop residues to the soil is a well-known practice to keep a sustainable soil quality in agriculture. In an orchard, pruning material could be returned for soil and water conservation or could be removed for energy production. Pruning wood decomposition rates and their impact on soil quality and greenhouse-gas emissions depend on climate, soil type, land management and water availability. Changing the soil management from leaving wood prunings on soil to removing them from the orchard is expected to result in a slow but lasting change of soil quality. Therefore a quick and cost-effective technique for soil quality evaluation is needed. This study aims to compare pruning wood decomposition effects on soil quality determined by soil chemistry (pH, C/N-ratio) or by Visual Soil Examination and Evaluation (VSEE). In addition, treatments effects on soil quality were compared for sampling positions in tree rows versus interrows. In a cherry orchard (Northeast Germany) six plots were established spreading over two planting rows. At each plot, three subplots with 1x (0.55 kg/m2), 2x (1.10 kg/m2) and 10x (5.50 kg/m2) the average pruning wood rates were installed in both tree and interrows. 5 months later the soils were sampled and a Visual Soil Evaluation and Examination (VSEE) was applied. To relate wood decomposition to impacts on soil quality, wood bags were placed in each plot and were sampled in time intervals of 5 weeks (till a maximum of 20 weeks). Wood decomposition was characterized by decomposition rates and changes in carbon and nitrogen contents. To assess environmental effects, CO2, N2O and CH4 emissions or uptake from soils with different pruning rates were determined with the closed chamber method. There were no significant differences in pH and C/N-ratio between the 3 pruning rates. However, pH was significant higher in the tree row compared to the interrow for the 10-fold pruning rate. The 10-fold pruning rate had significant higher VSEE

  2. Effects of pruning intensity on jujube transpiration and soil moisture of plantation in the Loess Plateau

    Science.gov (United States)

    Nie, Zhenyi; Wang, Xing; Wang, Youke; Ma, Jianpeng; Wei, Xinguang; Chen, Dianyu

    2017-01-01

    In order to ease soil desiccation and prevent ecological deterioration in the Loess Plateau, where jujube (Zizyphus jujube MIll) is widely cultivated as a drought tolerant plant, four pruning intensities (PI), from PI-1 (light) to PI-4 (heavy) were set up based on total length of secondary branches to study the effects of pruning on transpiration and soil moisture in jujube plantations. Furthermore, growth indexes were regularly monitored to estimate jujubes biomass. Sap flow, meteorological and soil moisture conditions were monitored using thermal dissipation probes (TDP), weather station (RR-9100) and the combination of time domain transmission (TDT) technology and neutron moisture gauges (CNC503B), respectively. The results showed that daily actual transpiration of jujube was positively correlated with leaf biomass. Compared with PI-1, jujube transpiration during growth period under PI-2, PI-3, and PI-4 dropped by 11.1%, 29.2%, and 47.9%, respectively. On the contrary, annual water storage under PI-2, PI-3, and PI-4 increased by 6.29 mm, 25.78 mm and 34.74 mm while water use efficiency increased by 5.1%, 15.7% and 24.2%, respectively. Overall, increase in pruning intensity could significantly reduce water consumption of jujube and improve soil moisture in jujube plantations.

  3. Carbon footprint associated with four disposal scenarios for urban pruning waste.

    Science.gov (United States)

    Araújo, Yuri Rommel Vieira; de Góis, Monijany Lins; Junior, Luiz Moreira Coelho; Carvalho, Monica

    2017-11-04

    The inadequate disposal of urban pruning residues can cause significant environmental impacts. The objective of the study presented herein was to quantify the carbon footprint and analyze four disposal scenarios for the urban pruning waste of the city of Joao Pessoa (Northeast Brazil). Software SimaPro was utilized for the quantification of the carbon footprint, with the IPCC 2013 GWP 100y impact evaluation method. The end-of-life treatments considered were sanitary landfilling (with and without collection of methane), simple municipal incineration, and reutilization of wood (transformation into briquettes). The results indicated that simple disposal in sanitary landfill generated 136.34 kg CO2/t urban pruning waste collected (highest carbon footprint), sanitary landfill with methane collection emitted 113.43 kg CO2/t waste, municipal incineration generated 71.31 kg CO2/t waste, and reutilization of woody residues was the scenario with the lowest carbon footprint, with 27.82 kg CO2/t waste. This study demonstrated that reutilization of biomass, besides being environmentally viable, presents the potential to contribute to the city's environmental quality, including the possibility of being used to obtain carbon credits.

  4. [Prenatal diagnosis of prune belly syndrome occurring in siblings in 2 consecutive pregnancies].

    Science.gov (United States)

    Feige, A; Fiedler, K; Rempen, A; Osterhage, H R

    1984-01-01

    The present paper reports on the occurrence of Prune Belly syndrome in siblings. The mother, now 21, gave birth in 1981 to a boy with Prune Belly syndrome. In the second pregnancy the changes in the fetus were first diagnosed by sonography in the 26th week. An ovarian tumor was ruled out by transabdominal puncture of the tumor in the girl's lower abdomen; dilatation of the bladder was diagnosed by the subsequent sonographic check-up. At the same time amniotic fluid was taken in order to determine the fetal karyotype. As the quantity of amniotic fluid was normal, further treatment was put off: a vesicoamniotic shunt, which had been considered at one stage, was not constructed, even though the dilatory changes in the urinary tract increased. In the 37th week a girl was delivered by cesarean section. She had Prune Belly syndrome. Kidney function post part was normal; the prognosis for the infant is good, as it is for her brother who was not treated prenatally. The authors' observations, especially of the course of the second pregnancy, show that provided the quantity of amniotic fluid is normal, prenatal therapy with its attendant risks should only be considered with great reservation.

  5. Effect of summer pruning and CPPU on yield and quality of kiwi fruit (Actinidia deliciosa).

    Science.gov (United States)

    Pramanick, K K; Kashyap, Poonam; Kishore, D K; Sharma, Y P

    2015-03-01

    A field experiment was conducted on bearing vines of kiwifruit cv. Abbott to find the effect of CPPU (N-(2- chloro-4-pyridyl)-N-phenylurea) and summer pruning on fruit yield, fruit size and quality. CPPU greatly stimulated fruit growth indicating that it can be a powerful tool for improving kiwifruit cropping. Application of CPPU at 10 ppm concentration was done by dipping the fruits for 10 sec in the aqueous solution of compound at petal fall and 30 days after petal fall. CPPU applied fruits increased size by 20-70 g over control. Summer pruning along with CPPU application proved to be more effective in obtaining fruits of high grades with increased fruit weight (95.37 g fruit(-1)) and high quality. Summer pruning, when done by pinching 1/5th at Petal Fall stage + CPPU dipping (10 ml(-1)) and pinching 1/5th continued till harvest, at one month interval resulted in increased fruit yield (54.80 kg vine(-1)), high TSS (17.60 Brix), high total sugar (9.85%), advanced ripening by one week and reduced flesh firmness.

  6. Investigation of the Best Method for Winter Pruning of Kiwifruit (Actinidia deliciosa L., cv. Hayward

    Directory of Open Access Journals (Sweden)

    B. Adouli

    2013-03-01

    Full Text Available Winter pruning of kiwifruit has significant effect on yield and quality of fruits. But many growers do not follow a correct pattern for this practice. To determine the effect of this pruning on performance and introducing the appropriate method to do the pruning, a two-year project was carried out as a RCBD with two factors [number of canes per leader (6, 8, 10 and 12 and number of left buds for each cane (6, 12 and 18] with three replications and two vines of Hayward cultivar for each replication of each treatment. For pollination of the flowers, Tomuri variety was planted with 1: 8 ratio with Hayward cultivar. Obtained results showed that both factors had significant effects on yield and fruit size. The best fruits were produced by vines that had 10 or 12 canes per leader and 12 buds on each cane. By these treatments, the yield was improved approximately 5 t/ha more than local orchards. Quality of the fruits was not affected by the studied factors.

  7. Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods

    Directory of Open Access Journals (Sweden)

    Ling Pan

    2016-01-01

    Full Text Available The similarity-based retrieval of lung nodule computed tomography (CT images is an important task in the computer-aided diagnosis of lung lesions. It can provide similar clinical cases for physicians and help them make reliable clinical diagnostic decisions. However, when handling large-scale lung images with a general-purpose computer, traditional image retrieval methods may not be efficient. In this paper, a new retrieval framework based on a hashing method for lung nodule CT images is proposed. This method can translate high-dimensional image features into a compact hash code, so the retrieval time and required memory space can be reduced greatly. Moreover, a pruning algorithm is presented to further improve the retrieval speed, and a pruning-based decision rule is presented to improve the retrieval precision. Finally, the proposed retrieval method is validated on 2,450 lung nodule CT images selected from the public Lung Image Database Consortium (LIDC database. The experimental results show that the proposed pruning algorithm effectively reduces the retrieval time of lung nodule CT images and improves the retrieval precision. In addition, the retrieval framework is evaluated by differentiating benign and malignant nodules, and the classification accuracy can reach 86.62%, outperforming other commonly used classification methods.

  8. Rapid Retrieval of Lung Nodule CT Images Based on Hashing and Pruning Methods.

    Science.gov (United States)

    Pan, Ling; Qiang, Yan; Yuan, Jie; Wu, Lidong

    2016-01-01

    The similarity-based retrieval of lung nodule computed tomography (CT) images is an important task in the computer-aided diagnosis of lung lesions. It can provide similar clinical cases for physicians and help them make reliable clinical diagnostic decisions. However, when handling large-scale lung images with a general-purpose computer, traditional image retrieval methods may not be efficient. In this paper, a new retrieval framework based on a hashing method for lung nodule CT images is proposed. This method can translate high-dimensional image features into a compact hash code, so the retrieval time and required memory space can be reduced greatly. Moreover, a pruning algorithm is presented to further improve the retrieval speed, and a pruning-based decision rule is presented to improve the retrieval precision. Finally, the proposed retrieval method is validated on 2,450 lung nodule CT images selected from the public Lung Image Database Consortium (LIDC) database. The experimental results show that the proposed pruning algorithm effectively reduces the retrieval time of lung nodule CT images and improves the retrieval precision. In addition, the retrieval framework is evaluated by differentiating benign and malignant nodules, and the classification accuracy can reach 86.62%, outperforming other commonly used classification methods.

  9. Induction and pruning of classification rules for prediction of microseismic hazards in coal mines

    Energy Technology Data Exchange (ETDEWEB)

    Sikora, M. [Silesian Technical University, Gliwice (Poland)

    2011-06-15

    The paper presents results of application of a rule induction and pruning algorithm for classification of a microseismic hazard state in coal mines. Due to imbalanced distribution of examples describing states 'hazardous' and 'safe', the special algorithm was used for induction and rule pruning. The algorithm selects optimal parameters' values influencing rule induction and pruning based on training and tuning sets. A rule quality measure which decides about a form and classification abilities of rules that are induced is the basic parameter of the algorithm. The specificity and sensitivity of a classifier were used to evaluate its quality. Conducted tests show that the admitted method of rules induction and classifier's quality evaluation enables to get better results of classification of microseismic hazards than by methods currently used in mining practice. Results obtained by the rules-based classifier were also compared with results got by a decision tree induction algorithm and by a neuro-fuzzy system.

  10. Developmental downregulation of LIS1 expression limits axonal extension and allows axon pruning

    Directory of Open Access Journals (Sweden)

    Kanako Kumamoto

    2017-07-01

    Full Text Available The robust axonal growth and regenerative capacities of young neurons decrease substantially with age. This developmental downregulation of axonal growth may facilitate axonal pruning and neural circuit formation but limits functional recovery following nerve damage. While external factors influencing axonal growth have been extensively investigated, relatively little is known about the intrinsic molecular changes underlying the age-dependent reduction in regeneration capacity. We report that developmental downregulation of LIS1 is responsible for the decreased axonal extension capacity of mature dorsal root ganglion (DRG neurons. In contrast, exogenous LIS1 expression or endogenous LIS1 augmentation by calpain inhibition restored axonal extension capacity in mature DRG neurons and facilitated regeneration of the damaged sciatic nerve. The insulator protein CTCF suppressed LIS1 expression in mature DRG neurons, and this reduction resulted in excessive accumulation of phosphoactivated GSK-3β at the axon tip, causing failure of the axonal extension. Conversely, sustained LIS1 expression inhibited developmental axon pruning in the mammillary body. Thus, LIS1 regulation may coordinate the balance between axonal growth and pruning during maturation of neuronal circuits.

  11. Evaluation of fungicides to protect pruning wounds from Botryosphaeriaceae species infections on almond trees

    Directory of Open Access Journals (Sweden)

    Diego OLMO

    2017-05-01

    Full Text Available In vitro efficacy of ten fungicides was evaluated against four Botryosphaeriaceae spp. (Diplodia seriata, Neofusicoccum luteum, N. mediterraneum and N. parvum associated with branch cankers on almond trees. Cyproconazole, pyraclostrobin, tebuconazole, and thiophanate-methyl were effective for the inhibition of mycelial growth of most of these fungi. An experiment on 3-year-old almond trees evaluated boscalid, mancozeb, thiophanate-methyl, pyraclostrobin and tebuconazole for preventative ability against infections caused by the four pathogens. Five months after pruning and fungicide application, lesion length measurements and isolation percentages showed no significant differences among the four pathogens after they were inoculated onto the trees, and also between the two inoculation times tested (1 or 7 d after fungicide application. Thiophanate-methyl was the most effective fungicide, resulting in the shortest lesion lengths and the lowest isolation percentages from artificially inoculated pruning wounds. This chemical is therefore a candidate for inclusion in integrated disease management, to protect pruning wounds from infections caused by species of Botryosphaeriaceae. This study represents the first approach to development of chemical control strategies for the management of canker diseases caused by Botryosphaeriaceae fungi on almond trees. 

  12. Effectiveness of Natural Antifungal Compounds in Controlling Infection by Grapevine Trunk Disease Pathogens through Pruning Wounds.

    Science.gov (United States)

    Cobos, Rebeca; Mateos, Rosa María; Álvarez-Pérez, José Manuel; Olego, Miguel Angel; Sevillano, Silvia; González-García, Sandra; Garzón-Jimeno, Enrique; Coque, Juan José R

    2015-09-01

    Grapevine trunk fungal pathogens, such as Diplodia seriata and Phaeomoniella chlamydospora, can infect plants through pruning wounds. They cause grapevine trunk diseases and are involved in grapevine decline. Accordingly, the protection of pruning wounds is crucial for the management of grapevine trunk diseases. The efficacy of different natural antifungals in inhibiting the growth of several fungi causing grapevine trunk diseases was evaluated in vitro. The fungi showing greater in vitro efficacy were tested on autoclaved grape wood assays against D. seriata and P. chlamydospora. Based on results from these assays, chitosan oligosaccharide, vanillin, and garlic extract were selected for further evaluation on pruning wounds inoculated with D. seriata and P. chlamydospora in field trials. A significant decrease in plant mortality was observed after 2 years of growth in the plants treated with the different natural antifungals compared to the mortality rate observed in infected plants that were not treated with antifungals. Also, the infection rate for the inoculated pathogens was significantly reduced in plants treated with the selected natural antifungals. Therefore, natural antifungals represent a promising alternative for disease control and could provide significant economic benefits for the grape-growing industry. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

  13. A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks Based on Group Lasso Method.

    Science.gov (United States)

    Wang, Jian; Xu, Chen; Yang, Xifeng; Zurada, Jacek M

    2017-09-26

    In this paper, we propose four new variants of the backpropagation algorithm to improve the generalization ability for feedforward neural networks. The basic idea of these methods stems from the Group Lasso concept which deals with the variable selection problem at the group level. There are two main drawbacks when the Group Lasso penalty has been directly employed during network training. They are numerical oscillations and theoretical challenges in computing the gradients at the origin. To overcome these obstacles, smoothing functions have then been introduced by approximating the Group Lasso penalty. Numerical experiments for classification and regression problems demonstrate that the proposed algorithms perform better than the other three classical penalization methods, Weight Decay, Weight Elimination, and Approximate Smoother, on both generalization and pruning efficiency. In addition, detailed simulations based on a specific data set have been performed to compare with some other common pruning strategies, which verify the advantages of the proposed algorithm. The pruning abilities of the proposed strategy have been investigated in detail for a relatively large data set, MNIST, in terms of various smoothing approximation cases.

  14. Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.

    Science.gov (United States)

    Wang, ShaoPeng; Zhang, Yu-Hang; Huang, GuoHua; Chen, Lei; Cai, Yu-Dong

    2017-01-01

    Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  15. Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

    Science.gov (United States)

    Anam, Khairul; Al-Jumaily, Adel

    2017-01-01

    The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluates a fast classifier, extreme learning machine (ELM), to classify individual and combined finger movements on amputees and non-amputees. ELM is a single hidden layer feed-forward network (SLFN) that avoids iterative learning by determining input weights randomly and output weights analytically. Therefore, it can accelerate the training time of SLFNs. In addition to the classifier evaluation, this paper evaluates various feature combinations to improve the performance of M-PR and investigate some feature projections to improve the class separability of the features. Different from other studies on the implementation of ELM in the myoelectric controller, this paper presents a complete and thorough investigation of various types of ELMs including the node-based and kernel-based ELM. Furthermore, this paper provides comparisons of ELMs and other well-known classifiers such as linear discriminant analysis (LDA), k-nearest neighbour (kNN), support vector machine (SVM) and least-square SVM (LS-SVM). The experimental results show the most accurate ELM classifier is radial basis function ELM (RBF-ELM). The comparison of RBF-ELM and other well-known classifiers shows that RBF-ELM is as accurate as SVM and LS-SVM but faster than the SVM family; it is superior to LDA and kNN. The experimental results also indicate that the accuracy gap of the M-PR on the amputees and non-amputees is not too much with the accuracy of 98.55% on amputees and 99.5% on the non-amputees using six electromyography (EMG) channels. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Sistem Pendukung Keputusan Peramalan Jumlah Kunjungan Pasien Menggunakan Metode Extreme Learning Machine (Studi Kasus : Poli Gigi Rsu Dr. Wahidin Sudiro Husodo Mojokerto

    Directory of Open Access Journals (Sweden)

    Delia Putri Fardani

    2015-04-01

    Full Text Available In this research, a decision support system to predict the number of patients visit RSU Dr. Wahidin Sudiro Husodo Kota Mojokerto was designed and developed using Extreme Learning Machine (ELM method which aims to assist director in making decision for the hospital, managing human and financial resource, as well as distributing material resource properly especially in the Department of Dentistry. The design of this decision support system to predict the number of patients visit with ELM method is divided into several stages. The first stage is to identify the input data collection needed in the calculation method of ELM. The next stage is processing the data; the data is divided into training data and testing data and then normalized, in which training data is 80% (452 data and testing 579 data 20% (116 data. The third stage is problem solving using ELM. The last stage is the design and development of systems using sysflow and desktop-based system that includes the implementation and evaluation of the system. The result of this research is an application of decision supporting system to predict number of patients. By using 116 testing data based on the binary sigmoid activation function using 7 units of hidden layer and 500 Epoch then Optimal MSE value that was obtained is 0.027.

  17. [Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

    Science.gov (United States)

    Liu, Guo-hai; Jiang, Hui; Xiao, Xia-hong; Zhang, Dong-juan; Mei, Cong-li; Ding, Yu-han

    2012-04-01

    Fourier transform near-infrared (FT-NIR) spectroscopy was attempted to determine pH, which is one of the key process parameters in solid-state fermentation of crop straws. First, near infrared spectra of 140 solid-state fermented product samples were obtained by near infrared spectroscopy system in the wavelength range of 10 000-4 000 cm(-1), and then the reference measurement results of pH were achieved by pH meter. Thereafter, the extreme learning machine (ELM) was employed to calibrate model. In the calibration model, the optimal number of PCs and the optimal number of hidden-layer nodes of ELM network were determined by the cross-validation. Experimental results showed that the optimal ELM model was achieved with 1040-1 topology construction as follows: R(p) = 0.961 8 and RMSEP = 0.104 4 in the prediction set. The research achievement could provide technological basis for the on-line measurement of the process parameters in solid-state fermentation.

  18. Multimodal Discrimination of Schizophrenia Using Hybrid Weighted Feature Concatenation of Brain Functional Connectivity and Anatomical Features with an Extreme Learning Machine

    Science.gov (United States)

    Qureshi, Muhammad Naveed Iqbal; Oh, Jooyoung; Cho, Dongrae; Jo, Hang Joon; Lee, Boreom

    2017-01-01

    Multimodal features of structural and functional magnetic resonance imaging (MRI) of the human brain can assist in the diagnosis of schizophrenia. We performed a classification study on age, sex, and handedness-matched subjects. The dataset we used is publicly available from the Center for Biomedical Research Excellence (COBRE) and it consists of two groups: patients with schizophrenia and healthy controls. We performed an independent component analysis and calculated global averaged functional connectivity-based features from the resting-state functional MRI data for all the cortical and subcortical anatomical parcellation. Cortical thickness along with standard deviation, surface area, volume, curvature, white matter volume, and intensity measures from the cortical parcellation, as well as volume and intensity from sub-cortical parcellation and overall volume of cortex features were extracted from the structural MRI data. A novel hybrid weighted feature concatenation method was used to acquire maximal 99.29% (P extreme learning machine, and its efficiency was compared to linear and non-linear (radial basis function) support vector machines, linear discriminant analysis, and random forest bagged tree ensemble algorithms. This article reports the predictive accuracy of both unimodal and multimodal features after 10-by-10-fold nested cross-validation. A permutation test followed the classification experiment to assess the statistical significance of the classification results. It was concluded that, from a clinical perspective, this feature concatenation approach may assist the clinicians in schizophrenia diagnosis. PMID:28943848

  19. Analysis of the performance, emission and combustion characteristics of a turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends using kernel-based extreme learning machine.

    Science.gov (United States)

    Silitonga, Arridina Susan; Hassan, Masjuki Haji; Ong, Hwai Chyuan; Kusumo, Fitranto

    2017-11-01

    The purpose of this study is to investigate the performance, emission and combustion characteristics of a four-cylinder common-rail turbocharged diesel engine fuelled with Jatropha curcas biodiesel-diesel blends. A kernel-based extreme learning machine (KELM) model is developed in this study using MATLAB software in order to predict the performance, combustion and emission characteristics of the engine. To acquire the data for training and testing the KELM model, the engine speed was selected as the input parameter, whereas the performance, exhaust emissions and combustion characteristics were chosen as the output parameters of the KELM model. The performance, emissions and combustion characteristics predicted by the KELM model were validated by comparing the predicted data with the experimental data. The results show that the coefficient of determination of the parameters is within a range of 0.9805-0.9991 for both the KELM model and the experimental data. The mean absolute percentage error is within a range of 0.1259-2.3838. This study shows that KELM modelling is a useful technique in biodiesel production since it facilitates scientists and researchers to predict the performance, exhaust emissions and combustion characteristics of internal combustion engines with high accuracy.

  20. Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

    Directory of Open Access Journals (Sweden)

    Yi Liang

    2016-11-01

    Full Text Available The power industry is the main battlefield of CO2 emission reduction, which plays an important role in the implementation and development of the low carbon economy. The forecasting of electricity demand can provide a scientific basis for the country to formulate a power industry development strategy and further promote the sustained, healthy and rapid development of the national economy. Under the goal of low-carbon economy, medium and long term electricity demand forecasting will have very important practical significance. In this paper, a new hybrid electricity demand model framework is characterized as follows: firstly, integration of grey relation degree (GRD with induced ordered weighted harmonic averaging operator (IOWHA to propose a new weight determination method of hybrid forecasting model on basis of forecasting accuracy as induced variables is presented; secondly, utilization of the proposed weight determination method to construct the optimal hybrid forecasting model based on extreme learning machine (ELM forecasting model and multiple regression (MR model; thirdly, three scenarios in line with the level of realization of various carbon emission targets and dynamic simulation of effect of low-carbon economy on future electricity demand are discussed. The resulting findings show that, the proposed model outperformed and concentrated some monomial forecasting models, especially in boosting the overall instability dramatically. In addition, the development of a low-carbon economy will increase the demand for electricity, and have an impact on the adjustment of the electricity demand structure.