Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine.

PubMed

Kim, Jongin; Lee, Boreom

2018-05-07

Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and Aβ42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC). © 2018 Wiley Periodicals, Inc.

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

PubMed

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.

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

PubMed Central

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

2017-01-01

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. PMID:28420187

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

PubMed

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.

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

PubMed Central

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

Evolution patterns and parameter regimes in edge localized modes on the National Spherical Torus Experiment

DOE Data Explorer

Smith, D. R. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Bell, R. E. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Podesta, M. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Smith, D. R. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Fonck, R. J. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); McKee, G. R. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Diallo, A. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Kaye, S. M. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); LeBlanc, B. P. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States); Sabbagh, S. A. [Princeton Plasma Physics Lab. (PPPL), Princeton, NJ (United States)

2015-09-01

We implement unsupervised machine learning techniques to identify characteristic evolution patterns and associated parameter regimes in edge localized mode (ELM) events observed on the National Spherical Torus Experiment. Multi-channel, localized measurements spanning the pedestal region capture the complex evolution patterns of ELM events on Alfven timescales. Some ELM events are active for less than 100~microsec, but others persist for up to 1~ms. Also, some ELM events exhibit a single dominant perturbation, but others are oscillatory. Clustering calculations with time-series similarity metrics indicate the ELM database contains at least two and possibly three groups of ELMs with similar evolution patterns. The identified ELM groups trigger similar stored energy loss, but the groups occupy distinct parameter regimes for ELM-relevant quantities like plasma current, triangularity, and pedestal height. Notably, the pedestal electron pressure gradient is not an effective parameter for distinguishing the ELM groups, but the ELM groups segregate in terms of electron density gradient and electron temperature gradient. The ELM evolution patterns and corresponding parameter regimes can shape the formulation or validation of nonlinear ELM models. Finally, the techniques and results demonstrate an application of unsupervised machine learning at a data-rich fusion facility.

Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

PubMed

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.

Dynamic extreme learning machine and its approximation capability.

PubMed

Zhang, Rui; Lan, Yuan; Huang, Guang-Bin; Xu, Zong-Ben; Soh, Yeng Chai

2013-12-01

Extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks which need not be neuron alike and perform well in both regression and classification applications. The problem of determining the suitable network architectures is recognized to be crucial in the successful application of ELMs. This paper first proposes a dynamic ELM (D-ELM) where the hidden nodes can be recruited or deleted dynamically according to their significance to network performance, so that not only the parameters can be adjusted but also the architecture can be self-adapted simultaneously. Then, this paper proves in theory that such D-ELM using Lebesgue p-integrable hidden activation functions can approximate any Lebesgue p-integrable function on a compact input set. Simulation results obtained over various test problems demonstrate and verify that the proposed D-ELM does a good job reducing the network size while preserving good generalization performance.

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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 algorithms tested, the ELM was the more expeditious tool for prediction of drought and its related properties.

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

PubMed

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.

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine

PubMed Central

Liu, Yongxiang; Huo, Kai; Zhang, Zhongshuai

2018-01-01

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available. PMID:29320453

Radar HRRP Target Recognition Based on Stacked Autoencoder and Extreme Learning Machine.

PubMed

Zhao, Feixiang; Liu, Yongxiang; Huo, Kai; Zhang, Shuanghui; Zhang, Zhongshuai

2018-01-10

A novel radar high-resolution range profile (HRRP) target recognition method based on a stacked autoencoder (SAE) and extreme learning machine (ELM) is presented in this paper. As a key component of deep structure, the SAE does not only learn features by making use of data, it also obtains feature expressions at different levels of data. However, with the deep structure, it is hard to achieve good generalization performance with a fast learning speed. ELM, as a new learning algorithm for single hidden layer feedforward neural networks (SLFNs), has attracted great interest from various fields for its fast learning speed and good generalization performance. However, ELM needs more hidden nodes than conventional tuning-based learning algorithms due to the random set of input weights and hidden biases. In addition, the existing ELM methods cannot utilize the class information of targets well. To solve this problem, a regularized ELM method based on the class information of the target is proposed. In this paper, SAE and the regularized ELM are combined to make full use of their advantages and make up for each of their shortcomings. The effectiveness of the proposed method is demonstrated by experiments with measured radar HRRP data. The experimental results show that the proposed method can achieve good performance in the two aspects of real-time and accuracy, especially when only a few training samples are available.

Classification of large-sized hyperspectral imagery using fast machine learning algorithms

NASA Astrophysics Data System (ADS)

Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira

2017-07-01

We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.

A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant

NASA Astrophysics Data System (ADS)

Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad

2013-06-01

This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.

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

PubMed

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.

Adaptive control of nonlinear system using online error minimum neural networks.

PubMed

Jia, Chao; Li, Xiaoli; Wang, Kang; Ding, Dawei

2016-11-01

In this paper, a new learning algorithm named OEM-ELM (Online Error Minimized-ELM) is proposed based on ELM (Extreme Learning Machine) neural network algorithm and the spreading of its main structure. The core idea of this OEM-ELM algorithm is: online learning, evaluation of network performance, and increasing of the number of hidden nodes. It combines the advantages of OS-ELM and EM-ELM, which can improve the capability of identification and avoid the redundancy of networks. The adaptive control based on the proposed algorithm OEM-ELM is set up which has stronger adaptive capability to the change of environment. The adaptive control of chemical process Continuous Stirred Tank Reactor (CSTR) is also given for application. The simulation results show that the proposed algorithm with respect to the traditional ELM algorithm can avoid network redundancy and improve the control performance greatly. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Adaptive Online Sequential ELM for Concept Drift Tackling

PubMed Central

Basaruddin, Chan

2016-01-01

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition. PMID:27594879

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

PubMed Central

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

2015-01-01

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. PMID:26506359

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

PubMed Central

Mansourvar, Marjan; Shamshirband, Shahaboddin; Raj, Ram Gopal; Gunalan, Roshan; Mazinani, Iman

2015-01-01

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. PMID:26402795

Dimension Reduction With Extreme Learning Machine.

PubMed

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

2016-08-01

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 are not able to represent data as parts (e.g. nose in a face image). On the other hand, NMF and non-linear AE are 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 non-linear dimension reduction framework referred to as extreme learning machine AE (ELM-AE) and sparse ELM-AE (SELM-AE). In contrast to tied weight AE, the hidden neurons in ELM-AE and SELM-AE need not be tuned, and 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 data set, 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.

Prediction of laser cutting heat affected zone by extreme learning machine

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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.

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

PubMed

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.

Bringing Interpretability and Visualization with Artificial Neural Networks

ERIC Educational Resources Information Center

Gritsenko, Andrey

2017-01-01

Extreme Learning Machine (ELM) is a training algorithm for Single-Layer Feed-forward Neural Network (SLFN). The difference in theory of ELM from other training algorithms is in the existence of explicitly-given solution due to the immutability of initialed weights. In practice, ELMs achieve performance similar to that of other state-of-the-art…

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

2017-10-01

Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

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

PubMed

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.

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

PubMed Central

Li, Yuancheng; 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. PMID:29485990

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

NASA Astrophysics Data System (ADS)

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.

Classifying BCI signals from novice users with extreme learning machine

NASA Astrophysics Data System (ADS)

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.

Parsimonious extreme learning machine using recursive orthogonal least squares.

PubMed

Wang, Ning; Er, Meng Joo; Han, Min

2014-10-01

Novel constructive and destructive parsimonious extreme learning machines (CP- and DP-ELM) are proposed in this paper. By virtue of the proposed ELMs, parsimonious structure and excellent generalization of multiinput-multioutput single hidden-layer feedforward networks (SLFNs) are obtained. The proposed ELMs are developed by innovative decomposition of the recursive orthogonal least squares procedure into sequential partial orthogonalization (SPO). The salient features of the proposed approaches are as follows: 1) Initial hidden nodes are randomly generated by the ELM methodology and recursively orthogonalized into an upper triangular matrix with dramatic reduction in matrix size; 2) the constructive SPO in the CP-ELM focuses on the partial matrix with the subcolumn of the selected regressor including nonzeros as the first column while the destructive SPO in the DP-ELM operates on the partial matrix including elements determined by the removed regressor; 3) termination criteria for CP- and DP-ELM are simplified by the additional residual error reduction method; and 4) the output weights of the SLFN need not be solved in the model selection procedure and is derived from the final upper triangular equation by backward substitution. Both single- and multi-output real-world regression data sets are used to verify the effectiveness and superiority of the CP- and DP-ELM in terms of parsimonious architecture and generalization accuracy. Innovative applications to nonlinear time-series modeling demonstrate superior identification results.

ELM: an Algorithm to Estimate the Alpha Abundance from Low-resolution Spectra

NASA Astrophysics Data System (ADS)

Bu, Yude; Zhao, Gang; Pan, Jingchang; Bharat Kumar, Yerra

2016-01-01

We have investigated a novel methodology using the extreme learning machine (ELM) algorithm to determine the α abundance of stars. Applying two methods based on the ELM algorithm—ELM+spectra and ELM+Lick indices—to the stellar spectra from the ELODIE database, we measured the α abundance with a precision better than 0.065 dex. By applying these two methods to the spectra with different signal-to-noise ratios (S/Ns) and different resolutions, we found that ELM+spectra is more robust against degraded resolution and ELM+Lick indices is more robust against variation in S/N. To further validate the performance of ELM, we applied ELM+spectra and ELM+Lick indices to SDSS spectra and estimated α abundances with a precision around 0.10 dex, which is comparable to the results given by the SEGUE Stellar Parameter Pipeline. We further applied ELM to the spectra of stars in Galactic globular clusters (M15, M13, M71) and open clusters (NGC 2420, M67, NGC 6791), and results show good agreement with previous studies (within 1σ). A comparison of the ELM with other widely used methods including support vector machine, Gaussian process regression, artificial neural networks, and linear least-squares regression shows that ELM is efficient with computational resources and more accurate than other methods.

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT.

PubMed

Wang, Xiaoxin; Hu, Hongli; Jia, Huiqin; Tang, Kaihao

2017-12-09

Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object.

An AST-ELM Method for Eliminating the Influence of Charging Phenomenon on ECT

PubMed Central

Wang, Xiaoxin; Hu, Hongli; Jia, Huiqin; Tang, Kaihao

2017-01-01

Electrical capacitance tomography (ECT) is a promising imaging technology of permittivity distributions in multiphase flow. To reduce the effect of charging phenomenon on ECT measurement, an improved extreme learning machine method combined with adaptive soft-thresholding (AST-ELM) is presented and studied for image reconstruction. This method can provide a nonlinear mapping model between the capacitance values and medium distributions by using machine learning but not an electromagnetic-sensitive mechanism. Both simulation and experimental tests are carried out to validate the performance of the presented method, and reconstructed images are evaluated by relative error and correlation coefficient. The results have illustrated that the image reconstruction accuracy by the proposed AST-ELM method has greatly improved than that by the conventional methods under the condition with charging object. PMID:29232850

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

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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 while successfully satisfying all the regulatory constraints of the contaminated site.

Prediction and early detection of delirium in the intensive care unit by using heart rate variability and machine learning.

PubMed

Oh, Jooyoung; Cho, Dongrae; Park, Jaesub; Na, Se Hee; Kim, Jongin; Heo, Jaeseok; Shin, Cheung Soo; Kim, Jae-Jin; Park, Jin Young; Lee, Boreom

2018-03-27

Delirium is an important syndrome found in patients in the intensive care unit (ICU), however, it is usually under-recognized during treatment. This study was performed to investigate whether delirious patients can be successfully distinguished from non-delirious patients by using heart rate variability (HRV) and machine learning. Electrocardiography data of 140 patients was acquired during daily ICU care, and HRV data were analyzed. Delirium, including its type, severity, and etiologies, was evaluated daily by trained psychiatrists. HRV data and various machine learning algorithms including linear support vector machine (SVM), SVM with radial basis function (RBF) kernels, linear extreme learning machine (ELM), ELM with RBF kernels, linear discriminant analysis, and quadratic discriminant analysis were utilized to distinguish delirium patients from non-delirium patients. HRV data of 4797 ECGs were included, and 39 patients had delirium at least once during their ICU stay. The maximum classification accuracy was acquired using SVM with RBF kernels. Our prediction method based on HRV with machine learning was comparable to previous delirium prediction models using massive amounts of clinical information. Our results show that autonomic alterations could be a significant feature of patients with delirium in the ICU, suggesting the potential for the automatic prediction and early detection of delirium based on HRV with machine learning.

[Determination of process variable pH in solid-state fermentation by FT-NIR spectroscopy and extreme learning machine (ELM)].

PubMed

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.

Quantitative thickness prediction of tectonically deformed coal using Extreme Learning Machine and Principal Component Analysis: a case study

NASA Astrophysics Data System (ADS)

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.

ELM: AN ALGORITHM TO ESTIMATE THE ALPHA ABUNDANCE FROM LOW-RESOLUTION SPECTRA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bu, Yude; Zhao, Gang; Kumar, Yerra Bharat

We have investigated a novel methodology using the extreme learning machine (ELM) algorithm to determine the α abundance of stars. Applying two methods based on the ELM algorithm—ELM+spectra and ELM+Lick indices—to the stellar spectra from the ELODIE database, we measured the α abundance with a precision better than 0.065 dex. By applying these two methods to the spectra with different signal-to-noise ratios (S/Ns) and different resolutions, we found that ELM+spectra is more robust against degraded resolution and ELM+Lick indices is more robust against variation in S/N. To further validate the performance of ELM, we applied ELM+spectra and ELM+Lick indices to SDSSmore » spectra and estimated α abundances with a precision around 0.10 dex, which is comparable to the results given by the SEGUE Stellar Parameter Pipeline. We further applied ELM to the spectra of stars in Galactic globular clusters (M15, M13, M71) and open clusters (NGC 2420, M67, NGC 6791), and results show good agreement with previous studies (within 1σ). A comparison of the ELM with other widely used methods including support vector machine, Gaussian process regression, artificial neural networks, and linear least-squares regression shows that ELM is efficient with computational resources and more accurate than other methods.« less

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

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. PMID:27500640

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

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.

Parsimonious kernel extreme learning machine in primal via Cholesky factorization.

PubMed

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.

A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.

PubMed

Malar, E; Kandaswamy, A; Chakravarthy, D; Giri Dharan, A

2012-09-01

The objective of this paper is to reveal the effectiveness of wavelet based tissue texture analysis for microcalcification detection in digitized mammograms using Extreme Learning Machine (ELM). Microcalcifications are tiny deposits of calcium in the breast tissue which are potential indicators for early detection of breast cancer. The dense nature of the breast tissue and the poor contrast of the mammogram image prohibit the effectiveness in identifying microcalcifications. Hence, a new approach to discriminate the microcalcifications from the normal tissue is done using wavelet features and is compared with different feature vectors extracted using Gray Level Spatial Dependence Matrix (GLSDM) and Gabor filter based techniques. A total of 120 Region of Interests (ROIs) extracted from 55 mammogram images of mini-Mias database, including normal and microcalcification images are used in the current research. The network is trained with the above mentioned features and the results denote that ELM produces relatively better classification accuracy (94%) with a significant reduction in training time than the other artificial neural networks like Bayesnet classifier, Naivebayes classifier, and Support Vector Machine. ELM also avoids problems like local minima, improper learning rate, and over fitting. Copyright © 2012 Elsevier Ltd. All rights reserved.

Test Generation Algorithm for Fault Detection of Analog Circuits Based on Extreme Learning Machine

PubMed Central

Zhou, Jingyu; Tian, Shulin; Yang, Chenglin; Ren, Xuelong

2014-01-01

This paper proposes a novel test generation algorithm based on extreme learning machine (ELM), and such algorithm is cost-effective and low-risk for analog device under test (DUT). This method uses test patterns derived from the test generation algorithm to stimulate DUT, and then samples output responses of the DUT for fault classification and detection. The novel ELM-based test generation algorithm proposed in this paper contains mainly three aspects of innovation. Firstly, this algorithm saves time efficiently by classifying response space with ELM. Secondly, this algorithm can avoid reduced test precision efficiently in case of reduction of the number of impulse-response samples. Thirdly, a new process of test signal generator and a test structure in test generation algorithm are presented, and both of them are very simple. Finally, the abovementioned improvement and functioning are confirmed in experiments. PMID:25610458

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

PubMed

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

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

PubMed

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.

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

PubMed Central

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

2017-01-01

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. PMID:28796187

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

PubMed

Khellal, Atmane; Ma, Hongbin; Fei, Qing

2018-05-09

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

Prediction of mortality after radical cystectomy for bladder cancer by machine learning techniques.

PubMed

Wang, Guanjin; Lam, Kin-Man; Deng, Zhaohong; Choi, Kup-Sze

2015-08-01

Bladder cancer is a common cancer in genitourinary malignancy. For muscle invasive bladder cancer, surgical removal of the bladder, i.e. radical cystectomy, is in general the definitive treatment which, unfortunately, carries significant morbidities and mortalities. Accurate prediction of the mortality of radical cystectomy is therefore needed. Statistical methods have conventionally been used for this purpose, despite the complex interactions of high-dimensional medical data. Machine learning has emerged as a promising technique for handling high-dimensional data, with increasing application in clinical decision support, e.g. cancer prediction and prognosis. Its ability to reveal the hidden nonlinear interactions and interpretable rules between dependent and independent variables is favorable for constructing models of effective generalization performance. In this paper, seven machine learning methods are utilized to predict the 5-year mortality of radical cystectomy, including back-propagation neural network (BPN), radial basis function (RBFN), extreme learning machine (ELM), regularized ELM (RELM), support vector machine (SVM), naive Bayes (NB) classifier and k-nearest neighbour (KNN), on a clinicopathological dataset of 117 patients of the urology unit of a hospital in Hong Kong. The experimental results indicate that RELM achieved the highest average prediction accuracy of 0.8 at a fast learning speed. The research findings demonstrate the potential of applying machine learning techniques to support clinical decision making. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation.

PubMed

Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei

2017-05-18

The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( K * approach). The results show that the predictions of MLAs are superior to those of K * approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

A fast and accurate online sequential learning algorithm for feedforward networks.

PubMed

Liang, Nan-Ying; Huang, Guang-Bin; Saratchandran, P; Sundararajan, N

2006-11-01

In this paper, we develop an online sequential learning algorithm for single hidden layer feedforward networks (SLFNs) with additive or radial basis function (RBF) hidden nodes in a unified framework. The algorithm is referred to as online sequential extreme learning machine (OS-ELM) and can learn data one-by-one or chunk-by-chunk (a block of data) with fixed or varying chunk size. The activation functions for additive nodes in OS-ELM can be any bounded nonconstant piecewise continuous functions and the activation functions for RBF nodes can be any integrable piecewise continuous functions. In OS-ELM, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. The algorithm uses the ideas of ELM of Huang et al. developed for batch learning which has been shown to be extremely fast with generalization performance better than other batch training methods. Apart from selecting the number of hidden nodes, no other control parameters have to be manually chosen. Detailed performance comparison of OS-ELM is done with other popular sequential learning algorithms on benchmark problems drawn from the regression, classification and time series prediction areas. The results show that the OS-ELM is faster than the other sequential algorithms and produces better generalization performance.

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

Surface EMG signals based motion intent recognition using multi-layer ELM

NASA Astrophysics Data System (ADS)

Wang, Jianhui; Qi, Lin; Wang, Xiao

2017-11-01

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human's intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.

Deep Learning Methods for Underwater Target Feature Extraction and Recognition

PubMed Central

Peng, Yuan; Qiu, Mengran; Shi, Jianfei; Liu, Liangliang

2018-01-01

The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network. An underwater target recognition classifier is based on extreme learning machine. Although convolution neural networks can execute both feature extraction and classification, their function mainly relies on a full connection layer, which is trained by gradient descent-based; the generalization ability is limited and suboptimal, so an extreme learning machine (ELM) was used in classification stage. Firstly, CNN learns deep and robust features, followed by the removing of the fully connected layers. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on the actual data set of civil ships obtained 93.04% recognition rate; compared to the traditional Mel frequency cepstral coefficients and Hilbert-Huang feature, recognition rate greatly improved. PMID:29780407

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

NASA Astrophysics Data System (ADS)

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.

Study of Environmental Data Complexity using Extreme Learning Machine

NASA Astrophysics Data System (ADS)

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.

Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification.

PubMed

Liu, Yanqiu; Lu, Huijuan; Yan, Ke; Xia, Haixia; An, Chunlin

2016-01-01

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm as the cost-sensitive D-ELM (CS-D-ELM). Furthermore, we embed rejection cost into the CS-D-ELM to increase the classification stability of the proposed algorithm. Experimental results show that the rejection cost embedded CS-D-ELM algorithm effectively reduces the average and overall cost of the classification process, while the classification accuracy still remains competitive. The proposed method can be extended to classification problems of other redundant and imbalanced data.

Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

PubMed Central

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. PMID:24795876

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

NASA Astrophysics Data System (ADS)

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.

Hyperparameterization of soil moisture statistical models for North America with Ensemble Learning Models (Elm)

NASA Astrophysics Data System (ADS)

Steinberg, P. D.; Brener, G.; Duffy, D.; Nearing, G. S.; Pelissier, C.

2017-12-01

Hyperparameterization, of statistical models, i.e. automated model scoring and selection, such as evolutionary algorithms, grid searches, and randomized searches, can improve forecast model skill by reducing errors associated with model parameterization, model structure, and statistical properties of training data. Ensemble Learning Models (Elm), and the related Earthio package, provide a flexible interface for automating the selection of parameters and model structure for machine learning models common in climate science and land cover classification, offering convenient tools for loading NetCDF, HDF, Grib, or GeoTiff files, decomposition methods like PCA and manifold learning, and parallel training and prediction with unsupervised and supervised classification, clustering, and regression estimators. Continuum Analytics is using Elm to experiment with statistical soil moisture forecasting based on meteorological forcing data from NASA's North American Land Data Assimilation System (NLDAS). There Elm is using the NSGA-2 multiobjective optimization algorithm for optimizing statistical preprocessing of forcing data to improve goodness-of-fit for statistical models (i.e. feature engineering). This presentation will discuss Elm and its components, including dask (distributed task scheduling), xarray (data structures for n-dimensional arrays), and scikit-learn (statistical preprocessing, clustering, classification, regression), and it will show how NSGA-2 is being used for automate selection of soil moisture forecast statistical models for North America.

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

NASA Astrophysics Data System (ADS)

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.

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

PubMed

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

2018-06-01

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 decomposition algorithm, and matrix 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 speedup on a cluster with ten nodes, and reaches a speedup with 15 nodes, an speedup with 20 nodes, a speedup with 25 nodes, a speedup with 30 nodes, and a speedup with 35 nodes.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

PubMed

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.

Quality prediction modeling for sintered ores based on mechanism models of sintering and extreme learning machine based error compensation

NASA Astrophysics Data System (ADS)

Tiebin, Wu; Yunlian, Liu; Xinjun, Li; Yi, Yu; Bin, Zhang

2018-06-01

Aiming at the difficulty in quality prediction of sintered ores, a hybrid prediction model is established based on mechanism models of sintering and time-weighted error compensation on the basis of the extreme learning machine (ELM). At first, mechanism models of drum index, total iron, and alkalinity are constructed according to the chemical reaction mechanism and conservation of matter in the sintering process. As the process is simplified in the mechanism models, these models are not able to describe high nonlinearity. Therefore, errors are inevitable. For this reason, the time-weighted ELM based error compensation model is established. Simulation results verify that the hybrid model has a high accuracy and can meet the requirement for industrial applications.

A fast and precise indoor localization algorithm based on an online sequential extreme learning machine.

PubMed

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

2015-01-15

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.

Cost-sensitive AdaBoost algorithm for ordinal regression based on extreme learning machine.

PubMed

Riccardi, Annalisa; Fernández-Navarro, Francisco; Carloni, Sante

2014-10-01

In this paper, the well known stagewise additive modeling using a multiclass exponential (SAMME) boosting algorithm is extended to address problems where there exists a natural order in the targets using a cost-sensitive approach. The proposed ensemble model uses an extreme learning machine (ELM) model as a base classifier (with the Gaussian kernel and the additional regularization parameter). The closed form of the derived weighted least squares problem is provided, and it is employed to estimate analytically the parameters connecting the hidden layer to the output layer at each iteration of the boosting algorithm. Compared to the state-of-the-art boosting algorithms, in particular those using ELM as base classifier, the suggested technique does not require the generation of a new training dataset at each iteration. The adoption of the weighted least squares formulation of the problem has been presented as an unbiased and alternative approach to the already existing ELM boosting techniques. Moreover, the addition of a cost model for weighting the patterns, according to the order of the targets, enables the classifier to tackle ordinal regression problems further. The proposed method has been validated by an experimental study by comparing it with already existing ensemble methods and ELM techniques for ordinal regression, showing competitive results.

Change detection from synthetic aperture radar images based on neighborhood-based ratio and extreme learning machine

NASA Astrophysics Data System (ADS)

Gao, Feng; Dong, Junyu; Li, Bo; Xu, Qizhi; Xie, Cui

2016-10-01

Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

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 study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.

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

PubMed

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.

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

PubMed Central

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

Improved Extreme Learning Machine based on the Sensitivity Analysis

NASA Astrophysics Data System (ADS)

Cui, Licheng; Zhai, Huawei; Wang, Benchao; Qu, Zengtang

2018-03-01

Extreme learning machine and its improved ones is weak in some points, such as computing complex, learning error and so on. After deeply analyzing, referencing the importance of hidden nodes in SVM, an novel analyzing method of the sensitivity is proposed which meets people’s cognitive habits. Based on these, an improved ELM is proposed, it could remove hidden nodes before meeting the learning error, and it can efficiently manage the number of hidden nodes, so as to improve the its performance. After comparing tests, it is better in learning time, accuracy and so on.

Extreme learning machine based optimal embedding location finder for image steganography

PubMed Central

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. PMID:28196080

Community detection in complex networks using deep auto-encoded extreme learning machine

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-06-01

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

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

NASA Astrophysics Data System (ADS)

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.

Hierarchical extreme learning machine based reinforcement learning for goal localization

NASA Astrophysics Data System (ADS)

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.

The prediction of food additives in the fruit juice based on electronic nose with chemometrics.

PubMed

Qiu, Shanshan; Wang, Jun

2017-09-01

Food additives are added to products to enhance their taste, and preserve flavor or appearance. While their use should be restricted to achieve a technological benefit, the contents of food additives should be also strictly controlled. In this study, E-nose was applied as an alternative to traditional monitoring technologies for determining two food additives, namely benzoic acid and chitosan. For quantitative monitoring, support vector machine (SVM), random forest (RF), extreme learning machine (ELM) and partial least squares regression (PLSR) were applied to establish regression models between E-nose signals and the amount of food additives in fruit juices. The monitoring models based on ELM and RF reached higher correlation coefficients (R 2 s) and lower root mean square errors (RMSEs) than models based on PLSR and SVM. This work indicates that E-nose combined with RF or ELM can be a cost-effective, easy-to-build and rapid detection system for food additive monitoring. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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.

Tensor manifold-based extreme learning machine for 2.5-D face recognition

NASA Astrophysics Data System (ADS)

Chong, Lee Ying; Ong, Thian Song; Teoh, Andrew Beng Jin

2018-01-01

We explore the use of the Gabor regional covariance matrix (GRCM), a flexible matrix-based descriptor that embeds the Gabor features in the covariance matrix, as a 2.5-D facial descriptor and an effective means of feature fusion for 2.5-D face recognition problems. Despite its promise, matching is not a trivial problem for GRCM since it is a special instance of a symmetric positive definite (SPD) matrix that resides in non-Euclidean space as a tensor manifold. This implies that GRCM is incompatible with the existing vector-based classifiers and distance matchers. Therefore, we bridge the gap of the GRCM and extreme learning machine (ELM), a vector-based classifier for the 2.5-D face recognition problem. We put forward a tensor manifold-compliant ELM and its two variants by embedding the SPD matrix randomly into reproducing kernel Hilbert space (RKHS) via tensor kernel functions. To preserve the pair-wise distance of the embedded data, we orthogonalize the random-embedded SPD matrix. Hence, classification can be done using a simple ridge regressor, an integrated component of ELM, on the random orthogonal RKHS. Experimental results show that our proposed method is able to improve the recognition performance and further enhance the computational efficiency.

Soft computing prediction of economic growth based in science and technology factors

NASA Astrophysics Data System (ADS)

Marković, Dušan; Petković, Dalibor; Nikolić, Vlastimir; Milovančević, Miloš; Petković, Biljana

2017-01-01

The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to forecast the gross domestic product (GDP) growth rate. In this study the GDP growth was analyzed based on ten science and technology factors. These factors were: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ELM results were compared with genetic programming (GP), artificial neural network (ANN) and fuzzy logic results. Based upon simulation results, it is demonstrated that ELM has better forecasting capability for the GDP growth rate.

Axial calibration methods of piezoelectric load sharing dynamometer

NASA Astrophysics Data System (ADS)

Zhang, Jun; Chang, Qingbing; Ren, Zongjin; Shao, Jun; Wang, Xinlei; Tian, Yu

2018-06-01

The relationship between input and output of load sharing dynamometer is seriously non-linear in different loading points of a plane, so it's significant for accutately measuring force to precisely calibrate the non-linear relationship. In this paper, firstly, based on piezoelectric load sharing dynamometer, calibration experiments of different loading points are performed in a plane. And then load sharing testing system is respectively calibrated based on BP algorithm and ELM (Extreme Learning Machine) algorithm. Finally, the results show that the calibration result of ELM is better than BP for calibrating the non-linear relationship between input and output of loading sharing dynamometer in the different loading points of a plane, which verifies that ELM algorithm is feasible in solving force non-linear measurement problem.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples.

PubMed

Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan; Liu, Jingjing

2018-01-18

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33-100%, and ELM, with an accuracy rate of 98.01-100%. For level assessment, the R² related to the training set was above 0.97 and the R² related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016-0.3494, lower than the error of 0.5-1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

Mexican Hat Wavelet Kernel ELM for Multiclass Classification.

PubMed

Wang, Jie; Song, Yi-Fan; Ma, Tian-Lei

2017-01-01

Kernel extreme learning machine (KELM) is a novel feedforward neural network, which is widely used in classification problems. To some extent, it solves the existing problems of the invalid nodes and the large computational complexity in ELM. However, the traditional KELM classifier usually has a low test accuracy when it faces multiclass classification problems. In order to solve the above problem, a new classifier, Mexican Hat wavelet KELM classifier, is proposed in this paper. The proposed classifier successfully improves the training accuracy and reduces the training time in the multiclass classification problems. Moreover, the validity of the Mexican Hat wavelet as a kernel function of ELM is rigorously proved. Experimental results on different data sets show that the performance of the proposed classifier is significantly superior to the compared classifiers.

Assessing and comparison of different machine learning methods in parent-offspring trios for genotype imputation.

PubMed

Mikhchi, Abbas; Honarvar, Mahmood; Kashan, Nasser Emam Jomeh; Aminafshar, Mehdi

2016-06-21

Genotype imputation is an important tool for prediction of unknown genotypes for both unrelated individuals and parent-offspring trios. Several imputation methods are available and can either employ universal machine learning methods, or deploy algorithms dedicated to infer missing genotypes. In this research the performance of eight machine learning methods: Support Vector Machine, K-Nearest Neighbors, Extreme Learning Machine, Radial Basis Function, Random Forest, AdaBoost, LogitBoost, and TotalBoost compared in terms of the imputation accuracy, computation time and the factors affecting imputation accuracy. The methods employed using real and simulated datasets to impute the un-typed SNPs in parent-offspring trios. The tested methods show that imputation of parent-offspring trios can be accurate. The Random Forest and Support Vector Machine were more accurate than the other machine learning methods. The TotalBoost performed slightly worse than the other methods.The running times were different between methods. The ELM was always most fast algorithm. In case of increasing the sample size, the RBF requires long imputation time.The tested methods in this research can be an alternative for imputation of un-typed SNPs in low missing rate of data. However, it is recommended that other machine learning methods to be used for imputation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle.

PubMed

Deo, Ravinesh C; Downs, Nathan; Parisi, Alfio V; Adamowski, Jan F; Quilty, John M

2017-05-01

Exposure to erythemally-effective solar ultraviolet radiation (UVR) that contributes to malignant keratinocyte cancers and associated health-risk is best mitigated through innovative decision-support systems, with global solar UV index (UVI) forecast necessary to inform real-time sun-protection behaviour recommendations. It follows that the UVI forecasting models are useful tools for such decision-making. In this study, a model for computationally-efficient data-driven forecasting of diffuse and global very short-term reactive (VSTR) (10-min lead-time) UVI, enhanced by drawing on the solar zenith angle (θ s ) data, was developed using an extreme learning machine (ELM) algorithm. An ELM algorithm typically serves to address complex and ill-defined forecasting problems. UV spectroradiometer situated in Toowoomba, Australia measured daily cycles (0500-1700h) of UVI over the austral summer period. After trialling activations functions based on sine, hard limit, logarithmic and tangent sigmoid and triangular and radial basis networks for best results, an optimal ELM architecture utilising logarithmic sigmoid equation in hidden layer, with lagged combinations of θ s as the predictor data was developed. ELM's performance was evaluated using statistical metrics: correlation coefficient (r), Willmott's Index (WI), Nash-Sutcliffe efficiency coefficient (E NS ), root mean square error (RMSE), and mean absolute error (MAE) between observed and forecasted UVI. Using these metrics, the ELM model's performance was compared to that of existing methods: multivariate adaptive regression spline (MARS), M5 Model Tree, and a semi-empirical (Pro6UV) clear sky model. Based on RMSE and MAE values, the ELM model (0.255, 0.346, respectively) outperformed the MARS (0.310, 0.438) and M5 Model Tree (0.346, 0.466) models. Concurring with these metrics, the Willmott's Index for the ELM, MARS and M5 Model Tree models were 0.966, 0.942 and 0.934, respectively. About 57% of the ELM model's absolute errors were small in magnitude (±0.25), whereas the MARS and M5 Model Tree models generated 53% and 48% of such errors, respectively, indicating the latter models' errors to be distributed in larger magnitude error range. In terms of peak global UVI forecasting, with half the level of error, the ELM model outperformed MARS and M5 Model Tree. A comparison of the magnitude of hourly-cumulated errors of 10-min lead time forecasts for diffuse and global UVI highlighted ELM model's greater accuracy compared to MARS, M5 Model Tree or Pro6UV models. This confirmed the versatility of an ELM model drawing on θ s data for VSTR forecasting of UVI at near real-time horizon. When applied to the goal of enhancing expert systems, ELM-based accurate forecasts capable of reacting quickly to measured conditions can enhance real-time exposure advice for the public, mitigating the potential for solar UV-exposure-related disease. Crown Copyright © 2017. Published by Elsevier Inc. All rights reserved.

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

PubMed

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.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen

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 vectormore » 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.« less

Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

PubMed

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.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

PubMed Central

Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan

2018-01-01

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level. PMID:29346328

Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.

PubMed

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.

Discriminant analysis to predict the occurrence of ELMS in H-mode discharges

NASA Astrophysics Data System (ADS)

Kardaun, O. J. W. F.; Itoh, S.-I.; Itoh, K.; Kardaun, J. W. P. F.

1993-08-01

After an exposition of its theoretical background, discriminant analysis is applied to the H-mode confinement database to find the region in plasma parameter space in which H-mode with small ELM's (Edge Localized Modes) is likely to occur. The boundary of this region is determined by the condition that the probability of appearance of such a type of H-mode, as a function of the plasma parameters, should be larger than some threshold value and larger than the corresponding probability for other types of H-mode (i.e., H-mode without ELM's or with giant ELM's). In practice, the discrimination has been performed for the ASDEX, JET and JFT-2M tokamaks using four instantaneous plasma parameters (injected power Pinj, magnetic field Bt, plasma current Ip and line averaged electron density ne) and taking also memory effects of the plasma and the distance between the plasma and the wall into account, while using variables that are normalized with respect to machine size. Generally speaking, it is found that there is a substantial overlap between the region of H-mode with small ELM's and the region of the two other types of H-mode. However, the ELM-free and the giant ELM H-modes relatively rarely appear in the region, that, according to the analysis, is allocated to small ELM's. A reliable production of H-mode with only small ELM's seems well possible by choosing this regime in parameter space. In the present study, it was not attempted to arrive at a unified discrimination across the machines. So, projection from one machine to another remains difficult, and a reliable determination of the region where small ELM's occur still requires a training sample from the device under consideration.

Prediction model of dissolved oxygen in ponds based on ELM neural network

NASA Astrophysics Data System (ADS)

Li, Xinfei; Ai, Jiaoyan; Lin, Chunhuan; Guan, Haibin

2018-02-01

Dissolved oxygen in ponds is affected by many factors, and its distribution is unbalanced. In this paper, in order to improve the imbalance of dissolved oxygen distribution more effectively, the dissolved oxygen prediction model of Extreme Learning Machine (ELM) intelligent algorithm is established, based on the method of improving dissolved oxygen distribution by artificial push flow. Select the Lake Jing of Guangxi University as the experimental area. Using the model to predict the dissolved oxygen concentration of different voltage pumps, the results show that the ELM prediction accuracy is higher than the BP algorithm, and its mean square error is MSEELM=0.0394, the correlation coefficient RELM=0.9823. The prediction results of the 24V voltage pump push flow show that the discrete prediction curve can approximate the measured values well. The model can provide the basis for the artificial improvement of the dissolved oxygen distribution decision.

Using input feature information to improve ultraviolet retrieval in neural networks

NASA Astrophysics Data System (ADS)

Sun, Zhibin; Chang, Ni-Bin; Gao, Wei; Chen, Maosi; Zempila, Melina

2017-09-01

In neural networks, the training/predicting accuracy and algorithm efficiency can be improved significantly via accurate input feature extraction. In this study, some spatial features of several important factors in retrieving surface ultraviolet (UV) are extracted. An extreme learning machine (ELM) is used to retrieve the surface UV of 2014 in the continental United States, using the extracted features. The results conclude that more input weights can improve the learning capacities of neural networks.

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

PubMed Central

Xu, Yang; Luo, Xiong; Wang, Weiping; Zhao, Wenbing

2017-01-01

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes. PMID:28085084

Professors in the Trenches: Deployed Soldiers and Social Science Academics

DTIC Science & Technology

2009-01-01

CGSC Experiential Learning Model, or ELM . The ELM serves as the methodology for both lesson plan design at the Command and General Staff College and as...the experiential learning model ( ELM ) than the U.S. contractors – the ELM methodology, by design, addresses all four learning style preferences. The...Since the CGSC team used the experiential learning model as the way to teach, they modeled the ELM as they taught all their classes. After the first

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms.

PubMed

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Fu, Xiaolin

2018-05-08

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

Semi-supervised vibration-based classification and condition monitoring of compressors

NASA Astrophysics Data System (ADS)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

NASA Astrophysics Data System (ADS)

Liang, Xiaolin; Zhang, Hao; Lyu, Tingting; Xiao, Han; Gulliver, T. Aaron

2017-12-01

This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.

Predictive local receptive fields based respiratory motion tracking for motion-adaptive radiotherapy.

PubMed

Yubo Wang; Tatinati, Sivanagaraja; Liyu Huang; Kim Jeong Hong; Shafiq, Ghufran; Veluvolu, Kalyana C; Khong, Andy W H

2017-07-01

Extracranial robotic radiotherapy employs external markers and a correlation model to trace the tumor motion caused by the respiration. The real-time tracking of tumor motion however requires a prediction model to compensate the latencies induced by the software (image data acquisition and processing) and hardware (mechanical and kinematic) limitations of the treatment system. A new prediction algorithm based on local receptive fields extreme learning machines (pLRF-ELM) is proposed for respiratory motion prediction. All the existing respiratory motion prediction methods model the non-stationary respiratory motion traces directly to predict the future values. Unlike these existing methods, the pLRF-ELM performs prediction by modeling the higher-level features obtained by mapping the raw respiratory motion into the random feature space of ELM instead of directly modeling the raw respiratory motion. The developed method is evaluated using the dataset acquired from 31 patients for two horizons in-line with the latencies of treatment systems like CyberKnife. Results showed that pLRF-ELM is superior to that of existing prediction methods. Results further highlight that the abstracted higher-level features are suitable to approximate the nonlinear and non-stationary characteristics of respiratory motion for accurate prediction.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models.

PubMed

AlDahoul, Nouar; Md Sabri, Aznul Qalid; Mansoor, Ali Mohammed

2018-01-01

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

ELM - A SIMPLE TOOL FOR THERMAL-HYDRAULIC ANALYSIS OF SOLID-CORE NUCLEAR ROCKET FUEL ELEMENTS

NASA Technical Reports Server (NTRS)

Walton, J. T.

1994-01-01

ELM is a simple computational tool for modeling the steady-state thermal-hydraulics of propellant flow through fuel element coolant channels in nuclear thermal rockets. Written for the nuclear propulsion project of the Space Exploration Initiative, ELM evaluates the various heat transfer coefficient and friction factor correlations available for turbulent pipe flow with heat addition. In the past, these correlations were found in different reactor analysis codes, but now comparisons are possible within one program. The logic of ELM is based on the one-dimensional conservation of energy in combination with Newton's Law of Cooling to determine the bulk flow temperature and the wall temperature across a control volume. Since the control volume is an incremental length of tube, the corresponding pressure drop is determined by application of the Law of Conservation of Momentum. The size, speed, and accuracy of ELM make it a simple tool for use in fuel element parametric studies. ELM is a machine independent program written in FORTRAN 77. It has been successfully compiled on an IBM PC compatible running MS-DOS using Lahey FORTRAN 77, a DEC VAX series computer running VMS, and a Sun4 series computer running SunOS UNIX. ELM requires 565K of RAM under SunOS 4.1, 360K of RAM under VMS 5.4, and 406K of RAM under MS-DOS. Because this program is machine independent, no executable is provided on the distribution media. The standard distribution medium for ELM is one 5.25 inch 360K MS-DOS format diskette. ELM was developed in 1991. DEC, VAX, and VMS are trademarks of Digital Equipment Corporation. Sun4 and SunOS are trademarks of Sun Microsystems, Inc. IBM PC is a registered trademark of International Business Machines. MS-DOS is a registered trademark of Microsoft Corporation.

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

PubMed

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.

Swarm-wavelet based extreme learning machine for finger movement classification on transradial amputees.

PubMed

Anam, Khairul; Al-Jumaily, Adel

2014-01-01

The use of a small number of surface electromyography (EMG) channels on the transradial amputee in a myoelectric controller is a big challenge. This paper proposes a pattern recognition system using an extreme learning machine (ELM) optimized by particle swarm optimization (PSO). PSO is mutated by wavelet function to avoid trapped in a local minima. The proposed system is used to classify eleven imagined finger motions on five amputees by using only two EMG channels. The optimal performance of wavelet-PSO was compared to a grid-search method and standard PSO. The experimental results show that the proposed system is the most accurate classifier among other tested classifiers. It could classify 11 finger motions with the average accuracy of about 94 % across five amputees.

Integrated pillar scatterers for speeding up classification of cell holograms.

PubMed

Lugnan, Alessio; Dambre, Joni; Bienstman, Peter

2017-11-27

The computational power required to classify cell holograms is a major limit to the throughput of label-free cell sorting based on digital holographic microscopy. In this work, a simple integrated photonic stage comprising a collection of silica pillar scatterers is proposed as an effective nonlinear mixing interface between the light scattered by a cell and an image sensor. The light processing provided by the photonic stage allows for the use of a simple linear classifier implemented in the electric domain and applied on a limited number of pixels. A proof-of-concept of the presented machine learning technique, which is based on the extreme learning machine (ELM) paradigm, is provided by the classification results on samples generated by 2D FDTD simulations of cells in a microfluidic channel.

Neural architecture design based on extreme learning machine.

PubMed

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.

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

NASA Astrophysics Data System (ADS)

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.

Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence.

PubMed

Tseng, Chih-Jen; Lu, Chi-Jie; Chang, Chi-Chang; Chen, Gin-Den; Cheewakriangkrai, Chalong

2017-05-01

Ovarian cancer is the second leading cause of deaths among gynecologic cancers in the world. Approximately 90% of women with ovarian cancer reported having symptoms long before a diagnosis was made. Literature shows that recurrence should be predicted with regard to their personal risk factors and the clinical symptoms of this devastating cancer. In this study, ensemble learning and five data mining approaches, including support vector machine (SVM), C5.0, extreme learning machine (ELM), multivariate adaptive regression splines (MARS), and random forest (RF), were integrated to rank the importance of risk factors and diagnose the recurrence of ovarian cancer. The medical records and pathologic status were extracted from the Chung Shan Medical University Hospital Tumor Registry. Experimental results illustrated that the integrated C5.0 model is a superior approach in predicting the recurrence of ovarian cancer. Moreover, the classification accuracies of C5.0, ELM, MARS, RF, and SVM indeed increased after using the selected important risk factors as predictors. Our findings suggest that The International Federation of Gynecology and Obstetrics (FIGO), Pathologic M, Age, and Pathologic T were the four most critical risk factors for ovarian cancer recurrence. In summary, the above information can support the important influence of personality and clinical symptom representations on all phases of guide interventions, with the complexities of multiple symptoms associated with ovarian cancer in all phases of the recurrent trajectory. Copyright © 2017 Elsevier B.V. All rights reserved.

Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

PubMed

Vaughan, Adam; Bohac, Stanislav V

2015-10-01

Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with ϵ-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acasual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines. Copyright © 2015 Elsevier Ltd. All rights reserved.

Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

PubMed

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.

Method Extreme Learning Machine for Forecasting Number of Patients’ Visits in Dental Poli (A Case Study: Community Health Centers Kamal Madura Indonesia)

NASA Astrophysics Data System (ADS)

Sari Rochman, E. M.; Rachmad, A.; Syakur, M. A.; Suzanti, I. O.

2018-01-01

Community Health Centers (Puskesmas) are health service institutions that provide individual health services for outpatient, inpatient and emergency care services. In the outpatient service, there are several polyclinics, including the polyclinic of Ear, Nose, and Throat (ENT), Eyes, Dentistry, Children, and internal disease. Dental Poli is a form of dental and oral health services which is directed to the community. At this moment, the management team in dental poli often has difficulties when they do the preparation and planning to serve a number of patients. It is because the dental poli does not have the appropriate workers with the right qualification. The purpose of this study is to make the system of forecasting the patient’s visit to predict how many patients will come; so that the resources that have been provided will be in accordance with the needs of the Puskesmas. In the ELM method, input and bias weights are initially determined randomly to obtain final weights using Generalized Invers. The matrix used in the final weights is a matrix whose outputs are from each input to a hidden layer. So ELM has a fast learning speed. The result of the experiment of ELM method in this research is able to generate a prediction of a number of patient visit with the RMSE value which is equal to 0.0426.

Recent advances in environmental data mining

NASA Astrophysics Data System (ADS)

Leuenberger, Michael; Kanevski, Mikhail

2016-04-01

Due to the large amount and complexity of data available nowadays in geo- and environmental sciences, we face the need to develop and incorporate more robust and efficient methods for their analysis, modelling and visualization. An important part of these developments deals with an elaboration and application of a contemporary and coherent methodology following the process from data collection to the justification and communication of the results. Recent fundamental progress in machine learning (ML) can considerably contribute to the development of the emerging field - environmental data science. The present research highlights and investigates the different issues that can occur when dealing with environmental data mining using cutting-edge machine learning algorithms. In particular, the main attention is paid to the description of the self-consistent methodology and two efficient algorithms - Random Forest (RF, Breiman, 2001) and Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. Despite the fact that they are based on two different concepts, i.e. decision trees vs artificial neural networks, they both propose promising results for complex, high dimensional and non-linear data modelling. In addition, the study discusses several important issues of data driven modelling, including feature selection and uncertainties. The approach considered is accompanied by simulated and real data case studies from renewable resources assessment and natural hazards tasks. In conclusion, the current challenges and future developments in statistical environmental data learning are discussed. References - Breiman, L., 2001. Random Forests. Machine Learning 45 (1), 5-32. - 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.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

PubMed Central

Men, Hong; Shi, Yan; Fu, Songlin; Jiao, Yanan; Qiao, Yu; Liu, Jingjing

2017-01-01

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively. PMID:28753917

Appraisal of artificial neural network for forecasting of economic parameters

NASA Astrophysics Data System (ADS)

Kordanuli, Bojana; Barjaktarović, Lidija; Jeremić, Ljiljana; Alizamir, Meysam

2017-01-01

The main aim of this research is to develop and apply artificial neural network (ANN) with extreme learning machine (ELM) and back propagation (BP) to forecast gross domestic product (GDP) and Hirschman-Herfindahl Index (HHI). GDP could be developed based on combination of different factors. In this investigation GDP forecasting based on the agriculture and industry added value in gross domestic product (GDP) was analysed separately. Other inputs are final consumption expenditure of general government, gross fixed capital formation (investments) and fertility rate. The relation between product market competition and corporate investment is contentious. On one hand, the relation can be positive, but on the other hand, the relation can be negative. Several methods have been proposed to monitor market power for the purpose of developing procedures to mitigate or eliminate the effects. The most widely used methods are based on indices such as the Hirschman-Herfindahl Index (HHI). The reliability of the ANN models were accessed based on simulation results and using several statistical indicators. Based upon simulation results, it was presented that ELM shows better performances than BP learning algorithm in applications of GDP and HHI forecasting.

Building a database for statistical characterization of ELMs on DIII-D

NASA Astrophysics Data System (ADS)

Fritch, B. J.; Marinoni, A.; Bortolon, A.

2017-10-01

Edge localized modes (ELMs) are bursty instabilities which occur in the edge region of H-mode plasmas and have the potential to damage in-vessel components of future fusion machines by exposing the divertor region to large energy and particle fluxes during each ELM event. While most ELM studies focus on average quantities (e.g. energy loss per ELM), this work investigates the statistical distributions of ELM characteristics, as a function of plasma parameters. A semi-automatic algorithm is being used to create a database documenting trigger times of the tens of thousands of ELMs for DIII-D discharges in scenarios relevant to ITER, thus allowing statistically significant analysis. Probability distributions of inter-ELM periods and energy losses will be determined and related to relevant plasma parameters such as density, stored energy, and current in order to constrain models and improve estimates of the expected inter-ELM periods and sizes, both of which must be controlled in future reactors. Work supported in part by US DoE under the Science Undergraduate Laboratory Internships (SULI) program, DE-FC02-04ER54698 and DE-FG02- 94ER54235.

New Dandelion Algorithm Optimizes Extreme Learning Machine for Biomedical Classification Problems

PubMed Central

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

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

PubMed Central

Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam SM, Jahangir

2017-01-01

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. PMID:28422080

Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

PubMed

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.

Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Kong, Wenwen; Liu, Fei; Zhang, Chu; Bao, Yidan; Yu, Jiajia; He, Yong

2014-01-01

Tomatoes are cultivated around the world and gray mold is one of its most prominent and destructive diseases. An early disease detection method can decrease losses caused by plant diseases and prevent the spread of diseases. The activity of peroxidase (POD) is very important indicator of disease stress for plants. The objective of this study is to examine the possibility of fast detection of POD activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging data. Five pre-treatment methods were investigated. Genetic algorithm-partial least squares (GA-PLS) was applied to select optimal wavelengths. A new fast learning neural algorithm named extreme learning machine (ELM) was employed as multivariate analytical tool in this study. 21 optimal wavelengths were selected by GA-PLS and used as inputs of three calibration models. The optimal prediction result was achieved by ELM model with selected wavelengths, and the r and RMSEP in validation were 0.8647 and 465.9880 respectively. The results indicated that hyperspectral imaging could be considered as a valuable tool for POD activity prediction. The selected wavelengths could be potential resources for instrument development.

A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine.

PubMed

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

2017-02-15

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

Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity

NASA Astrophysics Data System (ADS)

Zhang, Ling; Yin, Jian-qin; Lin, Jia-ben; Feng, Zhi-quan; Zhou, Jin

2017-07-01

Coronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.

[Measurement of Water COD Based on UV-Vis Spectroscopy Technology].

PubMed

Wang, Xiao-ming; Zhang, Hai-liang; Luo, Wei; Liu, Xue-mei

2016-01-01

Ultraviolet/visible (UV/Vis) spectroscopy technology was used to measure water COD. A total of 135 water samples were collected from Zhejiang province. Raw spectra with 3 different pretreatment methods (Multiplicative Scatter Correction (MSC), Standard Normal Variate (SNV) and 1st Derivatives were compared to determine the optimal pretreatment method for analysis. Spectral variable selection is an important strategy in spectrum modeling analysis, because it tends to parsimonious data representation and can lead to multivariate models with better performance. In order to simply calibration models, the preprocessed spectra were then used to select sensitive wavelengths by competitive adaptive reweighted sampling (CARS), Random frog and Successive Genetic Algorithm (GA) methods. Different numbers of sensitive wavelengths were selected by different variable selection methods with SNV preprocessing method. Partial least squares (PLS) was used to build models with the full spectra, and Extreme Learning Machine (ELM) was applied to build models with the selected wavelength variables. The overall results showed that ELM model performed better than PLS model, and the ELM model with the selected wavelengths based on CARS obtained the best results with the determination coefficient (R2), RMSEP and RPD were 0.82, 14.48 and 2.34 for prediction set. The results indicated that it was feasible to use UV/Vis with characteristic wavelengths which were obtained by CARS variable selection method, combined with ELM calibration could apply for the rapid and accurate determination of COD in aquaculture water. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Initial Assessments of E-Learning Modules in Cytotechnology Education.

PubMed

Mukherjee, Maheswari S; Donnelly, Amber D

2018-01-01

Nine E-learning modules (ELMs) were developed in our program using Articulate software. This study assessed our cytotechnology (CT) students' perceptions on the content of the ELMs, and the perceived influence of the ELMs on students' performance during clinical rotations. All CT students watched nine ELMs before the related classroom lecture and group discussion. Following that, students completed nine preclinical rotation surveys. After their clinical rotations, students completed nine postclinical rotation surveys. Statements on the content of the ELMs regarding the quality of the video and audio, duration, navigation, and the materials presented, received positive responses from the majority of the students. While there were a few disagreements and neutral responses, most of the students responded positively saying that the ELMs better prepared them for their role, as well as helped them to better perform their roles during the clinical rotation. The majority of the students recommended developing more EMLs for cytology courses in the future. This study has given hope that the ELMs have potential to enhance our online curriculum and benefit students, within the United States and internationally, who have no easy access to cytology clinical laboratories for hands-on training.

V-ELMpiRNAPred: Identification of human piRNAs by the voting-based extreme learning machine (V-ELM) with a new hybrid feature.

PubMed

Pian, Cong; Chen, Yuan-Yuan; Zhang, Jin; Chen, Zhi; Zhang, Guang-Le; Li, Qiang; Yang, Tao; Zhang, Liang-Yun

2017-02-01

Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores. The first 462 are selected as the input feature vector in the classifier. Moreover, eight of 80 SSM features appear in the top 20. This indicates that these eight SSM features play an important part in the identification of piRNAs. Since five of the above eight SSM features are associated with nucleotide A and G ('A*G', 'A**G', 'A***G', 'A****G', 'A*****G'). So, we guess there may exist some biological significance. We also use a neural network algorithm called voting-based extreme learning machine (V-ELM) to identify real piRNAs. The Specificity (Sp) and Sensitivity (Sn) of our method are 95.48% and 94.61%, respectively in human species. This result shows that our method is more effective compared with those of the piRPred, piRNApredictor, Asym-Pibomd, Piano and McRUMs. The web service of V-ELMpiRNAPred is available for free at http://mm20132014.wicp.net:38601/velmprepiRNA/Main.jsp .

An Extreme Learning Machine-Based Neuromorphic Tactile Sensing System for Texture Recognition.

PubMed

Rasouli, Mahdi; Chen, Yi; Basu, Arindam; Kukreja, Sunil L; Thakor, Nitish V

2018-04-01

Despite significant advances in computational algorithms and development of tactile sensors, artificial tactile sensing is strikingly less efficient and capable than the human tactile perception. Inspired by efficiency of biological systems, we aim to develop a neuromorphic system for tactile pattern recognition. We particularly target texture recognition as it is one of the most necessary and challenging tasks for artificial sensory systems. Our system consists of a piezoresistive fabric material as the sensor to emulate skin, an interface that produces spike patterns to mimic neural signals from mechanoreceptors, and an extreme learning machine (ELM) chip to analyze spiking activity. Benefiting from intrinsic advantages of biologically inspired event-driven systems and massively parallel and energy-efficient processing capabilities of the ELM chip, the proposed architecture offers a fast and energy-efficient alternative for processing tactile information. Moreover, it provides the opportunity for the development of low-cost tactile modules for large-area applications by integration of sensors and processing circuits. We demonstrate the recognition capability of our system in a texture discrimination task, where it achieves a classification accuracy of 92% for categorization of ten graded textures. Our results confirm that there exists a tradeoff between response time and classification accuracy (and information transfer rate). A faster decision can be achieved at early time steps or by using a shorter time window. This, however, results in deterioration of the classification accuracy and information transfer rate. We further observe that there exists a tradeoff between the classification accuracy and the input spike rate (and thus energy consumption). Our work substantiates the importance of development of efficient sparse codes for encoding sensory data to improve the energy efficiency. These results have a significance for a wide range of wearable, robotic, prosthetic, and industrial applications.

1. DETAIL OF TUBE ICE MACHINE OUTLET AT SOUTHWEST CORNER ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. DETAIL OF TUBE ICE MACHINE OUTLET AT SOUTHWEST CORNER OF BUILDING 162; ICE MANUFACTURED INSIDE THE BUILDING WAS AUGURED THROUGH THE WALL AND DROPPED INTO COMPARTMENTS IN REFIGERATED RAIL CARS - Rath Packing Company, Cooler Building, Sycamore Street between Elm & Eighteenth Streets, Waterloo, Black Hawk County, IA

Single image super-resolution via regularized extreme learning regression for imagery from microgrid polarimeters

NASA Astrophysics Data System (ADS)

Sargent, Garrett C.; Ratliff, Bradley M.; Asari, Vijayan K.

2017-08-01

The advantage of division of focal plane imaging polarimeters is their ability to obtain temporally synchronized intensity measurements across a scene; however, they sacrifice spatial resolution in doing so due to their spatially modulated arrangement of the pixel-to-pixel polarizers and often result in aliased imagery. Here, we propose a super-resolution method based upon two previously trained extreme learning machines (ELM) that attempt to recover missing high frequency and low frequency content beyond the spatial resolution of the sensor. This method yields a computationally fast and simple way of recovering lost high and low frequency content from demosaicing raw microgrid polarimetric imagery. The proposed method outperforms other state-of-the-art single-image super-resolution algorithms in terms of structural similarity and peak signal-to-noise ratio.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Explaining Electronic Learning Management Systems (ELMS) Continued Usage Intentions among Facilitators in Higher Education Institutions (HEIs) in Tanzania

ERIC Educational Resources Information Center

Muries, Bruckse; Masele, Juma James

2017-01-01

This study sought to explain ELMS continued usage intentions among HEIs in Tanzania. The study was guided by main research question, "What explains ELMS continued usage intentions among facilitators in HEIs in Tanzania?" The study used descriptive cross sectional design administered to 264 respondents drawn from five universities…

Predicting the Adoption of E-Learning Management System: A Case of Selected Private Universities in Nigeria

ERIC Educational Resources Information Center

Nicholas-Omoregbe, Olanike Sharon; Azeta, Ambrose Agbon; Chiazor, Idowu Aigbovo; Omoregbe, Nicholas

2017-01-01

Despite the availability of studies on e-learning management system (eLMS) using information system models, its theoretical foundations have not yet captured social constructs that are peculiar to developing countries including Nigeria. This study was undertaken with the aim of investigating factors that could influence eLMS adoption in higher…

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Research and application of a novel hybrid decomposition-ensemble learning paradigm with error correction for daily PM10 forecasting

NASA Astrophysics Data System (ADS)

Luo, Hongyuan; Wang, Deyun; Yue, Chenqiang; Liu, Yanling; Guo, Haixiang

2018-03-01

In this paper, a hybrid decomposition-ensemble learning paradigm combining error correction is proposed for improving the forecast accuracy of daily PM10 concentration. The proposed learning paradigm is consisted of the following two sub-models: (1) PM10 concentration forecasting model; (2) error correction model. In the proposed model, fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) are applied to disassemble original PM10 concentration series and error sequence, respectively. The extreme learning machine (ELM) model optimized by cuckoo search (CS) algorithm is utilized to forecast the components generated by FEEMD and VMD. In order to prove the effectiveness and accuracy of the proposed model, two real-world PM10 concentration series respectively collected from Beijing and Harbin located in China are adopted to conduct the empirical study. The results show that the proposed model performs remarkably better than all other considered models without error correction, which indicates the superior performance of the proposed model.

2. DETAIL OF DISCHARGE CHUTES FROM VOGT AUTOMATIC TUBE ICE ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

2. DETAIL OF DISCHARGE CHUTES FROM VOGT AUTOMATIC TUBE ICE MACHINE IN SOUTHWEST CORNER OF LEVEL 5; ICE DROPPED INTO HOLDING BIN BEFORE BEING TRANSFERRED TO RAIL CARS OUTSIDE BUILDING (HENRY VOGT MACHINE COMPANY, LOUISVILLE, USA, PATENT NO. 2,200,424 - Rath Packing Company, Cooler Building, Sycamore Street between Elm & Eighteenth Streets, Waterloo, Black Hawk County, IA

Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging

NASA Astrophysics Data System (ADS)

Kong, Wenwen; Liu, Fei; Zhang, Chu; Zhang, Jianfeng; Feng, Hailin

2016-10-01

The feasibility of hyperspectral imaging with 400-1000 nm was investigated to detect malondialdehyde (MDA) content in oilseed rape leaves under herbicide stress. After comparing the performance of different preprocessing methods, linear and nonlinear calibration models, the optimal prediction performance was achieved by extreme learning machine (ELM) model with only 23 wavelengths selected by competitive adaptive reweighted sampling (CARS), and the result was RP = 0.929 and RMSEP = 2.951. Furthermore, MDA distribution map was successfully achieved by partial least squares (PLS) model with CARS. This study indicated that hyperspectral imaging technology provided a fast and nondestructive solution for MDA content detection in plant leaves.

Plasma core power exhaust in ELMy H-Mode in JET with ITER-Like Wall

NASA Astrophysics Data System (ADS)

Guillemaut, C.; Metzger, C.; Appel, L.; Drewelow, P.; Horvath, L.; Matthews, G. F.; Szepesi, G.; Solano, E. R.; contributors, JET

2018-07-01

The mitigation of target heat load in future steady state fusion devices will require dissipation of a significant amount of power through radiation. Plasma operations relying on ELMy H-modes could be problematic since ELMs may transport substantial amounts of power to the target without significant dissipation. Therefore, estimation of the average ELM power exhaust from the plasma core is crucial to evaluate the potential limitation on the power dissipation in ELMy H-mode regime. A series of more than 50 Type-I ELMy H-mode discharges in JET with ITER-Like Wall (JET-ILW) with a wide range of conditions has been used here to compare the average ELM power to the average input power. The effect of input power, ELM frequency, plasma current, confinement and radiation on ELM power exhaust has been studied and reported in this paper. Good agreement has been found here with previous studies made in carbon machines. This work suggests that it should not be possible to dissipate more than 70%–80% of the input power in Type-I ELMy H-modes in JET-ILW which is consistent with the maximum radiative fraction found experimentally.

Ideal MHD Stability and Characteristics of Edge Localized Modes on CFETR

NASA Astrophysics Data System (ADS)

Li, Zeyu; Chan, Vincent; Xu, Xueqiao; Wang, Xiaogang; Cfetr Physics Team

2017-10-01

Investigation on the equilibrium operation regime, its ideal magnetohydrodynamics (MHD) stability and edge localized modes (ELM) characteristics is performed for China Fusion Engineering Test Reactor (CFETR). The CFETR operation regime study starts with a baseline scenario derived from multi-code integrated modeling, with key parameters varied to build a systematic database. These parameters, under profile and pedestal constraints, provide the foundation for engineering design. The linear stabilities of low-n and intermediate-n peeling-ballooning modes for CFETR baseline scenario are analyzed. Multi-code benchmarking, including GATO, ELITE, BOUT + + and NIMROD, demonstrated good agreement in predicting instabilities. Nonlinear behavior of ELMs for the baseline scenario is simulated using BOUT + + . Instabilities are found both at the pedestal top and inside the pedestal region, which lead to a mix of grassy and type I ELMs. Pedestal structures extending inward beyond the pedestal top are also varied to study the influence on ELM characteristic. Preliminary results on the dependence of the Type-I ELM divertor heat load scaling on machine size and pedestal pressure will also be presented. Prepared by LLNL under Contract DE-AC52-07NA27344 and National Magnetic Confinement Fusion Research Program of China (Grant No. 2014GB110003 and 2014GB107004).

Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal.

PubMed

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.

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

PubMed

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.

Injected mass deposition thresholds for lithium granule instigated triggering of edge localized modes on EAST

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lunsford, R.; Sun, Zhen; Maingi, Rajesh

The ability of an injected lithium granule to promptly trigger an edge localized mode (ELM) has been established in multiple experiments. By horizontally injecting granules ranging in diameter from 200 microns to 1mm in diameter into the low field side of EAST H-mode discharges we have determined that granules with diameter > 600 microns are successful in triggering ELMs more than 95% of the time. Granules were radially injected from the outer midplane with velocities ~ 80 m/s into EAST upper-single null discharges with an ITER like tungsten monoblock divertor. ELM triggering was a prompt response to granule injection, andmore » for granules of a sufficient size there was no evidence of a "trigger lag" phenomenon as observed in full metal machines. We also demonstrated that the triggering efficiency decreased with granule size during dynamic size scans. These granules were individually tracked throughout their injection cycle in order to determine their efficacy at triggering an ELM. Furthermore, by simulating the granule injection with an experimentally benchmarked neutral gas shielding (NGS) model, the ablatant mass deposition required to promptly trigger an ELM is calculated and the fractional mass deposition is determined. Simulated 900 micron granules capable of triggering an ELM show a peaked mass deposition of 3.9 x 10 17 atoms per mm of penetration at a depth of approximately 5 cm past the separatrix.« less

Injected mass deposition thresholds for lithium granule instigated triggering of edge localized modes on EAST

DOE PAGES

Lunsford, R.; Sun, Zhen; Maingi, Rajesh; ...

2017-12-19

The ability of an injected lithium granule to promptly trigger an edge localized mode (ELM) has been established in multiple experiments. By horizontally injecting granules ranging in diameter from 200 microns to 1mm in diameter into the low field side of EAST H-mode discharges we have determined that granules with diameter > 600 microns are successful in triggering ELMs more than 95% of the time. Granules were radially injected from the outer midplane with velocities ~ 80 m/s into EAST upper-single null discharges with an ITER like tungsten monoblock divertor. ELM triggering was a prompt response to granule injection, andmore » for granules of a sufficient size there was no evidence of a "trigger lag" phenomenon as observed in full metal machines. We also demonstrated that the triggering efficiency decreased with granule size during dynamic size scans. These granules were individually tracked throughout their injection cycle in order to determine their efficacy at triggering an ELM. Furthermore, by simulating the granule injection with an experimentally benchmarked neutral gas shielding (NGS) model, the ablatant mass deposition required to promptly trigger an ELM is calculated and the fractional mass deposition is determined. Simulated 900 micron granules capable of triggering an ELM show a peaked mass deposition of 3.9 x 10 17 atoms per mm of penetration at a depth of approximately 5 cm past the separatrix.« less

Boosting instance prototypes to detect local dermoscopic features.

PubMed

Situ, Ning; Yuan, Xiaojing; Zouridakis, George

2010-01-01

Local dermoscopic features are useful in many dermoscopic criteria for skin cancer detection. We address the problem of detecting local dermoscopic features from epiluminescence (ELM) microscopy skin lesion images. We formulate the recognition of local dermoscopic features as a multi-instance learning (MIL) problem. We employ the method of diverse density (DD) and evidence confidence (EC) function to convert MIL to a single-instance learning (SIL) problem. We apply Adaboost to improve the classification performance with support vector machines (SVMs) as the base classifier. We also propose to boost the selection of instance prototypes through changing the data weights in the DD function. We validate the methods on detecting ten local dermoscopic features from a dataset with 360 images. We compare the performance of the MIL approach, its boosting version, and a baseline method without using MIL. Our results show that boosting can provide performance improvement compared to the other two methods.

Empirical Wavelet Transform Based Features for Classification of Parkinson's Disease Severity.

PubMed

Oung, Qi Wei; Muthusamy, Hariharan; Basah, Shafriza Nisha; Lee, Hoileong; Vijean, Vikneswaran

2017-12-29

Parkinson's disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers - K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level - with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal's information.

Non-destructive determination of Malondialdehyde (MDA) distribution in oilseed rape leaves by laboratory scale NIR hyperspectral imaging

PubMed Central

Kong, Wenwen; Liu, Fei; Zhang, Chu; Zhang, Jianfeng; Feng, Hailin

2016-01-01

The feasibility of hyperspectral imaging with 400–1000 nm was investigated to detect malondialdehyde (MDA) content in oilseed rape leaves under herbicide stress. After comparing the performance of different preprocessing methods, linear and nonlinear calibration models, the optimal prediction performance was achieved by extreme learning machine (ELM) model with only 23 wavelengths selected by competitive adaptive reweighted sampling (CARS), and the result was RP = 0.929 and RMSEP = 2.951. Furthermore, MDA distribution map was successfully achieved by partial least squares (PLS) model with CARS. This study indicated that hyperspectral imaging technology provided a fast and nondestructive solution for MDA content detection in plant leaves. PMID:27739491

Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.

PubMed

Xu, Wenjun; Chen, Jie; Lau, Henry Y K; Ren, Hongliang

2017-09-01

Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy. To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs. The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm. The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator. Copyright © 2016 John Wiley & Sons, Ltd.

Not all triads are created equal: further support for the importance of visual and semantic proximity in object identification.

PubMed

Schweizer, Tom A; Dixon, Mike J; Desmarais, Geneviève; Smith, Stephen D

2002-01-01

Identification deficits were investigated in ELM, a temporal lobe stroke patient with category-specific deficits. We replicated previous work done on FS, a patient with category specific deficits as a result of herpes viral encephalitis. ELM was tested using novel, computer generated shapes that were paired with artifact labels. We paired semantically close or disparate labels to shapes and ELM attempted to learn these pairings. Overall, ELM's shape-label confusions were most detrimentally affected when we used labels that referred to objects that were visually and semantically close. However, as with FS, ELM had as many errors when shapes were paired with the labels "donut," "tire," and "washer" as he did when they were paired with visually and semantically close artifact labels. Two explanations are put forth to account for the anomalous performance by both patients on the triad of donut-tire-washer.

Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology

NASA Astrophysics Data System (ADS)

Hashim, Roslan; Roy, Chandrabhushan; Motamedi, Shervin; Shamshirband, Shahaboddin; Petković, Dalibor; Gocic, Milan; Lee, Siew Cheng

2016-05-01

Rainfall is a complex atmospheric process that varies over time and space. Researchers have used various empirical and numerical methods to enhance estimation of rainfall intensity. We developed a novel prediction model in this study, with the emphasis on accuracy to identify the most significant meteorological parameters having effect on rainfall. For this, we used five input parameters: wet day frequency (dwet), vapor pressure (e̅a), and maximum and minimum air temperatures (Tmax and Tmin) as well as cloud cover (cc). The data were obtained from the Indian Meteorological Department for the Patna city, Bihar, India. Further, a type of soft-computing method, known as the adaptive-neuro-fuzzy inference system (ANFIS), was applied to the available data. In this respect, the observation data from 1901 to 2000 were employed for testing, validating, and estimating monthly rainfall via the simulated model. In addition, the ANFIS process for variable selection was implemented to detect the predominant variables affecting the rainfall prediction. Finally, the performance of the model was compared to other soft-computing approaches, including the artificial neural network (ANN), support vector machine (SVM), extreme learning machine (ELM), and genetic programming (GP). The results revealed that ANN, ELM, ANFIS, SVM, and GP had R2 of 0.9531, 0.9572, 0.9764, 0.9525, and 0.9526, respectively. Therefore, we conclude that the ANFIS is the best method among all to predict monthly rainfall. Moreover, dwet was found to be the most influential parameter for rainfall prediction, and the best predictor of accuracy. This study also identified sets of two and three meteorological parameters that show the best predictions.

Comparative Study on Interaction of Form and Motion Processing Streams by Applying Two Different Classifiers in Mechanism for Recognition of Biological Movement

PubMed Central

2014-01-01

Research on psychophysics, neurophysiology, and functional imaging shows particular representation of biological movements which contains two pathways. The visual perception of biological movements formed through the visual system called dorsal and ventral processing streams. Ventral processing stream is associated with the form information extraction; on the other hand, dorsal processing stream provides motion information. Active basic model (ABM) as hierarchical representation of the human object had revealed novelty in form pathway due to applying Gabor based supervised object recognition method. It creates more biological plausibility along with similarity with original model. Fuzzy inference system is used for motion pattern information in motion pathway creating more robustness in recognition process. Besides, interaction of these paths is intriguing and many studies in various fields considered it. Here, the interaction of the pathways to get more appropriated results has been investigated. Extreme learning machine (ELM) has been implied for classification unit of this model, due to having the main properties of artificial neural networks, but crosses from the difficulty of training time substantially diminished in it. Here, there will be a comparison between two different configurations, interactions using synergetic neural network and ELM, in terms of accuracy and compatibility. PMID:25276860

Rapid detection of talcum powder in tea using FT-IR spectroscopy coupled with chemometrics

PubMed Central

Li, Xiaoli; Zhang, Yuying; He, Yong

2016-01-01

This paper investigated the feasibility of Fourier transform infrared transmission (FT-IR) spectroscopy to detect talcum powder illegally added in tea based on chemometric methods. Firstly, 210 samples of tea powder with 13 dose levels of talcum powder were prepared for FT-IR spectra acquirement. In order to highlight the slight variations in FT-IR spectra, smoothing, normalize and standard normal variate (SNV) were employed to preprocess the raw spectra. Among them, SNV preprocessing had the best performance with high correlation of prediction (RP = 0.948) and low root mean square error of prediction (RMSEP = 0.108) of partial least squares (PLS) model. Then 18 characteristic wavenumbers were selected based on a hybrid of backward interval partial least squares (biPLS) regression, competitive adaptive reweighted sampling (CARS) algorithm and successive projections algorithm (SPA). These characteristic wavenumbers only accounted for 0.64% of the full wavenumbers. Following that, 18 characteristic wavenumbers were used to build linear and nonlinear determination models by PLS regression and extreme learning machine (ELM), respectively. The optimal model with RP = 0.963 and RMSEP = 0.137 was achieved by ELM algorithm. These results demonstrated that FT-IR spectroscopy with chemometrics could be used successfully to detect talcum powder in tea. PMID:27468701

Edge profile analysis of Joint European Torus (JET) Thomson scattering data: Quantifying the systematic error due to edge localised mode synchronisation.

PubMed

Leyland, M J; Beurskens, M N A; Flanagan, J C; Frassinetti, L; Gibson, K J; Kempenaars, M; Maslov, M; Scannell, R

2016-01-01

The Joint European Torus (JET) high resolution Thomson scattering (HRTS) system measures radial electron temperature and density profiles. One of the key capabilities of this diagnostic is measuring the steep pressure gradient, termed the pedestal, at the edge of JET plasmas. The pedestal is susceptible to limiting instabilities, such as Edge Localised Modes (ELMs), characterised by a periodic collapse of the steep gradient region. A common method to extract the pedestal width, gradient, and height, used on numerous machines, is by performing a modified hyperbolic tangent (mtanh) fit to overlaid profiles selected from the same region of the ELM cycle. This process of overlaying profiles, termed ELM synchronisation, maximises the number of data points defining the pedestal region for a given phase of the ELM cycle. When fitting to HRTS profiles, it is necessary to incorporate the diagnostic radial instrument function, particularly important when considering the pedestal width. A deconvolved fit is determined by a forward convolution method requiring knowledge of only the instrument function and profiles. The systematic error due to the deconvolution technique incorporated into the JET pedestal fitting tool has been documented by Frassinetti et al. [Rev. Sci. Instrum. 83, 013506 (2012)]. This paper seeks to understand and quantify the systematic error introduced to the pedestal width due to ELM synchronisation. Synthetic profiles, generated with error bars and point-to-point variation characteristic of real HRTS profiles, are used to evaluate the deviation from the underlying pedestal width. We find on JET that the ELM synchronisation systematic error is negligible in comparison to the statistical error when assuming ten overlaid profiles (typical for a pre-ELM fit to HRTS profiles). This confirms that fitting a mtanh to ELM synchronised profiles is a robust and practical technique for extracting the pedestal structure.

A Smart High Accuracy Silicon Piezoresistive Pressure Sensor Temperature Compensation System

PubMed Central

Zhou, Guanwu; Zhao, Yulong; Guo, Fangfang; Xu, Wenju

2014-01-01

Theoretical analysis in this paper indicates that the accuracy of a silicon piezoresistive pressure sensor is mainly affected by thermal drift, and varies nonlinearly with the temperature. Here, a smart temperature compensation system to reduce its effect on accuracy is proposed. Firstly, an effective conditioning circuit for signal processing and data acquisition is designed. The hardware to implement the system is fabricated. Then, a program is developed on LabVIEW which incorporates an extreme learning machine (ELM) as the calibration algorithm for the pressure drift. The implementation of the algorithm was ported to a micro-control unit (MCU) after calibration in the computer. Practical pressure measurement experiments are carried out to verify the system's performance. The temperature compensation is solved in the interval from −40 to 85 °C. The compensated sensor is aimed at providing pressure measurement in oil-gas pipelines. Compared with other algorithms, ELM acquires higher accuracy and is more suitable for batch compensation because of its higher generalization and faster learning speed. The accuracy, linearity, zero temperature coefficient and sensitivity temperature coefficient of the tested sensor are 2.57% FS, 2.49% FS, 8.1 × 10−5/°C and 29.5 × 10−5/°C before compensation, and are improved to 0.13%FS, 0.15%FS, 1.17 × 10−5/°C and 2.1 × 10−5/°C respectively, after compensation. The experimental results demonstrate that the proposed system is valid for the temperature compensation and high accuracy requirement of the sensor. PMID:25006998

A Novel Gravity Compensation Method for High Precision Free-INS Based on “Extreme Learning Machine”

PubMed Central

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

2016-01-01

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. PMID:27916856

Use of a Neural Net to Model the Impact of Optical Coherence Tomography Abnormalities on Vision in Age-related Macular Degeneration.

PubMed

Aslam, Tariq M; Zaki, Haider R; Mahmood, Sajjad; Ali, Zaria C; Ahmad, Nur A; Thorell, Mariana R; Balaskas, Konstantinos

2018-01-01

To develop a neural network for the estimation of visual acuity from optical coherence tomography (OCT) images of patients with neovascular age-related macular degeneration (AMD) and to demonstrate its use to model the impact of specific controlled OCT changes on vision. Artificial intelligence (neural network) study. We assessed 1400 OCT scans of patients with neovascular AMD. Fifteen physical features for each eligible OCT, as well as patient age, were used as input data and corresponding recorded visual acuity as the target data to train, validate, and test a supervised neural network. We then applied this network to model the impact on acuity of defined OCT changes in subretinal fluid, subretinal hyperreflective material, and loss of external limiting membrane (ELM) integrity. A total of 1210 eligible OCT scans were analyzed, resulting in 1210 data points, which were each 16-dimensional. A 10-layer feed-forward neural network with 1 hidden layer of 10 neurons was trained to predict acuity and demonstrated a root mean square error of 8.2 letters for predicted compared to actual visual acuity and a mean regression coefficient of 0.85. A virtual model using this network demonstrated the relationship of visual acuity to specific, programmed changes in OCT characteristics. When ELM is intact, there is a shallow decline in acuity with increasing subretinal fluid but a much steeper decline with equivalent increasing subretinal hyperreflective material. When ELM is not intact, all visual acuities are reduced. Increasing subretinal hyperreflective material or subretinal fluid in this circumstance reduces vision further still, but with a smaller gradient than when ELM is intact. The supervised machine learning neural network developed is able to generate an estimated visual acuity value from OCT images in a population of patients with AMD. These findings should be of clinical and research interest in macular degeneration, for example in estimating visual prognosis or highlighting the importance of developing treatments targeting more visually destructive pathologies. Copyright © 2017 Elsevier Inc. All rights reserved.

Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework.

PubMed

Yin, X-X; Zhang, Y; Cao, J; Wu, J-L; Hadjiloucas, S

2016-12-01

We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Impact of a Comprehensive Tier I Core Kindergarten Program on the Achievement of Students at Risk in Mathematics

ERIC Educational Resources Information Center

Clarke, Ben; Smolkowski, Keith; Baker, Scott K.; Fien, Hank; Doabler, Christian T.; Chard, David J.

2011-01-01

This study examined the efficacy of a core kindergarten mathematics program, ELM, a 120-lesson comprehensive curriculum providing instruction in (a) number operations, (b) geometry, (c) measurement, and (d) vocabulary. ELM is designed to address the learning needs of all students, including at-risk students in the general education or Tier I…

Some Problems in German to English Machine Translation

DTIC Science & Technology

1974-12-01

fron Benanti^e is a slippery business, especially when I have just clalwsd to subscribe to the idea that the structure of an utterance is intinately...from the English translation on page 15, the example paragraph can be divided Into elm 134 sections. These diviaions can be characterized at

Overview of progress in European medium sized tokamaks towards an integrated plasma-edge/wall solution

NASA Astrophysics Data System (ADS)

Meyer, H.; Eich, T.; Beurskens, M.; Coda, S.; Hakola, A.; Martin, P.; Adamek, J.; Agostini, M.; Aguiam, D.; Ahn, J.; Aho-Mantila, L.; Akers, R.; Albanese, R.; Aledda, R.; Alessi, E.; Allan, S.; Alves, D.; Ambrosino, R.; Amicucci, L.; Anand, H.; Anastassiou, G.; Andrèbe, Y.; Angioni, C.; Apruzzese, G.; Ariola, M.; Arnichand, H.; Arter, W.; Baciero, A.; Barnes, M.; Barrera, L.; Behn, R.; Bencze, A.; Bernardo, J.; Bernert, M.; Bettini, P.; Bilková, P.; Bin, W.; Birkenmeier, G.; Bizarro, J. P. S.; Blanchard, P.; Blanken, T.; Bluteau, M.; Bobkov, V.; Bogar, O.; Böhm, P.; Bolzonella, T.; Boncagni, L.; Botrugno, A.; Bottereau, C.; Bouquey, F.; Bourdelle, C.; Brémond, S.; Brezinsek, S.; Brida, D.; Brochard, F.; Buchanan, J.; Bufferand, H.; Buratti, P.; Cahyna, P.; Calabrò, G.; Camenen, Y.; Caniello, R.; Cannas, B.; Canton, A.; Cardinali, A.; Carnevale, D.; Carr, M.; Carralero, D.; Carvalho, P.; Casali, L.; Castaldo, C.; Castejón, F.; Castro, R.; Causa, F.; Cavazzana, R.; Cavedon, M.; Cecconello, M.; Ceccuzzi, S.; Cesario, R.; Challis, C. D.; Chapman, I. T.; Chapman, S.; Chernyshova, M.; Choi, D.; Cianfarani, C.; Ciraolo, G.; Citrin, J.; Clairet, F.; Classen, I.; Coelho, R.; Coenen, J. W.; Colas, L.; Conway, G.; Corre, Y.; Costea, S.; Crisanti, F.; Cruz, N.; Cseh, G.; Czarnecka, A.; D'Arcangelo, O.; De Angeli, M.; De Masi, G.; De Temmerman, G.; De Tommasi, G.; Decker, J.; Delogu, R. S.; Dendy, R.; Denner, P.; Di Troia, C.; Dimitrova, M.; D'Inca, R.; Dorić, V.; Douai, D.; Drenik, A.; Dudson, B.; Dunai, D.; Dunne, M.; Duval, B. P.; Easy, L.; Elmore, S.; Erdös, B.; Esposito, B.; Fable, E.; Faitsch, M.; Fanni, A.; Fedorczak, N.; Felici, F.; Ferreira, J.; Février, O.; Ficker, O.; Fietz, S.; Figini, L.; Figueiredo, A.; Fil, A.; Fishpool, G.; Fitzgerald, M.; Fontana, M.; Ford, O.; Frassinetti, L.; Fridström, R.; Frigione, D.; Fuchert, G.; Fuchs, C.; Furno Palumbo, M.; Futatani, S.; Gabellieri, L.; Gałązka, K.; Galdon-Quiroga, J.; Galeani, S.; Gallart, D.; Gallo, A.; Galperti, C.; Gao, Y.; Garavaglia, S.; Garcia, J.; Garcia-Carrasco, A.; Garcia-Lopez, J.; Garcia-Munoz, M.; Gardarein, J.-L.; Garzotti, L.; Gaspar, J.; Gauthier, E.; Geelen, P.; Geiger, B.; Ghendrih, P.; Ghezzi, F.; Giacomelli, L.; Giannone, L.; Giovannozzi, E.; Giroud, C.; Gleason González, C.; Gobbin, M.; Goodman, T. P.; Gorini, G.; Gospodarczyk, M.; Granucci, G.; Gruber, M.; Gude, A.; Guimarais, L.; Guirlet, R.; Gunn, J.; Hacek, P.; Hacquin, S.; Hall, S.; Ham, C.; Happel, T.; Harrison, J.; Harting, D.; Hauer, V.; Havlickova, E.; Hellsten, T.; Helou, W.; Henderson, S.; Hennequin, P.; Heyn, M.; Hnat, B.; Hölzl, M.; Hogeweij, D.; Honoré, C.; Hopf, C.; Horáček, J.; Hornung, G.; Horváth, L.; Huang, Z.; Huber, A.; Igitkhanov, J.; Igochine, V.; Imrisek, M.; Innocente, P.; Ionita-Schrittwieser, C.; Isliker, H.; Ivanova-Stanik, I.; Jacobsen, A. S.; Jacquet, P.; Jakubowski, M.; Jardin, A.; Jaulmes, F.; Jenko, F.; Jensen, T.; Jeppe Miki Busk, O.; Jessen, M.; Joffrin, E.; Jones, O.; Jonsson, T.; Kallenbach, A.; Kallinikos, N.; Kálvin, S.; Kappatou, A.; Karhunen, J.; Karpushov, A.; Kasilov, S.; Kasprowicz, G.; Kendl, A.; Kernbichler, W.; Kim, D.; Kirk, A.; Kjer, S.; Klimek, I.; Kocsis, G.; Kogut, D.; Komm, M.; Korsholm, S. B.; Koslowski, H. R.; Koubiti, M.; Kovacic, J.; Kovarik, K.; Krawczyk, N.; Krbec, J.; Krieger, K.; Krivska, A.; Kube, R.; Kudlacek, O.; Kurki-Suonio, T.; Labit, B.; Laggner, F. M.; Laguardia, L.; Lahtinen, A.; Lalousis, P.; Lang, P.; Lauber, P.; Lazányi, N.; Lazaros, A.; Le, H. B.; Lebschy, A.; Leddy, J.; Lefévre, L.; Lehnen, M.; Leipold, F.; Lessig, A.; Leyland, M.; Li, L.; Liang, Y.; Lipschultz, B.; Liu, Y. Q.; Loarer, T.; Loarte, A.; Loewenhoff, T.; Lomanowski, B.; Loschiavo, V. P.; Lunt, T.; Lupelli, I.; Lux, H.; Lyssoivan, A.; Madsen, J.; Maget, P.; Maggi, C.; Maggiora, R.; Magnussen, M. L.; Mailloux, J.; Maljaars, B.; Malygin, A.; Mantica, P.; Mantsinen, M.; Maraschek, M.; Marchand, B.; Marconato, N.; Marini, C.; Marinucci, M.; Markovic, T.; Marocco, D.; Marrelli, L.; Martin, Y.; Solis, J. R. Martin; Martitsch, A.; Mastrostefano, S.; Mattei, M.; Matthews, G.; Mavridis, M.; Mayoral, M.-L.; Mazon, D.; McCarthy, P.; McAdams, R.; McArdle, G.; McCarthy, P.; McClements, K.; McDermott, R.; McMillan, B.; Meisl, G.; Merle, A.; Meyer, O.; Milanesio, D.; Militello, F.; Miron, I. G.; Mitosinkova, K.; Mlynar, J.; Mlynek, A.; Molina, D.; Molina, P.; Monakhov, I.; Morales, J.; Moreau, D.; Morel, P.; Moret, J.-M.; Moro, A.; Moulton, D.; Müller, H. W.; Nabais, F.; Nardon, E.; Naulin, V.; Nemes-Czopf, A.; Nespoli, F.; Neu, R.; Nielsen, A. H.; Nielsen, S. K.; Nikolaeva, V.; Nimb, S.; Nocente, M.; Nouailletas, R.; Nowak, S.; Oberkofler, M.; Oberparleiter, M.; Ochoukov, R.; Odstrčil, T.; Olsen, J.; Omotani, J.; O'Mullane, M. G.; Orain, F.; Osterman, N.; Paccagnella, R.; Pamela, S.; Pangione, L.; Panjan, M.; Papp, G.; Papřok, R.; Parail, V.; Parra, F. I.; Pau, A.; Pautasso, G.; Pehkonen, S.-P.; Pereira, A.; Perelli Cippo, E.; Pericoli Ridolfini, V.; Peterka, M.; Petersson, P.; Petrzilka, V.; Piovesan, P.; Piron, C.; Pironti, A.; Pisano, F.; Pisokas, T.; Pitts, R.; Ploumistakis, I.; Plyusnin, V.; Pokol, G.; Poljak, D.; Pölöskei, P.; Popovic, Z.; Pór, G.; Porte, L.; Potzel, S.; Predebon, I.; Preynas, M.; Primc, G.; Pucella, G.; Puiatti, M. E.; Pütterich, T.; Rack, M.; Ramogida, G.; Rapson, C.; Rasmussen, J. Juul; Rasmussen, J.; Rattá, G. A.; Ratynskaia, S.; Ravera, G.; Réfy, D.; Reich, M.; Reimerdes, H.; Reimold, F.; Reinke, M.; Reiser, D.; Resnik, M.; Reux, C.; Ripamonti, D.; Rittich, D.; Riva, G.; Rodriguez-Ramos, M.; Rohde, V.; Rosato, J.; Ryter, F.; Saarelma, S.; Sabot, R.; Saint-Laurent, F.; Salewski, M.; Salmi, A.; Samaddar, D.; Sanchis-Sanchez, L.; Santos, J.; Sauter, O.; Scannell, R.; Scheffer, M.; Schneider, M.; Schneider, B.; Schneider, P.; Schneller, M.; Schrittwieser, R.; Schubert, M.; Schweinzer, J.; Seidl, J.; Sertoli, M.; Šesnić, S.; Shabbir, A.; Shalpegin, A.; Shanahan, B.; Sharapov, S.; Sheikh, U.; Sias, G.; Sieglin, B.; Silva, C.; Silva, A.; Silva Fuglister, M.; Simpson, J.; Snicker, A.; Sommariva, C.; Sozzi, C.; Spagnolo, S.; Spizzo, G.; Spolaore, M.; Stange, T.; Stejner Pedersen, M.; Stepanov, I.; Stober, J.; Strand, P.; Šušnjara, A.; Suttrop, W.; Szepesi, T.; Tál, B.; Tala, T.; Tamain, P.; Tardini, G.; Tardocchi, M.; Teplukhina, A.; Terranova, D.; Testa, D.; Theiler, C.; Thornton, A.; Tolias, P.; Tophøj, L.; Treutterer, W.; Trevisan, G. L.; Tripsky, M.; Tsironis, C.; Tsui, C.; Tudisco, O.; Uccello, A.; Urban, J.; Valisa, M.; Vallejos, P.; Valovic, M.; Van den Brand, H.; Vanovac, B.; Varoutis, S.; Vartanian, S.; Vega, J.; Verdoolaege, G.; Verhaegh, K.; Vermare, L.; Vianello, N.; Vicente, J.; Viezzer, E.; Vignitchouk, L.; Vijvers, W. A. J.; Villone, F.; Viola, B.; Vlahos, L.; Voitsekhovitch, I.; Vondráček, P.; Vu, N. M. T.; Wagner, D.; Walkden, N.; Wang, N.; Wauters, T.; Weiland, M.; Weinzettl, V.; Westerhof, E.; Wiesenberger, M.; Willensdorfer, M.; Wischmeier, M.; Wodniak, I.; Wolfrum, E.; Yadykin, D.; Zagórski, R.; Zammuto, I.; Zanca, P.; Zaplotnik, R.; Zestanakis, P.; Zhang, W.; Zoletnik, S.; Zuin, M.; ASDEX Upgrade, the; MAST; TCV Teams

2017-10-01

Integrating the plasma core performance with an edge and scrape-off layer (SOL) that leads to tolerable heat and particle loads on the wall is a major challenge. The new European medium size tokamak task force (EU-MST) coordinates research on ASDEX Upgrade (AUG), MAST and TCV. This multi-machine approach within EU-MST, covering a wide parameter range, is instrumental to progress in the field, as ITER and DEMO core/pedestal and SOL parameters are not achievable simultaneously in present day devices. A two prong approach is adopted. On the one hand, scenarios with tolerable transient heat and particle loads, including active edge localised mode (ELM) control are developed. On the other hand, divertor solutions including advanced magnetic configurations are studied. Considerable progress has been made on both approaches, in particular in the fields of: ELM control with resonant magnetic perturbations (RMP), small ELM regimes, detachment onset and control, as well as filamentary scrape-off-layer transport. For example full ELM suppression has now been achieved on AUG at low collisionality with n  =  2 RMP maintaining good confinement {{H}\\text{H≤ft(98,\\text{y}2\\right)}}≈ 0.95 . Advances have been made with respect to detachment onset and control. Studies in advanced divertor configurations (Snowflake, Super-X and X-point target divertor) shed new light on SOL physics. Cross field filamentary transport has been characterised in a wide parameter regime on AUG, MAST and TCV progressing the theoretical and experimental understanding crucial for predicting first wall loads in ITER and DEMO. Conditions in the SOL also play a crucial role for ELM stability and access to small ELM regimes. In the future we will refer to the author list of the paper as the EUROfusion MST1 Team.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jia, J; Cao, R; Pei, X

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 themore » 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.« less

Novel images extraction model using improved delay vector variance feature extraction and multi-kernel neural network for EEG detection and prediction.

PubMed

Ge, Jing; Zhang, Guoping

2015-01-01

Advanced intelligent methodologies could help detect and predict diseases from the EEG signals in cases the manual analysis is inefficient available, for instance, the epileptic seizures detection and prediction. This is because the diversity and the evolution of the epileptic seizures make it very difficult in detecting and identifying the undergoing disease. Fortunately, the determinism and nonlinearity in a time series could characterize the state changes. Literature review indicates that the Delay Vector Variance (DVV) could examine the nonlinearity to gain insight into the EEG signals but very limited work has been done to address the quantitative DVV approach. Hence, the outcomes of the quantitative DVV should be evaluated to detect the epileptic seizures. To develop a new epileptic seizure detection method based on quantitative DVV. This new epileptic seizure detection method employed an improved delay vector variance (IDVV) to extract the nonlinearity value as a distinct feature. Then a multi-kernel functions strategy was proposed in the extreme learning machine (ELM) network to provide precise disease detection and prediction. The nonlinearity is more sensitive than the energy and entropy. 87.5% overall accuracy of recognition and 75.0% overall accuracy of forecasting were achieved. The proposed IDVV and multi-kernel ELM based method was feasible and effective for epileptic EEG detection. Hence, the newly proposed method has importance for practical applications.

Effect of nitrogen seeding on the energy losses and on the time scales of the electron temperature and density collapse of type-I ELMs in JET with the ITER-like wall

NASA Astrophysics Data System (ADS)

Frassinetti, L.; Dodt, D.; Beurskens, M. N. A.; Sirinelli, A.; Boom, J. E.; Eich, T.; Flanagan, J.; Giroud, C.; Jachmich, M. S.; Kempenaars, M.; Lomas, P.; Maddison, G.; Maggi, C.; Neu, R.; Nunes, I.; Perez von Thun, C.; Sieglin, B.; Stamp, M.; Contributors, JET-EFDA

2015-02-01

The baseline type-I ELMy H-mode scenario has been re-established in JET with the new tungsten MKII-HD divertor and beryllium on the main wall (hereafter called the ITER-like wall, JET-ILW). The first JET-ILW results show that the confinement is degraded by 20-30% in the baseline scenarios compared to the previous carbon wall JET (JET-C) plasmas. The degradation is mainly driven by the reduction in the pedestal temperature. Stored energies and pedestal temperature comparable to the JET-C have been obtained to date in JET-ILW baseline plasmas only in the high triangularity shape using N2 seeding. This work compares the energy losses during ELMs and the corresponding time scales of the temperature and density collapse in JET-ILW baseline plasmas with and without N2 seeding with similar JET-C baseline plasmas. ELMs in the JET-ILW differ from those with the carbon wall both in terms of time scales and energy losses. The ELM time scale, defined as the time to reach the minimum pedestal temperature soon after the ELM collapse, is ˜2 ms in the JET-ILW and lower than 1 ms in the JET-C. The energy losses are in the range ΔWELM/Wped ≈ 7-12% in the JET-ILW and ΔWELM/Wped ≈ 10-20% in JET-C, and fit relatively well with earlier multi-machine empirical scalings of ΔWELM/Wped with collisionality. The time scale of the ELM collapse seems to be related to the pedestal collisionality. Most of the non-seeded JET-ILW ELMs are followed by a further energy drop characterized by a slower time scale ˜8-10 ms (hereafter called slow transport events), that can lead to losses in the range ΔWslow/Wped ≈ 15-22%, slightly larger than the losses in JET-C. The N2 seeding in JET-ILW significantly affects the ELMs. The JET-ILW plasmas with N2 seeding are characterized by ELM energy losses and time scales similar to the JET-C and by the absence of the slow transport events.

[Remote intelligent Brunnstrom assessment system for upper limb rehabilitation for post-stroke based on extreme learning machine].

PubMed

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.

An efficient diagnosis system for Parkinson's disease using kernel-based extreme learning machine with subtractive clustering features weighting approach.

PubMed

Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Zhao, Xue-Hua

2014-01-01

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.

An Efficient Diagnosis System for Parkinson's Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

PubMed Central

Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Zhao, Xue-Hua

2014-01-01

A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance. PMID:25484912

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

PubMed Central

Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references. PMID:29581722

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

PubMed

Yu, Yunlong; Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks

NASA Astrophysics Data System (ADS)

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine; Hissel, Daniel

2016-08-01

Proton Exchange Membrane Fuel Cell (PEMFC) is considered the most versatile among available fuel cell technologies, which qualify for diverse applications. However, the large-scale industrial deployment of PEMFCs is limited due to their short life span and high exploitation costs. Therefore, ensuring fuel cell service for a long duration is of vital importance, which has led to Prognostics and Health Management of fuel cells. More precisely, prognostics of PEMFC is major area of focus nowadays, which aims at identifying degradation of PEMFC stack at early stages and estimating its Remaining Useful Life (RUL) for life cycle management. This paper presents a data-driven approach for prognostics of PEMFC stack using an ensemble of constraint based Summation Wavelet- Extreme Learning Machine (SW-ELM) models. This development aim at improving the robustness and applicability of prognostics of PEMFC for an online application, with limited learning data. The proposed approach is applied to real data from two different PEMFC stacks and compared with ensembles of well known connectionist algorithms. The results comparison on long-term prognostics of both PEMFC stacks validates our proposition.

Laser projection positioning of spatial contour curves via a galvanometric scanner

NASA Astrophysics Data System (ADS)

Tu, Junchao; Zhang, Liyan

2018-04-01

The technology of laser projection positioning is widely applied in advanced manufacturing fields (e.g. composite plying, parts location and installation). In order to use it better, a laser projection positioning (LPP) system is designed and implemented. Firstly, the LPP system is built by a laser galvanometric scanning (LGS) system and a binocular vision system. Applying Single-hidden Layer Feed-forward Neural Network (SLFN), the system model is constructed next. Secondly, the LGS system and the binocular system, which are respectively independent, are integrated through a datadriven calibration method based on extreme learning machine (ELM) algorithm. Finally, a projection positioning method is proposed within the framework of the calibrated SLFN system model. A well-designed experiment is conducted to verify the viability and effectiveness of the proposed system. In addition, the accuracy of projection positioning are evaluated to show that the LPP system can achieves the good localization effect.

A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being.

PubMed

Ravindran, Sindhu; Jambek, Asral Bahari; Muthusamy, Hariharan; Neoh, Siew-Chin

2015-01-01

A novel clinical decision support system is proposed in this paper for evaluating the fetal well-being from the cardiotocogram (CTG) dataset through an Improved Adaptive Genetic Algorithm (IAGA) and Extreme Learning Machine (ELM). IAGA employs a new scaling technique (called sigma scaling) to avoid premature convergence and applies adaptive crossover and mutation techniques with masking concepts to enhance population diversity. Also, this search algorithm utilizes three different fitness functions (two single objective fitness functions and multi-objective fitness function) to assess its performance. The classification results unfold that promising classification accuracy of 94% is obtained with an optimal feature subset using IAGA. Also, the classification results are compared with those of other Feature Reduction techniques to substantiate its exhaustive search towards the global optimum. Besides, five other benchmark datasets are used to gauge the strength of the proposed IAGA algorithm.

Kinase Identification with Supervised Laplacian Regularized Least Squares

PubMed Central

Zhang, He; Wang, Minghui

2015-01-01

Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms. PMID:26448296

Short-Term State Forecasting-Based Optimal Voltage Regulation in Distribution Systems: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Rui; Jiang, Huaiguang; Zhang, Yingchen

2017-05-17

A novel short-term state forecasting-based optimal power flow (OPF) approach for distribution system voltage regulation is proposed in this paper. An extreme learning machine (ELM) based state forecaster is developed to accurately predict system states (voltage magnitudes and angles) in the near future. Based on the forecast system states, a dynamically weighted three-phase AC OPF problem is formulated to minimize the voltage violations with higher penalization on buses which are forecast to have higher voltage violations in the near future. By solving the proposed OPF problem, the controllable resources in the system are optimally coordinated to alleviate the potential severemore » voltage violations and improve the overall voltage profile. The proposed approach has been tested in a 12-bus distribution system and simulation results are presented to demonstrate the performance of the proposed approach.« less

Kinase Identification with Supervised Laplacian Regularized Least Squares.

PubMed

Li, Ao; Xu, Xiaoyi; Zhang, He; Wang, Minghui

2015-01-01

Phosphorylation is catalyzed by protein kinases and is irreplaceable in regulating biological processes. Identification of phosphorylation sites with their corresponding kinases contributes to the understanding of molecular mechanisms. Mass spectrometry analysis of phosphor-proteomes generates a large number of phosphorylated sites. However, experimental methods are costly and time-consuming, and most phosphorylation sites determined by experimental methods lack kinase information. Therefore, computational methods are urgently needed to address the kinase identification problem. To this end, we propose a new kernel-based machine learning method called Supervised Laplacian Regularized Least Squares (SLapRLS), which adopts a new method to construct kernels based on the similarity matrix and minimizes both structure risk and overall inconsistency between labels and similarities. The results predicted using both Phospho.ELM and an additional independent test dataset indicate that SLapRLS can more effectively identify kinases compared to other existing algorithms.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dr. Ricardo Maqueda; Dr. Fred M. Levinton

Nova Photonics, Inc. has a collaborative effort at the National Spherical Torus Experiment (NSTX). This collaboration, based on fast imaging of visible phenomena, has provided key insights on edge turbulence, intermittency, and edge phenomena such as edge localized modes (ELMs) and multi-faceted axisymmetric radiation from the edge (MARFE). Studies have been performed in all these areas. The edge turbulence/intermittency studies make use of the Gas Puff Imaging diagnostic developed by the Principal Investigator (Ricardo Maqueda) together with colleagues from PPPL. This effort is part of the International Tokamak Physics Activity (ITPA) edge, scrape-off layer and divertor group joint activity (DSOL-15:more » Inter-machine comparison of blob characteristics). The edge turbulence/blob study has been extended from the current location near the midplane of the device to the lower divertor region of NSTX. The goal of this effort was to study turbulence born blobs in the vicinity of the X-point region and their circuit closure on divertor sheaths or high density regions in the divertor. In the area of ELMs and MARFEs we have studied and characterized the mode structure and evolution of the ELM types observed in NSTX, as well as the study of the observed interaction between MARFEs and ELMs. This interaction could have substantial implications for future devices where radiative divertor regions are required to maintain detachment from the divertor plasma facing components.« less

Inter-ELM evolution of the pedestal structures in type-I ELMy H-mode plasmas with LHW and NBI heating on EAST

NASA Astrophysics Data System (ADS)

Han, X.; Zang, Q.; Xiao, S.; Wang, T.; Hu, A.; Tian, B.; Li, D.; Zhou, H.; Zhao, J.; Hsieh, C.; Li, M.; Yan, N.; Gong, X.; Hu, L.; Xu, G.; Gao, X.; the EAST Team

2017-04-01

The evolution characteristics of type-I ELMy high-confinement mode pedestal are examined in EAST based on the recently developed Thomson scattering system. The influence of the plasma current on pedestal evolvement has been confirmed experimentally. In the higher I p case (500 kA) the pedestal height shows an increase trend until the onset of next ELM and in the lower I p cases (300 and 400 kA), however, this buildup saturates at the first ˜30% of the ELM cycle. In contrast, the width increases only during the first ˜70% of the ELM cycle and then keeps almost stable in three I p cases, but resulting in different widening size of ˜1.5, 1 and 0.5 cm for 300, 400 and 500 kA respectively. Experimental results show that the pedestal pressure width has good correlation with poloidal beta as {{{Δ }}}{{p}{{e}},\\psi }=0.16\\sqrt{{{β }}{{p}{{o}}{{l}}}}, where the fitting coefficient 0.16 is not changed with different plasma currents but a little larger than that of other machines. For each current level, the pedestal density increases while the pedestal temperature decreases. But with increasing {I}{{p}} platforms, the pedestal height prior to the ELM onset shows a near quadratic (within error bars) increase. Experimental measurements demonstrate that the decrease of {{Δ }}{W}{{E}{{L}}{{M}}} with increasing {ν }{{p}{{e}}{{d}}}* comes mostly from the reduction of the plasma temperature drop, while the pedestal density height keeps relatively stable. Additional injection of LHW has been proved to modify the pedestal structure which should be responsible for the remaining scatter of the experimental data.

Designing to Support Command and Control in Urban Firefighting

DTIC Science & Technology

2008-06-01

complex human- machine systems. Keywords: Command and control, firefighting, cognitive systems engineering, cognitive task analysis 1...Elm, W. (2000). Bootstrapping multiple converging cognitive task analysis techniques for system design. In J.M.C. Schraagen, S.F. Chipman, & V.L...Shalin, (Eds.), Cognitive Task Analysis . (pp. 317-340). Mahwah, NJ: Lawrence Erlbaum. Rasmussen, J., Pejtersen, A., Goodman, L. (1994). Cognitive

Effective Data-Driven Calibration for a Galvanometric Laser Scanning System Using Binocular Stereo Vision.

PubMed

Tu, Junchao; Zhang, Liyan

2018-01-12

A new solution to the problem of galvanometric laser scanning (GLS) system calibration is presented. Under the machine learning framework, we build a single-hidden layer feedforward neural network (SLFN）to represent the GLS system, which takes the digital control signal at the drives of the GLS system as input and the space vector of the corresponding outgoing laser beam as output. The training data set is obtained with the aid of a moving mechanism and a binocular stereo system. The parameters of the SLFN are efficiently solved in a closed form by using extreme learning machine (ELM). By quantitatively analyzing the regression precision with respective to the number of hidden neurons in the SLFN, we demonstrate that the proper number of hidden neurons can be safely chosen from a broad interval to guarantee good generalization performance. Compared to the traditional model-driven calibration, the proposed calibration method does not need a complex modeling process and is more accurate and stable. As the output of the network is the space vectors of the outgoing laser beams, it costs much less training time and can provide a uniform solution to both laser projection and 3D-reconstruction, in contrast with the existing data-driven calibration method which only works for the laser triangulation problem. Calibration experiment, projection experiment and 3D reconstruction experiment are respectively conducted to test the proposed method, and good results are obtained.

Learning a single-hidden layer feedforward neural network using a rank correlation-based strategy with application to high dimensional gene expression and proteomic spectra datasets in cancer detection.

PubMed

Belciug, Smaranda; Gorunescu, Florin

2018-06-08

Methods based on microarrays (MA), mass spectrometry (MS), and machine learning (ML) algorithms have evolved rapidly in recent years, allowing for early detection of several types of cancer. A pitfall of these approaches, however, is the overfitting of data due to large number of attributes and small number of instances -- a phenomenon known as the 'curse of dimensionality'. A potentially fruitful idea to avoid this drawback is to develop algorithms that combine fast computation with a filtering module for the attributes. The goal of this paper is to propose a statistical strategy to initiate the hidden nodes of a single-hidden layer feedforward neural network (SLFN) by using both the knowledge embedded in data and a filtering mechanism for attribute relevance. In order to attest its feasibility, the proposed model has been tested on five publicly available high-dimensional datasets: breast, lung, colon, and ovarian cancer regarding gene expression and proteomic spectra provided by cDNA arrays, DNA microarray, and MS. The novel algorithm, called adaptive SLFN (aSLFN), has been compared with four major classification algorithms: traditional ELM, radial basis function network (RBF), single-hidden layer feedforward neural network trained by backpropagation algorithm (BP-SLFN), and support vector-machine (SVM). Experimental results showed that the classification performance of aSLFN is competitive with the comparison models. Copyright © 2018. Published by Elsevier Inc.

A Fast Visible Camera Divertor-Imaging Diagnostic on DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roquemore, A; Maingi, R; Lasnier, C

2007-06-19

In recent campaigns, the Photron Ultima SE fast framing camera has proven to be a powerful diagnostic when applied to imaging divertor phenomena on the National Spherical Torus Experiment (NSTX). Active areas of NSTX divertor research addressed with the fast camera include identification of types of EDGE Localized Modes (ELMs)[1], dust migration, impurity behavior and a number of phenomena related to turbulence. To compare such edge and divertor phenomena in low and high aspect ratio plasmas, a multi-institutional collaboration was developed for fast visible imaging on NSTX and DIII-D. More specifically, the collaboration was proposed to compare the NSTX smallmore » type V ELM regime [2] and the residual ELMs observed during Type I ELM suppression with external magnetic perturbations on DIII-D[3]. As part of the collaboration effort, the Photron camera was installed recently on DIII-D with a tangential view similar to the view implemented on NSTX, enabling a direct comparison between the two machines. The rapid implementation was facilitated by utilization of the existing optics that coupled the visible spectral output from the divertor vacuum ultraviolet UVTV system, which has a view similar to the view developed for the divertor tangential TV camera [4]. A remote controlled filter wheel was implemented, as was the radiation shield required for the DIII-D installation. The installation and initial operation of the camera are described in this paper, and the first images from the DIII-D divertor are presented.« less

Multi-channel EEG-based sleep stage classification with joint collaborative representation and multiple kernel learning.

PubMed

Shi, Jun; Liu, Xiao; Li, Yan; Zhang, Qi; Li, Yingjie; Ying, Shihui

2015-10-30

Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state-of-the-art unsupervised feature learning method suitable for EEG feature representation. Collaborative representation (CR) is an effective data coding method used as a classifier. Here we use CR as a data representation method to learn features from the EEG signal. A joint collaboration model is established to develop a multi-view learning algorithm, and generate joint CR (JCR) codes to fuse and represent multi-channel EEG signals. A two-stage multi-view learning-based sleep staging framework is then constructed, in which JCR and joint sparse representation (JSR) algorithms first fuse and learning the feature representation from multi-channel EEG signals, respectively. Multi-view JCR and JSR features are then integrated and sleep stages recognized by a multiple kernel extreme learning machine (MK-ELM) algorithm with grid search. The proposed two-stage multi-view learning algorithm achieves superior performance for sleep staging. With a K-means clustering based dictionary, the mean classification accuracy, sensitivity and specificity are 81.10 ± 0.15%, 71.42 ± 0.66% and 94.57 ± 0.07%, respectively; while with the dictionary learned using the submodular optimization method, they are 80.29 ± 0.22%, 71.26 ± 0.78% and 94.38 ± 0.10%, respectively. The two-stage multi-view learning based sleep staging framework outperforms all other classification methods compared in this work, while JCR is superior to JSR. The proposed multi-view learning framework has the potential for sleep staging based on multi-channel or multi-modality polysomnography signals. Copyright © 2015 Elsevier B.V. All rights reserved.

Wants and Needs: SAMS’ Relationship With the Army

DTIC Science & Technology

2008-05-19

Experiential Learning Model (ELM) is a theory most closely attributed to David Kolb and his book, Experiential Learning : Experience...experience alone.57 This last principle is an important aspect of the Experiential Learning Model, which is the prevailing theory of adult education in use...depth to learn the theory behind current methods and techniques, and thus achieve mastery of the art o war at the tactical and operational level.” ,

Non-boronized compared with boronized operation of ASDEX Upgrade with full-tungsten plasma facing components

NASA Astrophysics Data System (ADS)

Kallenbach, A.; Dux, R.; Mayer, M.; Neu, R.; Pütterich, T.; Bobkov, V.; Fuchs, J. C.; Eich, T.; Giannone, L.; Gruber, O.; Herrmann, A.; Horton, L. D.; Maggi, C. F.; Meister, H.; Müller, H. W.; Rohde, V.; Sips, A.; Stäbler, A.; Stober, J.; ASDEX Upgrade Team

2009-04-01

After completion of the tungsten coating of all plasma facing components, ASDEX Upgrade has been operated without boronization for 1 1/2 experimental campaigns. This has allowed the study of fuel retention under conditions of relatively low D co-deposition with low-Z impurities as well as the operational space of a full-tungsten device for the unfavourable condition of a relatively high intrinsic impurity level. Restrictions in operation were caused by the central accumulation of tungsten in combination with density peaking, resulting in H-L backtransitions induced by too low separatrix power flux. Most important control parameters have been found to be the central heating power, as delivered predominantly by ECRH, and the ELM frequency, most easily controlled by gas puffing. Generally, ELMs exhibit a positive impact, with the effect of impurity flushing out of the pedestal region overbalancing the ELM-induced W source. The restrictions of plasma operation in the unboronized W machine occurred predominantly under low or medium power conditions. Under medium-high power conditions, stable operation with virtually no difference between boronized and unboronized discharges was achieved. Due to the reduced intrinsic radiation with boronization and the limited power handling capability of VPS coated divertor tiles (≈10 MW m-2), boronized operation at high heating powers was possible only with radiative cooling. To enable this, a previously developed feedback system using (thermo-)electric current measurements as approximate sensor for the divertor power flux was introduced into the standard AUG operation. To avoid the problems with reduced ELM frequency due to core plasma radiation, nitrogen was selected as radiating species since its radiative characteristic peaks at lower electron temperatures in comparison with Ne and Ar, favouring SOL and divertor radiative losses. Nitrogen seeding resulted not only in the desired divertor power load reduction but also in improved energy confinement, as well as in smaller ELMs.

Elm genetic diversity and hybridization in the presence of Dutch elm disease

Treesearch

Johanne Brunet; Raymond P. Guries

2017-01-01

The impact of Dutch elm disease (DED) on the genetic diversity of slippery elm (Ulmus rubra) is summarized and its potential impact on the genetic diversity of other North American native elms, American elm (U. americana), rock elm (U. thomasii), winged elm (U. alata), cedar elm (

Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm.

PubMed

Ren, Fulong; Cao, Peng; Li, Wei; Zhao, Dazhe; Zaiane, Osmar

2017-01-01

Diabetic retinopathy (DR) is a progressive disease, and its detection at an early stage is crucial for saving a patient's vision. An automated screening system for DR can help in reduce the chances of complete blindness due to DR along with lowering the work load on ophthalmologists. Among the earliest signs of DR are microaneurysms (MAs). However, current schemes for MA detection appear to report many false positives because detection algorithms have high sensitivity. Inevitably some non-MAs structures are labeled as MAs in the initial MAs identification step. This is a typical "class imbalance problem". Class imbalanced data has detrimental effects on the performance of conventional classifiers. In this work, we propose an ensemble based adaptive over-sampling algorithm for overcoming the class imbalance problem in the false positive reduction, and we use Boosting, Bagging, Random subspace as the ensemble framework to improve microaneurysm detection. The ensemble based over-sampling methods we proposed combine the strength of adaptive over-sampling and ensemble. The objective of the amalgamation of ensemble and adaptive over-sampling is to reduce the induction biases introduced from imbalanced data and to enhance the generalization classification performance of extreme learning machines (ELM). Experimental results show that our ASOBoost method has higher area under the ROC curve (AUC) and G-mean values than many existing class imbalance learning methods. Copyright © 2016 Elsevier Ltd. All rights reserved.

HOW to Differentiate Dutch Elm Disease from Elm Phloem Necrosis

Treesearch

Lester Paul Gibson; Arthur R. Hastings; Leon A. LeMadeliene

1981-01-01

Dutch elm disease (DED) and elm phloem necrosis are the two most serious diseases of elm in the United States (Figs. 1 and 2). Most native species of elm are susceptible to both diseases. Dutch elm disease is caused by a fungus, Ceratocystis u1mi (Buisman) C. Moreau, and is transmitted by two species of elm bark beetles-the smaller European elm bark beetle, Scolytus...

Thermal sensation prediction by soft computing methodology.

PubMed

Jović, Srđan; Arsić, Nebojša; Vilimonović, Jovana; Petković, Dalibor

2016-12-01

Thermal comfort in open urban areas is very factor based on environmental point of view. Therefore it is need to fulfill demands for suitable thermal comfort during urban planning and design. Thermal comfort can be modeled based on climatic parameters and other factors. The factors are variables and they are changed throughout the year and days. Therefore there is need to establish an algorithm for thermal comfort prediction according to the input variables. The prediction results could be used for planning of time of usage of urban areas. Since it is very nonlinear task, in this investigation was applied soft computing methodology in order to predict the thermal comfort. The main goal was to apply extreme leaning machine (ELM) for forecasting of physiological equivalent temperature (PET) values. Temperature, pressure, wind speed and irradiance were used as inputs. The prediction results are compared with some benchmark models. Based on the results ELM can be used effectively in forecasting of PET. Copyright © 2016 Elsevier Ltd. All rights reserved.

Elm genetic diversity and hybridization in the presence of Dutch elm disease

USDA-ARS?s Scientific Manuscript database

Dutch elm disease (DED) has devastated native North American elm species for more than 75 years. The impact of DED on the genetic diversity of one native elm species, U. rubra or slippery elm, is summarized and its potential impact on the genetic diversity of the other four North American native elm...

Detection of Oil Chestnuts Infected by Blue Mold Using Near-Infrared Hyperspectral Imaging Combined with Artificial Neural Networks.

PubMed

Feng, Lei; Zhu, Susu; Lin, Fucheng; Su, Zhenzhu; Yuan, Kangpei; Zhao, Yiying; He, Yong; Zhang, Chu

2018-06-15

Mildew damage is a major reason for chestnut poor quality and yield loss. In this study, a near-infrared hyperspectral imaging system in the 874⁻1734 nm spectral range was applied to detect the mildew damage to chestnuts caused by blue mold. Principal component analysis (PCA) scored images were firstly employed to qualitatively and intuitively distinguish moldy chestnuts from healthy chestnuts. Spectral data were extracted from the hyperspectral images. A successive projections algorithm (SPA) was used to select 12 optimal wavelengths. Artificial neural networks, including back propagation neural network (BPNN), evolutionary neural network (ENN), extreme learning machine (ELM), general regression neural network (GRNN) and radial basis neural network (RBNN) were used to build models using the full spectra and optimal wavelengths to distinguish moldy chestnuts. BPNN and ENN models using full spectra and optimal wavelengths obtained satisfactory performances, with classification accuracies all surpassing 99%. The results indicate the potential for the rapid and non-destructive detection of moldy chestnuts by hyperspectral imaging, which would help to develop online detection system for healthy and blue mold infected chestnuts.

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

PubMed Central

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

Dutch elm disease pathogen transmission by the banded elm bark beetle Scolytus schevyrewi

Treesearch

W. R. Jacobi; R. D. Koski; J. F. Negron

2013-01-01

Dutch Elm Disease (DED) is a vascular wilt disease of Ulmus species (elms) incited in North America primarily by the exotic fungus Ophiostoma novo-ulmi. The pathogen is transmitted via root grafts and elm bark beetle vectors, including the native North American elm bark beetle, Hylurgopinus rufipes and the exotic smaller European elm bark beetle, Scolytus multistriatus...

Critical Thinking Training for Army Officers Volume One: Overview of Research Program

DTIC Science & Technology

2008-06-01

Interview respondents favored a particular model of curriculum for adult learners, the Experiential Learning Model (ELM) ( Kolb , 1984) which has been...Psychologist, 58, 697-720. Klein, G. (1999). Sources of power. Cambridge, MA: MIT Press. Kolb , D.A. (1984). Experiential learning : Experience as... theory based, comprehensive, and widely available program of training is needed. Moreover, the scientific literature on critical thinking is highly

Plasma shaping and its impact on the pedestal of ASDEX Upgrade: edge stability and inter-ELM dynamics at varied triangularity

NASA Astrophysics Data System (ADS)

Laggner, F. M.; Wolfrum, E.; Cavedon, M.; Dunne, M. G.; Birkenmeier, G.; Fischer, R.; Willensdorfer, M.; Aumayr, F.; The EUROfusion MST1 Team; The ASDEX Upgrade Team

2018-04-01

The plasma shape, in particular the triangularity (δ), impacts on the pedestal stability. A scan of δ including a variation of heating power (P heat) and gas puff was performed to study the behaviour of edge localised modes (ELMs) and the pre-ELM pedestal stability for different plasma shapes. Generally, at higher δ the pedestal top electron density (n e) is enhanced and the ELM repetition frequency (f ELM) is reduced. For all δ, the pedestal top n e is already fully established to its pre-ELM value during the initial recovery phase of the n e pedestal, which takes place immediately after the ELM crash. The lowering of the f ELM with increasing δ is related to longer pedestal recovery phases, especially the last pre-ELM phase with clamped pedestal gradients (after the recovery phases of the n e and electron temperature (T e) pedestal) is extended. In all investigated discharge intervals, the pre-ELM pedestal profiles are in agreement with peeling-ballooning (PB) theory. Over the investigated range of δ, two well-separated f ELM bands are observed in several discharge intervals. Their occurrence is linked to the inter-ELM pedestal stability. In both kinds of ELM cycles the pedestal evolves similarly, however, the ‘fast’ ELM cycle occurs before the global plasma stored energy (W MHD) increases, which then provides a stabilising effect on the pedestal, extending the inter-ELM period in the case of the ‘slow’ ELM cycle. At the end of a ‘fast’ ELM cycle the n e profile is radially shifted inwards relative to the n e profile at the end of a ‘slow’ ELM cycle, leading to a reduced pressure gradient. The appearance of two f ELM bands suggests that the pedestal becomes more likely PB unstable in certain phases of the inter-ELM evolution. Such a behaviour is possible because the evolution of the global plasma is not rigidly coupled to the evolution of the pedestal structure on the timescales of an ELM cycle.

Proceedings of the American elm restoration workshop 2016

Treesearch

Cornelia C. Pinchot; Kathleen S. Knight; Linda M. Haugen; Charles E. Flower; James M. Slavicek

2017-01-01

Proceedings from the 2016 American Elm Restoration Workshop in Lewis Center, OH. The published proceedings include 16 papers pertaining to elm pathogens, American elm ecology, and American elm reintroduction.

Modelling of edge localised modes and edge localised mode control [Modelling of ELMs and ELM control

DOE PAGES

Huijsmans, G. T. A.; Chang, C. S.; Ferraro, N.; ...

2015-02-07

Edge Localised Modes (ELMs) in ITER Q = 10 H-mode plasmas are likely to lead to large transient heat loads to the divertor. In order to avoid an ELM induced reduction of the divertor lifetime, the large ELM energy losses need to be controlled. In ITER, ELM control is foreseen using magnetic field perturbations created by in-vessel coils and the injection of small D2 pellets. ITER plasmas are characterised by low collisionality at a high density (high fraction of the Greenwald density limit). These parameters cannot simultaneously be achieved in current experiments. Thus, the extrapolation of the ELM properties andmore » the requirements for ELM control in ITER relies on the development of validated physics models and numerical simulations. Here, we describe the modelling of ELMs and ELM control methods in ITER. The aim of this paper is not a complete review on the subject of ELM and ELM control modelling but rather to describe the current status and discuss open issues.« less

Edge localized mode characteristics during edge localized mode mitigation by supersonic molecular beam injection in Korea Superconducting Tokamak Advanced Research

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, H. Y.; Hong, J. H.; Jang, J. H.

It has been reported that supersonic molecular beam injection (SMBI) is an effective means of edge localized mode (ELM) mitigation. This paper newly reports the changes in the ELM, plasma profiles, and fluctuation characteristics during ELM mitigation by SMBI in Korea Superconducting Tokamak Advanced Research. During the mitigated ELM phase, the ELM frequency increased by a factor of 2–3 and the ELM size, which was estimated from the D{sub α} amplitude, the fractional changes in the plasma-stored energy and the line-averaged electron density, and divertor heat flux during an ELM burst, decreased by a factor of 0.34–0.43. Reductions in themore » electron and ion temperatures rather than in the electron density were observed during the mitigated ELM phase. In the natural ELM phase, frequency chirping of the plasma fluctuations was observed before the ELM bursts; however, the ELM bursts occurred without changes in the plasma fluctuation frequency in the mitigated ELM phase.« less

Effect of pedestal fluctuation on ELM frequency in the EAST tokamak

NASA Astrophysics Data System (ADS)

Zhong, F. B.; Zhang, T.; Liu, Z. X.; Qu, H.; Liu, H. Q.; Li, G. Q.; Liu, Y.; Gao, W.; Duan, Y. M.; Yang, Y.; Zeng, L.; Xiang, H. M.; Geng, K. N.; Wen, F.; Zhang, S. B.; Gao, X.; the EAST Team

2018-05-01

The dependence of ELM frequency on heating power has been studied on the Experimental Advanced Superconducting Tokamak (EAST). It is found that the ELM frequency (f ELM) generally increases with the power through separatrix (P sep), indicating type-I ELM in these plasmas. However, there are two data points, named ‘abnormal ELM’ in the present paper, which have much lower f ELM than the ‘normal ELM’, while both types of ELM have similar ELM energy losses. The ‘abnormal ELM’ occurs at a phase with increased radiation power due to metal impurity influx events. The increased radiation power cannot explain the much lower f ELM for ‘abnormal ELM’, since the reduction of P sep is weaker than proportional to the observed reduction of the ELM frequency. The ‘abnormal ELM’ feature can be attributed to the enhanced amplitude of a coherent mode in the pedestal region. Comparing the pedestal evolutions for the two types of ELM with similar separatrix power P sep, it is actually found that the more pronounced pedestal coherent mode in the plasma with ‘abnormal ELM’ leads to a slower pressure pedestal recovery during the inter-ELM phase. This experimental result implies that the physical mechanism for ‘abnormal ELM’ is that the more pronounced pedestal fluctuation induces larger outward transport, slows down the pedestal evolution and leads to longer inter-ELM phase, i.e. lower ELM frequency.

HOW to Save Dutch Elm Diseased Trees by Pruning

Treesearch

J.R. Allison; G.F. Gregory

1979-01-01

Dutch elm disease (DED), caused by the fungus, Ceratocystis ulmi, is the most devastating shade tree disease in the United States. Healthy elms can become diseased by 1) elm bark beetles that carry the fungus from elm to elm, or 2) through root grafting with already infected trees. Along with wilt symptoms, streaking (sapwood discoloration), a characteristic internal...

Report from the street: elm reintroduction

Treesearch

Tom. Zetterstrom

2017-01-01

Since the introduction of Dutch elm disease (DED) to North America, heritage elm preservation was paramount, but elm restoration is held as the long-term solution. Various elm restoration efforts have been advanced and encouraged over the past half century, and all have inspired the euphoria of the "Return of the American Elm." The changing cast of characters...

Dutch elm disease

Treesearch

James W. Walters

1992-01-01

Since its discovery in the United States in 1930, Dutch elm disease has killed thousands of native elms. The three native elms, American, slippery, and rock, have little or no resistance to Dutch elm disease, but individual trees within each species vary in susceptibility to the disease. The most important of these, American elm, is scattered in upland stands but is...

Dutch Elm Disease Control: Intensive Sanitation and Survey Economics

Treesearch

William N., Jr. Cannon; Jack H. Barger; David P. Worley

1977-01-01

Recent research has shown that prompt removal of diseased elms reduces the incidence of Dutch elm disease more than sanitation practice that allows diseased elms to remain standing into the dormant season. The key to prompt removal is repeated surveys to detect diseased elms as early as possible. Intensive sanitation can save more elms and cost less than the more...

ELM mitigation with pellet ELM triggering and implications for PFCs and plasma performance in ITER

DOE PAGES

Baylor, Larry R.; Lang, P. T.; Allen, Steve L.; ...

2014-10-05

The triggering of rapid small edge localized modes (ELMs) by high frequency pellet injection has been proposed as a method to prevent large naturally occurring ELMs that can erode the ITER plasma facing components. Deuterium pellet injection has been used to successfully demonstrate the on-demand triggering of edge localized modes (ELMs) at much higher rates and with much smaller intensity than natural ELMs. The proposed hypothesis for the triggering mechanism of ELMs by pellets is the local pressure perturbation resulting from reheating of the pellet cloud that can exceed the local high-n ballooning mode threshold where the pellet is injected.more » Nonlinear MHD simulations of the pellet ELM triggering show destabilization of high-n ballooning modes by such a local pressure perturbation. A review of the recent pellet ELM triggering results from ASDEX Upgrade (AUG), DIII-D, and JET reveals that a number of uncertainties about this ELM mitigation technique still remain. These include the heat flux impact pattern on the divertor and wall from pellet triggered and natural ELMs, the necessary pellet size and injection location to reliably trigger ELMs, and the level of fueling to be expected from ELM triggering pellets and synergy with larger fueling pellets. The implications of these issues for pellet ELM mitigation in ITER and its impact on the PFCs are presented along with the design features of the pellet injection system for ITER.« less

ELM mitigation with pellet ELM triggering and implications for PFCs and plasma performance in ITER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baylor, Larry R.; Lang, P.; Allen, S. L.

2015-08-01

The triggering of rapid small edge localized modes (ELMs) by high frequency pellet injection has been proposed as a method to prevent large naturally occurring ELMs that can erode the ITER plasma facing components (PFCs). Deuterium pellet injection has been used to successfully demonstrate the on-demand triggering of edge localized modes (ELMs) at much higher rates and with much smaller intensity than natural ELMs. The proposed hypothesis for the triggering mechanism of ELMs by pellets is the local pressure perturbation resulting from reheating of the pellet cloud that can exceed the local high-n ballooning mode threshold where the pellet ismore » injected. Nonlinear MHD simulations of the pellet ELM triggering show destabilization of high-n ballooning modes by such a local pressure perturbation.A review of the recent pellet ELM triggering results from ASDEX Upgrade (AUG), DIII-D, and JET reveals that a number of uncertainties about this ELM mitigation technique still remain. These include the heat flux impact pattern on the divertor and wall from pellet triggered and natural ELMs, the necessary pellet size and injection location to reliably trigger ELMs, and the level of fueling to be expected from ELM triggering pellets and synergy with larger fueling pellets. The implications of these issues for pellet ELM mitigation in ITER and its impact on the PFCs are presented along with the design features of the pellet injection system for ITER.« less

Overview of the EUROfusion Medium Size Tokamak scientific program

NASA Astrophysics Data System (ADS)

Bernert, Matthias; Bolzonella, Tommaso; Coda, Stefano; Hakola, Antti; Meyer, Hendrik; Eurofusion Mst1 Team; Tcv Team; Mast-U Team; ASDEX Upgrade Team

2017-10-01

Under the EUROfusion MST1 program, coordinated experiments are conducted at three European medium sized tokamaks (ASDEX Upgrade, TCV and MAST-U). It complements the JET program for preparing a safe and efficient operation for ITER and DEMO. Work under MST1 benefits from cross-machine comparisons but also makes use of the unique capabilities of each device. For the 2017/2018 campaign 25 topic areas were defined targeting three main objectives: 1) Development towards an edge and wall compatible H-mode scenario with small or no ELMs. 2) Investigation of disruptions in order to achieve better predictions and improve avoidance or mitigation schemes. 3) Exploring conventional and alternative divertor configurations for future high P/R scenarios. This contribution will give an overview of the work done under MST1 exemplified by the highlight results for each top objective from the last campaigns, such as evaluation of natural small ELM scenarios, runaway mitigation and control, assessment of detachment in alternative divertor configurations and highly radiative scenarios. See author list of ``H. Meyer et al. 2017 Nucl. Fusion 57, 102014''.

Reduction of ELM Intensity on DIII-D by On-demand Triggering With High Frequency Pellet Injection and Implications for ITER

NASA Astrophysics Data System (ADS)

Baylor, L. R.

2012-10-01

Deuterium pellet injection was used on the DIII-D tokamak to successfully demonstrate for the first time the on-demand triggering of ELMs at a 10x higher rate, and with much smaller intensity, than natural edge localized modes (ELMs). The triggering of small ELMs by high frequency pellet injection has been proposed as a method to prevent large ELMs that can erode the ITER plasma facing components [1]. The demonstration was made by injecting slow (<200 m/s) 1.3 mm diameter deuterium pellets at 60 Hz from the low field side in an ITER similar plasma with 5 Hz natural ELM frequency. The input power was only slightly above the H-mode threshold. Similar non-pellet discharges had ELM energy losses up to 55 kJ (˜8% of total stored energy), while the case with pellets demonstrated ELMs with an average energy loss less than 3 kJ (<1% of the total). Total divertor ELM heat flux was reduced by more than a factor of 10. Central accumulation of Ni was significantly reduced in the pellet triggered ELM case. No significant increase in density or decrease in energy confinement was observed. Stability analysis of these discharges shows that the pedestal parameters are approaching the peeling unstable region just before a natural ELM crash. In the rapid pellet small ELM case, the pedestal conditions are well within the stable region with a narrower pedestal width observed. This narrower width is consistent with a picture in which the pellets are triggering the ELMs before the width expands to the critical ELM width. Nonlinear MHD simulations of the pellet ELM triggering show destabilization of ballooning modes by a local pressure perturbation. The implications of these results for pellet ELM pacing in ITER will be discussed. 6pt [1] P.T. Lang et al., Nucl. Fusion 44, 665 (2004).

Modeling elm growth and Dutch elm disease susceptibility

Treesearch

Alberto Santini; Luisa Ghelardini

2012-01-01

Elm susceptibility to Dutch elm disease (DED) displays strong seasonal variation. The period during which elms can become infected and express DED symptoms is generally restricted to several weeks after growth resumption in spring, although it can vary among species, provenances, and environmental conditions. The reason for this phenomenon is not understood, but the...

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

Treesearch

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....

Edge-localized-modes in tokamaks

DOE PAGES

Leonard, Anthony W.

2014-09-11

Edge-localized-modes (ELMs) are a ubiquitous feature of H-mode in tokamaks. When gradients in the H-mode transport barrier grow to exceed the MHD stability limit the ELM instability grows explosively rapidly transporting energy and particles onto open field lines and material surfaces. Though ELMs provide additional particle and impurity transport through the H-mode transport barrier, enabling steady operation, the resulting heat flux transients to plasma facing surfaces project to large amplitude in future low collisionality burning plasma tokamaks. Measurements of the ELM heat flux deposition onto material surfaces in the divertor and main chamber indicate significant broadening compared to inter-ELM heatmore » flux, with a timescale for energy deposition that is consistent with sonic ion flow and numerical simulation. Comprehensive ELM simulation is highlighting the important physics processes of ELM transport including parallel transport due to magnetic reconnection and turbulence resulting from collapse of the H-mode transport barrier. As a result, encouraging prospects for ELM control and/or suppression in future tokamaks include intrinsic modes of ELM free operation, ELM triggering with frequent small pellet injection and the application of 3D magnetic fields.« less

Edge-localized-modes in tokamaksa)

NASA Astrophysics Data System (ADS)

Leonard, A. W.

2014-09-01

Edge-localized-modes (ELMs) are a ubiquitous feature of H-mode in tokamaks. When gradients in the H-mode transport barrier grow to exceed the MHD stability limit the ELM instability grows explosively, rapidly transporting energy and particles onto open field lines and material surfaces. Though ELMs provide additional particle and impurity transport through the H-mode transport barrier, enabling steady operation, the resulting heat flux transients to plasma facing surfaces project to large amplitude in future low collisionality burning plasma tokamaks. Measurements of the ELM heat flux deposition onto material surfaces in the divertor and main chamber indicate significant broadening compared to inter-ELM heat flux, with a timescale for energy deposition that is consistent with sonic ion flow and numerical simulation. Comprehensive ELM simulation is highlighting the important physics processes of ELM transport including parallel transport due to magnetic reconnection and turbulence resulting from collapse of the H-mode transport barrier. Encouraging prospects for ELM control and/or suppression in future tokamaks include intrinsic modes of ELM free operation, ELM triggering with frequent small pellet injection and the application of 3D magnetic fields.

Operation REDWING 1956

DTIC Science & Technology

1982-08-01

AIRCRAFT UNIT Hq TAU 2 0 1 0 Eniwetok Is. 20 Mar Hq USAF Elm 0 4 0 0 Eniwetok Is. 30 Apr Hq USAF Elm 4 3 2 0 Parry Is. 30 Apr 4g Drop & Cann Elm 15 41 0...0 Eniwrv k Is. 1 5-36 16 Mar i 4 2 5-52 mqý Effects Elm 14 7 8 53 Eniwecck Is. 1 B-52 21 Mar 1 B-47 I B-57 1 B-66 2 F-84F 1 F-101 IBDA Elm 11 35 0 0...Eniwetok Is. 3 B-47 30 Apr Ionosphere Elm 6 12 0 0 Eniwetok Is. 1 C-97 30 Apr * "Navy Effects Elm 8 31 13 0 I A3D "I P2V Early Pe.ietr Elm 16 40 0 0

Physics of increased edge electron temperature and density turbulence during ELM-free QH-mode operation on DIII-D

NASA Astrophysics Data System (ADS)

Sung, C.; Rhodes, T. L.; Staebler, G. M.; Yan, Z.; McKee, G. R.; Smith, S. P.; Osborne, T. H.; Peebles, W. A.

2018-05-01

For the first time, we report increased edge electron temperature and density turbulence levels ( T˜ e and n˜ e) in Edge Localized Mode free Quiescent H-mode (ELM-free QH-mode) plasmas as compared to the ELMing time period. ELMs can severely damage plasma facing components in fusion plasma devices due to their large transient energy transport, making ELM-free operation a highly sought after goal. The QH-mode is a candidate for this goal as it is ELM-free for times limited only by hardware constraints. It is found that the driving gradients decrease during the QH-mode compared to the ELMing phase, however, a significant decrease in the ExB shearing rate is also observed that taken together is consistent with the increased turbulence. These results are significant as the prediction and control of ELM-free H-mode regimes are crucial for the operation of future fusion devices such as ITER. The changes in the linear growth rates calculated by CGYRO [Candy et al., J. Comput. Phys. 324, 73 (2016)] and the measured ExB shearing rate between ELMing and QH-mode phases are qualitatively consistent with these turbulence changes. Comparison with ELMing and 3D fields ELM suppressed H-mode finds a similar increase in T˜ e and n˜ e, however, with distinctly different origins, the increased driving gradients rather than the changes in the ExB shearing rate in 3D fields ELM suppressed the H-mode. However, linear gyrokinetic calculation results are generally consistent with the increased turbulence in both ELM-controlled discharges.

Integrated simulations of H-mode operation in ITER including core fuelling, divertor detachment and ELM control

NASA Astrophysics Data System (ADS)

Polevoi, A. R.; Loarte, A.; Dux, R.; Eich, T.; Fable, E.; Coster, D.; Maruyama, S.; Medvedev, S. Yu.; Köchl, F.; Zhogolev, V. E.

2018-05-01

ELM mitigation to avoid melting of the tungsten (W) divertor is one of the main factors affecting plasma fuelling and detachment control at full current for high Q operation in ITER. Here we derive the ITER operational space, where ELM mitigation to avoid melting of the W divertor monoblocks top surface is not required and appropriate control of W sources and radiation in the main plasma can be ensured through ELM control by pellet pacing. We apply the experimental scaling that relates the maximum ELM energy density deposited at the divertor with the pedestal parameters and this eliminates the uncertainty related with the ELM wetted area for energy deposition at the divertor and enables the definition of the ITER operating space through global plasma parameters. Our evaluation is thus based on this empirical scaling for ELM power loads together with the scaling for the pedestal pressure limit based on predictions from stability codes. In particular, our analysis has revealed that for the pedestal pressure predicted by the EPED1  +  SOLPS scaling, ELM mitigation to avoid melting of the W divertor monoblocks top surface may not be required for 2.65 T H-modes with normalized pedestal densities (to the Greenwald limit) larger than 0.5 to a level of current of 6.5–7.5 MA, which depends on assumptions on the divertor power flux during ELMs and between ELMs that expand the range of experimental uncertainties. The pellet and gas fuelling requirements compatible with control of plasma detachment, core plasma tungsten accumulation and H-mode operation (including post-ELM W transient radiation) have been assessed by 1.5D transport simulations for a range of assumptions regarding W re-deposition at the divertor including the most conservative assumption of zero prompt re-deposition. With such conservative assumptions, the post-ELM W transient radiation imposes a very stringent limit on ELM energy losses and the associated minimum required ELM frequency. Depending on W transport assumptions during the ELM, a maximum ELM frequency is also identified above which core tungsten accumulation takes place.

Ulmus crassifolia Nutt. Cedar Elm

Treesearch

John J. Stransky; Sylvia M. Bierschenk

1990-01-01

Cedar elm (Ulmus cassifolia) grows rapidly to medium or large size in the Southern United States and northeastern Mexico, where it may sometimes be called basket elm, red elm, southern rock elm, or olmo (Spanish) It usually is found on moist, limestone soils along water courses with other bottomland trees, but it also paws on dry limestone hills. The...

Elm yellows: a widespread and overlooked killer of elm trees across the United States

Treesearch

Charles E. Flower; Nancy Hayes-Plazolles; Cristina Rosa; James M. Slavicek

2017-01-01

The elm yellows phytoplasma (Candidatus Phytoplasma ulmi) is a geographically widespread pathogen that poses a significant threat to most native wild elms in North America (Ulmus americana, U. rubra, U. alata, U. crassifolia, and U. serotina) (Mäurer et al. 1993), as well as to the success of American elm...

Do mites phoretic on elm bark beetles contribute to the transmission of Dutch elm disease?

Treesearch

John Moser; Heino Konrad; Stacy Blomquist; Thomas Kirisits

2010-01-01

Dutch elm disease (DED) is a destructive vascular wilt disease of elm (Ulmus) trees caused by the introduced Ascomycete fungus Ophiostoma novo-ulmi. In Europe, this DED pathogen is transmitted by elm bark beetles in the genus Scolytus. These insects carry phoretic mites to new, suitable habitats. The aim of this...

Edge localized mode rotation and the nonlinear dynamics of filaments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Morales, J. A.; Bécoulet, M.; Garbet, X.

2016-04-15

Edge Localized Modes (ELMs) rotating precursors were reported few milliseconds before an ELM crash in several tokamak experiments. Also, the reversal of the filaments rotation at the ELM crash is commonly observed. In this article, we present a mathematical model that reproduces the rotation of the ELM precursors as well as the reversal of the filaments rotation at the ELM crash. Linear ballooning theory is used to establish a formula estimating the rotation velocity of ELM precursors. The linear study together with nonlinear magnetohydrodynamic simulations give an explanation to the rotations observed experimentally. Unstable ballooning modes, localized at the pedestal,more » grow and rotate in the electron diamagnetic direction in the laboratory reference frame. Approaching the ELM crash, this rotation decreases corresponding to the moment when the magnetic reconnection occurs. During the highly nonlinear ELM crash, the ELM filaments are cut from the main plasma due to the strong sheared mean flow that is nonlinearly generated via the Maxwell stress tensor.« less

Three-dimensional equilibria and island energy transport due to resonant magnetic perturbation edge localized mode suppression on DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, J. D.; Strait, E. J.; Nazikian, R.

In this research, we conducted experiments in the DIII-D tokamak that show that the plasma responds to resonant magnetic perturbations (RMPs) with toroidalmode numbers of n=2 and n=3 without field line reconnection, consistent with resistive magnetohydrodynamic predictions, while a strong nonlinear bifurcation is apparent when edge localized modes(ELMs) are suppressed. The magnetic response associated with this bifurcation is localized to the high field side of the machine and exhibits a dominant n=1 component despite the application of a constant amplitude, slowly toroidally rotating, n=2 applied field. The n=1 mode is born locked to the vacuum vessel wall, while the n=2more » mode is entrained to the rotating field. Based on these magnetic response measurements and Thomson scattering measurements of flattening of the electron temperature profile, it is likely that these modes are magnetic island chains near the H-mode pedestal. The reduction in ∇Te occurs near the q=4 and 5 rational surfaces, suggesting five unique islands are possible (m=8, 9, or 10 for n=2) and (m=4 or 5 for n=1). In all cases, the island width is estimated to be 2–3 cm. The Chang-Callen calculated confinement degradation due to the presence of an individual island of this size is 8%–12%, which is close to the 13%–14% measured between the ELMs and suppressed states. In conclusion, this suggests that edge tearing modes may alter the pedestal causing peeling-ballooning stability during RMP induced ELM suppression.« less

Three-dimensional equilibria and island energy transport due to resonant magnetic perturbation edge localized mode suppression on DIII-D

DOE PAGES

King, J. D.; Strait, E. J.; Nazikian, R.; ...

2015-11-16

In this research, we conducted experiments in the DIII-D tokamak that show that the plasma responds to resonant magnetic perturbations (RMPs) with toroidalmode numbers of n=2 and n=3 without field line reconnection, consistent with resistive magnetohydrodynamic predictions, while a strong nonlinear bifurcation is apparent when edge localized modes(ELMs) are suppressed. The magnetic response associated with this bifurcation is localized to the high field side of the machine and exhibits a dominant n=1 component despite the application of a constant amplitude, slowly toroidally rotating, n=2 applied field. The n=1 mode is born locked to the vacuum vessel wall, while the n=2more » mode is entrained to the rotating field. Based on these magnetic response measurements and Thomson scattering measurements of flattening of the electron temperature profile, it is likely that these modes are magnetic island chains near the H-mode pedestal. The reduction in ∇Te occurs near the q=4 and 5 rational surfaces, suggesting five unique islands are possible (m=8, 9, or 10 for n=2) and (m=4 or 5 for n=1). In all cases, the island width is estimated to be 2–3 cm. The Chang-Callen calculated confinement degradation due to the presence of an individual island of this size is 8%–12%, which is close to the 13%–14% measured between the ELMs and suppressed states. In conclusion, this suggests that edge tearing modes may alter the pedestal causing peeling-ballooning stability during RMP induced ELM suppression.« less

Three-dimensional equilibria and island energy transport due to resonant magnetic perturbation edge localized mode suppression on DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

King, J. D.; Strait, Edward J.; Nazikian, Raffi

2015-11-01

Experiments in the DIII-D tokamak show that the plasma responds to resonant magnetic perturbations (RMP) with toroidal mode numbers of n = 2 and n = 3 without field line reconnection, consistent with resistive magnetohydrodynamic predictions (MHD), while a strong nonlinear bifurcation is apparent when edge localized modes (ELM) are suppressed. The magnetic response associated with this bifurcation is localized to the high field side (HFS) of the machine and exhibits a dominant n = 1 component despite the application of a constant amplitude, slowly toroidally rotating, n = 2 applied field. The n = 1 mode is born lockedmore » to the vacuum vessel wall, while the n = 2 mode is entrained to the rotating field. Based on these magnetic response measurements, and Thomson scattering measurements of flattening of the electron temperature profile it is likely that these modes are magnetic island chains near the H-mode pedestal. The reduction in ∇T e occurs near the q = 4 and 5 rational surfaces, suggesting five unique islands are possible (m = 8, 9 or 10 for n = 2) and (m = 4 or 5 for n = 1). In all cases, the island width is estimated to be 2 ~ 3 cm. The Chang-Callen calculated confinement degradation due to the presence of an individual island of this size is 8 ~ 12%, which is close to the 13 ~ 14% measured between the ELMing and suppressed states. This suggests that edge tearing modes may alter the pedestal causing peeling ballooning stability during resonant magnetic perturbation (RMP) induced ELM suppression.« less

Elm diseases

Treesearch

John W. Peacock

1989-01-01

Dutch elm disease was found in Cleveland, Ohio, in 1930, and is now in most of the contiguous 48 states. The disease is caused by a fungus that has killed millions of wild and planted elms. Losses have been the greatest in the eastern United States. The fungus attacks all elms, but our native species, American, slippery, and rock elm have little or no resistance to the...

HOW to Identify and Manage Dutch Elm Disease

Treesearch

Linda Haugen

1998-01-01

At one time, the American elm was considered to be an ideal street tree because it was graceful, long-lived, fast growing, and tolerant of compacted soils and air pollution. Then Dutch elm disease (DED) was introduced and began devastating the elm population. Estimates of DED losses of elm in communities and woodlands across the U.S. are staggering (figure 1). Because...

Elms and Dutch elm disease: a quick overview

Treesearch

Michael Marcotrigiano

2017-01-01

In the 1930s Dutch elm disease (DED) was accidentally introduced from Europe into the United States. It had a devastating impact on American elm (Ulmus americana) and its relatives in urban and riparian environments. In the United States, the three-part pathosystem for DED is unique in that the affected elm species are North American, the pathogen originated in Asia,...

Hybridization and introgression between the exotic Siberian elm, Ulmus pumila, and the native Field elm, U. minor, in Italy

USDA-ARS?s Scientific Manuscript database

In response to the first Dutch elm disease (DED) pandemic, Siberian elm, Ulmus pumila, was planted to replace the native elm, U. minor, in Italy. The potential for hybridization between these two species is high and repeated hybridization could result in the genetic swamping of the native species an...

Dutch Elm Disease (DED) and the American Elm (Pest Alert)

Treesearch

USDA Forest Service

1999-01-01

For decades the American elm was one of our most treasured trees, gracing streets and parks of many cities with beautiful form and dense foliage. The American elm was particularly well suited to urban sites because it grows quickly, is long-lived, and is tolerant of compacted soils and air pollution. However, in most communities Dutch elm disease (DED) killed a...

Control of edge localized modes by pedestal deposited impurity in the HL-2A tokamak

NASA Astrophysics Data System (ADS)

Zhang, Y. P.; Mazon, D.; Zou, X. L.; Zhong, W. L.; Gao, J. M.; Zhang, K.; Sun, P.; Dong, C. F.; Cui, Z. Y.; Liu, Yi; Shi, Z. B.; Yu, D. L.; Cheng, J.; Jiang, M.; Xu, J. Q.; Isobe, M.; Xiao, G. L.; Chen, W.; Song, S. D.; Bai, X. Y.; Zhang, P. F.; Yuan, G. L.; Ji, X. Q.; Li, Y. G.; Zhou, Y.; Delpech, L.; Ekedahl, A.; Giruzzi, G.; Hoang, T.; Peysson, Y.; Song, X. M.; Song, X. Y.; Li, X.; Ding, X. T.; Dong, J. Q.; Yang, Q. W.; Xu, M.; Duan, X. R.; Liu, Y.; the HL-2A Team

2018-04-01

Effect of the pedestal deposited impurity on the edge-localized mode (ELM) behaviour has been observed and intensively investigated in the HL-2A tokamak. Impurities have been externally seeded by a newly developed laser blow-off (LBO) system. Both mitigation and suppression of ELMs have been realized by LBO-seeded impurity. Measurements have shown that the LBO-seeded impurity particles are mainly deposited in the pedestal region. During the ELM mitigation phase, the pedestal density fluctuation is significantly increased, indicating that the ELM mitigation may be achieved by the enhancement of the pedestal transport. The transition from ELM mitigation to ELM suppression was triggered when the number of the LBO-seeded impurity exceeds a threshold value. During the ELM suppression phase, a harmonic coherent mode (HCM) is excited by the LBO-seeded impurity, and the pedestal density fluctuation is significantly decreased, the electron density is continuously increased, implying that HCM may reduce the pedestal turbulence, suppress ELMs, increase the pedestal pressure, thus extending the Peeling-Ballooning instability limit. It has been found that the occurance of the ELM mitigation and ELM suppression closely depends on the LBO laser spot diameter.

ELM mitigation studies in JET and implications for ITER

NASA Astrophysics Data System (ADS)

de La Luna, Elena

2009-11-01

Type I edge localized modes (ELMs) remain a serious concern for ITER because of the high transient heat and particle flux that can lead to rapid erosion of the divertor plates. This has stimulated worldwide research on exploration of different methods to avoid or at least mitigate the ELM energy loss while maintaining adequate confinement. ITER will require reliable ELM control over a wide range of operating conditions, including changes in the edge safety factor, therefore a suite of different techniques is highly desirable. In JET several techniques have been demonstrated for control the frequency and size of type I ELMs, including resonant perturbations of the edge magnetic field (RMP), ELM magnetic triggering by fast vertical movement of the plasma column (``vertical kicks'') and ELM pacing using pellet injection. In this paper we present results from recent dedicated experiments in JET focusing on integrating the different ELM mitigation methods into similar plasma scenarios. Plasma parameter scans provide comparison of the performance of the different techniques in terms of both the reduction in ELM size and on the impact of each control method on plasma confinement. The compatibility of different ELM mitigation schemes has also been investigated. The plasma response to RMP and vertical kicks during the ELM mitigation phase shares common features: the reduction in ELM size (up to a factor of 3) is accompanied by a reduction in pedestal pressure (mainly due to a loss of density) with only minor (< 10%) reduction of the stored energy. Interestingly, it has been found that the combined application of RMP and kicks leads to a reduction of the threshold perturbation level (vertical displacement in the case of the kicks) necessary for the ELM mitigation to occur. The implication of these results for ITER will be discussed.

An Explanation of the Relationship between Instructor Humor and Student Learning: Instructional Humor Processing Theory

ERIC Educational Resources Information Center

Wanzer, Melissa B.; Frymier, Ann B.; Irwin, Jeffrey

2010-01-01

This paper proposes the Instructional Humor Processing Theory (IHPT), a theory that incorporates elements of incongruity-resolution theory, disposition theory, and the elaboration likelihood model (ELM) of persuasion. IHPT is proposed and offered as an explanation for why some types of instructor-generated humor result in increased student…

Evaluating Initial Teacher Education Programmes: Perspectives from the Republic of Ireland

ERIC Educational Resources Information Center

Clarke, Marie; Lodge, Anne; Shevlin, Michael

2012-01-01

Research studies in teacher education have focussed on the outcomes of preparatory programmes. Less attention has been paid to the processes through which professional learning is acquired. This article argues that the study of attitudes and persuasion is very important in teacher education. The elaboration likelihood model (ELM) of persuasion…

The Evolution of School Improvement from the Classroom Teacher's Perspective.

ERIC Educational Resources Information Center

Thompson, Marci; Mitchell, Deborah

2002-01-01

Highlights changes that have occurred since 1992 at Elm Dale Elementary School (Greenfield, Wisconsin) through the school improvement process. Describes how teachers have become involved in and developed ownership of the improvement process, and how they have learned to analyze data. Asserts that the school improvement process has changed the…

Toward Intelligent Machine Learning Algorithms

DTIC Science & Technology

1988-05-01

Machine learning is recognized as a tool for improving the performance of many kinds of systems, yet most machine learning systems themselves are not...directed systems, and with the addition of a knowledge store for organizing and maintaining knowledge to assist learning, a learning machine learning (L...ML) algorithm is possible. The necessary components of L-ML systems are presented along with several case descriptions of existing machine learning systems

Web Mining: Machine Learning for Web Applications.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chau, Michael

2004-01-01

Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining…

Genotype x environment interaction and growth stability of several elm clones resistant to Dutch elm disease

Treesearch

Alberto Santini; Francesco Pecori; Alessia L. Pepori; Luisa Ghelardini

2012-01-01

The elm breeding program carried out in Italy at the Institute of Plant Protection - Consiglio Nazionale delle Ricercje (CNR) during the last 40 years aimed to develop Dutch elm disease (DED)-resistant elm selections specific to the Mediterranean environment. The need for genotypes adapted to Mediterranean conditions was evident from the poor performance of the Dutch...

Novel insights into the elm yellows phytoplasma genome and into the metagenome of elm yellows-infected elms

Treesearch

Christina Rosa; Paolo Margaria; Scott M. Geib; Erin D. Scully

2017-01-01

In North America, American elms were historically present throughout the northeastern United States and southeastern Canada. The longevity of these trees, their resistance to the harsh urban environment, and their aesthetics led to their wide use in landscaping and streetscaping over several decades. American elms were one of most cultivated plants in the United States...

Recovery of copper and cyanide from waste cyanide solutions using emulsion liquid membrane with LIX 7950 as the carrier.

PubMed

Xie, Feng; Wang, Wei

2017-08-01

The feasibility of using emulsion liquid membranes (ELMs) with the guanidine extractant LIX 7950 as the mobile carrier for detoxifying copper-containing waste cyanide solutions has been determined. Relatively stable ELMs can be maintained under suitable stirring speed during mixing ELMs and the external solution. Effective extraction of copper cyanides by ELMs only occurs at pH below 11. High copper concentration in the external phase and high volume ratio of the external phase to ELMs result in high transport rates of copper and cyanide. High molar ratio of cyanide to copper tends to suppress copper extraction. The presence of thiocyanate ion significantly depresses the transport of copper and cyanide through the membrane while the thiosulfate ion produces less impact on copper removal by ELMs. Zinc and nickel cyanides can also be effectively extracted by ELMs. More than 90% copper and cyanide can be effectively removed from alkaline cyanide solutions by ELMs under suitable experimental conditions, indicating the effectiveness of using the designed ELM for recovering copper and cyanide from waste cyanide solutions.

Using Machine Learning to Advance Personality Assessment and Theory.

PubMed

Bleidorn, Wiebke; Hopwood, Christopher James

2018-05-01

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

Development of new Dutch Elm disease-tolerant selections for restoration of the American Elm in urban and forested landscapes

Treesearch

C.C. Pinchot; C.E. Flower; K.S. Knight; C. Marks; R. Minocha; D. Lesser; K. Woeste; P.G. Schaberg; B. Baldwin; D.M. Delatte; T.D. Fox; N. Hayes-Plazolles; B. Held; K. Lehtoma; S. Long; S. Mattix; A. Sipes; J.M. Slavicek

2017-01-01

The goal of our research and development efforts is to restore American elm (Ulmus americana) as a species in both natural and urban landscapes. Accomplishing this goal requires identification/generation of additional American elm cultivars that are tolerant to Dutch elm disease (DED) caused by Ophiostoma ulmi and O. novo-ulmi, and development of methods to reintroduce...

Relative importance of root grafts and bark beetles to the spread of Dutch elm disease

Treesearch

R. A. Cuthbert; W. N., Jr. Cannon; J. W. Peacock

1975-01-01

Root-graft transmission of Dutch elm disease (DED) is sometimes ignored in both research studies and city programs to control DED. Our results indicate that elms adjacent to 1-, 2-, or 3-year-old stumps have a disease rate three to five times higher than elms not adjacent to stumps. We conclude that in Detroit, which has elm plantings typical of many United States...

QUANTITATIVE TESTS OF ELMS AS INTERMEDIATE N PEELING-BALOONING MODES

DOE Office of Scientific and Technical Information (OSTI.GOV)

LAO,LL; SNYDER,PB; LEONARD,AW

2003-03-01

A271 QUANTITATIVE TESTS OF ELMS AS INTERMEDIATE N PEELING-BALOONING MODES. Several testable features of the working model of edge localized modes (ELMs) as intermediate toroidal mode number peeling-ballooning modes are evaluated quantitatively using DIII-D and JT-60U experimental data and the ELITE MHD stability code. These include the hypothesis that ELM sizes are related to the radial widths of the unstable MHD modes, the unstable modes have a strong ballooning character localized in the outboard bad curvature region, and ELM size generally becomes smaller at high edge collisionality. ELMs are triggered when the growth rates of the unstable MHD modes becomemore » significantly large. These testable features are consistent with many ELM observations in DIII-D and JT-60U discharges.« less

Broadening of the divertor heat flux footprint with increasing number of ELM filaments in NSTX

NASA Astrophysics Data System (ADS)

Ahn, Joon-Wook

2014-10-01

We report on the broadening (narrowing) of the ELM heat flux footprint with increasing (decreasing) number of filamentary striations from in-depth thermography measurements in NSTX. Edge localized modes (ELMs) represent a challenge to future fusion devices, due to the high heat fluxes on plasma facing surfaces. One ameliorating factor has been that the divertor heat flux characteristic profile width (λq) has been observed to broaden with the size of ELM, as compared with the inter-ELM λq, which keeps the peak heat flux (qpeak) from increasing. In contrast, λq has been observed to narrow during ELMs under certain conditions in NSTX, for both naturally occurring and 3-D fields triggered ELMs. Fast thermographic measurements and detailed analysis demonstrate that the ELM λq increases with the number of observed filamentary striations, i . e . , profile narrowing (broadening) occurs when the number of striations is smaller (larger) than 3-4. With profile narrowing, qpeak at ELM peak times is inversely related (proportional) to λq (the ELM size), exacerbating the heat flux problem. Edge stability analysis shows that NSTX ELMs almost always lie on the current-driven kink/peeling mode side with low toroidal mode number (n = 1--5), consistent with the typical numbers of striations in NSTX (0-8) in comparison 10--15 striations are normally observed in intermediate-n peeling-ballooning ELMs, e.g., from JET. The NSTX characteristics may translate directly to ITER, which is also projected to lie on the low-n kink/peeling stability boundary. This work was supported by the U.S. DOE, Contract DE-AC05-00OR22725 (ORNL) and DE-AC02-09CH11466 (PPPL).

1. EXTERIOR VIEW OF ELM CITY PLANT (A. FRANCIS WALKER, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

1. EXTERIOR VIEW OF ELM CITY PLANT (A. FRANCIS WALKER, 1905-07) FROM SECOND AVENUE ON OPPOSITE SIDE OF ENTRANCE. THIS STRUCTURE WAS ORIGINALLY BUILT AS THE ELM CITY COTTON MILL OF CALLAWAY MILLS. NOTE RESERVOIR IN FOREGROUND. THIS PHOTOGRAPH IS THE LEFT SIDE OF A PANORAMA VIEW THAT INCLUDES HAER Nos. GA-128-2 AND GA-128-3. - Elm City Cotton Mill, 1000 Elm Street, La Grange, Troup County, GA

KSC-08pd3751

NASA Image and Video Library

2008-11-19

CAPE CANAVERAL, Fla. – In the Space Station Processing Facility at NASA's Kennedy Space Center in Florida, workers check the mast deployment on the SEDA-AP or Space Environment Data Acquisition equipment--Attached Payload. SEDA-AP will measure space environment in ISS orbit and environmental effects on materials and electronic devices to investigate the interaction with and from the environment at the Kibo exposed facility. The payload will be installed on the Japanese Experiment Module's Experiment Logistics Module-Exposed Section, or ELM-ES. The ELM-ES is one of the final components of the Japan Aerospace Exploration Agency's Kibo laboratory for the International Space Station. It can provide payload storage space and can carry up to three payloads at launch. In addition, the ELM-ES provides a logistics function where it can be returned to the ground aboard the space shuttle. The ELM-ES will be carried aboard space shuttle Endeavour on the STS-127 mission targeted for launch May 15. Photo credit: NASA/Cory Huston

KSC-08pd3750

NASA Image and Video Library

2008-11-19

CAPE CANAVERAL, Fla. – In the Space Station Processing Facility at NASA's Kennedy Space Center in Florida, workers deploy the mast on the SEDA-AP or Space Environment Data Acquisition equipment--Attached Payload. SEDA-AP will measure space environment in ISS orbit and environmental effects on materials and electronic devices to investigate the interaction with and from the environment at the Kibo exposed facility. The payload will be installed on the Japanese Experiment Module's Experiment Logistics Module-Exposed Section, or ELM-ES. The ELM-ES is one of the final components of the Japan Aerospace Exploration Agency's Kibo laboratory for the International Space Station. It can provide payload storage space and can carry up to three payloads at launch. In addition, the ELM-ES provides a logistics function where it can be returned to the ground aboard the space shuttle. The ELM-ES will be carried aboard space shuttle Endeavour on the STS-127 mission targeted for launch May 15. Photo credit: NASA/Cory Huston

KSC-08pd3752

NASA Image and Video Library

2008-11-19

CAPE CANAVERAL, Fla. – In the Space Station Processing Facility at NASA's Kennedy Space Center in Florida, workers check the mast deployment on the SEDA-AP or Space Environment Data Acquisition equipment--Attached Payload. SEDA-AP will measure space environment in ISS orbit and environmental effects on materials and electronic devices to investigate the interaction with and from the environment at the Kibo exposed facility. The payload will be installed on the Japanese Experiment Module's Experiment Logistics Module-Exposed Section, or ELM-ES. The ELM-ES is one of the final components of the Japan Aerospace Exploration Agency's Kibo laboratory for the International Space Station. It can provide payload storage space and can carry up to three payloads at launch. In addition, the ELM-ES provides a logistics function where it can be returned to the ground aboard the space shuttle. The ELM-ES will be carried aboard space shuttle Endeavour on the STS-127 mission targeted for launch May 15. Photo credit: NASA/Cory Huston

Flambeau Mining Corporation, Ladysmith, Rusk County, Wisconsin. Proposed Open Pit Copper Mine and Waste Containment Area, Draft Environmental Impact Statement.

DTIC Science & Technology

1976-08-01

American elm Lonicera tatarica - tartarian honeysuckle Ulmus rubra - slippery elm Siiibucus canadenis - common elder Ulmus thoiii~sii - cork elm ...community borders the marshes and swamps. 2.060 The predominant species are the trembling aspen (Populus tremuloides), red maple (Acer rubrum), the elms ...succession. The most numerous trees (in descending order) are: white birch, red maple, aspen, sugar maple, black ash, basswood, elm (Ulmus sp.), hemlock

Ecological Investigation of a Greentree Reservoir in the Delta National Forest, Mississippi.

DTIC Science & Technology

1981-09-01

elm (Ulmus americana) and slippery elm (U. rubra) oc- curred in the study area, with American elm the more abundant species. No distinction was made...spectively, on both areas (see Appendix C for nomenclature). American elm * and water hickory ranked fifth and sixth in importance on the ref- erence area...between these two species; the label "American elm " includes data for both. 18 boundaries, water hickory increment cores were not analyzed. Table 4

Measurements and modeling of intra-ELM tungsten sourcing and transport in DIII-D

NASA Astrophysics Data System (ADS)

Abrams, T.; Leonard, A. W.; Thomas, D. M.; McLean, A. G.; Makowski, M. A.; Wang, H. Q.; Unterberg, E. A.; Briesemeister, A. R.; Rudakov, D. L.; Bykov, I.; Donovan, D.

2017-10-01

Intra-ELM tungsten erosion profiles in the DIII-D divertor, acquired via W I spectroscopy with high temporal and spatial resolution, are consistent with SDTrim.SP sputtering modeling using measured ion saturation currents and impact energies during ELMs as input and an ad-hoc 2% C2+ impurity flux. The W sputtering profile peaks close to the OSP both during and between ELMs in the favorable BT direction. In reverse BT the W source peaks close to the OSP between ELMs but strongly broadens and shifts outboard during ELMs, heuristically consistent with radially outward ion transport via ExB drifts. Ion impact energies during ELMs (inferred taking the ratio of divertor heat flux to the ion saturation current) are found to be approximately equal to Te,ped, lower than the 4*Te,ped value predicted by the Fundamenski/Moulton free streaming model. These impact energies imply both D main ions and C impurities contribute strongly to W sputtering during ELMs on DIII-D. This work represents progress towards a predictive model to link upstream conditions (i.e., pedestal height) and SOL impurity levels to the ELM-induced W impurity source at both the strike-point and far-target regions in the ITER divertor. Correlations between ELM size/frequency and SOL W fluxes measured via a midplane deposition probe will also be presented. Work supported by US DOE under DE-FC02-04ER54698.

Prompt triggering of edge localized modes through lithium granule injection on EAST

NASA Astrophysics Data System (ADS)

Lunsford, Robert; Sun, Z.; Hu, J. S.; Xu, W.; Zuo, G. Z.; Gong, X. Z.; Wan, B. N.; Li, J. G.; Huang, M.; Maingi, R.; Diallo, A.; Tritz, K.; the EAST Team

2017-10-01

We report successful triggering of edge localized mode (ELMs) in EAST with Lithium (Li) micropellets, and the observed dependence of ELM triggering efficiency on granule size. ELM control is essential for successful ITER operation throughout the entire campaign, relying on magnetic perturbations for ELM suppression and ELM frequency enhancement via pellet injection. To separate the task of fueling from ELM pacing, we initiate the prompt generation of ELMs via impurity granule injection. Lithium granules ranging in size from 200 - 1000 microns are mechanically injected into upper-single null EAST long pulse H-mode discharges. The injections are monitored for their effect on high Z impurity accumulation and to assess the pressure perturbation required for reliable ELM triggering. We have determined that granules of diameter larger than 600 microns (corresponding to 5.2 x 1018 Li atoms) are successful at triggering ELMs more than 90% of the time. The triggering efficiency drops precipitously to less than 40% as the granule size is reduced to 400 microns (1.5 x 1018 Li atoms), indicating a triggering threshold has been crossed. Using this information an optimal impurity granule size which will regularly trigger a prompt ELM in these EAST discharges is determined. Coupling these results with alternate discharge scenarios on EAST and similar experiments performed on DIII-D provides the possibility of extrapolation to future devices.

Elm leaf beetle performance on ozone-fumigated elm

Treesearch

Jack H. Barger; Richard W. Hall; Alden M. Townsend; Alden M. Townsend

1992-01-01

Leaves (1986) from elm hybrids ('Pioneer', 'Homestead', '970') previously fumigated in open-top chambers with ozone or with charcoal-filtered air (CFA) were evaluated for water and nitrogen content or were fed to adult elm leaf beetles (ELB), Xanthogaleruca = (Pyrrhallta) luteola (Muller), to determine host suitability for beetle fecundity...

Using Dutch elm disease-tolerant elm to restore floodplains impacted by emerald ash borer

Treesearch

Kathleen S. Knight; James M. Slavicek; Rachel Kappler; Elizabeth Pisarczyk; Bernadette Wiggin; Karen. Menard

2012-01-01

American elm (Ulmus Americana L.) was a dominant species in floodplains and swamps of the Midwest before Dutch elm disease (DED) (Ophiostoma ulmi and O.novo-ulmi) reduced its populations. In many areas, ash (Fraxinus spp.) became dominant in these ecosystems. Emerald ash borer (EAB) (...

Evaluation of an Integrated Multi-Task Machine Learning System with Humans in the Loop

DTIC Science & Technology

2007-01-01

machine learning components natural language processing, and optimization...was examined with a test explicitly developed to measure the impact of integrated machine learning when used by a human user in a real world setting...study revealed that integrated machine learning does produce a positive impact on overall performance. This paper also discusses how specific machine learning components contributed to human-system

A Study of Vegetation Development in Relation to Age of River Stabilization Structures Along a Channelized Segment of the Missouri River.

DTIC Science & Technology

1975-01-25

s.ame time period, the first shrub species begin to appear in the sites. Ulmus rubra ( slippery elm ), Celtis occidentalis (hackberry), and Platanus...boxelder), Ulmus americana (American elm ), Ulmus rubra ( slippery elm ), Fra:inus pennsyl- vanica (green ash), and Juglans nigra (black walnut). These may...be replaced by Tilia americana (basswood), the Ulmus americana (American elm ) and Ulmus rubra ( slippery elm ) remaining as co- dominants. Weaver (1960

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

PubMed Central

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. PMID:26262687

Quantum machine learning.

PubMed

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Quantum machine learning

NASA Astrophysics Data System (ADS)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-01

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Modeling of Plasma Pressure Effects on ELM Suppression With RMP in DIII-D

NASA Astrophysics Data System (ADS)

Orlov, D. M.; Moyer, R. A.; Mordijck, S.; Evans, T. E.; Osborne, T. H.; Snyder, P. B.; Unterberg, E. A.; Fenstermacher, M. E.

2009-11-01

Resonant magnetic perturbations (RMPs) are used to control the pedestal pressure gradient in both low and high (ν3^*) DIII-D plasmas. In this work we have analyzed several discharges with different levels of triangularity, different neutral beam injection power levels, and with, βN ranging from 1.5 to 2.3. The field line integration code TRIP3D was used to model the magnetic perturbation in ELMing and ELM suppressed phases during the RMP pulse. The results of this modeling showed very little effect of βN on the structure of the vacuum magnetic field during ELM suppression using n=3 RMPs. Kinetic equilibrium reconstructions showed a decrease in bootstrap current during RMP. Linear peeling-ballooning stability analysis performed with the ELITE code suggested that the ELMs, which persist during RMP, i.e. ELMing still is observed, are not Type I ELMs. Identification of these Dα spikes is an ongoing work.

Ten-year performance of the United States national elm trial

Treesearch

Jason J. Griffin; William R. Jacobi; E. Gregory McPherson; Clifford S. Sadof; James R. McKenna; Mark L. Gleason; Nicole Ward Gauthier; Daniel A. Potter; David R. Smitley; Gerard C. Adams; Ann Brooks Gould; Christian R. Cash; James A. Walla; Mark C. Starrett; Gary Chastagner; Jeff L. Sibley; Vera A. Krischik; Adam F. Newby

2017-01-01

Ulmus americana (American elm) was an important urban tree in North America prior to the introduction of the Dutch elm disease pathogen in 1930. Subsequently, urban and community forests were devastated by the loss of large canopies. Tree improvement programs produced disease tolerant American and Eurasian elm cultivars and introduced them into the...

FRONT ELEVATION OF RESIDENCE, SHOWING ELM DRIVE AND SIDEWALK IN ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

FRONT ELEVATION OF RESIDENCE, SHOWING ELM DRIVE AND SIDEWALK IN THE FOREGROUND. VIEW FACING NORTH - Camp H.M. Smith and Navy Public Works Center Manana Title VII (Capehart) Housing, Three-Bedroom Single-Family Types 8 and 11, Birch Circle, Elm Drive, Elm Circle, and Date Drive, Pearl City, Honolulu County, HI

Establishment and characterization of American elm cell suspension cultures

Treesearch

Steven M. Eshita; Joseph C. Kamalay; Vicki M. Gingas; Daniel A. Yaussy

2000-01-01

Cell suspension cultures of Dutch elm disease (DED)-tolerant and DED-susceptible American elms clones have been established and characterized as prerequisites for contrasts of cellular responses to pathogen-derived elicitors. Characteristics of cultured elm cell growth were monitored by A700 and media conductivity. Combined cell growth data for all experiments within a...

Novel insights into the Elm Yellows phytoplasma genome and into the metagenome of Elm Yellows-infected elms

USDA-ARS?s Scientific Manuscript database

In North America, American elms were historically present throughout the northeastern United States and southeastern Canada. The longevity of these trees, their resistance to the harsh urban environment, and their aesthetics led to their wide use in landscaping and streetscaping over several decade...

Modeling carbon production and transport during ELMs in DIII-D

NASA Astrophysics Data System (ADS)

Hogan, J.; Wade, M.; Coster, D.; Lasnier, C.

2004-11-01

Large-scale Type I ELM events could provide a significant C source in ITER, and C production rates depend on incident D flux density and surface temperature, quantities which can vary significantly during an ELM event. Recent progress on DIII-D has improved opportunities for code comparison. Fast time-scale measurements of divertor CIII evolution [1] and fast edge CER measurements of C profile evolution during low-density DIII-D LSN ELMy H-modes (type I) [2] have been modeled using the solps5.0/Eirene99 coupled edge code and time dependent thermal analysis codes. An ELM model based on characteristics of MHD peeling-ballooning modes reproduces the pedestal evolution. Qualitative agreement for the CIII evolution during an ELM event is found using the Roth et al annealing model for chemical sputtering and the sensitivity to other models is described. Significant ELM-to-ELM variations in observed maximum divertor target IR temperature during nominally identical ELMs are investigated with models for C emission from micron-scale dust particles. [1] M Groth, M Fenstermacher et al J Nucl Mater 2003, [2] M Wade, K Burrell et al PSI-16

SIGNIFICANCE OF PREOPERATIVE EXTERNAL LIMITING MEMBRANE HEIGHT ON VISUAL PROGNOSIS IN PATIENTS UNDERGOING MACULAR HOLE SURGERY.

PubMed

Geenen, Caspar; Murphy, Declan C; Sandinha, Maria T; Rees, Jon; Steel, David H W

2018-03-05

To investigate the association between the vertical elevation of the external limiting membrane (ELM) and visual outcome in patients undergoing surgery for idiopathic full-thickness macular hole. Retrospective observational study of a consecutive cohort of patients undergoing vitrectomy to treat macular hole. The greatest vertical height of the central ELM above the retinal pigment epithelium (ELM height) was measured on spectral domain optical coherence tomography preoperatively. The relationship of ELM height to other preoperative and postoperative variables, including macular hole width and height, and visual acuity was analyzed. Data from 91 eyes of 91 patients who had undergone successful hole closure were included. The mean ELM height was 220 μm (range 100-394). There were significant correlations between the ELM height and the diameter of the hole, hole height, and worsening preoperative visual acuity. For holes less than 400 μm in width, better postoperative visual acuity was significantly predicted by a lower ELM height. The ELM height varies widely in idiopathic macular hole. It is higher in eyes where the hole is wider and also when the hole itself is higher. For holes of less than 400 μm in width, a lower ELM height is a strong independent predictor of a good postoperative outcome.

The Relationships Between ELM Suppression, Pedestal Profiles, and Lithium Wall Coatings in NSTX

DOE Office of Scientific and Technical Information (OSTI.GOV)

D.P. Boyle, R. Maingi, P.B. Snyder, J. Manickam, T.H. Osborne, R.E. Bell, B.P. LeBlanc, and the NSTX Team

2012-08-17

Recently in the National Spherical Torus Experiment (NSTX), increasing lithium wall coatings suppressed edge localized modes (ELMs), gradually but not quite monotonically. This work details profile and stability analysis as ELMs disappeared throughout the lithium scan. While the quantity of lithium deposited between discharges did not uniquely determine the presence of ELMs, profile analysis demonstrated that lithium was correlated to wider density and pressure pedestals with peak gradients farther from the separatrix. Moreover, the ELMy and ELM-free discharges were cleanly separated by their density and pedestal widths and peak gradient locations. Ultimately, ELMs were only suppressed when lithium caused themore » density pedestal to widen and shift inward. These changes in the density gradient were directly reflected in the pressure gradient and calculated bootstrap current. This supports the theory that ELMs in NSTX are caused by peeling and/or ballooning modes, as kink/peeling modes are stabilized when the edge current and pressure gradient shift away from the separatrix. Edge stability analysis using ELITE corroborated this picture, as reconstructed equilibria from ELM-free discharges were generally farther from their kink/peeling stability boundaries than ELMy discharges. We conclude that density profile control provided by lithium is the key first step to ELM suppression in NSTX« less

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Observations of the effect of lower hybrid waves on ELM behaviour in EAST

NASA Astrophysics Data System (ADS)

Chen, R.; Xu, G. S.; Liang, Y.; Wang, H. Q.; Zhou, C.; Liu, A. D.; Wang, L.; Qian, J. P.; Gan, K. F.; Yang, J. H.; Duan, Y. M.; Li, Y. L.; Ding, S. Y.; Wu, X. Q.; Yan, N.; Chen, L.; Shao, L. M.; Zhang, W.; Hu, G. H.; Zhao, N.; Liu, S. C.; Kong, D. F.; Gong, X. Z.

2015-03-01

Dedicated experiments focusing on the influence of lower hybrid waves (LHWs) on edge-localized modes (ELMs) were first performed during the 2012 experimental campaign of EAST, via modulating the input power of LHWs in the high-confinement-mode (H-mode) plasma mainly sustained by ion cyclotron resonant heating. Natural ELMs are effectively mitigated (ELM frequency increases, while its intensity decreases dramatically) as the LHW is applied, observed over a fairly wide range of plasma current or edge safety factor. By scanning the modulation frequency (fm) of LHW injected power in a target plasma dominated by the so-called small ELMs, we conclude that large ELMs with markedly larger amplitudes and lower frequencies are reproduced at low modulation frequencies (fm < 100 Hz). Analysis of the evolution of edge extreme ultraviolet radiation signals further indicates that plasma fluctuations at the pedestal region indistinctively respond to rapid modulation (fm ⩾ 100 Hz) of LHW injected power. This is proposed as the mechanism responsible for the observed fm dependence of the mitigation effect induced by LHWs on large ELMs. In addition, a critical threshold of LHW input power PLHW is estimated as PLHWthr≃800 kW , beyond which the impact of applied LHWs on ELM behaviours can be achieved. Finally, Langmuir probe measurements suggest that, rather than the concentration of free energy into a narrowband quasi-coherent precursor commonly observed growing until the ELM crash, the continuous development of broadband turbulence during the ELM-absent phase with the application of LHWs might contribute to the avoidance of ELM crashes. These results present new insights into existing experiments, and also provide some foundations and references for the next-step research about exploring in more depth and improving this new attractive method to effectively control the ELM-induced very large transient heat and particle flux.

Suppressive response of confections containing the extractive from leaves of Morus Alba on postprandial blood glucose and insulin in healthy human subjects

PubMed Central

Nakamura, Mariko; Nakamura, Sadako; Oku, Tsuneyuki

2009-01-01

Background The first aim of this study was to clarify the effective ratio of extractive from leaves of Morus Alba (ELM) to sucrose so as to apply this knowledge to the preparation of confections that could effectively suppress the elevation of postprandial blood glucose and insulin. The second aim was to identify the efficacy of confections prepared with the optimally effective ratio determined from the first study, using healthy human subjects. Methods Ten healthy females (22.3 years, BMI 21.4 kg/m2) participated in this within-subject, repeated measures study. For the first aim of this study, the test solutions containing 30 g of sucrose and 1.2 or 3.0 g of ELM were repeatedly and randomly given to each subject. To identify the practically suppressive effects on postprandial blood glucose and insulin, some confections with added ELM were prepared as follows: Mizu-yokan, 30 g of sucrose with the addition of 1.5 or 3.0 g ELM; Daifuku-mochi, 9.0 g of starch in addition to 30 g of sucrose and 1.5 or 3.0 g ELM; Chiffon-cake, 24 g of sucrose, starch, and 3.0 or 6.0 g of ELM, and were ingested by each subject. Blood and end-expiration were collected at selected periods after test food ingestion. Results When 30 g of sucrose with 1.2 or 3.0 g of ELM were ingested by subjects, the elevations of postprandial blood glucose and insulin were effectively suppressed (p < 0.01), and the most effective ratio of ELM to sucrose was evaluated to be 1:10. AUC (area under the curve) of breath hydrogen excretion for 6 h after the ingestion of an added 3 g of ELM significantly increased (p < 0.01). When AUCs-3h of incremental blood glucose of confections without ELM was 100, that of Mizu-yokan and Daifuku-mochi with the ratio (1:10) of ELM to sucrose was decreased to 53.4 and 58.2, respectively. Chiffon-cake added one-fourth ELM was 29.0. Conclusion ELM-containing confections for which the ratio of ELM and sucrose is one-tenth effectively suppress the postprandial blood glucose and insulin by inhibiting the intestinal sucrase, thus creating a prebiotic effect. The development of confections with ELM can therefore contribute to the prevention and the quality of life for prediabetic and diabetic patients. PMID:19602243

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment

PubMed Central

2011-01-01

Background Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. Results This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. Conclusions AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements. PMID:21798025

AZOrange - High performance open source machine learning for QSAR modeling in a graphical programming environment.

PubMed

Stålring, Jonna C; Carlsson, Lars A; Almeida, Pedro; Boyer, Scott

2011-07-28

Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally efficient machine learning algorithms, easily available to researchers without extensive machine learning knowledge. In granting the scientific principles of transparency and reproducibility, Open Source solutions are increasingly acknowledged by regulatory authorities. Thus, an Open Source state-of-the-art high performance machine learning platform, interfacing multiple, customized machine learning algorithms for both graphical programming and scripting, to be used for large scale development of QSAR models of regulatory quality, is of great value to the QSAR community. This paper describes the implementation of the Open Source machine learning package AZOrange. AZOrange is specially developed to support batch generation of QSAR models in providing the full work flow of QSAR modeling, from descriptor calculation to automated model building, validation and selection. The automated work flow relies upon the customization of the machine learning algorithms and a generalized, automated model hyper-parameter selection process. Several high performance machine learning algorithms are interfaced for efficient data set specific selection of the statistical method, promoting model accuracy. Using the high performance machine learning algorithms of AZOrange does not require programming knowledge as flexible applications can be created, not only at a scripting level, but also in a graphical programming environment. AZOrange is a step towards meeting the needs for an Open Source high performance machine learning platform, supporting the efficient development of highly accurate QSAR models fulfilling regulatory requirements.

The Efficacy of Machine Learning Programs for Navy Manpower Analysis

DTIC Science & Technology

1993-03-01

This thesis investigated the efficacy of two machine learning programs for Navy manpower analysis. Two machine learning programs, AIM and IXL, were...to generate models from the two commercial machine learning programs. Using a held out sub-set of the data the capabilities of the three models were...partial effects. The author recommended further investigation of AIM’s capabilities, and testing in an operational environment.... Machine learning , AIM, IXL.

The Security of Machine Learning

DTIC Science & Technology

2008-04-24

Machine learning has become a fundamental tool for computer security, since it can rapidly evolve to changing and complex situations. That...adaptability is also a vulnerability: attackers can exploit machine learning systems. We present a taxonomy identifying and analyzing attacks against machine ...We use our framework to survey and analyze the literature of attacks against machine learning systems. We also illustrate our taxonomy by showing

Source and Message Factors in Persuasion: A Reply to Stiff's Critique of the Elaboration Likelihood Model.

ERIC Educational Resources Information Center

Petty, Richard E.; And Others

1987-01-01

Answers James Stiff's criticism of the Elaboration Likelihood Model (ELM) of persuasion. Corrects certain misperceptions of the ELM and criticizes Stiff's meta-analysis that compares ELM predictions with those derived from Kahneman's elastic capacity model. Argues that Stiff's presentation of the ELM and the conclusions he draws based on the data…

American elm clones of importance in Dutch elm disease tolerance studies

Treesearch

Linda M. Haugen; Susan E. Bentz

2017-01-01

We present the background and characteristics of American elm clones that are commercially available or of interest in research on Dutch elm disease (DED) tolerance in the United States. The characteristics of interest include origin, ploidy level, whether available in nursery trade, evidence of DED tolerance, and other comments. The list includes 10 named commercially...

First Results of ELM Triggering With a Multichamber Lithium Granule Injector Into EAST Discharges

DOE PAGES

Sun, Z.; Lunsford, R.; Maingi, R.; ...

2017-12-12

A critical challenge facing the basic long-pulse H-mode for ITER is to control edge-localized modes (ELMs). A new method using a multichamber lithium (Li) granule injector (LGI) for ELM triggering experiments has been developed in Experimental Advanced Superconducting Tokamak (EAST). First experimental results of the control of ELMs are obtained in EAST with a tungsten divertor. It is found that the injector has good capacities, i.e., allowing good flexibilities in granule size selection, injection rate, and injection velocity. In conclusion, LGI has successfully triggered ELMs during the H-mode. These results indicate the LGI would be a promising method to controlmore » ELMs in long-pulse steady-state tokamaks.« less

First Results of ELM Triggering With a Multichamber Lithium Granule Injector Into EAST Discharges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sun, Z.; Lunsford, R.; Maingi, R.

A critical challenge facing the basic long-pulse H-mode for ITER is to control edge-localized modes (ELMs). A new method using a multichamber lithium (Li) granule injector (LGI) for ELM triggering experiments has been developed in Experimental Advanced Superconducting Tokamak (EAST). First experimental results of the control of ELMs are obtained in EAST with a tungsten divertor. It is found that the injector has good capacities, i.e., allowing good flexibilities in granule size selection, injection rate, and injection velocity. In conclusion, LGI has successfully triggered ELMs during the H-mode. These results indicate the LGI would be a promising method to controlmore » ELMs in long-pulse steady-state tokamaks.« less

Phylogeography: English elm is a 2,000-year-old Roman clone.

PubMed

Gil, Luis; Fuentes-Utrilla, Pablo; Soto, Alvaro; Cervera, M Teresa; Collada, Carmen

2004-10-28

The outbreak of Dutch elm disease in the 1970s ravaged European elm populations, killing more than 25 million trees in Britain alone; the greatest impact was on Ulmus procera, otherwise known as the English elm. Here we use molecular and historical information to show that this elm derives from a single clone that the Romans transported from Italy to the Iberian peninsula, and from there to Britain, for the purpose of supporting and training vines. Its highly efficient vegetative reproduction and its inability to set seeds have preserved this clone unaltered for 2,000 years as the core of the English elm population--and the preponderance of this susceptible variety may have favoured a rapid spread of the disease.

Entanglement-Based Machine Learning on a Quantum Computer

NASA Astrophysics Data System (ADS)

Cai, X.-D.; Wu, D.; Su, Z.-E.; Chen, M.-C.; Wang, X.-L.; Li, Li; Liu, N.-L.; Lu, C.-Y.; Pan, J.-W.

2015-03-01

Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

High frequency pacing of edge localized modes by injection of lithium granules in DIII-D H-mode discharges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bortolon, A.; Maingi, R.; Mansfield, D. K.

A newly installed Lithium Granule Injector (LGI) was used to pace edge localized modes (ELM) in DIII-D. ELM pacing efficiency was studied injecting lithium granules of nominal diameter 0.3–0.9mm, speed of 50–120 m s -1 and average injection rates up to 100 Hz for 0.9mm granules and up to 700 Hz for 0.3mm granules. The efficiency of ELM triggering was found to depend strongly on size of the injected granules, with triggering efficiency close to 100% obtained with 0.9mm diameter granules, lower with smaller sizes, and weakly depending on granule velocity. Robust ELM pacing was demonstrated in ITER-like plasmas formore » the entire shot length, at ELM frequencies 3–5 times larger than the ‘natural’ ELM frequency observed in reference discharges. Within the range of ELM frequencies obtained, the peak ELM heat flux at the outer strike point was reduced with increasing pacing frequency. The peak heat flux reduction at the inner strike point appears to saturate at high pacing frequency. Lithium was found in the plasma core, with a concurrent reduction of metallic impurities and carbon. Altogether, high frequency ELM pacing using the lithium granule injection appears to be compatible with both H-mode energy confinement and attractive H-mode pedestal characteristics, but further assessment is need« less

High frequency pacing of edge localized modes by injection of lithium granules in DIII-D H-mode discharges

DOE PAGES

Bortolon, A.; Maingi, R.; Mansfield, D. K.; ...

2016-04-08

A newly installed Lithium Granule Injector (LGI) was used to pace edge localized modes (ELM) in DIII-D. ELM pacing efficiency was studied injecting lithium granules of nominal diameter 0.3–0.9mm, speed of 50–120 m s -1 and average injection rates up to 100 Hz for 0.9mm granules and up to 700 Hz for 0.3mm granules. The efficiency of ELM triggering was found to depend strongly on size of the injected granules, with triggering efficiency close to 100% obtained with 0.9mm diameter granules, lower with smaller sizes, and weakly depending on granule velocity. Robust ELM pacing was demonstrated in ITER-like plasmas formore » the entire shot length, at ELM frequencies 3–5 times larger than the ‘natural’ ELM frequency observed in reference discharges. Within the range of ELM frequencies obtained, the peak ELM heat flux at the outer strike point was reduced with increasing pacing frequency. The peak heat flux reduction at the inner strike point appears to saturate at high pacing frequency. Lithium was found in the plasma core, with a concurrent reduction of metallic impurities and carbon. Altogether, high frequency ELM pacing using the lithium granule injection appears to be compatible with both H-mode energy confinement and attractive H-mode pedestal characteristics, but further assessment is need« less

Numerical Analysis of the Effects of Normalized Plasma Pressure on RMP ELM Suppression in DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Orlov, D. M.; Moyer, R.A.; Evans, T. E.

2010-01-01

The effect of normalized plasma pressure as characterized by normalized pressure parameter (beta(N)) on the suppression of edge localized modes (ELMs) using resonant magnetic perturbations (RMPs) is studied in low-collisionality (nu* <= 0.2) H-mode plasmas with low-triangularity ( = 0.25) and ITER similar shapes ( = 0.51). Experimental results have suggested that ELM suppression by RMPs requires a minimum threshold in plasma pressure as characterized by beta(N). The variations in the vacuum field topology with beta(N) due to safety factor profile and island overlap changes caused by variation of the Shafranov shift and pedestal bootstrap current are examined numerically withmore » the field line integration code TRIP3D. The results show very small differences in the vacuum field structure in terms of the Chirikov (magnetic island overlap) parameter, Poincare sections and field line loss fractions. These differences do not appear to explain the observed threshold in beta(N) for ELM suppression. Linear peeling-ballooning stability analysis with the ELITE code suggests that the ELMs which persist during the RMPs when beta(N) is below the observed threshold are not type I ELMs, because the pedestal conditions are deep within the stable regime for peeling-ballooning modes. These ELMs have similarities to type III ELMs or low density ELMs.« less

Antibiotic Injections Control Elm Phloem Necrosis in the Urban Ecosystem

Treesearch

T. H. Filer

1976-01-01

When nine American and cedar elms showing symptoms of elm phloem necrosis were given repeated injections of tetracycline antibiotics for several years, all treated trees recovered and appeared healthy by 1976. All but one of the untreated checks died. Of 10 severely infected American elms treated only during the summer of 1972, seven died and the other three showed...

The Italian elm breeding program for Dutch elm disease resistance

Treesearch

Alberto Santini; Francesco Pecori; Luisa Ghelardini

2012-01-01

In the 20th century, elms across Europe and North America were devastated by two pandemics of Dutch elm disease (DED), caused by the introduction of two fungal pathogens: Ophiostoma ulmi, followed by O. novo-ulmi. At the end of 1920s, research into a resistance to DED began in Europe and then in the United States. No...

Generation of American elm trees with tolerance to Dutch elm disease through controlled crosses and selection

Treesearch

James M. Slavicek; Kathleen S. Knight

2012-01-01

The goal of our research and development efforts is to generate new and/or improved selections of the American elm (Ulmus americana L.) with tolerance/resistance to Dutch elm disease (DED). The approaches we are taking for this effort include: 1) controlled breeding using known DED -tolerant selections, 2) controlled breeding using DED-tolerant...

Pressure injection of methyl 2-benzimidazole carbamate hydrochloride solution as a control for Dutch elm disease

Treesearch

Garold F. Gregory; Thomas W. Jones

1973-01-01

A preliminary evaluation of the effectiveness of injecting methyl 2-benzimidazole carbamate hydrochloride solution into elms for prevention or cure of Dutch elm disease is reported. Symptom development was diminished or prevented in elms injected with fungicide before inoculation. Symptom development was arrested in all crown-inoculated diseased trees injected with the...

The ecological role of American elm (Ulmus americana L.) in floodplain forests of northeastern North America

Treesearch

Christian O. Marks

2017-01-01

Before Dutch elm disease, the American elm (Ulmus americana L.) was a leading dominant tree species in the better drained parts of floodplain forests where flooding occurs about 1 percent of the time. Although still common in these habitats today, U. americana now rarely lives long enough to reach the forest canopy because elm...

Development of methods for the restoration of the American elm in forested landscapes

Treesearch

James M. Slavicek

2013-01-01

A project was initiated in 2003 to establish test sites to develop methods to reintroduce the American elm (Ulmus americana L.) in forested landscapes. American elm tree strains with high levels of tolerance to Dutch elm disease (DED) were established in areas where the trees can naturally regenerate and spread. The process of regeneration will...

Study of ELM Density Turbulence using the Upgraded Phase Contrast Imaging on DIII-D

NASA Astrophysics Data System (ADS)

Rost, J. C.; Davis, E. M.; Marinoni, A.; Porkolab, M.; Burrell, K. H.

2016-10-01

Recent studies of the turbulent density fluctuations accompanying ELMs in mixed ELM-type discharges have exploited the expanded wavenumber range of the upgraded Phase Contrast Imaging (PCI) diagnostic. The PCI data demonstrate the difference between the fluctuations generated by Type I ELMs, which are broadband in frequency and wavelength, and those generated by Type III ELMs, which are similar in amplitude but restricted to long wavelengths, suggesting that turbulence may play a significant role in Type I ELM transport. The high frequency response of PCI makes it ideal for studying the ELM-associated density fluctuations, which are observed at frequencies up to several MHz, evolve on time scales of 10s of μs, and persist after the magnetic component of the ELM has decayed away. The upgraded PCI, with independent systems for long and short wavelength detection (k < 5 cm-1 and 1 < k < 30 cm-1 respectively), demonstrated coverage of the full wavenumber range of interest. Work supported in part by the US Department of Energy under DE-FG02-94ER54235, DE-FC02-04ER54698, and DE-FC02-99ER54512.

ELM Mitigation in Low-rotation ITER Baseline Scenario Plasmas on DIII-D with Deuterium Pellet Injection

NASA Astrophysics Data System (ADS)

Baylor, L. R.

2016-10-01

ELM mitigation using high frequency D2 pellet ELM pacing has been demonstrated in ITER baseline scenario plasmas on DIII D with low rotation obtained with low NBI input torque. The ITER burning plasmas will have relatively low input torque and are expected to have low rotation. ELM mitigation by on-demand pellet ELM triggering has not been observed before in these conditions. New experiments on DIII-D in these conditions with 90 Hz D2 pellets have shown that significant mitigation of the divertor ELM peak heat flux by a factor of 8 is possible without detrimental effects to the plasma confinement. High-Z impurity accumulation is dramatically reduced at all input torques from 0.1 to 2.5 N-m. Fueling with high field side injection of D2 pellets has been employed to demonstrate that density buildup can be obtained simultaneously with ELM mitigation. The implications are that rapid pellet injection remains a promising technique to trigger on-demand ELMs in low rotating plasmas with greatly reduced peak flux while preventing impurity accumulation in ITER. Supported by the US DOE under DE-AC05-00OR22725, DE-FC02-04ER54698.

ELM elimination with Li powder injection in EAST discharges using the tungsten upper divertor

DOE PAGES

Maingi, R.; Hu, J. S.; Sun, Z.; ...

2018-01-05

Here, we report the first successful use of lithium (Li) to eliminate edge-localized modes (ELMs) with tungsten divertor plasma-facing components in the EAST device. Li powder injected into the scrape-off layer of the tungsten upper divertor successfully eliminated ELMs for 3–5 s in EAST. The ELM elimination became progressively more effective in consecutive discharges at constant lithium delivery rates, and the divertor D α baseline emission was reduced, both signatures of improved wall conditioning. A modest decrease in stored energy and normalized energy confinement was also observed, but the confinement relative to H98 remained well above 1, extending the previousmore » ELM elimination results via Li injection into the lower carbon divertor in EAST. These results can be compared with recent observations with lithium pellets in ASDEX-Upgrade that failed to mitigate ELMs, highlighting one comparative advantage of continuous powder injection for real-time ELM elimination.« less

ELM elimination with Li powder injection in EAST discharges using the tungsten upper divertor

NASA Astrophysics Data System (ADS)

Maingi, R.; Hu, J. S.; Sun, Z.; Tritz, K.; Zuo, G. Z.; Xu, W.; Huang, M.; Meng, X. C.; Canik, J. M.; Diallo, A.; Lunsford, R.; Mansfield, D. K.; Osborne, T. H.; Gong, X. Z.; Wang, Y. F.; Li, Y. Y.; EAST Team

2018-02-01

We report the first successful use of lithium (Li) to eliminate edge-localized modes (ELMs) with tungsten divertor plasma-facing components in the EAST device. Li powder injected into the scrape-off layer of the tungsten upper divertor successfully eliminated ELMs for 3-5 s in EAST. The ELM elimination became progressively more effective in consecutive discharges at constant lithium delivery rates, and the divertor D α baseline emission was reduced, both signatures of improved wall conditioning. A modest decrease in stored energy and normalized energy confinement was also observed, but the confinement relative to H98 remained well above 1, extending the previous ELM elimination results via Li injection into the lower carbon divertor in EAST (Hu et al 2015 Phys. Rev. Lett. 114 055001). These results can be compared with recent observations with lithium pellets in ASDEX-Upgrade that failed to mitigate ELMs (Lang et al 2017 Nucl. Fusion 57 016030), highlighting one comparative advantage of continuous powder injection for real-time ELM elimination.

ELM elimination with Li powder injection in EAST discharges using the tungsten upper divertor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maingi, R.; Hu, J. S.; Sun, Z.

Here, we report the first successful use of lithium (Li) to eliminate edge-localized modes (ELMs) with tungsten divertor plasma-facing components in the EAST device. Li powder injected into the scrape-off layer of the tungsten upper divertor successfully eliminated ELMs for 3–5 s in EAST. The ELM elimination became progressively more effective in consecutive discharges at constant lithium delivery rates, and the divertor D α baseline emission was reduced, both signatures of improved wall conditioning. A modest decrease in stored energy and normalized energy confinement was also observed, but the confinement relative to H98 remained well above 1, extending the previousmore » ELM elimination results via Li injection into the lower carbon divertor in EAST. These results can be compared with recent observations with lithium pellets in ASDEX-Upgrade that failed to mitigate ELMs, highlighting one comparative advantage of continuous powder injection for real-time ELM elimination.« less

A Machine Learning and Optimization Toolkit for the Swarm

DTIC Science & Technology

2014-11-17

Machine   Learning  and  Op0miza0on   Toolkit  for  the  Swarm   Ilge  Akkaya,  Shuhei  Emoto...3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE A Machine Learning and Optimization Toolkit for the Swarm 5a. CONTRACT NUMBER... machine   learning   methodologies  by  providing  the  right  interfaces  between   machine   learning  tools  and

Progress on the application of ELM control schemes to ITER scenarios from the non-active phase to DT operation

NASA Astrophysics Data System (ADS)

Loarte, A.; Huijsmans, G.; Futatani, S.; Baylor, L. R.; Evans, T. E.; Orlov, D. M.; Schmitz, O.; Becoulet, M.; Cahyna, P.; Gribov, Y.; Kavin, A.; Sashala Naik, A.; Campbell, D. J.; Casper, T.; Daly, E.; Frerichs, H.; Kischner, A.; Laengner, R.; Lisgo, S.; Pitts, R. A.; Saibene, G.; Wingen, A.

2014-03-01

Progress in the definition of the requirements for edge localized mode (ELM) control and the application of ELM control methods both for high fusion performance DT operation and non-active low-current operation in ITER is described. Evaluation of the power fluxes for low plasma current H-modes in ITER shows that uncontrolled ELMs will not lead to damage to the tungsten (W) divertor target, unlike for high-current H-modes in which divertor damage by uncontrolled ELMs is expected. Despite the lack of divertor damage at lower currents, ELM control is found to be required in ITER under these conditions to prevent an excessive contamination of the plasma by W, which could eventually lead to an increased disruptivity. Modelling with the non-linear MHD code JOREK of the physics processes determining the flow of energy from the confined plasma onto the plasma-facing components during ELMs at the ITER scale shows that the relative contribution of conductive and convective losses is intrinsically linked to the magnitude of the ELM energy loss. Modelling of the triggering of ELMs by pellet injection for DIII-D and ITER has identified the minimum pellet size required to trigger ELMs and, from this, the required fuel throughput for the application of this technique to ITER is evaluated and shown to be compatible with the installed fuelling and tritium re-processing capabilities in ITER. The evaluation of the capabilities of the ELM control coil system in ITER for ELM suppression is carried out (in the vacuum approximation) and found to have a factor of ˜2 margin in terms of coil current to achieve its design criterion, although such a margin could be substantially reduced when plasma shielding effects are taken into account. The consequences for the spatial distribution of the power fluxes at the divertor of ELM control by three-dimensional (3D) fields are evaluated and found to lead to substantial toroidal asymmetries in zones of the divertor target away from the separatrix. Therefore, specifications for the rotation of the 3D perturbation applied for ELM control in order to avoid excessive localized erosion of the ITER divertor target are derived. It is shown that a rotation frequency in excess of 1 Hz for the whole toroidally asymmetric divertor power flux pattern is required (corresponding to n Hz frequency in the variation of currents in the coils, where n is the toroidal symmetry of the perturbation applied) in order to avoid unacceptable thermal cycling of the divertor target for the highest power fluxes and worst toroidal power flux asymmetries expected. The possible use of the in-vessel vertical stability coils for ELM control as a back-up to the main ELM control systems in ITER is described and the feasibility of its application to control ELMs in low plasma current H-modes, foreseen for initial ITER operation, is evaluated and found to be viable for plasma currents up to 5-10 MA depending on modelling assumptions.

Development of Japanese experiment module remote manipulator system

NASA Technical Reports Server (NTRS)

Matsueda, Tatsuo; Kuwao, Fumihiro; Motohasi, Shoichi; Okamura, Ryo

1994-01-01

National Space Development Agency of Japan (NASDA) is developing the Japanese Experiment Module (JEM), as its contribution to the International Space Station. The JEM consists of the pressurized module (PM), the exposed facility (EF), the experiment logistics module pressurized section (ELM-PS), the experiment logistics module exposed section (ELM-ES) and the Remote Manipulator System (RMS). The JEMRMS services for the JEM EF, which is a space experiment platform, consists of the Main Arm (MA), the Small Fine Arm (SFA) and the RMS console. The MA handles the JEM EF payloads, the SFA and the JEM element, such as ELM-ES.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

PubMed

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

PubMed

Macesic, Nenad; Polubriaginof, Fernanda; Tatonetti, Nicholas P

2017-12-01

Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

Next-Generation Machine Learning for Biological Networks.

PubMed

Camacho, Diogo M; Collins, Katherine M; Powers, Rani K; Costello, James C; Collins, James J

2018-06-14

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology. Copyright © 2018 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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.

MLBCD: a machine learning tool for big clinical data.

PubMed

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

Slippery Elm

MedlinePlus

Slippery elm is a tree that is native to eastern Canada and the eastern and central United States. ... whole bark) is used as medicine. People take slippery elm by mouth for coughs, sore throat, colic, diarrhea, ...

Evaluating the Security of Machine Learning Algorithms

DTIC Science & Technology

2008-05-20

Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a...computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions... machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a

Using human brain activity to guide machine learning.

PubMed

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Quantum-Enhanced Machine Learning

NASA Astrophysics Data System (ADS)

Dunjko, Vedran; Taylor, Jacob M.; Briegel, Hans J.

2016-09-01

The emerging field of quantum machine learning has the potential to substantially aid in the problems and scope of artificial intelligence. This is only enhanced by recent successes in the field of classical machine learning. In this work we propose an approach for the systematic treatment of machine learning, from the perspective of quantum information. Our approach is general and covers all three main branches of machine learning: supervised, unsupervised, and reinforcement learning. While quantum improvements in supervised and unsupervised learning have been reported, reinforcement learning has received much less attention. Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements. As an example, we show that quadratic improvements in learning efficiency, and exponential improvements in performance over limited time periods, can be obtained for a broad class of learning problems.

Energetic ion losses caused by magnetohydrodynamic activity resonant and non-resonant with energetic ions in Large Helical Device

NASA Astrophysics Data System (ADS)

Ogawa, Kunihiro; Isobe, Mitsutaka; Toi, Kazuo; Shimizu, Akihiro; Spong, Donald A.; Osakabe, Masaki; Yamamoto, Satoshi; the LHD Experiment Group

2014-09-01

Experiments to reveal energetic ion dynamics associated with magnetohydrodynamic activity are ongoing in the Large Helical Device (LHD). Interactions between beam-driven toroidal Alfvén eigenmodes (TAEs) and energetic ions have been investigated. Energetic ion losses induced by beam-driven burst TAEs have been observed using a scintillator-based lost fast-ion probe (SLIP) in neutral beam-heated high β plasmas. The loss flux of co-going beam ions increases as the TAE amplitude increases. In addition to this, the expulsion of beam ions associated with edge-localized modes (ELMs) has been also recognized in LHD. The SLIP has indicated that beam ions having co-going and barely co-going orbits are affected by ELMs. The relation between ELM amplitude and ELM-induced loss has a dispersed structure. To understand the energetic ion loss process, a numerical simulation based on an orbit-following model, DELTA5D, that incorporates magnetic fluctuations is performed. The calculation result shows that energetic ions confined in the interior region are lost due to TAE instability, with a diffusive process characterizing their loss. For the ELM, energetic ions existing near the confinement/loss boundary are lost through a convective process. We found that the ELM-induced loss flux measured by SLIP changes with the ELM phase. This relation between the ELM amplitude and measured ELM-induced loss results in a more dispersed loss structure.

Myths and legends in learning classification rules

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

A discussion is presented of machine learning theory on empirically learning classification rules. Six myths are proposed in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, universal learning algorithms, and interactive learning. Some of the problems raised are also addressed from a Bayesian perspective. Questions are suggested that machine learning researchers should be addressing both theoretically and experimentally.

Machine Learning Based Malware Detection

DTIC Science & Technology

2015-05-18

A TRIDENT SCHOLAR PROJECT REPORT NO. 440 Machine Learning Based Malware Detection by Midshipman 1/C Zane A. Markel, USN...COVERED (From - To) 4. TITLE AND SUBTITLE Machine Learning Based Malware Detection 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...suitably be projected into realistic performance. This work explores several aspects of machine learning based malware detection . First, we

Interpreting Medical Information Using Machine Learning and Individual Conditional Expectation.

PubMed

Nohara, Yasunobu; Wakata, Yoshifumi; Nakashima, Naoki

2015-01-01

Recently, machine-learning techniques have spread many fields. However, machine-learning is still not popular in medical research field due to difficulty of interpreting. In this paper, we introduce a method of interpreting medical information using machine learning technique. The method gave new explanation of partial dependence plot and individual conditional expectation plot from medical research field.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

PubMed

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

Source localization in an ocean waveguide using supervised machine learning.

PubMed

Niu, Haiqiang; Reeves, Emma; Gerstoft, Peter

2017-09-01

Source localization in ocean acoustics is posed as a machine learning problem in which data-driven methods learn source ranges directly from observed acoustic data. The pressure received by a vertical linear array is preprocessed by constructing a normalized sample covariance matrix and used as the input for three machine learning methods: feed-forward neural networks (FNN), support vector machines (SVM), and random forests (RF). The range estimation problem is solved both as a classification problem and as a regression problem by these three machine learning algorithms. The results of range estimation for the Noise09 experiment are compared for FNN, SVM, RF, and conventional matched-field processing and demonstrate the potential of machine learning for underwater source localization.

Machine Learning for Medical Imaging

PubMed Central

Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054

Machine Learning for Medical Imaging.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Endangered Species Management Plan for Fort Hood, Texas: FY06-10

DTIC Science & Technology

2007-05-01

Texas red oak, post oak, Texas ash (Fraxinus texensis), shin oak, blackjack oak (Quercus marilandica), slippery elm (Ulmus rubra), cedar elm ...by Ashe juniper and Texas oak. Other important tree species included live oak, cedar elm (Ulmus crassifolia), Lacey oak (Quercus laceyi), Arizona...0.83 m (Cimprich 2005). Nest substrates include shin oak, Texas red oak, Texas redbud, Ashe juniper, Texas ash, Plateau live oak, cedar elm , rusty

The banded elm bark beetle: A new threat to elms in North America

Treesearch

Jose F. Negron; Jeffrey J. Witcosky; Robert J. Cain; James R. LaBonte; Donald A. Duerr; Sally J. McElwey; Jana C. Lee; Steven J. Seybold

2005-01-01

An exotic bark beetle from Asia, the banded elm bark beetle, Scolytus schevyrewi, has been discovered in 21 states of the United States. Although its point of entry is not known, a survey of museum specimens suggests that it has been in the US for at least 10 years. It is most abundant in western states, attacks primarily American and Siberian elms, and carries spores...

The relationships between edge localized modes suppression, pedestal profiles and lithium wall coatings in NSTX

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boyle, D. P.; Maingi, R.; Snyder, P. B.

2011-01-01

Recently in the National Spherical Torus Experiment (NSTX), increasing lithium wall coatings suppressed edge localized modes (ELMs), gradually but not quite monotonically. This work details profile and stability analysis as ELMs disappeared throughout the lithium scan. While the quantity of lithium deposited between discharges did not uniquely determine the presence of ELMs, profile analysis demonstrated that lithium was correlated with wider density and pressure pedestals with peak gradients farther from the separatrix. Moreover, the ELMy and ELM-free discharges were cleanly separated by their density and pedestal widths and peak gradient locations. Ultimately, ELMs were only suppressed when lithium caused themore » density pedestal to widen and shift inward. These changes in the density gradient were directly reflected in the pressure gradient and calculated bootstrap current. This supports the theory that ELMs in NSTX are caused by peeling and/or ballooning modes, as kink/peeling modes are stabilized when the edge current and pressure gradient shift away from the separatrix. Edge stability analysis using ELITE corroborated this picture, as reconstructed equilibria from ELM-free discharges were generally farther from their kink/peeling stability boundaries than ELMy discharges. We conclude that density profile control provided by lithium is the key first step to ELM suppression in NSTX.« less

Machine learning in heart failure: ready for prime time.

PubMed

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Human Machine Learning Symbiosis

ERIC Educational Resources Information Center

Walsh, Kenneth R.; Hoque, Md Tamjidul; Williams, Kim H.

2017-01-01

Human Machine Learning Symbiosis is a cooperative system where both the human learner and the machine learner learn from each other to create an effective and efficient learning environment adapted to the needs of the human learner. Such a system can be used in online learning modules so that the modules adapt to each learner's learning state both…

Machine learning in cardiovascular medicine: are we there yet?

PubMed

Shameer, Khader; Johnson, Kipp W; Glicksberg, Benjamin S; Dudley, Joel T; Sengupta, Partho P

2018-01-19

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Parameter dependence of ELM loss reduction by magnetic perturbations at low pedestal density and collisionality in ASDEX upgrade

NASA Astrophysics Data System (ADS)

Leuthold, N.; Suttrop, W.; Fischer, R.; Kappatou, A.; Kirk, A.; McDermott, R.; Mlynek, A.; Valovič, M.; Willensdorfer, M.; the ASDEX Upgrade Team; the EUROfusion MST1 Team

2017-05-01

ELM mitigation by magnetic perturbations is studied at low pedestal collisionalities down to ITER-like values ({ν }{e,{PED}}* =0.1) in ASDEX Upgrade. A comprehensive database of ELM energy losses for varying plasma density, heating power, edge safety factor and magnetic perturbation structure has been assembled to investigate parameter dependencies of ELM mitigation. It is found that magnetic perturbations with a toroidal mode number n = 2 can reduce the ELM energy loss normalized to the energy stored in the plasma pedestal from about 30% to less than 5%, i.e. by a factor of six, below an electron pedestal collisionality of {ν }{e,{PED}}* =0.4. At this level of ELM mitigation a significant reduction of the pedestal pressure and, therefore, global plasma confinement occurs. This pedestal pressure reduction is mostly due to a reduction of plasma density, the so-called ‘pump-out’ effect. Refueling by neutral beams and in particular by pellet injection is possible and can re-establish confinement, however, the ELM energy loss increases as well with increasing density.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Myths and legends in learning classification rules

NASA Technical Reports Server (NTRS)

Buntine, Wray

1990-01-01

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine learning community that address issues of bias, learning as search, computational learning theory, Occam's razor, 'universal' learning algorithms, and interactive learnings. Some of the problems raised are also addressed from a Bayesian perspective. The paper concludes by suggesting questions that machine learning researchers should be addressing both theoretically and experimentally.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.

Lower Mississippi River Environmental Program. Report 2. A Physical Description of Main Stem Levee Borrow Pits along the Lower Mississippi River

DTIC Science & Technology

1988-02-01

Eastern cottonwood, green ash, sugarberry. box elder, bald cypress, willow honey locust, slippery elm , overcup oak and bitter pecan. Principle...vines and understory. Woody vegetation surrounds the borrow pit and consists of American and slippery elms , silver maple, black willow, cottonwood, pin...aquatica Water elm Ulmus rubra Slippery elm Urtica dioica Stinging nettle Vaccinium sp. Blueberry Vaccinium spp. Vaccinum Vernonia altissima Ironweed

Water Resources Development Miami River, Little Miami River, and Mill Creek Basins, Southwest Ohio. Volume 2. Appendices A-G.

DTIC Science & Technology

1981-10-01

the area. Common species include boxelder, black locust, hackberry, tree of heaven, sycamore, Osage orange, black willow, mulberry, slippery elm ...and maple include tulip poplar, white ash, red elm , American elm , black cherry, hackberry, walnut, basswood, buckeye, white oak, shagbark hickory, and...willow. Other canopy species in these communities include buckeye, elm , beech, black locust, hackberry, walnut, and silver maple. The most prevalent

Time and materials needed to survey, inject systemic fungicides, and install root-graft barriers for Dutch elm disease management

Treesearch

William N., Jr. Cannon; Jack H. Barger; Charles J. Kostichka; Charles J. Kostichka

1986-01-01

Dutch elm disease control practice in 15 communities showed a wide range of time and material required to apply control methods. The median time used for each method was: sanitation survey, 9.8 hours per square mile; symptom survey, 96 hours per thousand elms; systemic fungicide injection, 1.4 hours per elm; and root-graft barrier installation, 2.2 hours per barrier (5...

Characteristics of the Secondary Divertor on DIII-D

NASA Astrophysics Data System (ADS)

Watkins, J. G.; Lasnier, C. J.; Leonard, A. W.; Evans, T. E.; Pitts, R.; Stangeby, P. C.; Boedo, J. A.; Moyer, R. A.; Rudakov, D. L.

2009-11-01

In order to address a concern that the ITER secondary divertor strike plates may be insufficiently robust to handle the incident pulses of particles and energy from ELMs, we performed dedicated studies of the secondary divertor plasma and scrape-off layer (SOL). Detailed measurements of the ELM energy and particle deposition footprint on the secondary divertor target plates were made with a fast IR camera and Langmuir probes and SOL profile and transport measurements were made with reciprocating probes. The secondary divertor and SOL conditions depended on changes in the magnetic balance and the core plasma density. Larger density resulted in smaller ELMs and the magnetic balance affected how many ELM particles coupled to the secondary SOL and divertor. Particularly striking are the images from a new fast IR camera that resolve ELM heat pulses and show spiral patterns with multiple peaks during ELMs in the secondary divertor.

Recycling Reduction and Density Control with Lithium Injection in DIII-D

NASA Astrophysics Data System (ADS)

Jackson, G. L.; Chrobak, C. P.; Maingi, R.; Mansfield, D.; Roquemore, A.; McLean, A. G.

2013-10-01

Lithium conditioning has been effective in tokamaks for reducing recycling and providing density control, particularly in NSTX and EAST. Since DIII-D has not injected lithium in more than a decade (and then in only very small amounts, 0.4 g total), a unique opportunity exists to extend this experience and examine the physical effects of lithium in a well-conditioned lithium-free machine. A lithium dropper, developed by PPPL, has recently been installed on DIII-D. By injecting 0.09 g of lithium we have observed reductions in recycling, density, and ELM frequency from the first discharge with significant lithium injection. Although modeling of individual 40 μ m diam. Li granules predicts virtually no penetration beyond the separatrix in auxiliary heated H-mode pulses, LiIII emission was detected in the core plasma, albeit with no increase in radiated power. On subsequent discharges without injection no core Li was detected, and only LiI emission was observed in the SOL and divertor regions. We will present the effects of Li on recycling, ELM frequency, and the edge pedestal, and discuss the long-term observations of lithium on plasma facing components. Work supported by the US Department of Energy under DE-FC02-04ER54698, DE-AC02-09CH11466, and DE-AC52-07NA27344.

Circulating Elastin Fragments Are Not Affected by Hepatic, Renal and Hemodynamic Changes, But Reflect Survival in Cirrhosis with TIPS.

PubMed

Nielsen, M J; Lehmann, J; Leeming, D J; Schierwagen, R; Klein, S; Jansen, C; Strassburg, C P; Bendtsen, F; Møller, S; Sauerbruch, T; Karsdal, M A; Krag, A; Trebicka, J

2015-11-01

Progressive fibrosis increases hepatic resistance and causes portal hypertension with complications. During progressive fibrosis remodeling and deposition of collagens and elastin occur. Elastin remodeling is crucially involved in fibrosis progression in animal models and human data. This study investigated the association of circulating elastin with the clinical outcome in cirrhotic patients with severe portal hypertension receiving transjugular intrahepatic porto-systemic shunt (TIPS). We analyzed portal and hepatic venous samples of 110 cirrhotic patients obtained at TIPS insertion and 2 weeks later. The circulating levels of elastin fragments (ELM) were determined using specific monoclonal ELISA. The relationship of ELM with clinical short-time follow-up and long-term outcome was investigated. Circulating levels of ELM showed a gradient across the liver before TIPS with higher levels in the hepatic vein. Interestingly, the circulating ELM levels remained unchanged after TIPS. The circulating levels of ELM in portal and hepatic veins correlated with platelet counts and inversely with serum sodium. Hepatic venous levels of ELM were higher in CHILD C compared to CHILD A and B and were associated with the presence of ascites. Patients with high levels of ELM in the hepatic veins before TIPS showed poorer survival. In multivariate analysis ELM levels in the hepatic veins and MELD were independent predictors of mortality in these patients. This study demonstrated that circulating levels of ELM are not associated with hemodynamic changes, but might reflect fibrosis remodeling and predict survival in patients with severe portal hypertension receiving TIPS independently of MELD.

First results from the US-PRC PMI collaboration on EAST

NASA Astrophysics Data System (ADS)

Maingi, R.; Lunsford, R.; Mansfield, D.; Diallo, A.; Hu, J.; Sun, Z.; Zuo, G.; Gong, X.; Tritz, K.; Canik, J.; Osborne, T.; EAST Team

2017-10-01

A US-PRC collaboration was formed to understand the plasma-material interface for improved long pulse discharge performance in EAST, with an emphasis on Li conditioning techniques. The US multi-institutional team consists of participants from PPPL, UI-UC, UT-K, ORNL, MIT, LANL, and JHU. In Dec. 2016, this team co-led experiments on the use of Li aerosol injection to mitigate ELMs, Li granule injection to pace ELMs, and a flowing liquid Li limiter to serve as a primary plasma-facing component. Li aerosol injection was shown to eliminate ELMs using the upper ITER-like W divertor, extending previous results of ELM suppression in the lower cabon divertor (J.S. Hu, PRL 2015). In addition Li granule injection was shown to trigger and even pace ELMs, although the paced ELM frequency was slower than the natural ELM frequency in this set of experiments; previously paced ELM frequency was comparable to natural ELMs frequency (D.K. Mansfield, NF 2013). Finally a second generation flowing liquid Li limiter was shown to be compatible with ELMy H-mode plasmas, pushed within 1 cm of the separatrix. The surface showed no damage to PMI and improved wetting as compared to the first generation limiter experiments (J.S. Hu, NF 2016 and G.Z. Zuo, NF 2017). US scientists supported in part by US DoE contracts DE-AC02-09CH11466, DE-FG02-09ER55012, DE-AC05-00OR22725, and DE-FC02-04ER54698, and ASIPP scientists by Contract No. 11625524, No. 11075185, No. 11021565, and No. 2013GB114004.

Applications of Machine Learning and Rule Induction,

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

Lower Mississippi River Environmental Program. Report 3. Bird and Mammal Use of Main Stem Levee Borrow Pits Along the Lower Mississippi River.

DTIC Science & Technology

1986-02-01

overcup oak, tupelo gum, Nuttall oak, American elm , slippery elm , i , hickories, persimmon, silver maple, deciduous holly, swamp privet, and rose mallows...cottonwood, green ash, sugarberry, box elder, deciduous holly, osage orange, swamp privet, hickories, overcup oak, water locust, honey locust, slippery elm ...wood, box elder, osage orange, hickories, honey locust, water locust, slippery elm , swamp privet, sugarberry, persimmon, and rose mallows. Where

Characterizing Tungsten Sourcing and SOL Transport during the Metal Rings Campaign

NASA Astrophysics Data System (ADS)

Thomas, D. M.; Abrams, T.; Unterberg, E. A.; Donovan, D.; Elder, J. D.; Wampler, W. R.; DIII-D Team

2017-10-01

The Metal Rings Campaign on DIII-D utilized two isotopically and poloidally distinct toroidal arrays of tungsten coated inserts in the lower divertor to study W divertor erosion near the outer strike point (OSP) and divertor entrance and subsequent migration in a mixed-material (C-W) environment. In AT hybrid discharges (PAUX = 14 MW, H98 = 1.6, βN = 3.7) with rapid ELMs (fELM 200 Hz, δW/W 0.7%) W impurities are seen to reach the midplane predominantly from the OSP region rather than the divertor entrance (far-SOL). Conversely, in scenarios with less frequent larger ELMs (fELM 60 Hz, δW/W 3.6%), the W impurities are found to transport equally from the OSP and entrance region. ELM-resolved spectroscopic measurements of W sourcing indicate that large ELMs can source W at many times the inter ELM rate. The peak W erosion rate can shift radially outwards consistent with the ELM energy flux, thereby shifting the balance between strikepoint and far-SOL sources. Changes in the peak erosion locations between forward and reversed Bt discharges are consistent with ExB ion drift effects. Evidence for a near-SOL impurity buildup between the divertors driven by the parallel grad-Ti force is also seen. Work supported under USDOE Cooperative Agreement DE-FC02-04ER54698.

Quantitative analysis of external limiting membrane, ellipsoid zone and interdigitation zone defects in patients with macular holes.

PubMed

Houly, Jacques Ramos; Veloso, Carlos Eduardo; Passos, Elke; Nehemy, Márcio Bittar

2017-07-01

To investigate the correlation between the length of external limiting membrane (ELM), ellipsoid zone (EZ) and interdigitation zone (IZ) defects and visual prognosis in patients undergoing macular hole (MH) surgery, using spectral-domain optical coherence tomography (SD-OCT). This is a retrospective, consecutive, observational case series study. Fifty-two eyes of 52 patients with primary MH were evaluated. A quantitative analysis of ELM, EZ and IZ defects was performed preoperatively and at 3 and 6 months postoperatively using SD-OCT. The correlation between pre- and postoperative ELM, EZ and IZ defects and the best-corrected visual acuity (BCVA) was investigated. The lengths of ELM, EZ and IZ defects correlated significantly with BCVA in each study period (P < 0.001). Preoperative measures of these band defects were also associated with visual outcomes 3 and 6 months after surgery (P < 0.05). Considering all preoperative parameters, the length of the ELM defect was the factor most strongly correlated with BCVA at 6 months (β = 0.643, P < 0.012). The integrity of the ELM was the only factor significantly associated with BCVA at 6 months (β = 0.427; P = 0.004). The preoperative length of the ELM defect is the strongest predictor of visual acuity after MH surgery. Postoperative integrity of the ELM is significantly associated with visual restoration after surgical treatment of MH.

iELM—a web server to explore short linear motif-mediated interactions

PubMed Central

Weatheritt, Robert J.; Jehl, Peter; Dinkel, Holger; Gibson, Toby J.

2012-01-01

The recent expansion in our knowledge of protein–protein interactions (PPIs) has allowed the annotation and prediction of hundreds of thousands of interactions. However, the function of many of these interactions remains elusive. The interactions of Eukaryotic Linear Motif (iELM) web server provides a resource for predicting the function and positional interface for a subset of interactions mediated by short linear motifs (SLiMs). The iELM prediction algorithm is based on the annotated SLiM classes from the Eukaryotic Linear Motif (ELM) resource and allows users to explore both annotated and user-generated PPI networks for SLiM-mediated interactions. By incorporating the annotated information from the ELM resource, iELM provides functional details of PPIs. This can be used in proteomic analysis, for example, to infer whether an interaction promotes complex formation or degradation. Furthermore, details of the molecular interface of the SLiM-mediated interactions are also predicted. This information is displayed in a fully searchable table, as well as graphically with the modular architecture of the participating proteins extracted from the UniProt and Phospho.ELM resources. A network figure is also presented to aid the interpretation of results. The iELM server supports single protein queries as well as large-scale proteomic submissions and is freely available at http://i.elm.eu.org. PMID:22638578

Experimental Realization of a Quantum Support Vector Machine

NASA Astrophysics Data System (ADS)

Li, Zhaokai; Liu, Xiaomei; Xu, Nanyang; Du, Jiangfeng

2015-04-01

The fundamental principle of artificial intelligence is the ability of machines to learn from previous experience and do future work accordingly. In the age of big data, classical learning machines often require huge computational resources in many practical cases. Quantum machine learning algorithms, on the other hand, could be exponentially faster than their classical counterparts by utilizing quantum parallelism. Here, we demonstrate a quantum machine learning algorithm to implement handwriting recognition on a four-qubit NMR test bench. The quantum machine learns standard character fonts and then recognizes handwritten characters from a set with two candidates. Because of the wide spread importance of artificial intelligence and its tremendous consumption of computational resources, quantum speedup would be extremely attractive against the challenges of big data.

Workshop on Fielded Applications of Machine Learning

DTIC Science & Technology

1994-05-11

This report summaries the talks presented at the Workshop on Fielded Applications of Machine Learning , and draws some initial conclusions about the state of machine learning and its potential for solving real-world problems.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Impact of Te and ne on edge current density profiles in ELM mitigated regimes on ASDEX Upgrade

NASA Astrophysics Data System (ADS)

Dunne, M. G.; Rathgeber, S.; Burckhart, A.; Fischer, R.; Giannone, L.; McCarthy, P. J.; Schneider, P. A.; Wolfrum, E.; the ASDEX Upgrade Team

2015-01-01

ELM resolved edge current density profiles are reconstructed using the CLISTE equilibrium code. As input, highly spatially and temporally resolved edge electron temperature and density profiles are used in addition to data from the extensive set of external poloidal field measurements available at ASDEX Upgrade, flux loop difference measurements, and current measurements in the scrape-off layer. Both the local and flux surface averaged current density profiles are analysed for several ELM mitigation regimes. The focus throughout is on the impact of altered temperature and density profiles on the current density. In particular, many ELM mitigation regimes rely on operation at high density. Two reference plasmas with type-I ELMs are analysed, one with a deuterium gas puff and one without, in order to provide a reference for the behaviour in type-II ELMy regimes and high density ELM mitigation with external magnetic perturbations at ASDEX Upgrade. For type-II ELMs it is found that while a similar pedestal top pressure is sustained at the higher density, the temperature gradient decreases in the pedestal. This results in lower local and flux surface averaged current densities in these phases, which reduces the drive for the peeling mode. No significant differences between the current density measured in the type-I phase and ELM mitigated phase is seen when external perturbations are applied, though the pedestal top density was increased. Finally, ELMs during the nitrogen seeded phase of a high performance discharge are analysed and compared to ELMs in the reference phase. An increased pedestal pressure gradient, which is the source of confinement improvement in impurity seeded discharges, causes a local current density increase. However, the increased Zeff in the pedestal acts to reduce the flux surface averaged current density. This dichotomy, which is not observed in other mitigation regimes, could act to stabilize both the ballooning mode and the peeling mode at the same time.

Machine Learning. Part 1. A Historical and Methodological Analysis.

DTIC Science & Technology

1983-05-31

Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern Al systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. Part 1 of this paper presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by

Toward Harnessing User Feedback For Machine Learning

DTIC Science & Technology

2006-10-02

machine learning systems. If this resource-the users themselves-could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users? understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

Stabilizing effect of resistivity towards ELM-free H-mode discharge in lithium-conditioned NSTX

NASA Astrophysics Data System (ADS)

Banerjee, Debabrata; Zhu, Ping; Maingi, Rajesh

2017-07-01

Linear stability analysis of the national spherical torus experiment (NSTX) Li-conditioned ELM-free H-mode equilibria is carried out in the context of the extended magneto-hydrodynamic (MHD) model in NIMROD. The purpose is to investigate the physical cause behind edge localized mode (ELM) suppression in experiment after the Li-coating of the divertor and the first wall of the NSTX tokamak. Besides ideal MHD modeling, including finite-Larmor radius effect and two-fluid Hall and electron diamagnetic drift contributions, a non-ideal resistivity model is employed, taking into account the increase of Z eff after Li-conditioning in ELM-free H-mode. Unlike an earlier conclusion from an eigenvalue code analysis of these equilibria, NIMROD results find that after reduced recycling from divertor plates, profile modification is necessary but insufficient to explain the mechanism behind complete ELMs suppression in ideal two-fluid MHD. After considering the higher plasma resistivity due to higher Z eff, the complete stabilization could be explained. A thorough analysis of both pre-lithium ELMy and with-lithium ELM-free cases using ideal and non-ideal MHD models is presented, after accurately including a vacuum-like cold halo region in NIMROD to investigate ELMs.

Comparative ELM study between the observation by ECEI and linear/nonlinear simulation in the KSTAR plasmas

NASA Astrophysics Data System (ADS)

Kim, Minwoo; Park, Hyeon K.; Yun, Gunsu; Lee, Jaehyun; Lee, Jieun; Lee, Woochang; Jardin, Stephen; Xu, X. Q.; Kstar Team

2015-11-01

The modeling of the Edge-localized-mode (ELM) should be rigorously pursued for reliable and robust ELM control for steady-state long-pulse H-mode operation in ITER as well as DEMO. In the KSTAR discharge #7328, a linear stability of the ELMs is investigated using M3D-C1 and BOUT + + codes. This is achieved by linear simulation for the n = 8 mode structure of the ELM observed by the KSTAR electron cyclotron emission imaging (ECEI) systems. In the process of analysis, variations due to the plasma equilibrium profiles and transport coefficients on the ELM growth rate are investigated and simulation results with the two codes are compared. The numerical simulations are extended to nonlinear phase of the ELM dynamics, which includes saturation and crash of the modes. Preliminary results of the nonlinear simulations are compared with the measured images especially from the saturation to the crash. This work is supported by NRF of Korea under contract no. NRF-2014M1A7A1A03029865, US DoE by LLNL under contract DE-AC52-07NA27344 and US DoE by PPPL under contract DE-AC02-09CH11466.

Plant signals during beetle (Scolytus multistriatus) feeding in American elm (Ulmus americana Planch).

PubMed

Saremba, Brett M; Tymm, Fiona J M; Baethke, Kathy; Rheault, Mark R; Sherif, Sherif M; Saxena, Praveen K; Murch, Susan J

2017-05-04

American Elms were devastated by an outbreak of Dutch Elm Disease is caused by the fungus Ophiostoma novo-ulmi Brasier that originated in Asia and arrived in the early 1900s. In spite of decades of study, the specific mechanisms and disease resistance in some trees is not well understood. the fungus is spread by several species of bark beetles in the genus Scolytus, during their dispersal and feeding. Our objective was to understand elm responses to beetle feeding in the absence of the fungus to identify potential resistance mechanisms. A colony of Scolytus multistriatus was established from wild-caught beetles and beetles were co-incubated with susceptible or resistant American elm varieties in a controlled environment chamber. Beetles burrowed into the auxillary meristems of the young elm shoots. The trees responded to the beetle damage by a series of spikes in the concentration of plant growth regulating compounds, melatonin, serotonin, and jasmonic acid. Spikes in melatonin and serotonin represented a 7,000-fold increase over resting levels. Spikes in jasmonic acid were about 10-fold higher than resting levels with one very large spike observed. Differences were noted between susceptible and resistant elms that provide new understanding of plant defenses.

Language Acquisition and Machine Learning.

DTIC Science & Technology

1986-02-01

machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, the authors propose four component tasks involved in learning from experience-aggregation, clustering, characterization, and storage. They then consider four common problems studied by machine learning researchers-learning from examples, heuristics learning, conceptual clustering, and learning macro-operators-describing each in terms of our framework. After this, they turn to the problem of grammar

Behavioral Profiling of Scada Network Traffic Using Machine Learning Algorithms

DTIC Science & Technology

2014-03-27

BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS THESIS Jessica R. Werling, Captain, USAF AFIT-ENG-14-M-81 DEPARTMENT...subject to copyright protection in the United States. AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ...AFIT-ENG-14-M-81 BEHAVIORAL PROFILING OF SCADA NETWORK TRAFFIC USING MACHINE LEARNING ALGORITHMS Jessica R. Werling, B.S.C.S. Captain, USAF Approved

Statistical Machine Learning for Structured and High Dimensional Data

DTIC Science & Technology

2014-09-17

AFRL-OSR-VA-TR-2014-0234 STATISTICAL MACHINE LEARNING FOR STRUCTURED AND HIGH DIMENSIONAL DATA Larry Wasserman CARNEGIE MELLON UNIVERSITY Final...Re . 8-98) v Prescribed by ANSI Std. Z39.18 14-06-2014 Final Dec 2009 - Aug 2014 Statistical Machine Learning for Structured and High Dimensional...area of resource-constrained statistical estimation. machine learning , high-dimensional statistics U U U UU John Lafferty 773-702-3813 > Research under

Machine learning in genetics and genomics

PubMed Central

Libbrecht, Maxwell W.; Noble, William Stafford

2016-01-01

The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

Learning from history, predicting the future: the UK Dutch elm disease outbreak in relation to contemporary tree disease threats

PubMed Central

Potter, Clive; Harwood, Tom; Knight, Jon; Tomlinson, Isobel

2011-01-01

Expanding international trade and increased transportation are heavily implicated in the growing threat posed by invasive pathogens to biodiversity and landscapes. With trees and woodland in the UK now facing threats from a number of disease systems, this paper looks to historical experience with the Dutch elm disease (DED) epidemic of the 1970s to see what can be learned about an outbreak and attempts to prevent, manage and control it. The paper draws on an interdisciplinary investigation into the history, biology and policy of the epidemic. It presents a reconstruction based on a spatial modelling exercise underpinned by archival research and interviews with individuals involved in the attempted management of the epidemic at the time. The paper explores what, if anything, might have been done to contain the outbreak and discusses the wider lessons for plant protection. Reading across to present-day biosecurity concerns, the paper looks at the current outbreak of ramorum blight in the UK and presents an analysis of the unfolding epidemiology and policy of this more recent, and potentially very serious, disease outbreak. The paper concludes by reflecting on the continuing contemporary relevance of the DED experience at an important juncture in the evolution of plant protection policy. PMID:21624917

Assembly interdependence among the S. cerevisiae bud neck ring proteins Elm1p, Hsl1p and Cdc12p.

PubMed

Thomas, Courtney L; Blacketer, Melissa J; Edgington, Nicholas P; Myers, Alan M

2003-07-15

In Saccharomyces cerevisiae, a complex comprising more than 20 different polypeptides assembles in a ring at the neck between the mother cell and the bud. This complex functions to coordinate cell morphology with cell division. Relatively little is known about this control system, including the physical relationships between the components of the neck ring. This study addressed the assembly interactions of three components of the ring, specifically the protein kinases Elm1p and Hsl1p and the septin Cdc12p. Specific amino acid substitutions in each of these three proteins were identified that either cause or suppress a characteristic phenotype of abnormally elongated cells and delay in the G(2)-M transition. Each protein was fused to green fluorescent protein, and its ability to localize at the neck was monitored in vivo in cells of various genotypes. Localization of Hsl1p to the neck requires Elm1p function. Elm1p localized normally in the absence of Hsl1p, although a specific point mutation in Hsl1p clearly affected Elm1p localization. The cdc12-122 mutation prevented assembly of Elm1p or Hsl1p into the neck ring. Normal assembly of Cdc12p at the neck was dependent upon Elm1p and also, to a smaller extent, on Hsl1p. Ectopic localization of Cdc12p at the bud tip was observed frequently in elm1 mutants and also, to a lesser extent, in hsl1 mutants. Thus, Elm1p is a key factor in the assembly and/or maintenance of Hsl1p, as well as at least one septin, into the bud neck ring. Copyright 2003 John Wiley & Sons, Ltd.

Physics of thermal transport and increased electron temperature turbulence in the edge pedestal of ELM-free, H-mode regimes on DIII-D

NASA Astrophysics Data System (ADS)

Sung, Choongki

2017-10-01

It has been observed, for the first time, that suppression of Edge Localized Modes (ELMs) in tokamak plasmas is accompanied by an increase in electron temperature turbulence. A correlation electron cyclotron emission technique has been utilized to quantify the observed increase: 40% increase in Quiescent H-mode (QH-mode) and 70% increase in 3D field ELM suppressed H-mode. Since reliable ELM-free H-mode operation is essential for future burning plasma experiments, it is crucial to develop a validated predictive capability for these plasmas. Linear stability analysis using TGLF has provided an explanation for the observations and has indicated that the underlying physical mechanisms are different in the two regimes. In QH-mode, profile gradients and the associated linear growth rate are decreased compared to ELMing H-mode. However, the ExB shearing rate is reduced by an even greater factor such that turbulent transport is no longer suppressed by flow shear. In contrast, during 3D field ELM suppressed H-mode, gradients are increased and TGLF predicts that a large increase in linear growth rate is primarily responsible for the increased turbulence. Power balance analysis using ONETWO is also consistent with the changes in electron thermal transport being due to the increased turbulence. These new findings are significant since they i) provide a physics explanation of these changes via TGLF analysis and enable validation of the model in the key pedestal region, and ii) support the hypothesis that turbulent transport partially replaces ELM-dominated transport during ELM-free operation. These results form a basis to develop a predictive understanding of pedestal regulation in ELM suppressed regimes. Supported by the US DOE under DE-FG02-08ER54984, DE-FC02-04ER54698.

Experimental validation of coil phase parametrisation on ASDEX Upgrade, and extension to ITER

NASA Astrophysics Data System (ADS)

Ryan, D. A.; Liu, Y. Q.; Kirk, A.; Suttrop, W.; Dudson, B.; Dunne, M.; Willensdorfer, M.; the ASDEX Upgrade team; the EUROfusion MST1 team

2018-06-01

It has been previously demonstrated in Li et al (2016 Nucl. Fusion 56 126007) that the optimum upper/lower coil phase shift ΔΦopt for alignment of RMP coils for ELM mitigation depends sensitively on q 95, and other equilibrium plasma parameters. Therefore, ΔΦopt is expected to vary widely during the current ramp of ITER plasmas, with negative implications for ELM mitigation during this period. A previously derived and numerically benchmarked parametrisation of the coil phase for optimal ELM mitigation on ASDEX Upgrade (Ryan et al 2017 Plasma Phys. Control. Fusion 59 024005) is validated against experimental measurements of ΔΦopt, made by observing the changes to the ELM frequency as the coil phase is scanned. It is shown that the parametrisation may predict the optimal coil phase to within 32° of the experimental measurement for n = 2 applied perturbations. It is explained that this agreement is sufficient to ensure that the ELM mitigation is not compromised by poor coil alignment. It is also found that the phase which maximises ELM mitigation is shifted from the phase which maximizes density pump-out, in contrast to theoretical expectations that ELM mitigation and density pump out have the same ΔΦ ul dependence. A time lag between the ELM frequency response and density response to the RMP is suggested as the cause. The method for numerically deriving the parametrisation is repeated for the ITER coil set, using the baseline scenario as a reference equilibrium, and the parametrisation coefficients given for future use in a feedback coil alignment system. The relative merits of square or sinusoidal toroidal current waveforms for ELM mitigation are briefly discussed.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Addressing uncertainty in atomistic machine learning.

PubMed

Peterson, Andrew A; Christensen, Rune; Khorshidi, Alireza

2017-05-10

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate of the uncertainty when the width is comparable to that in the training data. Intriguingly, we also show that the uncertainty can be localized to specific atoms in the simulation, which may offer hints for the generation of training data to strategically improve the machine-learned representation.

On the Conditioning of Machine-Learning-Assisted Turbulence Modeling

NASA Astrophysics Data System (ADS)

Wu, Jinlong; Sun, Rui; Wang, Qiqi; Xiao, Heng

2017-11-01

Recently, several researchers have demonstrated that machine learning techniques can be used to improve the RANS modeled Reynolds stress by training on available database of high fidelity simulations. However, obtaining improved mean velocity field remains an unsolved challenge, restricting the predictive capability of current machine-learning-assisted turbulence modeling approaches. In this work we define a condition number to evaluate the model conditioning of data-driven turbulence modeling approaches, and propose a stability-oriented machine learning framework to model Reynolds stress. Two canonical flows, the flow in a square duct and the flow over periodic hills, are investigated to demonstrate the predictive capability of the proposed framework. The satisfactory prediction performance of mean velocity field for both flows demonstrates the predictive capability of the proposed framework for machine-learning-assisted turbulence modeling. With showing the capability of improving the prediction of mean flow field, the proposed stability-oriented machine learning framework bridges the gap between the existing machine-learning-assisted turbulence modeling approaches and the demand of predictive capability of turbulence models in real applications.

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

PubMed

Zeng, Xueqiang; Luo, Gang

2017-12-01

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

Bypassing the Kohn-Sham equations with machine learning.

PubMed

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Statistical Characterization and Classification of Edge-Localized Plasma Instabilities

NASA Astrophysics Data System (ADS)

Webster, A. J.; Dendy, R. O.

2013-04-01

The statistics of edge-localized plasma instabilities (ELMs) in toroidal magnetically confined fusion plasmas are considered. From first principles, standard experimentally motivated assumptions are shown to determine a specific probability distribution for the waiting times between ELMs: the Weibull distribution. This is confirmed empirically by a statistically rigorous comparison with a large data set from the Joint European Torus. The successful characterization of ELM waiting times enables future work to progress in various ways. Here we present a quantitative classification of ELM types, complementary to phenomenological approaches. It also informs us about the nature of ELM processes, such as whether they are random or deterministic. The methods are extremely general and can be applied to numerous other quasiperiodic intermittent phenomena.

VizieR Online Data Catalog: The ELM survey. VI. 11 new ELM WD binaries (Gianninas+, 2015)

NASA Astrophysics Data System (ADS)

Gianninas, A.; Kilic, M.; Brown, W. R.; Canton, P.; Kenyon, S. J.

2016-02-01

We used the 6.5m MMT telescope equipped with the Blue Channel spectrograph, the 200 inch Hale telescope equipped with the Double spectrograph, the Kitt Peak National Observatory 4m telescope equipped with the R-C spectrograph, and more recently with Kitt Peak Ohio State Multi-Object Spectrograph (KOSMOS), to obtain spectroscopy of our 11 targets in several observing runs. We have also been obtaining radial-velocity measurements for candidates from other sources including the Large Sky Area Multi-Object Spectroscopy Telescope (LAMOST). Those 11 new Extremely low-mass white dwarf (ELM WD) binaries bring the total of ELM WDs identified by the ELM Survey up to 73. (4 data files).

Dutch Elm Disease and Methoxychlor

Treesearch

Jack H. Barger

1976-01-01

American elm trees, Ulmus americana L., in Milwaukee, Wisconsin, were sprayed with methoxychlor by helicopter or mist blower once each year for 3 years to control the smaller European elm bark beetle Scolytus multistriatus (Marsham). Twig crotches were collected from sprayed trees each year for bioassay. Methoxychlor residues...

Mitigation of divertor heat flux by high-frequency ELM pacing with non-fuel pellet injection in DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bortolon, A.; Maingi, R.; Mansfield, D. K.

Experiments have been conducted on DIII-D investigating high repetition rate injection of non-fuel pellets as a tool for pacing Edge Localized Modes (ELMs) and mitigating their transient divertor heat loads. Effective ELM pacing was obtained with injection of Li granules in different H-mode scenarios, at frequencies 3–5 times larger than the natural ELM frequency, with subsequent reduction of strike-point heat flux. However, in scenarios with high pedestal density (~6 × 10 19 m –3), the magnitude of granule triggered ELMs shows a broad distribution, in terms of stored energy loss and peak heat flux, challenging the effectiveness of ELM mitigation.more » Furthermore, transient heat-flux deposition correlated with granule injections was observed far from the strike-points. As a result, field line tracing suggest this phenomenon to be consistent with particle loss into the mid-plane far scrape-off layer, at toroidal location of the granule injection.« less

Mitigation of divertor heat flux by high-frequency ELM pacing with non-fuel pellet injection in DIII-D

DOE PAGES

Bortolon, A.; Maingi, R.; Mansfield, D. K.; ...

2017-03-23

Experiments have been conducted on DIII-D investigating high repetition rate injection of non-fuel pellets as a tool for pacing Edge Localized Modes (ELMs) and mitigating their transient divertor heat loads. Effective ELM pacing was obtained with injection of Li granules in different H-mode scenarios, at frequencies 3–5 times larger than the natural ELM frequency, with subsequent reduction of strike-point heat flux. However, in scenarios with high pedestal density (~6 × 10 19 m –3), the magnitude of granule triggered ELMs shows a broad distribution, in terms of stored energy loss and peak heat flux, challenging the effectiveness of ELM mitigation.more » Furthermore, transient heat-flux deposition correlated with granule injections was observed far from the strike-points. As a result, field line tracing suggest this phenomenon to be consistent with particle loss into the mid-plane far scrape-off layer, at toroidal location of the granule injection.« less

An Evolutionary Machine Learning Framework for Big Data Sequence Mining

ERIC Educational Resources Information Center

Kamath, Uday Krishna

2014-01-01

Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

Neuromorphic Optical Signal Processing and Image Understanding for Automated Target Recognition

DTIC Science & Technology

1989-12-01

34 Stochastic Learning Machine " Neuromorphic Target Identification * Cognitive Networks 3. Conclusions ..... ................ .. 12 4. Publications...16 5. References ...... ................... . 17 6. Appendices ....... .................. 18 I. Optoelectronic Neural Networks and...Learning Machines. II. Stochastic Optical Learning Machine. III. Learning Network for Extrapolation AccesFon For and Radar Target Identification

An iterative learning control method with application for CNC machine tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

Learning dominance relations in combinatorial search problems

NASA Technical Reports Server (NTRS)

Yu, Chee-Fen; Wah, Benjamin W.

1988-01-01

Dominance relations commonly are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem, the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.

Thutmose - Investigation of Machine Learning-Based Intrusion Detection Systems

DTIC Science & Technology

2016-06-01

research is being done to incorporate the field of machine learning into intrusion detection. Machine learning is a branch of artificial intelligence (AI...adversarial drift." Proceedings of the 2013 ACM workshop on Artificial intelligence and security. ACM. (2013) Kantarcioglu, M., Xi, B., and Clifton, C. "A...34 Proceedings of the 4th ACM workshop on Security and artificial intelligence . ACM. (2011) Dua, S., and Du, X. Data Mining and Machine Learning in

Large-Scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation

DTIC Science & Technology

2016-08-10

AFRL-AFOSR-JP-TR-2016-0073 Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation ...2016 4.  TITLE AND SUBTITLE Large-scale Linear Optimization through Machine Learning: From Theory to Practical System Design and Implementation 5a...performances on various machine learning tasks and it naturally lends itself to fast parallel implementations . Despite this, very little work has been

ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines

DTIC Science & Technology

2014-05-16

ML-o-scope: a diagnostic visualization system for deep machine learning pipelines Daniel Bruckner Electrical Engineering and Computer Sciences... machine learning pipelines 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f...the system as a support for tuning large scale object-classification pipelines. 1 Introduction A new generation of pipelined machine learning models

WebWatcher: Machine Learning and Hypertext

DTIC Science & Technology

1995-05-29

WebWatcher: Machine Learning and Hypertext Thorsten Joachims, Tom Mitchell, Dayne Freitag, and Robert Armstrong School of Computer Science Carnegie...HTML-page about machine learning in which we in- serted a hyperlink to WebWatcher (line 6). The user follows this hyperlink and gets to a page which...AND SUBTITLE WebWatcher: Machine Learning and Hypertext 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

INCREASED SERUM LEVELS OF UREA AND CREATININE ARE SURROGATE MARKERS FOR DISRUPTION OF RETINAL PHOTORECEPTOR EXTERNAL LIMITING MEMBRANE AND INNER SEGMENT ELLIPSOID ZONE IN TYPE 2 DIABETES MELLITUS.

PubMed

Saxena, Sandeep; Ruia, Surabhi; Prasad, Senthamizh; Jain, Astha; Mishra, Nibha; Natu, Shankar M; Meyer, Carsten H; Gilhotra, Jagjit S; Kruzliak, Peter; Akduman, Levent

2017-02-01

To evaluate the role of serum urea and creatinine as surrogate markers for disruption of retinal photoreceptor external limiting membrane (ELM) and inner segment ellipsoid zone (EZ) in Type 2 diabetic retinopathy (DR) using spectral-domain optical coherence tomography, for the first time. One hundred and seventeen consecutive cases of Type 2 diabetes mellitus (diabetes without retinopathy [No DR; n = 39], nonproliferative diabetic retinopathy [NPDR; n = 39], proliferative diabetic retinopathy [PDR; n = 39]) and 40 healthy control subjects were included. Serum levels of urea and creatinine were assessed using standard protocol. Spectral-domain optical coherence tomography was used to grade the disruption of ELM and EZ as follows: Grade 0, no disruption of ELM and EZ; Grade 1, ELM disrupted, EZ intact; Grade 2, ELM and EZ disrupted. Data were analyzed statistically. Increase in serum levels of urea (F = 22.93) and creatinine (F = 15.82) and increased grades of disruption of ELM and EZ (γ = 116.3) were observed with increased severity of DR (P < 0.001). Increase in serum levels of urea (F = 10.45) and creatinine (F = 6.89) was observed with increased grades of disruption of ELM and EZ (P = 0.001). Serum levels of urea and creatinine are surrogate markers for disruption of retinal photoreceptor ELM and EZ on spectral-domain optical coherence tomography in DR.

American Elm clones of importance in DED tolerance studies

USDA-ARS?s Scientific Manuscript database

We present the background and characteristics of American elm clones that are commercially available or of interest in research on Dutch elm disease (DED) tolerance in the United States. The characteristics of interest include origin, ploidy level, whether available in nursery trade, evidence of DED...

Machine learning for medical images analysis.

PubMed

Criminisi, A

2016-10-01

This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Machine Learning.

ERIC Educational Resources Information Center

Kirrane, Diane E.

1990-01-01

As scientists seek to develop machines that can "learn," that is, solve problems by imitating the human brain, a gold mine of information on the processes of human learning is being discovered, expert systems are being improved, and human-machine interactions are being enhanced. (SK)

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Quantum Machine Learning over Infinite Dimensions

DOE PAGES

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George; ...

2017-02-21

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

Quantum Machine Learning over Infinite Dimensions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lau, Hoi-Kwan; Pooser, Raphael; Siopsis, George

Machine learning is a fascinating and exciting eld within computer science. Recently, this ex- citement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the nite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practi- cal, in nite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that achieve an exponential speedup compared to their equivalent classical counterparts. Finally, we also map out an experi- mental implementation which can be used as amore » blueprint for future photonic demonstrations.« less

Machine learning and medicine: book review and commentary.

PubMed

Koprowski, Robert; Foster, Kenneth R

2018-02-01

This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book.

Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models

DTIC Science & Technology

2015-09-12

AFRL-AFOSR-VA-TR-2015-0278 DERIVATIVE FREE OPTIMIZATION OF COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS Katya Scheinberg...COMPLEX SYSTEMS WITH THE USE OF STATISTICAL MACHINE LEARNING MODELS 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-11-1-0239 5c.  PROGRAM ELEMENT...developed, which has been the focus of our research. 15. SUBJECT TERMS optimization, Derivative-Free Optimization, Statistical Machine Learning 16. SECURITY

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

The Elaboration Likelihood Model: Implications for the Practice of School Psychology.

ERIC Educational Resources Information Center

Petty, Richard E.; Heesacker, Martin; Hughes, Jan N.

1997-01-01

Reviews a contemporary theory of attitude change, the Elaboration Likelihood Model (ELM) of persuasion, and addresses its relevance to school psychology. Claims that a key postulate of ELM is that attitude change results from thoughtful (central route) or nonthoughtful (peripheral route) processes. Illustrations of ELM's utility for school…

Cephalosporium Wilt of Elm in the Lower Mississippi Valley

Treesearch

T. H. Filer; F. I. McCracken; E. R. Toole

1968-01-01

Dead and dying American elms (Ulmus americana) and cedar elms (U. crassifolia) were observed on the Delta Experimental Forest, Stoneville, Mississippi, and in Desha County, Arkansas, near the Mississippi River (about 30 miles northwest of the experimental forest) during August 1967. The only fungus consistently isolated from these...

77 FR 36206 - Airworthiness Directives; The Boeing Company Airplanes

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-18

... experienced smoke and heat damage from insulation blankets that smoldered after molten debris from a P200 ELMS power panel fell on the insulation blankets. When a contactor in the ELMS panel fails and overheats, the... ELMS contactor breakdown, consequent smoke and heat damage to airplane structure and equipment during...

Ulmus americana is a polyploid complex

USDA-ARS?s Scientific Manuscript database

The elms (the genus Ulmus) are one of the most important tree crops for the $4.7-billion per year nursery industry. The commercial importance of the genus centers on the American elm, Ulmus americana. Once decimated by Dutch Elm Disease, the recent introduction of cultivars resistant to the diseas...

Suppression of type-I ELMs with reduced RMP coil set on DIII-D

DOE PAGES

Orlov, Dmitriy M.; Moyer, Richard A.; Evans, Todd E.; ...

2016-02-19

Recent experiments on DIII-D have demonstrated that having a toroidally-monochromatic spectral content of edge-resonant magnetic perturbations (RMPs) is not a necessary condition for suppression of Edge Localized Modes (ELMs). Robust ELM suppression has been reproducibly obtained on DIII-D during experiments in which various non-axisymmetric coil loops were turned off pseudo-randomly producing a variety of n=1, n=2, and n=3 spectral contributions. It was shown that RMP ELM suppression could be achieved with as few as 5 out of 12 internal coil loops (I-coils) on DIII-D at similar coil currents and with good plasma confinement. Linear MHD plasma response (M3DC1, IPEC, MARS)more » and vacuum (SURFMN, TRIP3D) modeling have been performed in order to understand the effects of the perturbation spectrum on the plasma response and ELM suppression. The results suggest that reduction of the dominant n=3 perturbation field is compensated by increased n=2 field in the plasma that may lead to RMP ELM suppression at lower levels of n=3 perturbative magnetic flux from the I-coils. These results provide additional confidence that ITER may be capable of RMP ELM suppression in the event of multiple internal coil failures.« less

Stabilizing effect of resistivity towards ELM-free H-mode discharge in lithium-conditioned NSTX

DOE Office of Scientific and Technical Information (OSTI.GOV)

Banerjee, Debabrata; Zhu, Ping; Maingi, Rajesh

Linear stability analysis of the national spherical torus experiment (NSTX) Li-conditioned ELM-free H-mode equilibria is carried out in the context of the extended magneto-hydrodynamic (MHD) model in NIMROD. Our purpose is to investigate the physical cause behind edge localized mode (ELM) suppression in experiment after the Li-coating of the divertor and the first wall of the NSTX tokamak. Besides ideal MHD modeling, including finite-Larmor radius effect and two-fluid Hall and electron diamagnetic drift contributions, a non-ideal resistivity model is employed, taking into account the increase of Z eff after Li-conditioning in ELM-free H-mode. And unlike an earlier conclusion from anmore » eigenvalue code analysis of these equilibria, NIMROD results find that after reduced recycling from divertor plates, profile modification is necessary but insufficient to explain the mechanism behind complete ELMs suppression in ideal two-fluid MHD. After considering the higher plasma resistivity due to higher Z eff, the complete stabilization could be explained. Furthermore, a thorough analysis of both pre-lithium ELMy and with-lithium ELM-free cases using ideal and non-ideal MHD models is presented, after accurately including a vacuum-like cold halo region in NIMROD to investigate ELMs.« less

Stabilizing effect of resistivity towards ELM-free H-mode discharge in lithium-conditioned NSTX

DOE PAGES

Banerjee, Debabrata; Zhu, Ping; Maingi, Rajesh

2017-05-12

Linear stability analysis of the national spherical torus experiment (NSTX) Li-conditioned ELM-free H-mode equilibria is carried out in the context of the extended magneto-hydrodynamic (MHD) model in NIMROD. Our purpose is to investigate the physical cause behind edge localized mode (ELM) suppression in experiment after the Li-coating of the divertor and the first wall of the NSTX tokamak. Besides ideal MHD modeling, including finite-Larmor radius effect and two-fluid Hall and electron diamagnetic drift contributions, a non-ideal resistivity model is employed, taking into account the increase of Z eff after Li-conditioning in ELM-free H-mode. And unlike an earlier conclusion from anmore » eigenvalue code analysis of these equilibria, NIMROD results find that after reduced recycling from divertor plates, profile modification is necessary but insufficient to explain the mechanism behind complete ELMs suppression in ideal two-fluid MHD. After considering the higher plasma resistivity due to higher Z eff, the complete stabilization could be explained. Furthermore, a thorough analysis of both pre-lithium ELMy and with-lithium ELM-free cases using ideal and non-ideal MHD models is presented, after accurately including a vacuum-like cold halo region in NIMROD to investigate ELMs.« less

Understanding tungsten divertor sourcing and SOL transport using multiple poloidally-localized sources in DIII-D ELM-y H-mode discharges

NASA Astrophysics Data System (ADS)

Unterberg, Ea; Donovan, D.; Barton, J.; Wampler, Wr; Abrams, T.; Thomas, Dm; Petrie, T.; Guo, Hy; Stangeby, Pg; Elder, Jd; Rudakov, D.; Grierson, B.; Victor, B.

2017-10-01

Experiments using metal inserts with novel isotopically-enriched tungsten coatings at the outer divertor strike point (OSP) have provided unique insight into the ELM-induced sourcing, main-SOL transport, and core accumulation control mechanisms of W for a range of operating conditions. This experimental approach has used a multi-head, dual-facing collector probe (CP) at the outboard midplane, as well as W-I and core W spectroscopy. Using the CP system, the total amount of W deposited relative to source measurements shows a clear dependence on ELM size, ELM frequency, and strike point location, with large ELMs depositing significantly more W on the CP from the far-SOL source. Additionally, high spatial ( 1mm) and ELM resolved spectroscopic measurements of W sourcing indicate shifts in the peak erosion rate. Furthermore, high performance discharges with rapid ELMs show core W concentrations of few 10-5, and the CP deposition profile indicates W is predominantly transported to the midplane from the OSP rather than from the far-SOL region. The low central W concentration is shown to be due to flattening of the main plasma density profile, presumably by on-axis electron cyclotron heating. Work supported under USDOE Cooperative Agreement DE-FC02-04ER54698.

Plant signals during beetle (Scolytus multistriatus) feeding in American elm (Ulmus americana Planch)

PubMed Central

Saremba, Brett M.; Tymm, Fiona J. M.; Baethke, Kathy; Rheault, Mark R.; Sherif, Sherif M.; Saxena, Praveen K.; Murch, Susan J.

2017-01-01

ABSTRACT American Elms were devastated by an outbreak of Dutch Elm Disease is caused by the fungus Ophiostoma novo-ulmi Brasier that originated in Asia and arrived in the early 1900s. In spite of decades of study, the specific mechanisms and disease resistance in some trees is not well understood. the fungus is spread by several species of bark beetles in the genus Scolytus, during their dispersal and feeding. Our objective was to understand elm responses to beetle feeding in the absence of the fungus to identify potential resistance mechanisms. A colony of Scolytus multistriatus was established from wild-caught beetles and beetles were co-incubated with susceptible or resistant American elm varieties in a controlled environment chamber. Beetles burrowed into the auxillary meristems of the young elm shoots. The trees responded to the beetle damage by a series of spikes in the concentration of plant growth regulating compounds, melatonin, serotonin, and jasmonic acid. Spikes in melatonin and serotonin represented a 7,000-fold increase over resting levels. Spikes in jasmonic acid were about 10-fold higher than resting levels with one very large spike observed. Differences were noted between susceptible and resistant elms that provide new understanding of plant defenses. PMID:28448744

Approaches to Machine Learning.

DTIC Science & Technology

1984-02-16

The field of machine learning strives to develop methods and techniques to automatic the acquisition of new information, new skills, and new ways of organizing existing information. In this article, we review the major approaches to machine learning in symbolic domains, covering the tasks of learning concepts from examples, learning search methods, conceptual clustering, and language acquisition. We illustrate each of the basic approaches with paradigmatic examples. (Author)

Machine Learning in the Big Data Era: Are We There Yet?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sukumar, Sreenivas Rangan

In this paper, we discuss the machine learning challenges of the Big Data era. We observe that recent innovations in being able to collect, access, organize, integrate, and query massive amounts of data from a wide variety of data sources have brought statistical machine learning under more scrutiny and evaluation for gleaning insights from the data than ever before. In that context, we pose and debate the question - Are machine learning algorithms scaling with the ability to store and compute? If yes, how? If not, why not? We survey recent developments in the state-of-the-art to discuss emerging and outstandingmore » challenges in the design and implementation of machine learning algorithms at scale. We leverage experience from real-world Big Data knowledge discovery projects across domains of national security and healthcare to suggest our efforts be focused along the following axes: (i) the data science challenge - designing scalable and flexible computational architectures for machine learning (beyond just data-retrieval); (ii) the science of data challenge the ability to understand characteristics of data before applying machine learning algorithms and tools; and (iii) the scalable predictive functions challenge the ability to construct, learn and infer with increasing sample size, dimensionality, and categories of labels. We conclude with a discussion of opportunities and directions for future research.« less

Machine Learning

DTIC Science & Technology

1990-04-01

DTIC i.LE COPY RADC-TR-90-25 Final Technical Report April 1990 MACHINE LEARNING The MITRE Corporation Melissa P. Chase Cs) CTIC ’- CT E 71 IN 2 11990...S. FUNDING NUMBERS MACHINE LEARNING C - F19628-89-C-0001 PE - 62702F PR - MOlE S. AUTHO(S) TA - 79 Melissa P. Chase WUT - 80 S. PERFORMING...341.280.5500 pm I " Aw Sig rill Ia 2110-01 SECTION 1 INTRODUCTION 1.1 BACKGROUND Research in machine learning has taken two directions in the problem of

Workshop on Fielded Applications of Machine Learning Held in Amherst, Massachusetts on 30 June-1 July 1993. Abstracts.

DTIC Science & Technology

1993-01-01

engineering has led to many AI systems that are now regularly used in industry and elsewhere. The ultimate test of machine learning , the subfield of Al that...applications of machine learning suggest the time was ripe for a meeting on this topic. For this reason, Pat Langley (Siemens Corporate Research) and Yves...Kodratoff (Universite de Paris, Sud) organized an invited workshop on applications of machine learning . The goal of the gathering was to familiarize

Detection of tumor cell-specific mRNA and protein in exosome-like microvesicles from blood and saliva.

PubMed

Yang, Jieping; Wei, Fang; Schafer, Christopher; Wong, David T W

2014-01-01

The discovery of disease-specific biomarkers in oral fluids has revealed a new dimension in molecular diagnostics. Recent studies have reported the mechanistic involvement of tumor cells derived mediators, such as exosomes, in the development of saliva-based mRNA biomarkers. To further our understanding of the origins of disease-induced salivary biomarkers, we here evaluated the hypothesis that tumor-shed secretory lipidic vesicles called exosome-like microvesicles (ELMs) that serve as protective carriers of tissue-specific information, mRNAs, and proteins, throughout the vasculature and bodily fluids. RNA content was analyzed in cell free-saliva and ELM-enriched fractions of saliva. Our data confirmed that the majority of extracellular RNAs (exRNAs) in saliva were encapsulated within ELMs. Nude mice implanted with human lung cancer H460 cells expressing hCD63-GFP were used to follow the circulation of tumor cell specific protein and mRNA in the form of ELMs in vivo. We were able to identify human GAPDH mRNA in ELMs of blood and saliva of tumor bearing mice using nested RT-qPCR. ELMs positive for hCD63-GFP were detected in the saliva and blood of tumor bearing mice as well as using electric field-induced release and measurement (EFIRM). Altogether, our results demonstrate that ELMs carry tumor cell-specific mRNA and protein from blood to saliva in a xenografted mouse model of human lung cancer. These results therefore strengthen the link between distal tumor progression and the biomarker discovery of saliva through the ELMs.

Theory-based model for the pedestal, edge stability and ELMs in tokamaks

NASA Astrophysics Data System (ADS)

Pankin, A. Y.; Bateman, G.; Brennan, D. P.; Schnack, D. D.; Snyder, P. B.; Voitsekhovitch, I.; Kritz, A. H.; Janeschitz, G.; Kruger, S.; Onjun, T.; Pacher, G. W.; Pacher, H. D.

2006-04-01

An improved model for triggering edge localized mode (ELM) crashes is developed for use within integrated modelling simulations of the pedestal and ELM cycles at the edge of H-mode tokamak plasmas. The new model is developed by using the BALOO, DCON and ELITE ideal MHD stability codes to derive parametric expressions for the ELM triggering threshold. The whole toroidal mode number spectrum is studied with these codes. The DCON code applies to low mode numbers, while the BALOO code applies to only high mode numbers and the ELITE code applies to intermediate and high mode numbers. The variables used in the parametric stability expressions are the normalized pressure gradient and the parallel current density, which drive ballooning and peeling modes. Two equilibria motivated by DIII-D geometry with different plasma triangularities are studied. It is found that the stable region in the high triangularity discharge covers a much larger region of parameter space than the corresponding stability region in the low triangularity discharge. The new ELM trigger model is used together with a previously developed model for pedestal formation and ELM crashes in the ASTRA integrated modelling code to follow the time evolution of the temperature profiles during ELM cycles. The ELM frequencies obtained in the simulations of low and high triangularity discharges are observed to increase with increasing heating power. There is a transition from second stability to first ballooning mode stability as the heating power is increased in the high triangularity simulations. The results from the ideal MHD stability codes are compared with results from the resistive MHD stability code NIMROD.

Systematic Poisoning Attacks on and Defenses for Machine Learning in Healthcare.

PubMed

Mozaffari-Kermani, Mehran; Sur-Kolay, Susmita; Raghunathan, Anand; Jha, Niraj K

2015-11-01

Machine learning is being used in a wide range of application domains to discover patterns in large datasets. Increasingly, the results of machine learning drive critical decisions in applications related to healthcare and biomedicine. Such health-related applications are often sensitive, and thus, any security breach would be catastrophic. Naturally, the integrity of the results computed by machine learning is of great importance. Recent research has shown that some machine-learning algorithms can be compromised by augmenting their training datasets with malicious data, leading to a new class of attacks called poisoning attacks. Hindrance of a diagnosis may have life-threatening consequences and could cause distrust. On the other hand, not only may a false diagnosis prompt users to distrust the machine-learning algorithm and even abandon the entire system but also such a false positive classification may cause patient distress. In this paper, we present a systematic, algorithm-independent approach for mounting poisoning attacks across a wide range of machine-learning algorithms and healthcare datasets. The proposed attack procedure generates input data, which, when added to the training set, can either cause the results of machine learning to have targeted errors (e.g., increase the likelihood of classification into a specific class), or simply introduce arbitrary errors (incorrect classification). These attacks may be applied to both fixed and evolving datasets. They can be applied even when only statistics of the training dataset are available or, in some cases, even without access to the training dataset, although at a lower efficacy. We establish the effectiveness of the proposed attacks using a suite of six machine-learning algorithms and five healthcare datasets. Finally, we present countermeasures against the proposed generic attacks that are based on tracking and detecting deviations in various accuracy metrics, and benchmark their effectiveness.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

PubMed

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Improved Sanitation Practice for Control of Dutch Elm Disease

Treesearch

Jack H. Barger

1977-01-01

In Detroit, Michigan, 12 plots, each containing about 600 American elm trees, Ulmus americana L., were subjected for 3 years to intensive and conventional sanitation treatments to control Dutch elm disease. In the intensive treatment, three disease surveys were conducted each year; each followed by tree removal within 20 working days. In the...

Semiochemical-MediatedFlight Strategies of Two Invasive Elm Bark Beetles: A Potential Factor in Competitive Displacement

USDA-ARS?s Scientific Manuscript database

A recent seven-state survey revealed that the newly invasive banded elm bark beetle, Scolytus schevyrewi, was abundant in areas of Colorado and Wyoming, USA, whereas the long-established European elm bark beetle, S. multistriatus was not as abundant. Behavioral trials were conducted by hanging sm...

Genome size variation in elms (Ulmus spp.) and related genera

USDA-ARS?s Scientific Manuscript database

The elms (the genus Ulmus) are one of the most important tree crops for the $4.7-billion per year U.S. nursery industry. Utilization of these plants has been limited in recent decades by diseases introduced from the Old World, especially Dutch elm disease. Past research and breeding have been based ...

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

PubMed

Nakai, Yasushi; Takiguchi, Tetsuya; Matsui, Gakuyo; Yamaoka, Noriko; Takada, Satoshi

2017-10-01

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.

Scaling Relationships for ELM Diverter Heat Flux on DIII D

NASA Astrophysics Data System (ADS)

Peters, E. A.; Makowski, M. A.; Leonard, A. W.

2015-11-01

Edge Localized Modes (ELMs) are periodic plasma instabilities that occur during H-mode operation in tokamaks. Left unmitigated, these instabilities result in concentrated particle and heat fluxes at the divertor and stand to cause serious damage to the plasma facing components of tokamaks. The purpose of this research is to find scaling relationships that predict divertor heat flux due to ELMs based on plasma parameters at the time of instability. This will be accomplished by correlating characteristic ELM parameters with corresponding plasma measurements and analyzing the data for trends. One early assessment is the effect of the heat transmission coefficient ? on the in/out asymmetry of the calculated ELM heat fluxes. Using IR camera data, further assessments in this study will continue to emphasize in/out asymmetry in ELMs, as this has important implications for ITER operation. Work supported in part by the US DOE, DE-AC52-07NA27344, DE-FC02-04ER54698, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI).

KSC-08pd2916

NASA Image and Video Library

2008-09-24

CAPE CANAVERAL, Fla. - On the Shuttle Landing Facility at NASA's Kennedy Space Center in Florida, ramps are in place for the offloading of the primary cargo from the Russian Antonov AH-124-100 cargo airplane. The plane carries the final components of the Japan Aerospace Exploration Agency's Kibo laboratory for the International Space Station: the Kibo Exposed Facility, or EF, and the Experiment Logistics Module Exposed Section, or ELM-ES. The EF provides a multipurpose platform where science experiments can be deployed and operated in the exposed environment. The payloads attached to the EF can be exchanged or retrieved by Kibo's robotic arm, the JEM Remote Manipulator System. The ELM-ES will be attached to the end of the EF to provide payload storage space and can carry up to three payloads at launch. In addition, the ELM-ES provides a logistics function where it can be detached from the EF and returned to the ground aboard the space shuttle. The two JEM components will be carried aboard space shuttle Endeavour on the STS-127 mission targeted for launch in May 2009. Photo credit: NASA/Jim Grossmann

KSC-08pd2915

NASA Image and Video Library

2008-09-24

CAPE CANAVERAL, Fla. - On the Shuttle Landing Facility at NASA's Kennedy Space Center in Florida, workers remove material from a cargo box before offloading the primary cargo from the Russian Antonov AH-124-100 cargo airplane. The plane carries the final components of the Japan Aerospace Exploration Agency's Kibo laboratory for the International Space Station: the Kibo Exposed Facility, or EF, and the Experiment Logistics Module Exposed Section, or ELM-ES. The EF provides a multipurpose platform where science experiments can be deployed and operated in the exposed environment. The payloads attached to the EF can be exchanged or retrieved by Kibo's robotic arm, the JEM Remote Manipulator System. The ELM-ES will be attached to the end of the EF to provide payload storage space and can carry up to three payloads at launch. In addition, the ELM-ES provides a logistics function where it can be detached from the EF and returned to the ground aboard the space shuttle. The two JEM components will be carried aboard space shuttle Endeavour on the STS-127 mission targeted for launch in May 2009. Photo credit: NASA/Jim Grossmann

KSC-08pd2914

NASA Image and Video Library

2008-09-24

CAPE CANAVERAL, Fla. - On the Shuttle Landing Facility at NASA's Kennedy Space Center in Florida, equipment is removed from the Russian Antonov AH-124-100 cargo airplane to facilitate offloading of the primary cargo, the final components of the Japan Aerospace Exploration Agency's Kibo laboratory for the International Space Station. The components are the Kibo Exposed Facility, or EF, and the Experiment Logistics Module Exposed Section, or ELM-ES. The EF provides a multipurpose platform where science experiments can be deployed and operated in the exposed environment. The payloads attached to the EF can be exchanged or retrieved by Kibo's robotic arm, the JEM Remote Manipulator System. The ELM-ES will be attached to the end of the EF to provide payload storage space and can carry up to three payloads at launch. In addition, the ELM-ES provides a logistics function where it can be detached from the EF and returned to the ground aboard the space shuttle. The two JEM components will be carried aboard space shuttle Endeavour on the STS-127 mission targeted for launch in May 2009. Photo credit: NASA/Jim Grossmann

ELM Behavior in High- βp EAST-Demonstration Plasmas on DIII-D

NASA Astrophysics Data System (ADS)

Li, G. Q.; Gong, X. Z.; Garofalo, A. M.; Lao, L. L.; Meneghini, O.; Snyder, P. B.; Ren, Q. L.; Ding, S. Y.; Guo, W. F.; Qian, J. P.; Wan, B. N.; Xu, G. S.; Holcomb, C. T.; Solomon, W. M.

2015-11-01

In the DIII-D high- βp EAST-demonstration experiment, for several similar discharges when the experimental parameters such as the toroidal magnetic field or ECH power are varied slightly, the changes in ELM frequency response are observed to be much larger. Kinetic EFIT equilibrium reconstructions for these discharges have been performed, which suggest that the ELM frequency changes are likely due to the variations of pedestal width, height, and edge current density. Kinetic profile analyses further indicate that the strong ITB that are located at large minor radii (rho=0.6 ~0.7) in these discharges are affecting the pedestal structure. The ITB could broaden the pedestal width and decrease the pedestal height, thus changing the ELM frequency and size. With the GATO and ELITE MHD codes, the linear growth rates and mode structures of these ELMs are analyzed. The impact of ITB on the ELMs behavior will be discussed. Work supported by China MOST under 2014GB106001 and 2015GB102001 and US DOE under DE-FC02-04ER54698 and DE-FG03-95ER54309.

Broadening of divertor heat flux profile with increasing number of ELM filaments in NSTX

DOE PAGES

Ahn, J. -W.; Maingi, R.; Canik, J. M.; ...

2014-11-13

Edge localized modes (ELMs) represent a challenge to future fusion devices, owing to cyclical high peak heat fluxes on divertor plasma facing surfaces. One ameliorating factor has been that the heat flux characteristic profile width has been observed to broaden with the size of the ELM, as compared with the inter-ELM heat flux profile. In contrast, the heat flux profile has been observed to narrow during ELMs under certain conditions in NSTX. Here we show that the ELM heat flux profile width increases with the number of filamentary striations observed, i.e., profile narrowing is observed with zero or very fewmore » striations. Because NSTX often lies on the long wavelength current-driven mode side of ideal MHD instabilities, few filamentary structures can be expected under many conditions. Lastly, ITER is also projected to lie on the current driven low-n stability boundary, and therefore detailed projections of the unstable modes expected in ITER and the heat flux driven in ensuing filamentary structures is needed.« less

Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

PubMed

Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

2018-03-01

This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms ( k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene ( XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

Probabilistic machine learning and artificial intelligence.

PubMed

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

NASA Astrophysics Data System (ADS)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Machine Learning Techniques in Clinical Vision Sciences.

PubMed

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration, and diabetic retinopathy, these ocular pathologies being the major causes of irreversible visual impairment.

Multi-Stage Convex Relaxation Methods for Machine Learning

DTIC Science & Technology

2013-03-01

Many problems in machine learning can be naturally formulated as non-convex optimization problems. However, such direct nonconvex formulations have...original nonconvex formulation. We will develop theoretical properties of this method and algorithmic consequences. Related convex and nonconvex machine learning methods will also be investigated.

Machine Learning for the Knowledge Plane

DTIC Science & Technology

2006-06-01

this idea is to combine techniques from machine learning with new architectural concepts in networking to make the internet self-aware and self...work on the machine learning portion of the Knowledge Plane. This consisted of three components: (a) we wrote a document formulating the various

ELM mitigation techniques

NASA Astrophysics Data System (ADS)

Evans, T. E.

2013-07-01

Large edge-localized mode (ELM) control techniques must be developed to help ensure the success of burning and ignited fusion plasma devices such as tokamaks and stellarators. In full performance ITER tokamak discharges, with QDT = 10, the energy released by a single ELM could reach ˜30 MJ which is expected to result in an energy density of 10-15 MJ/m2on the divertor targets. This will exceed the estimated divertor ablation limit by a factor of 20-30. A worldwide research program is underway to develop various types of ELM control techniques in preparation for ITER H-mode plasma operations. An overview of the ELM control techniques currently being developed is discussed along with the requirements for applying these techniques to plasmas in ITER. Particular emphasis is given to the primary approaches, pellet pacing and resonant magnetic perturbation fields, currently being considered for ITER.

Machine learning and data science in soft materials engineering

NASA Astrophysics Data System (ADS)

Ferguson, Andrew L.

2018-01-01

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ‘de-jargonizing’ data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Machine learning and data science in soft materials engineering.

PubMed

Ferguson, Andrew L

2018-01-31

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors

PubMed Central

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-01-01

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors. PMID:28934163

Machine Learning Approaches for Clinical Psychology and Psychiatry.

PubMed

Dwyer, Dominic B; Falkai, Peter; Koutsouleris, Nikolaos

2018-05-07

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.

Divertor, scrape-off layer and pedestal particle dynamics in the ELM cycle on ASDEX Upgrade

NASA Astrophysics Data System (ADS)

Laggner, F. M.; Keerl, S.; Gnilsen, J.; Wolfrum, E.; Bernert, M.; Carralero, D.; Guimarais, L.; Nikolaeva, V.; Potzel, S.; Cavedon, M.; Mink, F.; Dunne, M. G.; Birkenmeier, G.; Fischer, R.; Viezzer, E.; Willensdorfer, M.; Wischmeier, M.; Aumayr, F.; the EUROfusion MST1 Team; the ASDEX Upgrade Team

2018-02-01

In addition to the relaxation of the pedestal, edge localised modes (ELMs) introduce changes to the divertor and scrape-off layer (SOL) conditions. Their impact on the inter-ELM pedestal recovery is investigated, with emphasis on the electron density (n e) evolution. The typical ELM cycle occurring in an exemplary ASDEX Upgrade discharge interval at moderate applied gas puff and heating power is characterised, utilising several divertor, SOL and pedestal diagnostics. In the studied discharge interval the inner divertor target is detached before the ELM crash, while the outer target is attached. The particles and power expelled by the ELM crash lead to a re-attachment of the inner target plasma. After the ELM crash, the outer divertor target moves into a high recycling regime with large n e in front of the plate, which is accompanied by high main chamber neutral fluxes. On similar timescales, the inner target fully detaches and the high field side high density region (HFSHD) is formed reaching up to the high field side midplane. This state evolves again to the pre-ELM state, when the main chamber neutral fluxes are reduced later in the ELM cycle. Neither the timescale of the appearance of the HFSHD nor the increase of the main chamber neutral fluxes fit the timescale of the n e pedestal, which is faster. It is found that during the n e pedestal recovery, the magnetic activity at the low field side midplane is strongly reduced indicating a lower level of fluctuations. A rough estimation of the particle flux across the pedestal suggests that the particle flux is reduced in this period. In conclusion, the evolution of the n e pedestal is determined by a combination of neutral fluxes, HFSHD and reduced particle flux across the pedestal. A reduced particle flux explains the fast, experimentally observed re-establishment of the n e pedestal best, whereas neutrals and HFSHD impact on the evolution of the SOL and separatrix conditions.

Validation of the model for ELM suppression with 3D magnetic fields using low torque ITER baseline scenario discharges in DIII-D

NASA Astrophysics Data System (ADS)

Moyer, R. A.; Paz-Soldan, C.; Nazikian, R.; Orlov, D. M.; Ferraro, N. M.; Grierson, B. A.; Knölker, M.; Lyons, B. C.; McKee, G. R.; Osborne, T. H.; Rhodes, T. L.; Meneghini, O.; Smith, S.; Evans, T. E.; Fenstermacher, M. E.; Groebner, R. J.; Hanson, J. M.; La Haye, R. J.; Luce, T. C.; Mordijck, S.; Solomon, W. M.; Turco, F.; Yan, Z.; Zeng, L.; DIII-D Team

2017-10-01

Experiments have been executed in the DIII-D tokamak to extend suppression of Edge Localized Modes (ELMs) with Resonant Magnetic Perturbations (RMPs) to ITER-relevant levels of beam torque. The results support the hypothesis for RMP ELM suppression based on transition from an ideal screened response to a tearing response at a resonant surface that prevents expansion of the pedestal to an unstable width [Snyder et al., Nucl. Fusion 51, 103016 (2011) and Wade et al., Nucl. Fusion 55, 023002 (2015)]. In ITER baseline plasmas with I/aB = 1.4 and pedestal ν * ˜ 0.15, ELMs are readily suppressed with co- I p neutral beam injection. However, reducing the beam torque from 5 Nm to ≤ 3.5 Nm results in loss of ELM suppression and a shift in the zero-crossing of the electron perpendicular rotation ω ⊥ e ˜ 0 deeper into the plasma. The change in radius of ω ⊥ e ˜ 0 is due primarily to changes to the electron diamagnetic rotation frequency ωe * . Linear plasma response modeling with the resistive MHD code m3d-c1 indicates that the tearing response location tracks the inward shift in ω ⊥ e ˜ 0. At pedestal ν * ˜ 1, ELM suppression is also lost when the beam torque is reduced, but the ω ⊥ e change is dominated by collapse of the toroidal rotation v T . The hypothesis predicts that it should be possible to obtain ELM suppression at reduced beam torque by also reducing the height and width of the ωe * profile. This prediction has been confirmed experimentally with RMP ELM suppression at 0 Nm of beam torque and plasma normalized pressure β N ˜ 0.7. This opens the possibility of accessing ELM suppression in low torque ITER baseline plasmas by establishing suppression at low beta and then increasing beta while relying on the strong RMP-island coupling to maintain suppression.

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

Supersonic molecular beam injection effects on tokamak plasma applied non-axisymmetric magnetic perturbation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, Hyunsun, E-mail: hyunsun@nfri.re.kr; In, Y.; Jeon, Y. M.

The change of tokamak plasma behavior by supersonic molecular beam injection (SMBI) was investigated by applying a three-dimensional magnetic perturbation that could suppress edge localized modes (ELMs). From the time trace of decreasing electron temperature and with increasing plasma density keeping the total confined energy constant, the SMBI seems to act as a cold pulse on the plasma. However, the ELM behaviors were changed drastically (i.e., the symptom of ELM suppression has disappeared). The plasma collisionality in the edge-pedestal region could play a role in the change of the ELM behaviors.

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

A review of supervised machine learning applied to ageing research.

PubMed

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

Machine learning, social learning and the governance of self-driving cars.

PubMed

Stilgoe, Jack

2018-02-01

Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. 'Self-driving' or 'autonomous' cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.

Robust Fault Diagnosis in Electric Drives Using Machine Learning

DTIC Science & Technology

2004-09-08

detection of fault conditions of the inverter. A machine learning framework is developed to systematically select torque-speed domain operation points...were used to generate various fault condition data for machine learning . The technique is viable for accurate, reliable and fast fault detection in electric drives.

Agents Technology Research

DTIC Science & Technology

2010-02-01

multi-agent reputation management. State abstraction is a technique used to allow machine learning technologies to cope with problems that have large...state abstrac- tion process to enable reinforcement learning in domains with large state spaces. State abstraction is vital to machine learning ...across a collective of independent platforms. These individual elements, often referred to as agents in the machine learning community, should exhibit both

Machine learning approaches in medical image analysis: From detection to diagnosis.

PubMed

de Bruijne, Marleen

2016-10-01

Machine learning approaches are increasingly successful in image-based diagnosis, disease prognosis, and risk assessment. This paper highlights new research directions and discusses three main challenges related to machine learning in medical imaging: coping with variation in imaging protocols, learning from weak labels, and interpretation and evaluation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Testing meta tagger

DTIC Science & Technology

2017-12-21

rank , and computer vision. Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on...Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed.[1] Arthur Samuel...an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning " in 1959 while at IBM[2]. Evolved

Discovery of Three Pulsating, Mixed-atmosphere, Extremely Low-mass White Dwarf Precursors

NASA Astrophysics Data System (ADS)

Gianninas, A.; Curd, Brandon; Fontaine, G.; Brown, Warren R.; Kilic, Mukremin

2016-05-01

We report the discovery of pulsations in three mixed-atmosphere, extremely low-mass white dwarf (ELM WD, M ≤slant 0.3 M ⊙) precursors. Following the recent discoveries of pulsations in both ELM and pre-ELM WDs, we targeted pre-ELM WDs with mixed H/He atmospheres with high-speed photometry. We find significant optical variability in all three observed targets with periods in the range 320-590 s, consistent in timescale with theoretical predictions of p-mode pulsations in mixed-atmosphere ≈0.18 M ⊙ He-core pre-ELM WDs. This represents the first empirical evidence that pulsations in pre-ELM WDs can only occur if a significant amount of He is present in the atmosphere. Future, more extensive, timeseries photometry of the brightest of the three new pulsators offers an excellent opportunity to constrain the thickness of the surface H layer, which regulates the cooling timescales for ELM WDs. Based on observations obtained at the Gemini Observatory, which is operated by the Association of Universities for Research in Astronomy, Inc., under a cooperative agreement with the NSF on behalf of the Gemini partnership: the National Science Foundation (United States), the National Research Council (Canada), CONICYT (Chile), Ministerio de Ciencia, Tecnología e Innovación Productiva (Argentina), and Ministério da Ciência, Tecnologia e Inovação (Brazil).

RMP ELM Suppression in DIII-D Plasmas with ITER Similar Shapes and Collisionalities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Evans, T.E.; Fenstermacher, M. E.; Moyer, R.A.

2008-01-01

Large Type-I edge localized modes (ELMs) are completely eliminated with small n = 3 resonant magnetic perturbations (RMP) in low average triangularity, = 0.26, plasmas and in ITER similar shaped (ISS) plasmas, = 0.53, with ITER relevant collisionalities ve 0.2. Significant differences in the RMP requirements and in the properties of the ELM suppressed plasmas are found when comparing the two triangularities. In ISS plasmas, the current required to suppress ELMs is approximately 25% higher than in low average triangularity plasmas. It is also found that the width of the resonant q95 window required for ELM suppression is smaller inmore » ISS plasmas than in low average triangularity plasmas. An analysis of the positions and widths of resonant magnetic islands across the pedestal region, in the absence of resonant field screening or a self-consistent plasma response, indicates that differences in the shape of the q profile may explain the need for higher RMP coil currents during ELM suppression in ISS plasmas. Changes in the pedestal profiles are compared for each plasma shape as well as with changes in the injected neutral beam power and the RMP amplitude. Implications of these results are discussed in terms of requirements for optimal ELM control coil designs and for establishing the physics basis needed in order to scale this approach to future burning plasma devices such as ITER.« less

H-mode pedestal stability and ELMs in Alcator C-Mod

NASA Astrophysics Data System (ADS)

Mossessian, Dmitri

2002-11-01

For steady state H-mode operation, a relaxation mechanism is required to limit build-up of the edge gradient and impurity accumulation. The major relaxation mechanism seen on most of the existing tokamaks - large type I ELMs - drive high particle and energy fluxes that present a significant power load on the divertor plates. On Alcator C-Mod, however, type I ELMs are not observed. Instead, more benign mechanisms - EDA and small grassy ELMs - appear to drive enhanced particle transport at the edge of H-mode plasmas. Both have good energy confinement, no impurity accumulation, and are steady state. In EDA the edge relaxation mechanism is provided by a quasicoherent electromagnetic mode localized in the outer part of the pedestal. Non-linear gyrofluid and linear gyrokinetic simulations, as well as real geometry fluctuation modeling based on fluid equations show the presence of a coherent mode. Based on those results the observed mode is tentatively identified as resistive ballooning. At higher edge pressure gradient the mode is replaced by broadband fluctuations and small irregular ELMs are observed. Based on ideal MHD calculations that include effects of bootstrap current, these ELMs are identified as medium n coupled ideal peeling/ballooning modes. The stability threshold and modes structure of these modes are studied with recently developed linear MHD stability code ELITE and the results are compared with the observed dependence of the ELMs' character on pedestal parameters and plasma shape.

Cognitive learning: a machine learning approach for automatic process characterization from design

NASA Astrophysics Data System (ADS)

Foucher, J.; Baderot, J.; Martinez, S.; Dervilllé, A.; Bernard, G.

2018-03-01

Cutting edge innovation requires accurate and fast process-control to obtain fast learning rate and industry adoption. Current tools available for such task are mainly manual and user dependent. We present in this paper cognitive learning, which is a new machine learning based technique to facilitate and to speed up complex characterization by using the design as input, providing fast training and detection time. We will focus on the machine learning framework that allows object detection, defect traceability and automatic measurement tools.

Biology of the invasive banded elm bark beetle (Coleoptera: Scolytidae) in the western United States

Treesearch

Jana C. Lee; Jose F. Negron; Sally J. McElwey; Livy Williams; Jeffrey J. Witcosky; John B. Popp; Steven J. Seybold

2011-01-01

The banded elm bark beetle, Scolytus schevyrewi Semenov (Coleoptera: Scolytidae), native to Asia, was detected in the United States in 2003, and as of 2011 it is known to occur in 28 states and four Canadian provinces. S. schevyrewi infests the same elm (Ulmus spp.) hosts as the longestablished invasive...

Use of culture filtrates of Ceratocystis ulmi as a bioassay to screen for disease tolerant Ulmus americana

Treesearch

Paula M. Pijut; Subash C. Domir; R. Daniel Lineberger; Lawrence R. Schreiber

1990-01-01

Callus cultures of elm (Ulmus americana L.) derived from Dutch elm disease susceptible, intermediate-resistant, and resistant genotypes were exposed to the culture filtrates of three pathogenic isolates of Ceratocystis ulmi, the causal agent of Dutch elm disease. Callus fresh weights, cell viability, and reactions of stem cuttings...

ELM-ART--An Interactive and Intelligent Web-Based Electronic Textbook

ERIC Educational Resources Information Center

Weber, Gerhard; Brusilovsky, Peter

2016-01-01

This paper present provides a broader view on ELM-ART, one of the first Web-based Intelligent Educational systems that offered a creative combination of two different paradigms--Intelligent Tutoring and Adaptive Hypermedia technologies. The unique dual nature of ELM-ART contributed to its long life and research impact and was a result of…

Co-occurrence of the invasive banded and European elm bark beetles (Coleoptera: Scolytidae) in North America

Treesearch

Janna C. Lee; Ingrid Aguayo; Ray Aslin; Gail Durham; Shakeeb M. Hamud; Beruce D. Moltzan; A. Steve Munson; Jose F. Negron; Travis Peterson; Iral R. Ragenovich; Jeffrey J. Witcosky; Steven J. Seybold

2009-01-01

The invasive European elm bark beetle, Scolytus multistriatus (Marsham), was detected in Massachusetts a century ago, and it now occurs throughout the continental United States and southern Canada. The Asian banded elm bark beetle, Scolytus schevyrewi Semenov, was discovered in the United States in 2003, and now occurs in 28 states...

Challenge inoculations to test for Dutch elm disease tolerance: a summary of Methods used by various researchers

USDA-ARS?s Scientific Manuscript database

A variety of methods have been used by different research groups to “challenge” inoculate American elms (Ulmus americana) with the purpose of determining whether some clones may be resistant to the Dutch elm disease fungus. The methods used by seven research groups are described, along with observat...

Canopy decline assessment in American elm after inoculation with different doses of Ophiostoma ulmi and O. novo-ulmi

Treesearch

Charles E. Flower; James M. Slavicek; Dale Lesser; Steven Eshita; Cornelia C. Pinchot

2017-01-01

Restoration of American elm (Ulmus americana L.) in natural and urban landscapes necessitates the development of new selections that not only exhibit Dutch elm disease (DED, caused by the fungal pathogen Ophiostoma novo-ulmi and O. ulmi) tolerance, but also an increase the genetic variability of tolerant...

Effects of simulated acid rain, ozone and sulfur dioxide on suitability of elms for elm leaf beetle

Treesearch

Richard W. Hall; Jack H. Barger; Alden M. Townsend

1988-01-01

Cuttings from two clonally propagated elm hybrids ('Pioneer' and 'Homestead') were treated with ozone (03), sulfur dioxide (S02), simulated acid rain or left untreated. Fumigants were applied 7 hours per day, 5 days per week for 9 weeks in open-top chambers. Fumigation treatments were: 0.1 ppm 0

Combining Machine Learning and Natural Language Processing to Assess Literary Text Comprehension

ERIC Educational Resources Information Center

Balyan, Renu; McCarthy, Kathryn S.; McNamara, Danielle S.

2017-01-01

This study examined how machine learning and natural language processing (NLP) techniques can be leveraged to assess the interpretive behavior that is required for successful literary text comprehension. We compared the accuracy of seven different machine learning classification algorithms in predicting human ratings of student essays about…

Implementing Machine Learning in Radiology Practice and Research.

PubMed

Kohli, Marc; Prevedello, Luciano M; Filice, Ross W; Geis, J Raymond

2017-04-01

The purposes of this article are to describe concepts that radiologists should understand to evaluate machine learning projects, including common algorithms, supervised as opposed to unsupervised techniques, statistical pitfalls, and data considerations for training and evaluation, and to briefly describe ethical dilemmas and legal risk. Machine learning includes a broad class of computer programs that improve with experience. The complexity of creating, training, and monitoring machine learning indicates that the success of the algorithms will require radiologist involvement for years to come, leading to engagement rather than replacement.

Prediction of outcome in internet-delivered cognitive behaviour therapy for paediatric obsessive-compulsive disorder: A machine learning approach.

PubMed

Lenhard, Fabian; Sauer, Sebastian; Andersson, Erik; Månsson, Kristoffer Nt; Mataix-Cols, David; Rück, Christian; Serlachius, Eva

2018-03-01

There are no consistent predictors of treatment outcome in paediatric obsessive-compulsive disorder (OCD). One reason for this might be the use of suboptimal statistical methodology. Machine learning is an approach to efficiently analyse complex data. Machine learning has been widely used within other fields, but has rarely been tested in the prediction of paediatric mental health treatment outcomes. To test four different machine learning methods in the prediction of treatment response in a sample of paediatric OCD patients who had received Internet-delivered cognitive behaviour therapy (ICBT). Participants were 61 adolescents (12-17 years) who enrolled in a randomized controlled trial and received ICBT. All clinical baseline variables were used to predict strictly defined treatment response status three months after ICBT. Four machine learning algorithms were implemented. For comparison, we also employed a traditional logistic regression approach. Multivariate logistic regression could not detect any significant predictors. In contrast, all four machine learning algorithms performed well in the prediction of treatment response, with 75 to 83% accuracy. The results suggest that machine learning algorithms can successfully be applied to predict paediatric OCD treatment outcome. Validation studies and studies in other disorders are warranted. Copyright © 2017 John Wiley & Sons, Ltd.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

PubMed

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

On-line monitoring system of PV array based on internet of things technology

NASA Astrophysics Data System (ADS)

Li, Y. F.; Lin, P. J.; Zhou, H. F.; Chen, Z. C.; Wu, L. J.; Cheng, S. Y.; Su, F. P.

2017-11-01

The Internet of Things (IoT) Technology is used to inspect photovoltaic (PV) array which can greatly improve the monitoring, performance and maintenance of the PV array. In order to efficiently realize the remote monitoring of PV operating environment, an on-line monitoring system of PV array based on IoT is designed in this paper. The system includes data acquisition, data gateway and PV monitoring centre (PVMC) website. Firstly, the DSP-TMS320F28335 is applied to collect indicators of PV array using sensors, then the data are transmitted to data gateway through ZigBee network. Secondly, the data gateway receives the data from data acquisition part, obtains geographic information via GPS module, and captures the scenes around PV array via USB camera, then uploads them to PVMC website. Finally, the PVMC website based on Laravel framework receives all data from data gateway and displays them with abundant charts. Moreover, a fault diagnosis approach for PV array based on Extreme Learning Machine (ELM) is applied in PVMC. Once fault occurs, a user alert can be sent via E-mail. The designed system enables users to browse the operating conditions of PV array on PVMC website, including electrical, environmental parameters and video. Experimental results show that the presented monitoring system can efficiently real-time monitor the PV array, and the fault diagnosis approach reaches a high accuracy of 97.5%.

EEG channels reduction using PCA to increase XGBoost's accuracy for stroke detection

NASA Astrophysics Data System (ADS)

Fitriah, N.; Wijaya, S. K.; Fanany, M. I.; Badri, C.; Rezal, M.

2017-07-01

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

Pruning The ELM Survey: Characterizing Candidate Low-mass White Dwarfs through Photometric Variability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bell, Keaton J.; Winget, D. E.; Montgomery, M. H.

We assess the photometric variability of nine stars with spectroscopic T {sub eff} and log g values from the ELM Survey that locates them near the empirical extremely low-mass (ELM) white dwarf instability strip. We discover three new pulsating stars: SDSS J135512.34+195645.4, SDSS J173521.69+213440.6, and SDSS J213907.42+222708.9. However, these are among the few ELM Survey objects that do not show radial velocity (RV) variations that confirm the binary nature expected of helium-core white dwarfs. The dominant 4.31 hr pulsation in SDSS J135512.34+195645.4 far exceeds the theoretical cut-off for surface reflection in a white dwarf, and this target is likely amore » high-amplitude δ Scuti pulsator with an overestimated surface gravity. We estimate the probability to be less than 0.0008 that the lack of measured RV variations in four of eight other pulsating candidate ELM white dwarfs could be due to low orbital inclination. Two other targets exhibit variability as photometric binaries. Partial coverage of the 19.342 hr orbit of WD J030818.19+514011.5 reveals deep eclipses that imply a primary radius >0.4 R {sub ⊙}—too large to be consistent with an ELM white dwarf. The only object for which our time series photometry adds support to ELM white dwarf classification is SDSS J105435.78−212155.9, which has consistent signatures of Doppler beaming and ellipsoidal variations. We conclude that the ELM Survey contains multiple false positives from another stellar population at T {sub eff}≲9000 K, possibly related to the sdA stars recently reported from SDSS spectra.« less

Connection between plasma response and resonant magnetic perturbation (RMP) edge localized mode (ELM) suppression in DIII-D [Connection between plasma response and RMP ELM suppression in DIII-D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wingen, Andreas; Ferraro, Nathaniel M.; Shafer, Morgan W.

Calculations of the plasma response to applied non-axisymmetric fields in several DIII-D discharges show that predicted displacements depend strongly on the edge current density. This result is found using both a linear two-fluid-MHD model (M3D-C1) and a nonlinear ideal-MHD model (VMEC). Furthermore, it is observed that the probability of a discharge being edge localized mode (ELM)-suppressed is most closely related to the edge current density, as opposed to the pressure gradient. It is found that discharges with a stronger kink response are closer to the peeling–ballooning stability limit in ELITE simulations and eventually cross into the unstable region, causing ELMsmore » to reappear. Thus for effective ELM suppression, the RMP has to prevent the plasma from generating a large kink response, associated with ELM instability. Experimental observations are in agreement with the finding; discharges which have a strong kink response in the MHD simulations show ELMs or ELM mitigation during the RMP phase of the experiment, while discharges with a small kink response in the MHD simulations are fully ELM suppressed in the experiment by the applied resonant magnetic perturbation. The results are cross-checked against modeled 3D ideal MHD equilibria using the VMEC code. The procedure of constructing optimal 3D equilibria for diverted H-mode discharges using VMEC is presented. As a result, kink displacements in VMEC are found to scale with the edge current density, similar to M3D-C1, but the displacements are smaller. A direct correlation in the flux surface displacements to the bootstrap current is shown.« less

Connection between plasma response and resonant magnetic perturbation (RMP) edge localized mode (ELM) suppression in DIII-D [Connection between plasma response and RMP ELM suppression in DIII-D

DOE PAGES

Wingen, Andreas; Ferraro, Nathaniel M.; Shafer, Morgan W.; ...

2015-09-03

Calculations of the plasma response to applied non-axisymmetric fields in several DIII-D discharges show that predicted displacements depend strongly on the edge current density. This result is found using both a linear two-fluid-MHD model (M3D-C1) and a nonlinear ideal-MHD model (VMEC). Furthermore, it is observed that the probability of a discharge being edge localized mode (ELM)-suppressed is most closely related to the edge current density, as opposed to the pressure gradient. It is found that discharges with a stronger kink response are closer to the peeling–ballooning stability limit in ELITE simulations and eventually cross into the unstable region, causing ELMsmore » to reappear. Thus for effective ELM suppression, the RMP has to prevent the plasma from generating a large kink response, associated with ELM instability. Experimental observations are in agreement with the finding; discharges which have a strong kink response in the MHD simulations show ELMs or ELM mitigation during the RMP phase of the experiment, while discharges with a small kink response in the MHD simulations are fully ELM suppressed in the experiment by the applied resonant magnetic perturbation. The results are cross-checked against modeled 3D ideal MHD equilibria using the VMEC code. The procedure of constructing optimal 3D equilibria for diverted H-mode discharges using VMEC is presented. As a result, kink displacements in VMEC are found to scale with the edge current density, similar to M3D-C1, but the displacements are smaller. A direct correlation in the flux surface displacements to the bootstrap current is shown.« less

Pruning The ELM Survey: Characterizing Candidate Low-mass White Dwarfs through Photometric Variability

NASA Astrophysics Data System (ADS)

Bell, Keaton J.; Gianninas, A.; Hermes, J. J.; Winget, D. E.; Kilic, Mukremin; Montgomery, M. H.; Castanheira, B. G.; Vanderbosch, Z.; Winget, K. I.; Brown, Warren R.

2017-02-01

We assess the photometric variability of nine stars with spectroscopic Teff and log g values from the ELM Survey that locates them near the empirical extremely low-mass (ELM) white dwarf instability strip. We discover three new pulsating stars: SDSS J135512.34+195645.4, SDSS J173521.69+213440.6, and SDSS J213907.42+222708.9. However, these are among the few ELM Survey objects that do not show radial velocity (RV) variations that confirm the binary nature expected of helium-core white dwarfs. The dominant 4.31 hr pulsation in SDSS J135512.34+195645.4 far exceeds the theoretical cut-off for surface reflection in a white dwarf, and this target is likely a high-amplitude δ Scuti pulsator with an overestimated surface gravity. We estimate the probability to be less than 0.0008 that the lack of measured RV variations in four of eight other pulsating candidate ELM white dwarfs could be due to low orbital inclination. Two other targets exhibit variability as photometric binaries. Partial coverage of the 19.342 hr orbit of WD J030818.19+514011.5 reveals deep eclipses that imply a primary radius >0.4 R⊙—too large to be consistent with an ELM white dwarf. The only object for which our time series photometry adds support to ELM white dwarf classification is SDSS J105435.78-212155.9, which has consistent signatures of Doppler beaming and ellipsoidal variations. We conclude that the ELM Survey contains multiple false positives from another stellar population at Teff ≲ 9000 K, possibly related to the sdA stars recently reported from SDSS spectra.

Recent developments in machine learning applications in landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Lun, Na Kai; Liew, Mohd Shahir; Matori, Abdul Nasir; Zawawi, Noor Amila Wan Abdullah

2017-11-01

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field.

ELM induced divertor heat loads on TCV

NASA Astrophysics Data System (ADS)

Marki, J.; Pitts, R. A.; Horacek, J.; Tskhakaya, D.; TCV Team

2009-06-01

Results are presented for heat loads at the TCV outer divertor target during ELMing H-mode using a fast IR camera. Benefitting from a recent surface cleaning of the entire first wall graphite armour, a comparison of the transient thermal response of freshly cleaned and untreated tile surfaces (coated with thick co-deposited layers) has been performed. The latter routinely exhibit temperature transients exceeding those of the clean ones by a factor ˜3, even if co-deposition throughout the first days of operation following the cleaning process leads to the steady regrowth of thin layers. Filaments are occasionally observed during the ELM heat flux rise phase, showing a spatial structure consistent with energy release at discrete toroidal locations in the outer midplane vicinity and with individual filaments carrying ˜1% of the total ELM energy. The temporal waveform of the ELM heat load is found to be in good agreement with the collisionless free streaming particle model.

VizieR Online Data Catalog: Binary white dwarfs atmospheric parameters (Gianninas+, 2014)

NASA Astrophysics Data System (ADS)

Gianninas, A.; Dufour, P.; Kilic, M.; Brown, W. R.; Bergeron, P.; Hermes, J. J.

2017-04-01

The sample that we analyze includes a total of 61 ELM WD binaries from the ELM Survey (Brown et al. 2013, J/ApJ/769/66). The bulk of this sample is comprised of the 58 ELM WDs listed in Table 3 of Brown et al. (2013, J/ApJ/769/66), but also includes three additional ELM WDs that have been published in separate papers since then. The spectra of these 61 ELM WDs were obtained using five distinct setups on two different telescopes. A total of 57 targets were observed with the 6.5m MMT telescope with the Blue Channel spectrograph (Schmidt et al. 1989PASP..101..713S). The four remaining targets were observed using the Fred Lawrence Whipple Observatory's (FLWO) 1.5m Tilinghast telescope equipped with the FAST spectrograph (Fabricant et al. 1998PASP..110...79F) and the 600 line/mm grating. (2 data files).

Dynamics of energetic particle driven modes and MHD modes in wall-stabilized high-β plasmas on JT-60U and DIII-D

NASA Astrophysics Data System (ADS)

Matsunaga, G.; Okabayashi, M.; Aiba, N.; Boedo, J. A.; Ferron, J. R.; Hanson, J. M.; Hao, G. Z.; Heidbrink, W. W.; Holcomb, C. T.; In, Y.; Jackson, G. L.; Liu, Y. Q.; Luce, T. C.; McKee, G. R.; Osborne, T. H.; Pace, D. C.; Shinohara, K.; Snyder, P. B.; Solomon, W. M.; Strait, E. J.; Turnbull, A. D.; Van Zeeland, M. A.; Watkins, J. G.; Zeng, L.; the DIII-D Team; the JT-60 Team

2013-12-01

In the wall-stabilized high-β plasmas in JT-60U and DIII-D, interactions between energetic particle (EP) driven modes (EPdMs) and edge localized modes (ELMs) have been observed. The interaction between the EPdM and ELM are reproducibly observed. Many EP diagnostics indicate a strong correlation between the distorted waveform of the EPdM and the EP transport to the edge. The waveform distortion is composed of higher harmonics (n ⩾ 2) and looks like a density snake near the plasma edge. According to statistical analyses, ELM triggering by the EPdMs requires a finite level of waveform distortion and pedestal recovery. ELM pacing by the EPdMs occurs when the repetition frequency of the EPdMs is higher than the natural ELM frequency. EPs transported by EPdMs are thought to contribute to change the edge stability.

Machine vision systems using machine learning for industrial product inspection

NASA Astrophysics Data System (ADS)

Lu, Yi; Chen, Tie Q.; Chen, Jie; Zhang, Jian; Tisler, Anthony

2002-02-01

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Machine Learning, deep learning and optimization in computer vision

NASA Astrophysics Data System (ADS)

Canu, Stéphane

2017-03-01

As quoted in the Large Scale Computer Vision Systems NIPS workshop, computer vision is a mature field with a long tradition of research, but recent advances in machine learning, deep learning, representation learning and optimization have provided models with new capabilities to better understand visual content. The presentation will go through these new developments in machine learning covering basic motivations, ideas, models and optimization in deep learning for computer vision, identifying challenges and opportunities. It will focus on issues related with large scale learning that is: high dimensional features, large variety of visual classes, and large number of examples.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

PubMed

Lakhani, Paras; Prater, Adam B; Hutson, R Kent; Andriole, Kathy P; Dreyer, Keith J; Morey, Jose; Prevedello, Luciano M; Clark, Toshi J; Geis, J Raymond; Itri, Jason N; Hawkins, C Matthew

2018-02-01

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

The Next Era: Deep Learning in Pharmaceutical Research.

PubMed

Ekins, Sean

2016-11-01

Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.

PubMed

Luo, Wei; Phung, Dinh; Tran, Truyen; Gupta, Sunil; Rana, Santu; Karmakar, Chandan; Shilton, Alistair; Yearwood, John; Dimitrova, Nevenka; Ho, Tu Bao; Venkatesh, Svetha; Berk, Michael

2016-12-16

As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. ©Wei Luo, Dinh Phung, Truyen Tran, Sunil Gupta, Santu Rana, Chandan Karmakar, Alistair Shilton, John Yearwood, Nevenka Dimitrova, Tu Bao Ho, Svetha Venkatesh, Michael Berk. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.12.2016.

Establishment patterns of water-elm at Catahoula Lake, Louisiana

Treesearch

Karen S. Doerr; Sanjeev Joshi; Richard F. Keim

2015-01-01

At Catahoula Lake in central Louisiana, an internationally important lake for water fowl, hydrologic alterations to the surrounding rivers and the lake itself have led to an expansion of water-elm (Planera aquatic J.F. Gmel.) into the lake bed. In this study, we used dendrochronology and aerial photography to quantify the expansion of water-elm in the lake and identify...

Pruning cycles and storm damage: are young American elms failing prematurely?

Treesearch

Chad P. Giblin

2017-01-01

The use of Dutch elm disease-resistant elms as a common replacement tree in municipal planting schedules has amassed a large population of these trees in many cities throughout the eastern half of the United States. Reports from practitioners have suggested that this population is vulnerable to catastrophic losses due to severe canopy failures during wind-loading...

Challenge inoculations to test for Dutch elm disease tolerance: a summary of methods used by various researchers

Treesearch

Linda M. Haugen; Garrett L. Beier; Susan E. Bentz; Raymond P. Guries; James M. Slavicek

2017-01-01

A variety of methods have been used by different research groups to "challenge" inoculate American elms (Ulmus americana) with the purpose of determining whether some clones may be resistant to the Dutch elm disease fungus. The methods used by seven research groups are described, along with observations on complications and benefits...

Host acceptance and larval competition between the invasive banded and European elm bark beetles (Coleoptera: Scolytidae): Potential mechanisms for competitive displacement

Treesearch

J.C. Lee; S.J. Seybold

2009-01-01

A recent survey revealed that the newly invasive banded elm bark beetle, Scolytus schevyrewi, was much more abundant than the long-established European elm bark beetle, S. multistriatus, in areas of Colorado and Wyoming, USA. This study was initiated to determine whether competitive displacement of S. multistriatus...

Height preference of Scolytus schevyrewi Semenov, the banded elm bark beetle of American elm, Ulmus americana

Treesearch

S. McElwey; J. F. Negron; J. Withrow

2007-01-01

Scolytus schevyrewi Semenov, the banded elm bark beetle, was first detected in North America in April 2003 in Colorado and Utah by the Rapid Detection and Response Pilot Project sponsored by APHIS and USDA Forest Service, Forest Health Protection. It was captured in Lindgren funnel traps baited with a-pinene, ethanol, and the Ips typographus...

Observations of ELM stabilization during neutral beam injection in DIII-D

NASA Astrophysics Data System (ADS)

Bortolon, Alessandro; Kramer, Gerrit; Diallo, Ahmed; Knolker, Matthias; Maingi, Rajesh; Nazikian, Raffi; Degrassie, John; Osborne, Thomas

2017-10-01

Edge localized modes (ELMs) are generally interpreted as peeling-ballooning instabilities, driven by the pedestal current and pressure gradient, with other subdominant effects possibly relevant close to marginal stability. We report observations of transient stabilization of type-I ELMs during neutral beam injection (NBI), emerging from a combined dataset of DIII-D ELMy H-mode plasmas with moderate heating obtained through pulsed NBI waveforms. Statistical analysis of ELM onset times indicates that, in the selected dataset, the likelihood of onset of an ELM lowers significantly during NBI modulation pulses, with the stronger correlation found with counter-current NBI. The effect is also found in rf-heated H-modes, where ELMs appear inhibited when isolated diagnostic beam pulses are applied. Coherent average analysis is used to determine how plasma density, temperature, rotation as well as beam ion quantities evolve during a NB modulation cycle, finding relatively small changes ( 3%) of pedestal Te and ne and toroidal and poloidal rotation variations up to 5 km/s. The effect of these changes on pedestal stability will be discussed. Work supported by US DOE under DE-FC02-04ER54698, DE-AC02-09CH11466.

Resistance to Dutch Elm Disease Reduces Presence of Xylem Endophytic Fungi in Elms (Ulmus spp.)

PubMed Central

Martín, Juan A.; Witzell, Johanna; Blumenstein, Kathrin; Rozpedowska, Elzbieta; Helander, Marjo; Sieber, Thomas N.; Gil, Luis

2013-01-01

Efforts to introduce pathogen resistance into landscape tree species by breeding may have unintended consequences for fungal diversity. To address this issue, we compared the frequency and diversity of endophytic fungi and defensive phenolic metabolites in elm (Ulmus spp.) trees with genotypes known to differ in resistance to Dutch elm disease. Our results indicate that resistant U. minor and U. pumila genotypes exhibit a lower frequency and diversity of fungal endophytes in the xylem than susceptible U. minor genotypes. However, resistant and susceptible genotypes showed a similar frequency and diversity of endophytes in the leaves and bark. The resistant and susceptible genotypes could be discriminated on the basis of the phenolic profile of the xylem, but not on basis of phenolics in the leaves or bark. As the Dutch elm disease pathogen develops within xylem tissues, the defensive chemistry of resistant elm genotypes thus appears to be one of the factors that may limit colonization by both the pathogen and endophytes. We discuss a potential trade-off between the benefits of breeding resistance into tree species, versus concomitant losses of fungal endophytes and the ecosystem services they provide. PMID:23468900

ELM: the status of the 2010 eukaryotic linear motif resource

PubMed Central

Gould, Cathryn M.; Diella, Francesca; Via, Allegra; Puntervoll, Pål; Gemünd, Christine; Chabanis-Davidson, Sophie; Michael, Sushama; Sayadi, Ahmed; Bryne, Jan Christian; Chica, Claudia; Seiler, Markus; Davey, Norman E.; Haslam, Niall; Weatheritt, Robert J.; Budd, Aidan; Hughes, Tim; Paś, Jakub; Rychlewski, Leszek; Travé, Gilles; Aasland, Rein; Helmer-Citterich, Manuela; Linding, Rune; Gibson, Toby J.

2010-01-01

Linear motifs are short segments of multidomain proteins that provide regulatory functions independently of protein tertiary structure. Much of intracellular signalling passes through protein modifications at linear motifs. Many thousands of linear motif instances, most notably phosphorylation sites, have now been reported. Although clearly very abundant, linear motifs are difficult to predict de novo in protein sequences due to the difficulty of obtaining robust statistical assessments. The ELM resource at http://elm.eu.org/ provides an expanding knowledge base, currently covering 146 known motifs, with annotation that includes >1300 experimentally reported instances. ELM is also an exploratory tool for suggesting new candidates of known linear motifs in proteins of interest. Information about protein domains, protein structure and native disorder, cellular and taxonomic contexts is used to reduce or deprecate false positive matches. Results are graphically displayed in a ‘Bar Code’ format, which also displays known instances from homologous proteins through a novel ‘Instance Mapper’ protocol based on PHI-BLAST. ELM server output provides links to the ELM annotation as well as to a number of remote resources. Using the links, researchers can explore the motifs, proteins, complex structures and associated literature to evaluate whether candidate motifs might be worth experimental investigation. PMID:19920119

Inhibition of phenylpropanoid biosynthesis increases cell wall digestibility, protoplast isolation, and facilitates sustained cell division in American elm (Ulmus americana).

PubMed

Jones, A Maxwell P; Chattopadhyay, Abhishek; Shukla, Mukund; Zoń, Jerzy; Saxena, Praveen K

2012-05-30

Protoplast technologies offer unique opportunities for fundamental research and to develop novel germplasm through somatic hybridization, organelle transfer, protoclonal variation, and direct insertion of DNA. Applying protoplast technologies to develop Dutch elm disease resistant American elms (Ulmus americana L.) was proposed over 30 years ago, but has not been achieved. A primary factor restricting protoplast technology to American elm is the resistance of the cell walls to enzymatic degradation and a long lag phase prior to cell wall re-synthesis and cell division. This study suggests that resistance to enzymatic degradation in American elm was due to water soluble phenylpropanoids. Incubating tobacco (Nicotiana tabacum L.) leaf tissue, an easily digestible species, in aqueous elm extract inhibits cell wall digestion in a dose dependent manner. This can be mimicked by p-coumaric or ferulic acid, phenylpropanoids known to re-enforce cell walls. Culturing American elm tissue in the presence of 2-aminoindane-2-phosphonic acid (AIP; 10-150 μM), an inhibitor of phenylalanine ammonia lyase (PAL), reduced flavonoid content, decreased tissue browning, and increased isolation rates significantly from 11.8% (±3.27) in controls to 65.3% (±4.60). Protoplasts isolated from callus grown in 100 μM AIP developed cell walls by day 2, had a division rate of 28.5% (±3.59) by day 6, and proliferated into callus by day 14. Heterokaryons were successfully produced using electrofusion and fused protoplasts remained viable when embedded in agarose. This study describes a novel approach of modifying phenylpropanoid biosynthesis to facilitate efficient protoplast isolation which has historically been problematic for American elm. This isolation system has facilitated recovery of viable protoplasts capable of rapid cell wall re-synthesis and sustained cell division to form callus. Further, isolated protoplasts survived electrofusion and viable heterokaryons were produced. Together, these results provide the first evidence of sustained cell division, callus regeneration, and potential application of somatic cell fusion in American elm, suggesting that this source of protoplasts may be ideal for genetic manipulation of this species. The technological advance made with American elm in this study has potential implications in other woody species for fundamental and applied research which require availability of viable protoplasts.

Inhibition of phenylpropanoid biosynthesis increases cell wall digestibility, protoplast isolation, and facilitates sustained cell division in American elm (Ulmus americana)

PubMed Central

2012-01-01

Background Protoplast technologies offer unique opportunities for fundamental research and to develop novel germplasm through somatic hybridization, organelle transfer, protoclonal variation, and direct insertion of DNA. Applying protoplast technologies to develop Dutch elm disease resistant American elms (Ulmus americana L.) was proposed over 30 years ago, but has not been achieved. A primary factor restricting protoplast technology to American elm is the resistance of the cell walls to enzymatic degradation and a long lag phase prior to cell wall re-synthesis and cell division. Results This study suggests that resistance to enzymatic degradation in American elm was due to water soluble phenylpropanoids. Incubating tobacco (Nicotiana tabacum L.) leaf tissue, an easily digestible species, in aqueous elm extract inhibits cell wall digestion in a dose dependent manner. This can be mimicked by p-coumaric or ferulic acid, phenylpropanoids known to re-enforce cell walls. Culturing American elm tissue in the presence of 2-aminoindane-2-phosphonic acid (AIP; 10-150 μM), an inhibitor of phenylalanine ammonia lyase (PAL), reduced flavonoid content, decreased tissue browning, and increased isolation rates significantly from 11.8% (±3.27) in controls to 65.3% (±4.60). Protoplasts isolated from callus grown in 100 μM AIP developed cell walls by day 2, had a division rate of 28.5% (±3.59) by day 6, and proliferated into callus by day 14. Heterokaryons were successfully produced using electrofusion and fused protoplasts remained viable when embedded in agarose. Conclusions This study describes a novel approach of modifying phenylpropanoid biosynthesis to facilitate efficient protoplast isolation which has historically been problematic for American elm. This isolation system has facilitated recovery of viable protoplasts capable of rapid cell wall re-synthesis and sustained cell division to form callus. Further, isolated protoplasts survived electrofusion and viable heterokaryons were produced. Together, these results provide the first evidence of sustained cell division, callus regeneration, and potential application of somatic cell fusion in American elm, suggesting that this source of protoplasts may be ideal for genetic manipulation of this species. The technological advance made with American elm in this study has potential implications in other woody species for fundamental and applied research which require availability of viable protoplasts. PMID:22646730

Divertor power and particle fluxes between and during type-I ELMs in the ASDEX Upgrade

NASA Astrophysics Data System (ADS)

Kallenbach, A.; Dux, R.; Eich, T.; Fischer, R.; Giannone, L.; Harhausen, J.; Herrmann, A.; Müller, H. W.; Pautasso, G.; Wischmeier, M.; ASDEX Upgrade Team

2008-08-01

Particle, electric charge and power fluxes for type-I ELMy H-modes are measured in the divertor of the ASDEX Upgrade tokamak by triple Langmuir probes, shunts, infrared (IR) thermography and spectroscopy. The discharges are in the medium to high density range, resulting in predominantly convective edge localized modes (ELMs) with moderate fractional stored energy losses of 2% or below. Time resolved data over ELM cycles are obtained by coherent averaging of typically one hundred similar ELMs, spatial profiles from the flush-mounted Langmuir probes are obtained by strike point sweeps. The application of simple physics models is used to compare different diagnostics and to make consistency checks, e.g. the standard sheath model applied to the Langmuir probes yields power fluxes which are compared with the thermographic measurements. In between ELMs, Langmuir probe and thermography power loads appear consistent in the outer divertor, taking into account additional load due to radiation and charge exchange neutrals measured by thermography. The inner divertor is completely detached and no significant power flow by charged particles is measured. During ELMs, quite similar power flux profiles are found in the outer divertor by thermography and probes, albeit larger uncertainties in Langmuir probe evaluation during ELMs have to be taken into account. In the inner divertor, ELM power fluxes from thermography are a factor 10 larger than those derived from probes using the standard sheath model. This deviation is too large to be caused by deficiencies of probe analysis. The total ELM energy deposition from IR is about a factor 2 higher in the inner divertor compared with the outer divertor. Spectroscopic measurements suggest a quite moderate contribution of radiation to the target power load. Shunt measurements reveal a significant positive charge flow into the inner target during ELMs. The net number of elementary charges correlates well with the total core particle loss obtained from highly resolved density profiles. As a consequence, the discrepancy between probe and IR measurements is attributed to the ion power channel via a high mean impact energy of the ions at the inner target. The dominant contributing mechanism is proposed to be the directed loss of ions from the pedestal region into the inner divertor.

Development of E-Learning Materials for Machining Safety Education

NASA Astrophysics Data System (ADS)

Nakazawa, Tsuyoshi; Mita, Sumiyoshi; Matsubara, Masaaki; Takashima, Takeo; Tanaka, Koichi; Izawa, Satoru; Kawamura, Takashi

We developed two e-learning materials for Manufacturing Practice safety education: movie learning materials and hazard-detection learning materials. Using these video and sound media, students can learn how to operate machines safely with movie learning materials, which raise the effectiveness of preparation and review for manufacturing practice. Using these materials, students can realize safety operation well. Students can apply knowledge learned in lectures to the detection of hazards and use study methods for hazard detection during machine operation using the hazard-detection learning materials. Particularly, the hazard-detection learning materials raise students‧ safety consciousness and increase students‧ comprehension of knowledge from lectures and comprehension of operations during Manufacturing Practice.

An introduction to quantum machine learning

NASA Astrophysics Data System (ADS)

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

2015-04-01

Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Large-Scale Machine Learning for Classification and Search

ERIC Educational Resources Information Center

Liu, Wei

2012-01-01

With the rapid development of the Internet, nowadays tremendous amounts of data including images and videos, up to millions or billions, can be collected for training machine learning models. Inspired by this trend, this thesis is dedicated to developing large-scale machine learning techniques for the purpose of making classification and nearest…

Newton Methods for Large Scale Problems in Machine Learning

ERIC Educational Resources Information Center

Hansen, Samantha Leigh

2014-01-01

The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

ERIC Educational Resources Information Center

Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

2015-01-01

Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead…

An active role for machine learning in drug development

PubMed Central

Murphy, Robert F.

2014-01-01

Due to the complexity of biological systems, cutting-edge machine-learning methods will be critical for future drug development. In particular, machine-vision methods to extract detailed information from imaging assays and active-learning methods to guide experimentation will be required to overcome the dimensionality problem in drug development. PMID:21587249