Multi-variants synthesis of Petri nets for FPGA devices

NASA Astrophysics Data System (ADS)

Bukowiec, Arkadiusz; Doligalski, Michał

2015-09-01

There is presented new method of synthesis of application specific logic controllers for FPGA devices. The specification of control algorithm is made with use of control interpreted Petri net (PT type). It allows specifying parallel processes in easy way. The Petri net is decomposed into state-machine type subnets. In this case, each subnet represents one parallel process. For this purpose there are applied algorithms of coloring of Petri nets. There are presented two approaches of such decomposition: with doublers of macroplaces or with one global wait place. Next, subnets are implemented into two-level logic circuit of the controller. The levels of logic circuit are obtained as a result of its architectural decomposition. The first level combinational circuit is responsible for generation of next places and second level decoder is responsible for generation output symbols. There are worked out two variants of such circuits: with one shared operational memory or with many flexible distributed memories as a decoder. Variants of Petri net decomposition and structures of logic circuits can be combined together without any restrictions. It leads to existence of four variants of multi-variants synthesis.

Dominant modal decomposition method

NASA Astrophysics Data System (ADS)

Dombovari, Zoltan

2017-03-01

The paper deals with the automatic decomposition of experimental frequency response functions (FRF's) of mechanical structures. The decomposition of FRF's is based on the Green function representation of free vibratory systems. After the determination of the impulse dynamic subspace, the system matrix is formulated and the poles are calculated directly. By means of the corresponding eigenvectors, the contribution of each element of the impulse dynamic subspace is determined and the sufficient decomposition of the corresponding FRF is carried out. With the presented dominant modal decomposition (DMD) method, the mode shapes, the modal participation vectors and the modal scaling factors are identified using the decomposed FRF's. Analytical example is presented along with experimental case studies taken from machine tool industry.

Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

PubMed Central

2011-01-01

Background Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'. Methods The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples. Results For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684. Conclusions Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals. PMID:21244712

A Hybrid Generalized Hidden Markov Model-Based Condition Monitoring Approach for Rolling Bearings

PubMed Central

Liu, Jie; Hu, Youmin; Wu, Bo; Wang, Yan; Xie, Fengyun

2017-01-01

The operating condition of rolling bearings affects productivity and quality in the rotating machine process. Developing an effective rolling bearing condition monitoring approach is critical to accurately identify the operating condition. In this paper, a hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings is proposed, where interval valued features are used to efficiently recognize and classify machine states in the machine process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition (VMD). Parameters of the VMD, in the form of generalized intervals, provide a concise representation for aleatory and epistemic uncertainty and improve the robustness of identification. The multi-scale permutation entropy method is applied to extract state features from the decomposed signals in different operating conditions. Traditional principal component analysis is adopted to reduce feature size and computational cost. With the extracted features’ information, the generalized hidden Markov model, based on generalized interval probability, is used to recognize and classify the fault types and fault severity levels. Finally, the experiment results show that the proposed method is effective at recognizing and classifying the fault types and fault severity levels of rolling bearings. This monitoring method is also efficient enough to quantify the two uncertainty components. PMID:28524088

Decomposition of the compound Atwood machine

NASA Astrophysics Data System (ADS)

Lopes Coelho, R.

2017-11-01

Non-standard solving strategies for the compound Atwood machine problem have been proposed. The present strategy is based on a very simple idea. Taking an Atwood machine and replacing one of its bodies by another Atwood machine, we have a compound machine. As this operation can be repeated, we can construct any compound Atwood machine. This rule of construction is transferred to a mathematical model, whereby the equations of motion are obtained. The only difference between the machine and its model is that instead of pulleys and bodies, we have reference frames that move solidarily with these objects. This model provides us with the accelerations in the non-inertial frames of the bodies, which we will use to obtain the equations of motion. This approach to the problem will be justified by the Lagrange method and exemplified by machines with six and eight bodies.

Method for the protection of extreme ultraviolet lithography optics

DOEpatents

Grunow, Philip A.; Clift, Wayne M.; Klebanoff, Leonard E.

2010-06-22

A coating for the protection of optical surfaces exposed to a high energy erosive plasma. A gas that can be decomposed by the high energy plasma, such as the xenon plasma used for extreme ultraviolet lithography (EUVL), is injected into the EUVL machine. The decomposition products coat the optical surfaces with a protective coating maintained at less than about 100 .ANG. thick by periodic injections of the gas. Gases that can be used include hydrocarbon gases, particularly methane, PH.sub.3 and H.sub.2S. The use of PH.sub.3 and H.sub.2S is particularly advantageous since films of the plasma-induced decomposition products S and P cannot grow to greater than 10 .ANG. thick in a vacuum atmosphere such as found in an EUVL machine.

Fusion of infrared and visible images based on BEMD and NSDFB

NASA Astrophysics Data System (ADS)

Zhu, Pan; Huang, Zhanhua; Lei, Hai

2016-07-01

This paper presents a new fusion method based on the adaptive multi-scale decomposition of bidimensional empirical mode decomposition (BEMD) and the flexible directional expansion of nonsubsampled directional filter banks (NSDFB) for visible-infrared images. Compared with conventional multi-scale fusion methods, BEMD is non-parametric and completely data-driven, which is relatively more suitable for non-linear signals decomposition and fusion. NSDFB can provide direction filtering on the decomposition levels to capture more geometrical structure of the source images effectively. In our fusion framework, the entropies of the two patterns of source images are firstly calculated and the residue of the image whose entropy is larger is extracted to make it highly relevant with the other source image. Then, the residue and the other source image are decomposed into low-frequency sub-bands and a sequence of high-frequency directional sub-bands in different scales by using BEMD and NSDFB. In this fusion scheme, two relevant fusion rules are used in low-frequency sub-bands and high-frequency directional sub-bands, respectively. Finally, the fused image is obtained by applying corresponding inverse transform. Experimental results indicate that the proposed fusion algorithm can obtain state-of-the-art performance for visible-infrared images fusion in both aspects of objective assessment and subjective visual quality even for the source images obtained in different conditions. Furthermore, the fused results have high contrast, remarkable target information and rich details information that are more suitable for human visual characteristics or machine perception.

Fourier decomposition of segmented magnets with radial magnetization in surface-mounted PM machines

NASA Astrophysics Data System (ADS)

Tiang, Tow Leong; Ishak, Dahaman; Lim, Chee Peng

2017-11-01

This paper presents a generic field model of radial magnetization (RM) pattern produced by multiple segmented magnets per rotor pole in surface-mounted permanent magnet (PM) machines. The magnetization vectors from either odd- or even-number of magnet blocks per pole are described. Fourier decomposition is first employed to derive the field model, and later integrated with the exact 2D analytical subdomain method to predict the magnetic field distributions and other motor global quantities. For the assessment purpose, a 12-slot/8-pole surface-mounted PM motor with two segmented magnets per pole is investigated by using the proposed field model. The electromagnetic performances of the PM machines are intensively predicted by the proposed magnet field model which include the magnetic field distributions, airgap flux density, phase back-EMF, cogging torque, and output torque during either open-circuit or on-load operating conditions. The analytical results are evaluated and compared with those obtained from both 2D and 3D finite element analyses (FEA) where an excellent agreement has been achieved.

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.

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

Machine learning properties of materials and molecules with entropy-regularized kernels

NASA Astrophysics Data System (ADS)

Ceriotti, Michele; Bartók, Albert; CsáNyi, GáBor; de, Sandip

Application of machine-learning methods to physics, chemistry and materials science is gaining traction as a strategy to obtain accurate predictions of the properties of matter at a fraction of the typical cost of quantum mechanical electronic structure calculations. In this endeavor, one can leverage general-purpose frameworks for supervised-learning. It is however very important that the input data - for instance the positions of atoms in a molecule or solid - is processed into a form that reflects all the underlying physical symmetries of the problem, and that possesses the regularity properties that are required by machine-learning algorithms. Here we introduce a general strategy to build a representation of this kind. We will start from existing approaches to compare local environments (basically, groups of atoms), and combine them using techniques borrowed from optimal transport theory, discussing the relation between this idea and additive energy decompositions. We will present a few examples demonstrating the potential of this approach as a tool to predict molecular and materials' properties with an accuracy on par with state-of-the-art electronic structure methods. MARVEL NCCR (Swiss National Science Foundation) and ERC StG HBMAP (European Research Council, G.A. 677013).

A method to identify the main mode of machine tool under operating conditions

NASA Astrophysics Data System (ADS)

Wang, Daming; Pan, Yabing

2017-04-01

The identification of the modal parameters under experimental conditions is the most common procedure when solving the problem of machine tool structure vibration. However, the influence of each mode on the machine tool vibration in real working conditions remains unknown. In fact, the contributions each mode makes to the machine tool vibration during machining process are different. In this article, an active excitation modal analysis is applied to identify the modal parameters in operational condition, and the Operating Deflection Shapes (ODS) in frequencies of high level vibration that affect the quality of machining in real working conditions are obtained. Then, the ODS is decomposed by the mode shapes which are identified in operational conditions. So, the contributions each mode makes to machine tool vibration during machining process are got by decomposition coefficients. From the previous steps, we can find out the main modes which effect the machine tool more significantly in working conditions. This method was also verified to be effective by experiments.

Epileptic Seizures Prediction Using Machine Learning Methods

PubMed Central

Usman, Syed Muhammad

2017-01-01

Epileptic seizures occur due to disorder in brain functionality which can affect patient's health. Prediction of epileptic seizures before the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction. Our model predicts epileptic seizures' sufficient time before the onset of seizure starts and provides a better true positive rate. We have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects. PMID:29410700

Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding

NASA Astrophysics Data System (ADS)

Kang, Shouqiang; Ma, Danyang; Wang, Yujing; Lan, Chaofeng; Chen, Qingguo; Mikulovich, V. I.

2017-03-01

To effectively assess different fault locations and different degrees of performance degradation of a rolling bearing with a unified assessment index, a novel state assessment method based on the relative compensation distance of multiple-domain features and locally linear embedding is proposed. First, for a single-sample signal, time-domain and frequency-domain indexes can be calculated for the original vibration signal and each sensitive intrinsic mode function obtained by improved ensemble empirical mode decomposition, and the singular values of the sensitive intrinsic mode function matrix can be extracted by singular value decomposition to construct a high-dimensional hybrid-domain feature vector. Second, a feature matrix can be constructed by arranging each feature vector of multiple samples, the dimensions of each row vector of the feature matrix can be reduced by the locally linear embedding algorithm, and the compensation distance of each fault state of the rolling bearing can be calculated using the support vector machine. Finally, the relative distance between different fault locations and different degrees of performance degradation and the normal-state optimal classification surface can be compensated, and on the basis of the proposed relative compensation distance, the assessment model can be constructed and an assessment curve drawn. Experimental results show that the proposed method can effectively assess different fault locations and different degrees of performance degradation of the rolling bearing under certain conditions.

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

NASA Astrophysics Data System (ADS)

Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto

2017-04-01

This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.

The Distributed Diagonal Force Decomposition Method for Parallelizing Molecular Dynamics Simulations

PubMed Central

Boršnik, Urban; Miller, Benjamin T.; Brooks, Bernard R.; Janežič, Dušanka

2011-01-01

Parallelization is an effective way to reduce the computational time needed for molecular dynamics simulations. We describe a new parallelization method, the distributed-diagonal force decomposition method, with which we extend and improve the existing force decomposition methods. Our new method requires less data communication during molecular dynamics simulations than replicated data and current force decomposition methods, increasing the parallel efficiency. It also dynamically load-balances the processors' computational load throughout the simulation. The method is readily implemented in existing molecular dynamics codes and it has been incorporated into the CHARMM program, allowing its immediate use in conjunction with the many molecular dynamics simulation techniques that are already present in the program. We also present the design of the Force Decomposition Machine, a cluster of personal computers and networks that is tailored to running molecular dynamics simulations using the distributed diagonal force decomposition method. The design is expandable and provides various degrees of fault resilience. This approach is easily adaptable to computers with Graphics Processing Units because it is independent of the processor type being used. PMID:21793007

Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of nonsparse low-rank matrices

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Siopsis, George; Weedbrook, Christian

2018-02-01

With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.

Modeling and implementation of concurrent logic controllers with use of Petri nets, LSMs, and sequent calculus

NASA Astrophysics Data System (ADS)

Tkacz, J.; Bukowiec, A.; Doligalski, M.

2017-08-01

The paper presentes the method of modeling and implementation of concurrent controllers. Concurrent controllers are specified by Petri nets. Then Petri nets are decomposed using symbolic deduction method of analysis. Formal methods like sequent calculus system with considered elements of Thelen's algorithm have been used here. As a result, linked state machines (LSMs) are received. Each FSM is implemented using methods of structural decomposition during process of logic synthesis. The method of multiple encoding of microinstruction has been applied. It leads to decreased number of Boolean function realized by combinational part of FSM. The additional decoder could be implemented with the use of memory blocks.

jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

PubMed Central

2011-01-01

Background The decomposition of a chemical graph is a convenient approach to encode information of the corresponding organic compound. While several commercial toolkits exist to encode molecules as so-called fingerprints, only a few open source implementations are available. The aim of this work is to introduce a library for exactly defined molecular decompositions, with a strong focus on the application of these features in machine learning and data mining. It provides several options such as search depth, distance cut-offs, atom- and pharmacophore typing. Furthermore, it provides the functionality to combine, to compare, or to export the fingerprints into several formats. Results We provide a Java 1.6 library for the decomposition of chemical graphs based on the open source Chemistry Development Kit toolkit. We reimplemented popular fingerprinting algorithms such as depth-first search fingerprints, extended connectivity fingerprints, autocorrelation fingerprints (e.g. CATS2D), radial fingerprints (e.g. Molprint2D), geometrical Molprint, atom pairs, and pharmacophore fingerprints. We also implemented custom fingerprints such as the all-shortest path fingerprint that only includes the subset of shortest paths from the full set of paths of the depth-first search fingerprint. As an application of jCompoundMapper, we provide a command-line executable binary. We measured the conversion speed and number of features for each encoding and described the composition of the features in detail. The quality of the encodings was tested using the default parametrizations in combination with a support vector machine on the Sutherland QSAR data sets. Additionally, we benchmarked the fingerprint encodings on the large-scale Ames toxicity benchmark using a large-scale linear support vector machine. The results were promising and could often compete with literature results. On the large Ames benchmark, for example, we obtained an AUC ROC performance of 0.87 with a reimplementation of the extended connectivity fingerprint. This result is comparable to the performance achieved by a non-linear support vector machine using state-of-the-art descriptors. On the Sutherland QSAR data set, the best fingerprint encodings showed a comparable or better performance on 5 of the 8 benchmarks when compared against the results of the best descriptors published in the paper of Sutherland et al. Conclusions jCompoundMapper is a library for chemical graph fingerprints with several tweaking possibilities and exporting options for open source data mining toolkits. The quality of the data mining results, the conversion speed, the LPGL software license, the command-line interface, and the exporters should be useful for many applications in cheminformatics like benchmarks against literature methods, comparison of data mining algorithms, similarity searching, and similarity-based data mining. PMID:21219648

An inductance Fourier decomposition-based current-hysteresis control strategy for switched reluctance motors

NASA Astrophysics Data System (ADS)

Hua, Wei; Qi, Ji; Jia, Meng

2017-05-01

Switched reluctance machines (SRMs) have attracted extensive attentions due to the inherent advantages, including simple and robust structure, low cost, excellent fault-tolerance and wide speed range, etc. However, one of the bottlenecks limiting the SRMs for further applications is its unfavorable torque ripple, and consequently noise and vibration due to the unique doubly-salient structure and pulse-current-based power supply method. In this paper, an inductance Fourier decomposition-based current-hysteresis-control (IFD-CHC) strategy is proposed to reduce torque ripple of SRMs. After obtaining a nonlinear inductance-current-position model based Fourier decomposition, reference currents can be calculated by reference torque and the derived inductance model. Both the simulations and experimental results confirm the effectiveness of the proposed strategy.

Problem decomposition by mutual information and force-based clustering

NASA Astrophysics Data System (ADS)

Otero, Richard Edward

The scale of engineering problems has sharply increased over the last twenty years. Larger coupled systems, increasing complexity, and limited resources create a need for methods that automatically decompose problems into manageable sub-problems by discovering and leveraging problem structure. The ability to learn the coupling (inter-dependence) structure and reorganize the original problem could lead to large reductions in the time to analyze complex problems. Such decomposition methods could also provide engineering insight on the fundamental physics driving problem solution. This work forwards the current state of the art in engineering decomposition through the application of techniques originally developed within computer science and information theory. The work describes the current state of automatic problem decomposition in engineering and utilizes several promising ideas to advance the state of the practice. Mutual information is a novel metric for data dependence and works on both continuous and discrete data. Mutual information can measure both the linear and non-linear dependence between variables without the limitations of linear dependence measured through covariance. Mutual information is also able to handle data that does not have derivative information, unlike other metrics that require it. The value of mutual information to engineering design work is demonstrated on a planetary entry problem. This study utilizes a novel tool developed in this work for planetary entry system synthesis. A graphical method, force-based clustering, is used to discover related sub-graph structure as a function of problem structure and links ranked by their mutual information. This method does not require the stochastic use of neural networks and could be used with any link ranking method currently utilized in the field. Application of this method is demonstrated on a large, coupled low-thrust trajectory problem. Mutual information also serves as the basis for an alternative global optimizer, called MIMIC, which is unrelated to Genetic Algorithms. Advancement to the current practice demonstrates the use of MIMIC as a global method that explicitly models problem structure with mutual information, providing an alternate method for globally searching multi-modal domains. By leveraging discovered problem inter- dependencies, MIMIC may be appropriate for highly coupled problems or those with large function evaluation cost. This work introduces a useful addition to the MIMIC algorithm that enables its use on continuous input variables. By leveraging automatic decision tree generation methods from Machine Learning and a set of randomly generated test problems, decision trees for which method to apply are also created, quantifying decomposition performance over a large region of the design space.

Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging.

PubMed

Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D; Joel, Suresh; Pekar, James J; Mostofsky, Stewart H; Caffo, Brian

2012-01-01

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD.

Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging

PubMed Central

Eloyan, Ani; Muschelli, John; Nebel, Mary Beth; Liu, Han; Han, Fang; Zhao, Tuo; Barber, Anita D.; Joel, Suresh; Pekar, James J.; Mostofsky, Stewart H.; Caffo, Brian

2012-01-01

Successful automated diagnoses of attention deficit hyperactive disorder (ADHD) using imaging and functional biomarkers would have fundamental consequences on the public health impact of the disease. In this work, we show results on the predictability of ADHD using imaging biomarkers and discuss the scientific and diagnostic impacts of the research. We created a prediction model using the landmark ADHD 200 data set focusing on resting state functional connectivity (rs-fc) and structural brain imaging. We predicted ADHD status and subtype, obtained by behavioral examination, using imaging data, intelligence quotients and other covariates. The novel contributions of this manuscript include a thorough exploration of prediction and image feature extraction methodology on this form of data, including the use of singular value decompositions (SVDs), CUR decompositions, random forest, gradient boosting, bagging, voxel-based morphometry, and support vector machines as well as important insights into the value, and potentially lack thereof, of imaging biomarkers of disease. The key results include the CUR-based decomposition of the rs-fc-fMRI along with gradient boosting and the prediction algorithm based on a motor network parcellation and random forest algorithm. We conjecture that the CUR decomposition is largely diagnosing common population directions of head motion. Of note, a byproduct of this research is a potential automated method for detecting subtle in-scanner motion. The final prediction algorithm, a weighted combination of several algorithms, had an external test set specificity of 94% with sensitivity of 21%. The most promising imaging biomarker was a correlation graph from a motor network parcellation. In summary, we have undertaken a large-scale statistical exploratory prediction exercise on the unique ADHD 200 data set. The exercise produced several potential leads for future scientific exploration of the neurological basis of ADHD. PMID:22969709

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Distributed-Memory Computing With the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA)

NASA Technical Reports Server (NTRS)

Riley, Christopher J.; Cheatwood, F. McNeil

1997-01-01

The Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA), a Navier-Stokes solver, has been modified for use in a parallel, distributed-memory environment using the Message-Passing Interface (MPI) standard. A standard domain decomposition strategy is used in which the computational domain is divided into subdomains with each subdomain assigned to a processor. Performance is examined on dedicated parallel machines and a network of desktop workstations. The effect of domain decomposition and frequency of boundary updates on performance and convergence is also examined for several realistic configurations and conditions typical of large-scale computational fluid dynamic analysis.

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.

Progressive Vector Quantization on a massively parallel SIMD machine with application to multispectral image data

NASA Technical Reports Server (NTRS)

Manohar, Mareboyana; Tilton, James C.

1994-01-01

A progressive vector quantization (VQ) compression approach is discussed which decomposes image data into a number of levels using full search VQ. The final level is losslessly compressed, enabling lossless reconstruction. The computational difficulties are addressed by implementation on a massively parallel SIMD machine. We demonstrate progressive VQ on multispectral imagery obtained from the Advanced Very High Resolution Radiometer instrument and other Earth observation image data, and investigate the trade-offs in selecting the number of decomposition levels and codebook training method.

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

PubMed

Zhang, Ming-Huan; Ma, Jun-Shan; Shen, Ying; Chen, Ying

2016-09-01

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images. T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy). Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively. The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Nondestructive method for chemically machining crucibles or molds from their enclosed ingots and castings

DOEpatents

Stout, Norman D.; Newkirk, Herbert W.

1991-01-01

An inventive method is described for chemically machining rhenium, rhenium and tungsten alloy, and group 5b and 6b crucibles or molds from included ingots and castings comprised of oxide crystals including YAG and YAG based crystals, garnets, corundum crystals, and ceramic oxides. A mixture of potassium hydroxide and 15 to 90 weight percent of potassium nitrate is prepared and maintained at a temperature above melting and below the lower of 500 degrees centigrade or the temperature of decomposition of the mixture. The enveloping metal container together with its included oxide crystal object is rotated within the heated KOH-KNO.sub.3 mixture, until the container is safely chemically machined away from the included oxide crystal object.

A machine learning approach to galaxy-LSS classification - I. Imprints on halo merger trees

NASA Astrophysics Data System (ADS)

Hui, Jianan; Aragon, Miguel; Cui, Xinping; Flegal, James M.

2018-04-01

The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here, we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of 93 per cent. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine (SVM) to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time, and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for SVM.

Evaluating litter decomposition and soil organic matter dynamics in earth system models: contrasting analysis of long-term litter decomposition and steady-state soil carbon

NASA Astrophysics Data System (ADS)

Bonan, G. B.; Wieder, W. R.

2012-12-01

Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle-climate feedbacks are largely untested with respect to litter decomposition. Here, we demonstrate a protocol to document model performance with respect to both long-term (10 year) litter decomposition and steady-state soil carbon stocks. First, we test the soil organic matter parameterization of the Community Land Model version 4 (CLM4), the terrestrial component of the Community Earth System Model, with data from the Long-term Intersite Decomposition Experiment Team (LIDET). The LIDET dataset is a 10-year study of litter decomposition at multiple sites across North America and Central America. We show results for 10-year litter decomposition simulations compared with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes. We show additional simulations with DAYCENT, a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results reveal large discrepancy between the laboratory microcosm studies used to parameterize the CLM4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, despite using the LIDET-provided climatic decomposition index to constrain temperature and moisture effects on decomposition. Nitrogen immobilization is similarly biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, without requirement for nitrogen limitation of decomposition. Second, we compare global observationally-based datasets of soil carbon with simulated steady-state soil carbon stocks for both models. The models simulations were forced with observationally-based estimates of annual litterfall and model-derived climatic decomposition index. While comparison with the LIDET 10-year litterbag study reveals sharp contrasts between CLM4 and DAYCENT, simulations of steady-state soil carbon show less difference between models. Both CLM4 and DAYCENT significantly underestimate soil carbon. Sensitivity analyses highlight causes of the low soil carbon bias. The terrestrial biogeochemistry of earth system models must be critically tested with observations, and the consequences of particular model choices must be documented. Long-term litter decomposition experiments such as LIDET provide a real-world process-oriented benchmark to evaluate models and can critically inform model development. Analysis of steady-state soil carbon estimates reveal additional, but here different, inferences about model performance.

Data Mining in Earth System Science (DMESS 2011)

Treesearch

Forrest M. Hoffman; J. Walter Larson; Richard Tran Mills; Bhorn-Gustaf Brooks; Auroop R. Ganguly; William Hargrove; et al

2011-01-01

From field-scale measurements to global climate simulations and remote sensing, the growing body of very large and long time series Earth science data are increasingly difficult to analyze, visualize, and interpret. Data mining, information theoretic, and machine learning techniques—such as cluster analysis, singular value decomposition, block entropy, Fourier and...

Analysis of tasks for dynamic man/machine load balancing in advanced helicopters

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

Jorgensen, C.C.

1987-10-01

This report considers task allocation requirements imposed by advanced helicopter designs incorporating mixes of human pilots and intelligent machines. Specifically, it develops an analogy between load balancing using distributed non-homogeneous multiprocessors and human team functions. A taxonomy is presented which can be used to identify task combinations likely to cause overload for dynamic scheduling and process allocation mechanisms. Designer criteria are given for function decomposition, separation of control from data, and communication handling for dynamic tasks. Possible effects of n-p complete scheduling problems are noted and a class of combinatorial optimization methods are examined.

Padé spectrum decompositions of quantum distribution functions and optimal hierarchical equations of motion construction for quantum open systems

NASA Astrophysics Data System (ADS)

Hu, Jie; Luo, Meng; Jiang, Feng; Xu, Rui-Xue; Yan, YiJing

2011-06-01

Padé spectrum decomposition is an optimal sum-over-poles expansion scheme of Fermi function and Bose function [J. Hu, R. X. Xu, and Y. J. Yan, J. Chem. Phys. 133, 101106 (2010)], 10.1063/1.3484491. In this work, we report two additional members to this family, from which the best among all sum-over-poles methods could be chosen for different cases of application. Methods are developed for determining these three Padé spectrum decomposition expansions at machine precision via simple algorithms. We exemplify the applications of present development with optimal construction of hierarchical equations-of-motion formulations for nonperturbative quantum dissipation and quantum transport dynamics. Numerical demonstrations are given for two systems. One is the transient transport current to an interacting quantum-dots system, together with the involved high-order co-tunneling dynamics. Another is the non-Markovian dynamics of a spin-boson system.

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

Fast polar decomposition of an arbitrary matrix

NASA Technical Reports Server (NTRS)

Higham, Nicholas J.; Schreiber, Robert S.

1988-01-01

The polar decomposition of an m x n matrix A of full rank, where m is greater than or equal to n, can be computed using a quadratically convergent algorithm. The algorithm is based on a Newton iteration involving a matrix inverse. With the use of a preliminary complete orthogonal decomposition the algorithm can be extended to arbitrary A. How to use the algorithm to compute the positive semi-definite square root of a Hermitian positive semi-definite matrix is described. A hybrid algorithm which adaptively switches from the matrix inversion based iteration to a matrix multiplication based iteration due to Kovarik, and to Bjorck and Bowie is formulated. The decision when to switch is made using a condition estimator. This matrix multiplication rich algorithm is shown to be more efficient on machines for which matrix multiplication can be executed 1.5 times faster than matrix inversion.

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

NASA Astrophysics Data System (ADS)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Abstract quantum computing machines and quantum computational logics

NASA Astrophysics Data System (ADS)

Chiara, Maria Luisa Dalla; Giuntini, Roberto; Sergioli, Giuseppe; Leporini, Roberto

2016-06-01

Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.

Initial mechanisms for the unimolecular decomposition of electronically excited bisfuroxan based energetic materials.

PubMed

Yuan, Bing; Bernstein, Elliot R

2017-01-07

Unimolecular decomposition of energetic molecules, 3,3'-diamino-4,4'-bisfuroxan (labeled as A) and 4,4'-diamino-3,3'-bisfuroxan (labeled as B), has been explored via 226/236 nm single photon laser excitation/decomposition. These two energetic molecules, subsequent to UV excitation, create NO as an initial decomposition product at the nanosecond excitation energies (5.0-5.5 eV) with warm vibrational temperature (1170 ± 50 K for A, 1400 ± 50 K for B) and cold rotational temperature (<55 K). Initial decomposition mechanisms for these two electronically excited, isolated molecules are explored at the complete active space self-consistent field (CASSCF(12,12)/6-31G(d)) level with and without MP2 correction. Potential energy surface calculations illustrate that conical intersections play an essential role in the calculated decomposition mechanisms. Based on experimental observations and theoretical calculations, NO product is released through opening of the furoxan ring: ring opening can occur either on the S 1 excited or S 0 ground electronic state. The reaction path with the lowest energetic barrier is that for which the furoxan ring opens on the S 1 state via the breaking of the N1-O1 bond. Subsequently, the molecule moves to the ground S 0 state through related ring-opening conical intersections, and an NO product is formed on the ground state surface with little rotational excitation at the last NO dissociation step. For the ground state ring opening decomposition mechanism, the N-O bond and C-N bond break together in order to generate dissociated NO. With the MP2 correction for the CASSCF(12,12) surface, the potential energies of molecules with dissociated NO product are in the range from 2.04 to 3.14 eV, close to the theoretical result for the density functional theory (B3LYP) and MP2 methods. The CASMP2(12,12) corrected approach is essential in order to obtain a reasonable potential energy surface that corresponds to the observed decomposition behavior of these molecules. Apparently, highly excited states are essential for an accurate representation of the kinetics and dynamics of excited state decomposition of both of these bisfuroxan energetic molecules. The experimental vibrational temperatures of NO products of A and B are about 800-1000 K lower than previously studied energetic molecules with NO as a decomposition product.

Linear friction weld process monitoring of fixture cassette deformations using empirical mode decomposition

NASA Astrophysics Data System (ADS)

Bakker, O. J.; Gibson, C.; Wilson, P.; Lohse, N.; Popov, A. A.

2015-10-01

Due to its inherent advantages, linear friction welding is a solid-state joining process of increasing importance to the aerospace, automotive, medical and power generation equipment industries. Tangential oscillations and forge stroke during the burn-off phase of the joining process introduce essential dynamic forces, which can also be detrimental to the welding process. Since burn-off is a critical phase in the manufacturing stage, process monitoring is fundamental for quality and stability control purposes. This study aims to improve workholding stability through the analysis of fixture cassette deformations. Methods and procedures for process monitoring are developed and implemented in a fail-or-pass assessment system for fixture cassette deformations during the burn-off phase. Additionally, the de-noised signals are compared to results from previous production runs. The observed deformations as a consequence of the forces acting on the fixture cassette are measured directly during the welding process. Data on the linear friction-welding machine are acquired and de-noised using empirical mode decomposition, before the burn-off phase is extracted. This approach enables a direct, objective comparison of the signal features with trends from previous successful welds. The capacity of the whole process monitoring system is validated and demonstrated through the analysis of a large number of signals obtained from welding experiments.

Unifying neural-network quantum states and correlator product states via tensor networks

NASA Astrophysics Data System (ADS)

Clark, Stephen R.

2018-04-01

Correlator product states (CPS) are a powerful and very broad class of states for quantum lattice systems whose (unnormalised) amplitudes in a fixed basis can be sampled exactly and efficiently. They work by gluing together states of overlapping clusters of sites on the lattice, called correlators. Recently Carleo and Troyer (2017 Science 355 602) introduced a new type sampleable ansatz called neural-network quantum states (NQS) that are inspired by the restricted Boltzmann model used in machine learning. By employing the formalism of tensor networks we show that NQS are a special form of CPS with novel properties. Diagramatically a number of simple observations become transparent. Namely, that NQS are CPS built from extensively sized GHZ-form correlators making them uniquely unbiased geometrically. The appearance of GHZ correlators also relates NQS to canonical polyadic decompositions of tensors. Another immediate implication of the NQS equivalence to CPS is that we are able to formulate exact NQS representations for a wide range of paradigmatic states, including superpositions of weighed-graph states, the Laughlin state, toric code states, and the resonating valence bond state. These examples reveal the potential of using higher dimensional hidden units and a second hidden layer in NQS. The major outlook of this study is the elevation of NQS to correlator operators allowing them to enhance conventional well-established variational Monte Carlo approaches for strongly correlated fermions.

Methodology for identifying and representing knowledge in the scope of CMM inspection resource selection

NASA Astrophysics Data System (ADS)

Martínez, S.; Barreiro, J.; Cuesta, E.; Álvarez, B. J.; González, D.

2012-04-01

This paper is focused on the task of elicitation and structuring of knowledge related to selection of inspection resources. The final goal is to obtain an informal model of knowledge oriented to the inspection planning in coordinate measuring machines. In the first tasks, where knowledge is captured, it is necessary to use tools that make easier the analysis and structuring of knowledge, so that rules of selection can be easily stated to configure the inspection resources. In order to store the knowledge a so-called Onto-Process ontology has been developed. This ontology may be of application to diverse processes in manufacturing engineering. This paper describes the decomposition of the ontology in terms of general units of knowledge and others more specific for selection of sensor assemblies in inspection planning with touch sensors.

Joint optimization of maintenance, buffers and machines in manufacturing lines

NASA Astrophysics Data System (ADS)

Nahas, Nabil; Nourelfath, Mustapha

2018-01-01

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Development of Fast Algorithms Using Recursion, Nesting and Iterations for Computational Electromagnetics

NASA Technical Reports Server (NTRS)

Chew, W. C.; Song, J. M.; Lu, C. C.; Weedon, W. H.

1995-01-01

In the first phase of our work, we have concentrated on laying the foundation to develop fast algorithms, including the use of recursive structure like the recursive aggregate interaction matrix algorithm (RAIMA), the nested equivalence principle algorithm (NEPAL), the ray-propagation fast multipole algorithm (RPFMA), and the multi-level fast multipole algorithm (MLFMA). We have also investigated the use of curvilinear patches to build a basic method of moments code where these acceleration techniques can be used later. In the second phase, which is mainly reported on here, we have concentrated on implementing three-dimensional NEPAL on a massively parallel machine, the Connection Machine CM-5, and have been able to obtain some 3D scattering results. In order to understand the parallelization of codes on the Connection Machine, we have also studied the parallelization of 3D finite-difference time-domain (FDTD) code with PML material absorbing boundary condition (ABC). We found that simple algorithms like the FDTD with material ABC can be parallelized very well allowing us to solve within a minute a problem of over a million nodes. In addition, we have studied the use of the fast multipole method and the ray-propagation fast multipole algorithm to expedite matrix-vector multiplication in a conjugate-gradient solution to integral equations of scattering. We find that these methods are faster than LU decomposition for one incident angle, but are slower than LU decomposition when many incident angles are needed as in the monostatic RCS calculations.

Machine Learning Techniques for Global Sensitivity Analysis in Climate Models

NASA Astrophysics Data System (ADS)

Safta, C.; Sargsyan, K.; Ricciuto, D. M.

2017-12-01

Climate models studies are not only challenged by the compute intensive nature of these models but also by the high-dimensionality of the input parameter space. In our previous work with the land model components (Sargsyan et al., 2014) we identified subsets of 10 to 20 parameters relevant for each QoI via Bayesian compressive sensing and variance-based decomposition. Nevertheless the algorithms were challenged by the nonlinear input-output dependencies for some of the relevant QoIs. In this work we will explore a combination of techniques to extract relevant parameters for each QoI and subsequently construct surrogate models with quantified uncertainty necessary to future developments, e.g. model calibration and prediction studies. In the first step, we will compare the skill of machine-learning models (e.g. neural networks, support vector machine) to identify the optimal number of classes in selected QoIs and construct robust multi-class classifiers that will partition the parameter space in regions with smooth input-output dependencies. These classifiers will be coupled with techniques aimed at building sparse and/or low-rank surrogate models tailored to each class. Specifically we will explore and compare sparse learning techniques with low-rank tensor decompositions. These models will be used to identify parameters that are important for each QoI. Surrogate accuracy requirements are higher for subsequent model calibration studies and we will ascertain the performance of this workflow for multi-site ALM simulation ensembles.

Control system software, simulation, and robotic applications

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

All essential existing capabilities needed to create a man-machine interaction dynamics and performance (MMIDAP) capability are reviewed. The multibody system dynamics software program Order N DISCOS will be used for machine and musculo-skeletal dynamics modeling. The program JACK will be used for estimating and animating whole body human response to given loading situations and motion constraints. The basic elements of performance (BEP) task decomposition methodologies associated with the Human Performance Institute database will be used for performance assessment. Techniques for resolving the statically indeterminant muscular load sharing problem will be used for a detailed understanding of potential musculotendon or ligamentous fatigue, pain, discomfort, and trauma. The envisioned capacity is to be used for mechanical system design, human performance assessment, extrapolation of man/machine interaction test data, biomedical engineering, and soft prototyping within a concurrent engineering (CE) system.

Editorial: Mathematical Methods and Modeling in Machine Fault Diagnosis

DOE PAGES

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua; ...

2014-12-18

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Pseudo-fault signal assisted EMD for fault detection and isolation in rotating machines

NASA Astrophysics Data System (ADS)

Singh, Dheeraj Sharan; Zhao, Qing

2016-12-01

This paper presents a novel data driven technique for the detection and isolation of faults, which generate impacts in a rotating equipment. The technique is built upon the principles of empirical mode decomposition (EMD), envelope analysis and pseudo-fault signal for fault separation. Firstly, the most dominant intrinsic mode function (IMF) is identified using EMD of a raw signal, which contains all the necessary information about the faults. The envelope of this IMF is often modulated with multiple vibration sources and noise. A second level decomposition is performed by applying pseudo-fault signal (PFS) assisted EMD on the envelope. A pseudo-fault signal is constructed based on the known fault characteristic frequency of the particular machine. The objective of using external (pseudo-fault) signal is to isolate different fault frequencies, present in the envelope . The pseudo-fault signal serves dual purposes: (i) it solves the mode mixing problem inherent in EMD, (ii) it isolates and quantifies a particular fault frequency component. The proposed technique is suitable for real-time implementation, which has also been validated on simulated fault and experimental data corresponding to a bearing and a gear-box set-up, respectively.

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Excited electronic state decomposition mechanisms and dynamics of nitramine energetic materials and model systems

NASA Astrophysics Data System (ADS)

Greenfield, Margo

Energetic materials play an important role in aeronautics, the weapon industry, and the propellant industry due to their broad applications as explosives and fuels. RDX (1,3,5-trinitrohexahydro-s-triazine), HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine), and CL-20 (2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane) are compounds which contain high energy density. Although RDX and HMX have been studied extensively over the past several decades a complete understanding of their decomposition mechanisms and dynamics is unknown. Time of flight mass spectroscopy (TOFMS) UV photodissociation (ns) experiments of gas phase RDX, HMX, and CL-20 generate the NO molecule as the initial decomposition product. Four different vibronic transitions of the initial decomposition product, the NO molecule, are observed: A2Sigma(upsilon'=0)←X 2pi(upsilon"=0,1,2,3). Simulations of the rovibronic intensities for the A←X transitions demonstrate that NO dissociated from RDX, HMX, and CL-20 is rotationally cold (˜20 K) and vibrationally hot (˜1800 K). Conversely, experiments on the five model systems (nitromethane, dimethylnitramine (DMNA), nitropyrrolidine, nitropiperidine and dinitropiperazine) produce rotationally hot and vibrationally cold spectra. Laser induced fluorescence (LIF) experiments are performed to rule out the possible decomposition product OH, generated along with NO, perhaps from the suggested HONO elimination mechanism. The OH radical is not observed in the fluorescence experiments, indicating the HONO decomposition intermediate is not an important pathway for the excited electronic state decomposition of cyclic nitramines. The NO molecule is also employed to measure the dynamics of the excited state decomposition. A 226 nm, 180 fs light pulse is utilized to photodissociate the gas phase systems. Stable ion states of DMNA and nitropyrrolidine are observed while the energetic materials and remaining model systems present the NO molecule as the only observed product. Pump-probe transients of the resonant A←X (0-0) transition of the NO molecule show a constant signal indicating these materials decompose faster than the time duration of the 226 nm laser light. Calculational results together with the experimental results indicate the energetic materials decompose through an internal conversion to very highly excited (˜5 eV of vibrational energy) vibrational states of their ground electronic state, while the model systems follow an excited electronic state decomposition pathway.

NearFar: A computer program for nearside farside decomposition of heavy-ion elastic scattering amplitude

NASA Astrophysics Data System (ADS)

Cha, Moon Hoe

2007-02-01

The NearFar program is a package for carrying out an interactive nearside-farside decomposition of heavy-ion elastic scattering amplitude. The program is implemented in Java to perform numerical operations on the nearside and farside angular distributions. It contains a graphical display interface for the numerical results. A test run has been applied to the elastic O16+Si28 scattering at E=1503 MeV. Program summaryTitle of program: NearFar Catalogue identifier: ADYP_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADYP_v1_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Licensing provisions: none Computers: designed for any machine capable of running Java, developed on PC-Pentium-4 Operating systems under which the program has been tested: Microsoft Windows XP (Home Edition) Program language used: Java Number of bits in a word: 64 Memory required to execute with typical data: case dependent No. of lines in distributed program, including test data, etc.: 3484 Number of bytes distributed program, including test data, etc.: 142 051 Distribution format: tar.gz Other software required: A Java runtime interpreter, or the Java Development Kit, version 5.0 Nature of physical problem: Interactive nearside-farside decomposition of heavy-ion elastic scattering amplitude. Method of solution: The user must supply a external data file or PPSM parameters which calculates theoretical values of the quantities to be decomposed. Typical running time: Problem dependent. In a test run, it is about 35 s on a 2.40 GHz Intel P4-processor machine.

Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

DOE PAGES

Slaybaugh, R. N.; Ramirez-Zweiger, M.; Pandya, Tara; ...

2018-02-20

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MGmore » Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.« less

Eigenvalue Solvers for Modeling Nuclear Reactors on Leadership Class Machines

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

Slaybaugh, R. N.; Ramirez-Zweiger, M.; Pandya, Tara

In this paper, three complementary methods have been implemented in the code Denovo that accelerate neutral particle transport calculations with methods that use leadership-class computers fully and effectively: a multigroup block (MG) Krylov solver, a Rayleigh quotient iteration (RQI) eigenvalue solver, and a multigrid in energy (MGE) preconditioner. The MG Krylov solver converges more quickly than Gauss Seidel and enables energy decomposition such that Denovo can scale to hundreds of thousands of cores. RQI should converge in fewer iterations than power iteration (PI) for large and challenging problems. RQI creates shifted systems that would not be tractable without the MGmore » Krylov solver. It also creates ill-conditioned matrices. The MGE preconditioner reduces iteration count significantly when used with RQI and takes advantage of the new energy decomposition such that it can scale efficiently. Each individual method has been described before, but this is the first time they have been demonstrated to work together effectively. The combination of solvers enables the RQI eigenvalue solver to work better than the other available solvers for large reactors problems on leadership-class machines. Using these methods together, RQI converged in fewer iterations and in less time than PI for a full pressurized water reactor core. These solvers also performed better than an Arnoldi eigenvalue solver for a reactor benchmark problem when energy decomposition is needed. The MG Krylov, MGE preconditioner, and RQI solver combination also scales well in energy. Finally, this solver set is a strong choice for very large and challenging problems.« less

shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications

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

Alexandrov, Boian S.; Lliev, Filip L.; Stanev, Valentin G.

This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. Themore » code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.« less

An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

NASA Astrophysics Data System (ADS)

Peng, Yanfeng; Cheng, Junsheng; Liu, Yanfei; Li, Xuejun; Peng, Zhihua

2018-06-01

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique (DET) is proposed to predict the remaining useful life (RUL) of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statistical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings. Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.

Separable decompositions of bipartite mixed states

NASA Astrophysics Data System (ADS)

Li, Jun-Li; Qiao, Cong-Feng

2018-04-01

We present a practical scheme for the decomposition of a bipartite mixed state into a sum of direct products of local density matrices, using the technique developed in Li and Qiao (Sci. Rep. 8:1442, 2018). In the scheme, the correlation matrix which characterizes the bipartite entanglement is first decomposed into two matrices composed of the Bloch vectors of local states. Then, we show that the symmetries of Bloch vectors are consistent with that of the correlation matrix, and the magnitudes of the local Bloch vectors are lower bounded by the correlation matrix. Concrete examples for the separable decompositions of bipartite mixed states are presented for illustration.

Fault Tolerant State Machines

NASA Technical Reports Server (NTRS)

Burke, Gary R.; Taft, Stephanie

2004-01-01

State machines are commonly used to control sequential logic in FPGAs and ASKS. An errant state machine can cause considerable damage to the device it is controlling. For example in space applications, the FPGA might be controlling Pyros, which when fired at the wrong time will cause a mission failure. Even a well designed state machine can be subject to random errors us a result of SEUs from the radiation environment in space. There are various ways to encode the states of a state machine, and the type of encoding makes a large difference in the susceptibility of the state machine to radiation. In this paper we compare 4 methods of state machine encoding and find which method gives the best fault tolerance, as well as determining the resources needed for each method.

A system decomposition approach to the design of functional observers

NASA Astrophysics Data System (ADS)

Fernando, Tyrone; Trinh, Hieu

2014-09-01

This paper reports a system decomposition that allows the construction of a minimum-order functional observer using a state observer design approach. The system decomposition translates the functional observer design problem to that of a state observer for a smaller decomposed subsystem. Functional observability indices are introduced, and a closed-form expression for the minimum order required for a functional observer is derived in terms of those functional observability indices.

Initial decomposition mechanism for the energy release from electronically excited energetic materials: FOX-7 (1,1-diamino-2,2-dinitroethene, C{sub 2}H{sub 4}N{sub 4}O{sub 4})

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

Yuan, Bing; Yu, Zijun; Bernstein, Elliot R., E-mail: erb@lamar.Colostate.edu

Decomposition of the energetic material FOX-7 (1,1-diamino-2,2-dinitroethylene, C{sub 2}H{sub 4}N{sub 4}O{sub 4}) is investigated both theoretically and experimentally. The NO molecule is observed as an initial decomposition product subsequent to electronic excitation. The observed NO product is rotationally cold (<35 K) and vibrationally hot (2800 K). The initial decomposition mechanism is explored at the complete active space self-consistent field (CASSCF) level. Potential energy surface calculations at the CASSCF(12,8)/6-31G(d) level illustrate that conical intersections play an essential role in the decomposition mechanism. Electronically excited S{sub 2} FOX-7 can radiationlessly relax to lower electronic states through (S{sub 2}/S{sub 1}){sub CI} and (S{submore » 1}/S{sub 0}){sub CI} conical intersections and undergo a nitro-nitrite isomerization to generate NO product on the S{sub 0} state. The theoretically predicted mechanism is consistent with the experimental results. As FOX-7 decomposes on the ground electronic state, thus, the vibrational energy of the NO product from FOX-7 is high. The observed rotational energy distribution for NO is consistent with the final transition state structure on the S{sub 0} state. Ground state FOX-7 decomposition agrees with previous work: the nitro-nitrite isomerization has the lowest average energy barrier, the C–NH{sub 2} bond cleavage is unlikely under the given excitation conditions, and HONO formation on the ground state surface is energy accessible but not the main process.« less

Development of a classification method for a crack on a pavement surface images using machine learning

NASA Astrophysics Data System (ADS)

Hizukuri, Akiyoshi; Nagata, Takeshi

2017-03-01

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

Three-Dimensional High-Lift Analysis Using a Parallel Unstructured Multigrid Solver

NASA Technical Reports Server (NTRS)

Mavriplis, Dimitri J.

1998-01-01

A directional implicit unstructured agglomeration multigrid solver is ported to shared and distributed memory massively parallel machines using the explicit domain-decomposition and message-passing approach. Because the algorithm operates on local implicit lines in the unstructured mesh, special care is required in partitioning the problem for parallel computing. A weighted partitioning strategy is described which avoids breaking the implicit lines across processor boundaries, while incurring minimal additional communication overhead. Good scalability is demonstrated on a 128 processor SGI Origin 2000 machine and on a 512 processor CRAY T3E machine for reasonably fine grids. The feasibility of performing large-scale unstructured grid calculations with the parallel multigrid algorithm is demonstrated by computing the flow over a partial-span flap wing high-lift geometry on a highly resolved grid of 13.5 million points in approximately 4 hours of wall clock time on the CRAY T3E.

Stochastic thermodynamics, fluctuation theorems and molecular machines.

PubMed

Seifert, Udo

2012-12-01

Stochastic thermodynamics as reviewed here systematically provides a framework for extending the notions of classical thermodynamics such as work, heat and entropy production to the level of individual trajectories of well-defined non-equilibrium ensembles. It applies whenever a non-equilibrium process is still coupled to one (or several) heat bath(s) of constant temperature. Paradigmatic systems are single colloidal particles in time-dependent laser traps, polymers in external flow, enzymes and molecular motors in single molecule assays, small biochemical networks and thermoelectric devices involving single electron transport. For such systems, a first-law like energy balance can be identified along fluctuating trajectories. For a basic Markovian dynamics implemented either on the continuum level with Langevin equations or on a discrete set of states as a master equation, thermodynamic consistency imposes a local-detailed balance constraint on noise and rates, respectively. Various integral and detailed fluctuation theorems, which are derived here in a unifying approach from one master theorem, constrain the probability distributions for work, heat and entropy production depending on the nature of the system and the choice of non-equilibrium conditions. For non-equilibrium steady states, particularly strong results hold like a generalized fluctuation-dissipation theorem involving entropy production. Ramifications and applications of these concepts include optimal driving between specified states in finite time, the role of measurement-based feedback processes and the relation between dissipation and irreversibility. Efficiency and, in particular, efficiency at maximum power can be discussed systematically beyond the linear response regime for two classes of molecular machines, isothermal ones such as molecular motors, and heat engines such as thermoelectric devices, using a common framework based on a cycle decomposition of entropy production.

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

PubMed Central

Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S.; Phoon, Sin Ye

2016-01-01

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively. PMID:27271840

Bidding-based autonomous process planning and scheduling

NASA Astrophysics Data System (ADS)

Gu, Peihua; Balasubramanian, Sivaram; Norrie, Douglas H.

1995-08-01

Improving productivity through computer integrated manufacturing systems (CIMS) and concurrent engineering requires that the islands of automation in an enterprise be completely integrated. The first step in this direction is to integrate design, process planning, and scheduling. This can be achieved through a bidding-based process planning approach. The product is represented in a STEP model with detailed design and administrative information including design specifications, batch size, and due dates. Upon arrival at the manufacturing facility, the product registered in the shop floor manager which is essentially a coordinating agent. The shop floor manager broadcasts the product's requirements to the machines. The shop contains autonomous machines that have knowledge about their functionality, capabilities, tooling, and schedule. Each machine has its own process planner and responds to the product's request in a different way that is consistent with its capabilities and capacities. When more than one machine offers certain process(es) for the same requirements, they enter into negotiation. Based on processing time, due date, and cost, one of the machines wins the contract. The successful machine updates its schedule and advises the product to request raw material for processing. The concept was implemented using a multi-agent system with the task decomposition and planning achieved through contract nets. The examples are included to illustrate the approach.

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment.

PubMed

Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S; Phoon, Sin Ye

2016-06-07

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

Virtual Planning, Control, and Machining for a Modular-Based Automated Factory Operation in an Augmented Reality Environment

NASA Astrophysics Data System (ADS)

Pai, Yun Suen; Yap, Hwa Jen; Md Dawal, Siti Zawiah; Ramesh, S.; Phoon, Sin Ye

2016-06-01

This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

Program Helps Decompose Complex Design Systems

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Hall, Laura E.

1994-01-01

DeMAID (A Design Manager's Aid for Intelligent Decomposition) computer program is knowledge-based software system for ordering sequence of modules and identifying possible multilevel structure for design problem. Groups modular subsystems on basis of interactions among them. Saves considerable money and time in total design process, particularly in new design problem in which order of modules has not been defined. Available in two machine versions: Macintosh and Sun.

Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.

PubMed

Madsen, Kristoffer H; Krohne, Laerke G; Cai, Xin-Lu; Wang, Yi; Chan, Raymond C K

2018-03-15

Functional magnetic resonance imaging is capable of estimating functional activation and connectivity in the human brain, and lately there has been increased interest in the use of these functional modalities combined with machine learning for identification of psychiatric traits. While these methods bear great potential for early diagnosis and better understanding of disease processes, there are wide ranges of processing choices and pitfalls that may severely hamper interpretation and generalization performance unless carefully considered. In this perspective article, we aim to motivate the use of machine learning schizotypy research. To this end, we describe common data processing steps while commenting on best practices and procedures. First, we introduce the important role of schizotypy to motivate the importance of reliable classification, and summarize existing machine learning literature on schizotypy. Then, we describe procedures for extraction of features based on fMRI data, including statistical parametric mapping, parcellation, complex network analysis, and decomposition methods, as well as classification with a special focus on support vector classification and deep learning. We provide more detailed descriptions and software as supplementary material. Finally, we present current challenges in machine learning for classification of schizotypy and comment on future trends and perspectives.

Detection of inter-turn short-circuit at start-up of induction machine based on torque analysis

NASA Astrophysics Data System (ADS)

Pietrowski, Wojciech; Górny, Konrad

2017-12-01

Recently, interest in new diagnostics methods in a field of induction machines was observed. Research presented in the paper shows the diagnostics of induction machine based on torque pulsation, under inter-turn short-circuit, during start-up of a machine. In the paper three numerical techniques were used: finite element analysis, signal analysis and artificial neural networks (ANN). The elaborated numerical model of faulty machine consists of field, circuit and motion equations. Voltage excited supply allowed to determine the torque waveform during start-up. The inter-turn short-circuit was treated as a galvanic connection between two points of the stator winding. The waveforms were calculated for different amounts of shorted-turns from 0 to 55. Due to the non-stationary waveforms a wavelet packet decomposition was used to perform an analysis of the torque. The obtained results of analysis were used as input vector for ANN. The response of the neural network was the number of shorted-turns in the stator winding. Special attention was paid to compare response of general regression neural network (GRNN) and multi-layer perceptron neural network (MLP). Based on the results of the research, the efficiency of the developed algorithm can be inferred.

xEMD procedures as a data - Assisted filtering method

NASA Astrophysics Data System (ADS)

Machrowska, Anna; Jonak, Józef

2018-01-01

The article presents the possibility of using Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) algorithms for mechanical system condition monitoring applications. There were presented the results of the xEMD procedures used for vibration signals of system in different states of wear.

Unraveling the physical meaning of the Jaffe-Manohar decomposition of the nucleon spin

NASA Astrophysics Data System (ADS)

Wakamatsu, M.

2016-09-01

A general consensus now is that there are two physically inequivalent complete decompositions of the nucleon spin, i.e. the decomposition of the canonical type and that of mechanical type. The well-known Jaffe-Manohar decomposition is of the former type. Unfortunately, there is a wide-spread misbelief that this decomposition matches the partonic picture, which states that motion of quarks in the nucleon is approximately free. In the present monograph, we reveal that this understanding is not necessarily correct and that the Jaffe-Manohar decomposition is not such a decomposition, which natively reflects the intrinsic (or static) orbital angular momentum structure of the nucleon.

Effect of Hydration State of Martian Perchlorate Salts on Their Decomposition Temperatures During Thermal Extraction

NASA Astrophysics Data System (ADS)

Royle, Samuel H.; Montgomery, Wren; Kounaves, Samuel P.; Sephton, Mark A.

2017-12-01

Three Mars missions have analyzed the composition of surface samples using thermal extraction techniques. The temperatures of decomposition have been used as diagnostic information for the materials present. One compound of great current interest is perchlorate, a relatively recently discovered component of Mars' surface geochemistry that leads to deleterious effects on organic matter during thermal extraction. Knowledge of the thermal decomposition behavior of perchlorate salts is essential for mineral identification and possible avoidance of confounding interactions with organic matter. We have performed a series of experiments which reveal that the hydration state of magnesium perchlorate has a significant effect on decomposition temperature, with differing temperature releases of oxygen corresponding to different perchlorate hydration states (peak of O2 release shifts from 500 to 600°C as the proportion of the tetrahydrate form in the sample increases). Changes in crystallinity/crystal size may also have a secondary effect on the temperature of decomposition, and although these surface effects appear to be minor for our samples, further investigation may be warranted. A less than full appreciation of the hydration state of perchlorate salts during thermal extraction analyses could lead to misidentification of the number and the nature of perchlorate phases present.

Cellular Decomposition Based Hybrid-Hierarchical Control Systems with Applications to Flight Management Systems

NASA Technical Reports Server (NTRS)

Caines, P. E.

1999-01-01

The work in this research project has been focused on the construction of a hierarchical hybrid control theory which is applicable to flight management systems. The motivation and underlying philosophical position for this work has been that the scale, inherent complexity and the large number of agents (aircraft) involved in an air traffic system imply that a hierarchical modelling and control methodology is required for its management and real time control. In the current work the complex discrete or continuous state space of a system with a small number of agents is aggregated in such a way that discrete (finite state machine or supervisory automaton) controlled dynamics are abstracted from the system's behaviour. High level control may then be either directly applied at this abstracted level, or, if this is in itself of significant complexity, further layers of abstractions may be created to produce a system with an acceptable degree of complexity at each level. By the nature of this construction, high level commands are necessarily realizable at lower levels in the system.

Fault-Tolerant Coding for State Machines

NASA Technical Reports Server (NTRS)

Naegle, Stephanie Taft; Burke, Gary; Newell, Michael

2008-01-01

Two reliable fault-tolerant coding schemes have been proposed for state machines that are used in field-programmable gate arrays and application-specific integrated circuits to implement sequential logic functions. The schemes apply to strings of bits in state registers, which are typically implemented in practice as assemblies of flip-flop circuits. If a single-event upset (SEU, a radiation-induced change in the bit in one flip-flop) occurs in a state register, the state machine that contains the register could go into an erroneous state or could hang, by which is meant that the machine could remain in undefined states indefinitely. The proposed fault-tolerant coding schemes are intended to prevent the state machine from going into an erroneous or hang state when an SEU occurs. To ensure reliability of the state machine, the coding scheme for bits in the state register must satisfy the following criteria: 1. All possible states are defined. 2. An SEU brings the state machine to a known state. 3. There is no possibility of a hang state. 4. No false state is entered. 5. An SEU exerts no effect on the state machine. Fault-tolerant coding schemes that have been commonly used include binary encoding and "one-hot" encoding. Binary encoding is the simplest state machine encoding and satisfies criteria 1 through 3 if all possible states are defined. Binary encoding is a binary count of the state machine number in sequence; the table represents an eight-state example. In one-hot encoding, N bits are used to represent N states: All except one of the bits in a string are 0, and the position of the 1 in the string represents the state. With proper circuit design, one-hot encoding can satisfy criteria 1 through 4. Unfortunately, the requirement to use N bits to represent N states makes one-hot coding inefficient.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

Acid and alkali effects on the decomposition of HMX molecule: a computational study.

PubMed

Zhang, Chaoyang; Li, Yuzhen; Xiong, Ying; Wang, Xiaolin; Zhou, Mingfei

2011-11-03

The stored and wasted explosives are usually in an acid or alkali environment, leading to the importance of exploring the acid and alkali effects on the decomposition mechanism of explosives. The acid and alkali effects on the decomposition of HMX molecule in gaseous state and in aqueous solution at 298 K are studied using quantum chemistry and molecular force field calculations. The results show that both H(+) and OH(-) make the decomposition in gaseous state energetically favorable. However, the effect of H(+) is much different from that of OH(-) in aqueous solution: OH(-) can accelerate the decomposition but H(+) cannot. The difference is mainly caused by the large aqueous solvation energy difference between H(+) and OH(-). The results confirm that the dissociation of HMX is energetically favored only in the base solutions, in good agreement with previous HMX base hydrolysis experimental observations. The different acid and alkali effects on the HMX decomposition are dominated by the large aqueous solvation energy difference between H(+) and OH(-).

Two-qubit quantum cloning machine and quantum correlation broadcasting

NASA Astrophysics Data System (ADS)

Kheirollahi, Azam; Mohammadi, Hamidreza; Akhtarshenas, Seyed Javad

2016-11-01

Due to the axioms of quantum mechanics, perfect cloning of an unknown quantum state is impossible. But since imperfect cloning is still possible, a question arises: "Is there an optimal quantum cloning machine?" Buzek and Hillery answered this question and constructed their famous B-H quantum cloning machine. The B-H machine clones the state of an arbitrary single qubit in an optimal manner and hence it is universal. Generalizing this machine for a two-qubit system is straightforward, but during this procedure, except for product states, this machine loses its universality and becomes a state-dependent cloning machine. In this paper, we propose some classes of optimal universal local quantum state cloners for a particular class of two-qubit systems, more precisely, for a class of states with known Schmidt basis. We then extend our machine to the case that the Schmidt basis of the input state is deviated from the local computational basis of the machine. We show that more local quantum coherence existing in the input state corresponds to less fidelity between the input and output states. Also we present two classes of a state-dependent local quantum copying machine. Furthermore, we investigate local broadcasting of two aspects of quantum correlations, i.e., quantum entanglement and quantum discord, defined, respectively, within the entanglement-separability paradigm and from an information-theoretic perspective. The results show that although quantum correlation is, in general, very fragile during the broadcasting procedure, quantum discord is broadcasted more robustly than quantum entanglement.

Mass-spectrometric monitoring of the intravenous anesthetic concentration in the breathing circuit of an anesthesia machine

NASA Astrophysics Data System (ADS)

Elizarov, A. Yu.; Levshankov, A. I.

2011-04-01

Interaction between inhalational anesthetic sevoflurane and an absorber of CO2 (soda lime) in the breathing circuit of an anesthesia machine during low-flow anesthesia (0.5 l of a fresh gaseous mixture per minute) is studied with the mass-spectrometric method. Monitoring data for the concentration of sevoflurane and three toxic products of sevoflurane decompositions (substances A, B, and C) during anesthesia in the inspiration-expiration regime are presented. The highest concentration of substance A is found to be 65 ppm. The biochemical blood analysis before and after anesthesia shows that nephropathy is related to the function of liver toxicity. It is found that inhalational anesthetic sevoflurane influences the concentration of intravenous hypnotic propofol in blood.

Iterative variational mode decomposition based automated detection of glaucoma using fundus images.

PubMed

Maheshwari, Shishir; Pachori, Ram Bilas; Kanhangad, Vivek; Bhandary, Sulatha V; Acharya, U Rajendra

2017-09-01

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95.19% and 94.79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Low-rank plus sparse decomposition for exoplanet detection in direct-imaging ADI sequences. The LLSG algorithm

NASA Astrophysics Data System (ADS)

Gomez Gonzalez, C. A.; Absil, O.; Absil, P.-A.; Van Droogenbroeck, M.; Mawet, D.; Surdej, J.

2016-05-01

Context. Data processing constitutes a critical component of high-contrast exoplanet imaging. Its role is almost as important as the choice of a coronagraph or a wavefront control system, and it is intertwined with the chosen observing strategy. Among the data processing techniques for angular differential imaging (ADI), the most recent is the family of principal component analysis (PCA) based algorithms. It is a widely used statistical tool developed during the first half of the past century. PCA serves, in this case, as a subspace projection technique for constructing a reference point spread function (PSF) that can be subtracted from the science data for boosting the detectability of potential companions present in the data. Unfortunately, when building this reference PSF from the science data itself, PCA comes with certain limitations such as the sensitivity of the lower dimensional orthogonal subspace to non-Gaussian noise. Aims: Inspired by recent advances in machine learning algorithms such as robust PCA, we aim to propose a localized subspace projection technique that surpasses current PCA-based post-processing algorithms in terms of the detectability of companions at near real-time speed, a quality that will be useful for future direct imaging surveys. Methods: We used randomized low-rank approximation methods recently proposed in the machine learning literature, coupled with entry-wise thresholding to decompose an ADI image sequence locally into low-rank, sparse, and Gaussian noise components (LLSG). This local three-term decomposition separates the starlight and the associated speckle noise from the planetary signal, which mostly remains in the sparse term. We tested the performance of our new algorithm on a long ADI sequence obtained on β Pictoris with VLT/NACO. Results: Compared to a standard PCA approach, LLSG decomposition reaches a higher signal-to-noise ratio and has an overall better performance in the receiver operating characteristic space. This three-term decomposition brings a detectability boost compared to the full-frame standard PCA approach, especially in the small inner working angle region where complex speckle noise prevents PCA from discerning true companions from noise.

Kinetic Deuterium Isotope Effects in the Combustion of Nitramine Propellants

DTIC Science & Technology

1988-07-01

Transition state 33 7. Possible Isotope Effects in HMX -d., and RDX -d. 38 8. HMX synthesis 48 9. a- HMX 52 10. V- HMX 53 11. RDX Synthesis 55 12 Pellet...configuration of the transition state in HMX decomposition could be rade. KDIE in RDX Decomposition The KDIE values obtained for RDX decomposition -ire...0.13 HMX -d 8 60.3 35.7 8.6 0.10 RDX 61.2 36.7 11.8 0.10 RDX -de 53.7 22.8 8.3 0.11 DSC EXPERIMENTS The 13 -+ 8 phase

Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines

NASA Technical Reports Server (NTRS)

Le, Martin; Zheng, Xin; Katanyoutant, Sunant

2008-01-01

Single-event upsets (SEUs) pose great threats to avionic systems state machine control logic, which are frequently used to control sequence of events and to qualify protocols. The risks of SEUs manifest in two ways: (a) the state machine s state information is changed, causing the state machine to unexpectedly transition to another state; (b) due to the asynchronous nature of SEU, the state machine's state registers become metastable, consequently causing any combinational logic associated with the metastable registers to malfunction temporarily. Effect (a) can be mitigated with methods such as triplemodular redundancy (TMR). However, effect (b) cannot be eliminated and can degrade the effectiveness of any mitigation method of effect (a). Although there is no way to completely eliminate the risk of SEU-induced errors, the risk can be made very small by use of a combination of very fast state-machine logic and error-detection logic. Therefore, one goal of two main elements of the present method is to design the fastest state-machine logic circuitry by basing it on the fastest generic state-machine design, which is that of a one-hot state machine. The other of the two main design elements is to design fast error-detection logic circuitry and to optimize it for implementation in a field-programmable gate array (FPGA) architecture: In the resulting design, the one-hot state machine is fitted with a multiple-input XNOR gate for detection of illegal states. The XNOR gate is implemented with lookup tables and with pipelines for high speed. In this method, the task of designing all the logic must be performed manually because no currently available logic synthesis software tool can produce optimal solutions of design problems of this type. However, some assistance is provided by a script, written for this purpose in the Python language (an object-oriented interpretive computer language) to automatically generate hardware description language (HDL) code from state-transition rules.

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

PubMed Central

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

2016-01-01

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

Early diagenesis of mangrove leaves in a tropical estuary: Bulk chemical characterization using solid-state 13C NMR and elemental analyses

NASA Astrophysics Data System (ADS)

Benner, Ronald; Hatcher, Patrick G.; Hedges, John I.

1990-07-01

Changes in the chemical composition of mangrove ( Rhizophora mangle) leaves during decomposition in tropical estuarine waters were characterized using solid-state 13C nuclear magnetic resonance (NMR) and elemental (CHNO) analysis. Carbohydrates were the most abundant components of the leaves accounting for about 50 wt% of senescent tissues. Tannins were estimated to account for about 20 wt% of leaf tissues, and lipid components, cutin, and possibly other aliphatic biopolymers in leaf cuticles accounted for about 15 wt%. Carbohydrates were generally less resistant to decomposition than the other constituents and decreased in relative concentration during decomposition. Tannins were of intermediate resistance to decomposition and remained in fairly constant proportion during decomposition. Paraffinic components were very resistant to decomposition and increased in relative concentration as decomposition progressed. Lignin was a minor component of all leaf tissues. Standard methods for the colorimetric determination of tannins (Folin-Dennis reagent) and the gravimetric determination of lignin (Klason lignin) were highly inaccurate when applied to mangrove leaves. The N content of the leaves was particularly dynamic with values ranging from 1.27 wt% in green leaves to 0.65 wt% in senescent yellow leaves attached to trees. During decomposition in the water the N content initially decreased to 0.51 wt% due to leaching, but values steadily increased thereafter to 1.07 wt% in the most degraded leaf samples. The absolute mass of N in the leaves increased during decomposition indicating that N immobilization was occurring as decomposition progressed.

Early diagenesis of mangrove leaves in a tropical estuary: Bulk chemical characterization using solid-state 13C NMR and elemental analyses

USGS Publications Warehouse

Benner, R.; Hatcher, P.G.; Hedges, J.I.

1990-01-01

Changes in the chemical composition of mangrove (Rhizophora mangle) leaves during decomposition in tropical estuarine waters were characterized using solid-state 13C nuclear magnetic resonance (NMR) and elemental (CHNO) analysis. Carbohydrates were the most abundant components of the leaves accounting for about 50 wt% of senescent tissues. Tannins were estimated to account for about 20 wt% of leaf tissues, and lipid components, cutin, and possibly other aliphatic biopolymers in leaf cuticles accounted for about 15 wt%. Carbohydrates were generally less resistant to decomposition than the other constituents and decreased in relative concentration during decomposition. Tannins were of intermediate resistance to decomposition and remained in fairly constant proportion during decomposition. Paraffinic components were very resistant to decomposition and increased in relative concentration as decomposition progressed. Lignin was a minor component of all leaf tissues. Standard methods for the colorimetric determination of tannins (Folin-Dennis reagent) and the gravimetric determination of lignin (Klason lignin) were highly inaccurate when applied to mangrove leaves. The N content of the leaves was particularly dynamic with values ranging from 1.27 wt% in green leaves to 0.65 wt% in senescent yellow leaves attached to trees. During decomposition in the water the N content initially decreased to 0.51 wt% due to leaching, but values steadily increased thereafter to 1.07 wt% in the most degraded leaf samples. The absolute mass of N in the leaves increased during decomposition indicating that N immobilization was occurring as decomposition progressed. ?? 1990.

A Parallel Algorithm for Contact in a Finite Element Hydrocode

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

Pierce, Timothy G.

A parallel algorithm is developed for contact/impact of multiple three dimensional bodies undergoing large deformation. As time progresses the relative positions of contact between the multiple bodies changes as collision and sliding occurs. The parallel algorithm is capable of tracking these changes and enforcing an impenetrability constraint and momentum transfer across the surfaces in contact. Portions of the various surfaces of the bodies are assigned to the processors of a distributed-memory parallel machine in an arbitrary fashion, known as the primary decomposition. A secondary, dynamic decomposition is utilized to bring opposing sections of the contacting surfaces together on the samemore » processors, so that opposing forces may be balanced and the resultant deformation of the bodies calculated. The secondary decomposition is accomplished and updated using only local communication with a limited subset of neighbor processors. Each processor represents both a domain of the primary decomposition and a domain of the secondary, or contact, decomposition. Thus each processor has four sets of neighbor processors: (a) those processors which represent regions adjacent to it in the primary decomposition, (b) those processors which represent regions adjacent to it in the contact decomposition, (c) those processors which send it the data from which it constructs its contact domain, and (d) those processors to which it sends its primary domain data, from which they construct their contact domains. The latter three of these neighbor sets change dynamically as the simulation progresses. By constraining all communication to these sets of neighbors, all global communication, with its attendant nonscalable performance, is avoided. A set of tests are provided to measure the degree of scalability achieved by this algorithm on up to 1024 processors. Issues related to the operating system of the test platform which lead to some degradation of the results are analyzed. This algorithm has been implemented as the contact capability of the ALE3D multiphysics code, and is currently in production use.« less

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

Yan, Ruqiang; Chen, Xuefeng; Li, Weihua

Modern mathematics has commonly been utilized as an effective tool to model mechanical equipment so that their dynamic characteristics can be studied analytically. This will help identify potential failures of mechanical equipment by observing change in the equipment’s dynamic parameters. On the other hand, dynamic signals are also important and provide reliable information about the equipment’s working status. Modern mathematics has also provided us with a systematic way to design and implement various signal processing methods, which are used to analyze these dynamic signals, and to enhance intrinsic signal components that are directly related to machine failures. This special issuemore » is aimed at stimulating not only new insights on mathematical methods for modeling but also recently developed signal processing methods, such as sparse decomposition with potential applications in machine fault diagnosis. Finally, the papers included in this special issue provide a glimpse into some of the research and applications in the field of machine fault diagnosis through applications of the modern mathematical methods.« less

Effect of charged and excited states on the decomposition of 1,1-diamino-2,2-dinitroethylene molecules

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

Kimmel, Anna V.; Sushko, Peter V.; Shluger, Alexander L.

The authors have calculated the electronic structure of individual 1,1-diamino-2,2-dinitroethylene molecules (FOX-7) in the gas phase by means of density functional theory with the hybrid B3LYP functional and 6-31+G(d,p) basis set and considered their dissociation pathways. Positively and negatively charged states as well as the lowest excited states of the molecule were simulated. They found that charging and excitation can not only reduce the activation barriers for decomposition reactions but also change the dominating chemistry from endo- to exothermic type. In particular, they found that there are two competing primary initiation mechanisms of FOX-7 decomposition: C-NO{sub 2} bond fission andmore » C-NO{sub 2} to CONO isomerization. Electronic excitation or charging of FOX-7 disfavors CONO formation and, thus, terminates this channel of decomposition. However, if CONO is formed from the neutral FOX-7 molecule, charge trapping and/or excitation results in spontaneous splitting of an NO group accompanied by the energy release. Intramolecular hydrogen transfer is found to be a rare event in FOX-7 unless free electrons are available in the vicinity of the molecule, in which case HONO formation is a feasible exothermic reaction with a relatively low energy barrier. The effect of charged and excited states on other possible reactions is also studied. Implications of the obtained results to FOX-7 decomposition in condensed state are discussed.« less

Conductimetric determination of decomposition of silicate melts

NASA Technical Reports Server (NTRS)

Kroeger, C.; Lieck, K.

1986-01-01

A description of a procedure is given to detect decomposition of silicate systems in the liquid state by conductivity measurements. Onset of decomposition can be determined from the temperature curves of resistances measured on two pairs of electrodes, one above the other. Degree of decomposition can be estimated from temperature and concentration dependency of conductivity of phase boundaries. This procedure was tested with systems PbO-B2O3 and PbO-B2O3-SiO2.

Technical Note: Linking climate change and downed woody debris decomposition across forests of the eastern United States

USGS Publications Warehouse

Russell, Matthew B.; Woodall, Christopher W.; D'Amato, Anthony W.; Fraver, Shawn; Bradford, John B.

2014-01-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Forest carbon (C) is stored through photosynthesis and released via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years, depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics in addition to a traditional emphasis on live-tree demographics.

Azole energetic materials: Initial mechanisms for the energy release from electronical excited nitropyrazoles

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

Yuan, Bing; Yu, Zijun; Bernstein, Elliot R., E-mail: erb@lamar.Colostate.edu

2014-01-21

Decomposition of energetic material 3,4-dinitropyrazole (DNP) and two model molecules 4-nitropyrazole and 1-nitropyrazole is investigated both theoretically and experimentally. The initial decomposition mechanisms for these three nitropyrazoles are explored with complete active space self-consistent field (CASSCF) level. The NO molecule is observed as an initial decomposition product from all three materials subsequent to UV excitation. Observed NO products are rotationally cold (<50 K) for all three systems. The vibrational temperature of the NO product from DNP is (3850 ± 50) K, 1350 K hotter than that of the two model species. Potential energy surface calculations at the CASSCF(12,8)/6-31+G(d) level illustratemore » that conical intersections plays an essential role in the decomposition mechanism. Electronically excited S{sub 2} nitropyraozles can nonradiatively relax to lower electronic states through (S{sub 2}/S{sub 1}){sub CI} and (S{sub 1}/S{sub 0}){sub CI} conical intersection and undergo a nitro-nitrite isomerization to generate NO product either in the S{sub 1} state or S{sub 0} state. In model systems, NO is generated in the S{sub 1} state, while in the energetic material DNP, NO is produced on the ground state surface, as the S{sub 1} decomposition pathway is energetically unavailable. The theoretically predicted mechanism is consistent with the experimental results, as DNP decomposes in a lower electronic state than do the model systems and thus the vibrational energy in the NO product from DNP should be hotter than from the model systems. The observed rotational energy distributions for NO are consistent with the final structures of the respective transition states for each molecule.« less

Multivariate Approach for Alzheimer's Disease Detection Using Stationary Wavelet Entropy and Predator-Prey Particle Swarm Optimization.

PubMed

Zhang, Yudong; Wang, Shuihua; Sui, Yuxiu; Yang, Ming; Liu, Bin; Cheng, Hong; Sun, Junding; Jia, Wenjuan; Phillips, Preetha; Gorriz, Juan Manuel

2017-07-17

The number of patients with Alzheimer's disease is increasing rapidly every year. Scholars often use computer vision and machine learning methods to develop an automatic diagnosis system. In this study, we developed a novel machine learning system that can make diagnoses automatically from brain magnetic resonance images. First, the brain imaging was processed, including skull stripping and spatial normalization. Second, one axial slice was selected from the volumetric image, and stationary wavelet entropy (SWE) was done to extract the texture features. Third, a single-hidden-layer neural network was used as the classifier. Finally, a predator-prey particle swarm optimization was proposed to train the weights and biases of the classifier. Our method used 4-level decomposition and yielded 13 SWE features. The classification yielded an overall accuracy of 92.73±1.03%, a sensitivity of 92.69±1.29%, and a specificity of 92.78±1.51%. The area under the curve is 0.95±0.02. Additionally, this method only cost 0.88 s to identify a subject in online stage, after its volumetric image is preprocessed. In terms of classification performance, our method performs better than 10 state-of-the-art approaches and the performance of human observers. Therefore, this proposed method is effective in the detection of Alzheimer's disease.

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

PubMed

Lu, Na; Li, Tengfei; Ren, Xiaodong; Miao, Hongyu

2017-06-01

Motor imagery classification is an important topic in brain-computer interface (BCI) research that enables the recognition of a subject's intension to, e.g., implement prosthesis control. The brain dynamics of motor imagery are usually measured by electroencephalography (EEG) as nonstationary time series of low signal-to-noise ratio. Although a variety of methods have been previously developed to learn EEG signal features, the deep learning idea has rarely been explored to generate new representation of EEG features and achieve further performance improvement for motor imagery classification. In this study, a novel deep learning scheme based on restricted Boltzmann machine (RBM) is proposed. Specifically, frequency domain representations of EEG signals obtained via fast Fourier transform (FFT) and wavelet package decomposition (WPD) are obtained to train three RBMs. These RBMs are then stacked up with an extra output layer to form a four-layer neural network, which is named the frequential deep belief network (FDBN). The output layer employs the softmax regression to accomplish the classification task. Also, the conjugate gradient method and backpropagation are used to fine tune the FDBN. Extensive and systematic experiments have been performed on public benchmark datasets, and the results show that the performance improvement of FDBN over other selected state-of-the-art methods is statistically significant. Also, several findings that may be of significant interest to the BCI community are presented in this article.

Identification of Shearer Cutting Patterns Using Vibration Signals Based on a Least Squares Support Vector Machine with an Improved Fruit Fly Optimization Algorithm

PubMed Central

Si, Lei; Wang, Zhongbin; Liu, Xinhua; Tan, Chao; Liu, Ze; Xu, Jing

2016-01-01

Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system. PMID:26771615

Multilevel decomposition of complete vehicle configuration in a parallel computing environment

NASA Technical Reports Server (NTRS)

Bhatt, Vinay; Ragsdell, K. M.

1989-01-01

This research summarizes various approaches to multilevel decomposition to solve large structural problems. A linear decomposition scheme based on the Sobieski algorithm is selected as a vehicle for automated synthesis of a complete vehicle configuration in a parallel processing environment. The research is in a developmental state. Preliminary numerical results are presented for several example problems.

A highly efficient autothermal microchannel reactor for ammonia decomposition: Analysis of hydrogen production in transient and steady-state regimes

NASA Astrophysics Data System (ADS)

Engelbrecht, Nicolaas; Chiuta, Steven; Bessarabov, Dmitri G.

2018-05-01

The experimental evaluation of an autothermal microchannel reactor for H2 production from NH3 decomposition is described. The reactor design incorporates an autothermal approach, with added NH3 oxidation, for coupled heat supply to the endothermic decomposition reaction. An alternating catalytic plate arrangement is used to accomplish this thermal coupling in a cocurrent flow strategy. Detailed analysis of the transient operating regime associated with reactor start-up and steady-state results is presented. The effects of operating parameters on reactor performance are investigated, specifically, the NH3 decomposition flow rate, NH3 oxidation flow rate, and fuel-oxygen equivalence ratio. Overall, the reactor exhibits rapid response time during start-up; within 60 min, H2 production is approximately 95% of steady-state values. The recommended operating point for steady-state H2 production corresponds to an NH3 decomposition flow rate of 6 NL min-1, NH3 oxidation flow rate of 4 NL min-1, and fuel-oxygen equivalence ratio of 1.4. Under these flows, NH3 conversion of 99.8% and H2 equivalent fuel cell power output of 0.71 kWe is achieved. The reactor shows good heat utilization with a thermal efficiency of 75.9%. An efficient autothermal reactor design is therefore demonstrated, which may be upscaled to a multi-kW H2 production system for commercial implementation.

Utilization of a balanced steady state free precession signal model for improved fat/water decomposition.

PubMed

Henze Bancroft, Leah C; Strigel, Roberta M; Hernando, Diego; Johnson, Kevin M; Kelcz, Frederick; Kijowski, Richard; Block, Walter F

2016-03-01

Chemical shift based fat/water decomposition methods such as IDEAL are frequently used in challenging imaging environments with large B0 inhomogeneity. However, they do not account for the signal modulations introduced by a balanced steady state free precession (bSSFP) acquisition. Here we demonstrate improved performance when the bSSFP frequency response is properly incorporated into the multipeak spectral fat model used in the decomposition process. Balanced SSFP allows for rapid imaging but also introduces a characteristic frequency response featuring periodic nulls and pass bands. Fat spectral components in adjacent pass bands will experience bulk phase offsets and magnitude modulations that change the expected constructive and destructive interference between the fat spectral components. A bSSFP signal model was incorporated into the fat/water decomposition process and used to generate images of a fat phantom, and bilateral breast and knee images in four normal volunteers at 1.5 Tesla. Incorporation of the bSSFP signal model into the decomposition process improved the performance of the fat/water decomposition. Incorporation of this model allows rapid bSSFP imaging sequences to use robust fat/water decomposition methods such as IDEAL. While only one set of imaging parameters were presented, the method is compatible with any field strength or repetition time. © 2015 Wiley Periodicals, Inc.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Screening on oil-decomposing microorganisms and application in organic waste treatment machine.

PubMed

Lu, Yi-Tong; Chen, Xiao-Bin; Zhou, Pei; Li, Zhen-Hong

2005-01-01

As an oil-decomposable mixture of two bacteria strains (Bacillus sp. and Pseudomonas sp.), Y3 was isolated after 50 d domestication under the condition that oil was used as the limited carbon source. The decomposing rate by Y3 was higher than that by each separate individual strain, indicating a synergistic effect of the two bacteria. Under the conditions that T = 25-40 degrees C, pH = 6-8, HRT (Hydraulic retention time) = 36 h and the oil concentration at 0.1%, Y3 yielded the highest decomposing rate of 95.7%. Y3 was also applied in an organic waste treatment machine and a certain rate of activated bacteria was put into the stuffing. A series of tests including humidity, pH, temperature, C/N rate and oil percentage of the stuffing were carried out to check the efficacy of oil-decomposition. Results showed that the oil content of the stuffing with inoculums was only half of that of the control. Furthermore, the bacteria were also beneficial to maintain the stability of the machine operating. Therefore, the bacteria mixture as well as the machines in this study could be very useful for waste treatment.

State machine analysis of sensor data from dynamic processes

DOEpatents

Cook, William R.; Brabson, John M.; Deland, Sharon M.

2003-12-23

A state machine model analyzes sensor data from dynamic processes at a facility to identify the actual processes that were performed at the facility during a period of interest for the purpose of remote facility inspection. An inspector can further input the expected operations into the state machine model and compare the expected, or declared, processes to the actual processes to identify undeclared processes at the facility. The state machine analysis enables the generation of knowledge about the state of the facility at all levels, from location of physical objects to complex operational concepts. Therefore, the state machine method and apparatus may benefit any agency or business with sensored facilities that stores or manipulates expensive, dangerous, or controlled materials or information.

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Online Artifact Removal for Brain-Computer Interfaces Using Support Vector Machines and Blind Source Separation

PubMed Central

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method. PMID:18288259

Online artifact removal for brain-computer interfaces using support vector machines and blind source separation.

PubMed

Halder, Sebastian; Bensch, Michael; Mellinger, Jürgen; Bogdan, Martin; Kübler, Andrea; Birbaumer, Niels; Rosenstiel, Wolfgang

2007-01-01

We propose a combination of blind source separation (BSS) and independent component analysis (ICA) (signal decomposition into artifacts and nonartifacts) with support vector machines (SVMs) (automatic classification) that are designed for online usage. In order to select a suitable BSS/ICA method, three ICA algorithms (JADE, Infomax, and FastICA) and one BSS algorithm (AMUSE) are evaluated to determine their ability to isolate electromyographic (EMG) and electrooculographic (EOG) artifacts into individual components. An implementation of the selected BSS/ICA method with SVMs trained to classify EMG and EOG artifacts, which enables the usage of the method as a filter in measurements with online feedback, is described. This filter is evaluated on three BCI datasets as a proof-of-concept of the method.

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS), Version 5.0 (User’s Guide)

DTIC Science & Technology

2010-03-30

provides tools for common modeling functions, as well as regridding, data decomposition, and communication on parallel computers. NRL/MR/7320--10...specified gncomDir. If running COAMPS at the DSRC (e.g. BABBAGE, DAVINCI , or EINSTEIN), the global NCOM files will be copied to /scr/[user]/COAMPS/data...the site (DSRC or local) and the platform (BABBAGE. DAVINCI , EINSTEIN, or local machine) on which COAMPS is being run. site=navy_dsrc (for DSRC

Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS), Version 5.0, Revision 2.0 (User’s Guide)

DTIC Science & Technology

2012-05-03

output (I/O) system. The framework provides tools for common modeling functions, as well as regridding, data decomposition, and communication on...Within this script, the user must specify both the site (DSRC or local) and the platform ( DAVINCI , EINSTEIN, or local machine) on which COAMPS is...being run. For example: site=navy_dsrc (for DSRC usage) site=nrlssc (for local NRL-SSC usage) platform= davinci or einstein (for DSRC usage

Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) Version 5.0, Rev. 2.0 (User’s Guide)

DTIC Science & Technology

2012-05-03

output (I/O) system. The framework provides tools for common modeling functions, as well as regridding, data decomposition, and communication on...Within this script, the user must specify both the site (DSRC or local) and the platform ( DAVINCI , EINSTEIN, or local machine) on which COAMPS is...being run. For example: site=navy_dsrc (for DSRC usage) site=nrlssc (for local NRL-SSC usage) platform= davinci or einstein (for DSRC usage

Communication Efficient Gaussian Elimination with Partial Pivoting using a Shape Morphing Data Layout

DTIC Science & Technology

2013-02-21

support comes from ParLab affiliates National Instruments, Nokia, NVIDIA , Oracle and Samsung, as well as MathWorks. Research is also supported by DOE...affiliates National Instruments, Nokia, NVIDIA , Oracle and Samsung, as well as MathWorks. Research is also supported by DOE grants DE-SC0004938, DE-SC0005136...International Business Machines Company , 1966. [17] S. Toledo. Locality of reference in LU decomposition with partial pivoting. SIAM J. Matrix Anal. Appl., 18

Improving the Diagnostic Specificity of CT for Early Detection of Lung Cancer: 4D CT-Based Pulmonary Nodule Elastometry

DTIC Science & Technology

2013-08-01

as thin - plate spline (1-3) or elastic-body spline (4, 5), is locally controlled. One of the main motivations behind the use of B- spline ...FL. Principal warps: thin - plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence...Weese J, Kuhn MH. Landmark-based elastic registration using approximating thin - plate splines . IEEE Transactions on Medical Imaging. 2001;20(6):526-34

Impacts Of Long-Term Prescribed Fire On Decomposition And Litter Quality In Uneven-Aged Loblolly Pine Stands

Treesearch

Michele L. Renschin; Hal O. Liechty; Michael G. Shelton

2002-01-01

Abstract - Although fire has long been an important forest management tool in the southern United States, little is known concerning the effects of long-term fire use on nutrient cycling and decomposition. To better understand the effects of fire on these processes, decomposition rates, and foliage litter quality were quantified in a study...

Linking climate change and downed woody debris decomposition across forests of the eastern United States

Treesearch

M.B. Russell; C.W. Woodall; A.W. D' Amato; S. Fraver; J.B. Bradford

2014-01-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Forest carbon (C) is stored through photosynthesis and released via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased...

GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.

PubMed

Hess, Berk; Kutzner, Carsten; van der Spoel, David; Lindahl, Erik

2008-03-01

Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.

Koopman operator theory: Past, present, and future

NASA Astrophysics Data System (ADS)

Brunton, Steven; Kaiser, Eurika; Kutz, Nathan

2017-11-01

Koopman operator theory has emerged as a dominant method to represent nonlinear dynamics in terms of an infinite-dimensional linear operator. The Koopman operator acts on the space of all possible measurement functions of the system state, advancing these measurements with the flow of the dynamics. A linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. Dynamic mode decomposition has become the leading data-driven method to approximate the Koopman operator, although there are still open questions and challenges around how to obtain accurate approximations for strongly nonlinear systems. This talk will provide an introductory overview of modern Koopman operator theory, reviewing the basics and describing recent theoretical and algorithmic developments. Particular emphasis will be placed on the use of data-driven Koopman theory to characterize and control high-dimensional fluid dynamic systems. This talk will also address key advances in the rapidly growing fields of machine learning and data science that are likely to drive future developments.

Generalized decompositions of dynamic systems and vector Lyapunov functions

NASA Astrophysics Data System (ADS)

Ikeda, M.; Siljak, D. D.

1981-10-01

The notion of decomposition is generalized to provide more freedom in constructing vector Lyapunov functions for stability analysis of nonlinear dynamic systems. A generalized decomposition is defined as a disjoint decomposition of a system which is obtained by expanding the state-space of a given system. An inclusion principle is formulated for the solutions of the expansion to include the solutions of the original system, so that stability of the expansion implies stability of the original system. Stability of the expansion can then be established by standard disjoint decompositions and vector Lyapunov functions. The applicability of the new approach is demonstrated using the Lotka-Volterra equations.

Technical Note: Linking climate change and downed woody debris decomposition across forests of the eastern United States

NASA Astrophysics Data System (ADS)

Russell, M. B.; Woodall, C. W.; D'Amato, A. W.; Fraver, S.; Bradford, J. B.

2014-06-01

Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Long-term forest carbon (C) storage is determined by the balance between C fixation into biomass through photosynthesis and C release via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics, in addition to a traditional emphasis on live tree demographics.

Installation and Setup of Whole School Food Waste Composting Program

NASA Astrophysics Data System (ADS)

Zhang, A.; Forder, S. E.

2014-12-01

Hong Kong, one of the busiest trading harbors in the world, is also a city of 8 million of people. The biggest problem that the government faces is the lack of solid waste landfill space. Hong Kong produces around 13,500 tons of waste per day. There are three landfills in Hong Kong in operation. These three landfills will soon be exhausted in around 2020, and the solid waste in Hong Kong is still increasing. Out of the 13,500 tons of solid waste, 9,000 tons are organic solid waste or food waste. Food waste, especially domestic waste, is recyclable. The Independent Schools Foundation Academy has a project to collect domestic food waste (from the school cafeteria) for decomposition. Our school produces around 15 tons of food waste per year. The project includes a sub-project in the Primary school, which uses the organic soil produced by an aerobic food waste machine, the Rocket A900, to plant vegetables in school. This not only helps our school to process the waste, but also helps the Primary students to study agriculture and have greater opportunities for experimental learning. For this project, two types of machines will be used for food waste processing. Firstly, the Dehydra made by Tiny Planet reduces the volume and the mass of the food waste, by dehydrating the food waste and separating the ground food waste and the excessive water inside machine for further decomposition. Secondly, the A900 Rocket, also made by Tidy Planet; this is used to process the dehydrated ground food waste for around 14 days thereby producing usable organic soil. It grinds the food waste into tiny pieces so that it is easier to decompose. It also separates the wood chips inside the ground food waste. This machine runs an aerobic process, which includes O2 and will produce CO2 during the process and is less harmful to the environment. On the other hand, if it is an anaerobic process occurs during the operation, it will produce a greenhouse gas- CH4 -and smells bad.

Hierarchical Diagnosis of Vocal Fold Disorders

NASA Astrophysics Data System (ADS)

Nikkhah-Bahrami, Mansour; Ahmadi-Noubari, Hossein; Seyed Aghazadeh, Babak; Khadivi Heris, Hossein

This paper explores the use of hierarchical structure for diagnosis of vocal fold disorders. The hierarchical structure is initially used to train different second-level classifiers. At the first level normal and pathological signals have been distinguished. Next, pathological signals have been classified into neurogenic and organic vocal fold disorders. At the final level, vocal fold nodules have been distinguished from polyps in organic disorders category. For feature selection at each level of hierarchy, the reconstructed signal at each wavelet packet decomposition sub-band in 5 levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Davies-Bouldin criterion has been employed to find the most discriminant features. Finally, support vector machines have been adopted as classifiers at each level of hierarchy resulting in the diagnosis accuracy of 92%.

Radiation tolerant combinational logic cell

NASA Technical Reports Server (NTRS)

Maki, Gary R. (Inventor); Whitaker, Sterling (Inventor); Gambles, Jody W. (Inventor)

2009-01-01

A system has a reduced sensitivity to Single Event Upset and/or Single Event Transient(s) compared to traditional logic devices. In a particular embodiment, the system includes an input, a logic block, a bias stage, a state machine, and an output. The logic block is coupled to the input. The logic block is for implementing a logic function, receiving a data set via the input, and generating a result f by applying the data set to the logic function. The bias stage is coupled to the logic block. The bias stage is for receiving the result from the logic block and presenting it to the state machine. The state machine is coupled to the bias stage. The state machine is for receiving, via the bias stage, the result generated by the logic block. The state machine is configured to retain a state value for the system. The state value is typically based on the result generated by the logic block. The output is coupled to the state machine. The output is for providing the value stored by the state machine. Some embodiments of the invention produce dual rail outputs Q and Q'. The logic block typically contains combinational logic and is similar, in size and transistor configuration, to a conventional CMOS combinational logic design. However, only a very small portion of the circuits of these embodiments, is sensitive to Single Event Upset and/or Single Event Transients.

Experimental Machine Learning of Quantum States

NASA Astrophysics Data System (ADS)

Gao, Jun; Qiao, Lu-Feng; Jiao, Zhi-Qiang; Ma, Yue-Chi; Hu, Cheng-Qiu; Ren, Ruo-Jing; Yang, Ai-Lin; Tang, Hao; Yung, Man-Hong; Jin, Xian-Min

2018-06-01

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.

Theoretical studies of the decomposition mechanisms of 1,2,4-butanetriol trinitrate.

PubMed

Pei, Liguan; Dong, Kehai; Tang, Yanhui; Zhang, Bo; Yu, Chang; Li, Wenzuo

2017-12-06

Density functional theory (DFT) and canonical variational transition-state theory combined with a small-curvature tunneling correction (CVT/SCT) were used to explore the decomposition mechanisms of 1,2,4-butanetriol trinitrate (BTTN) in detail. The results showed that the γ-H abstraction reaction is the initial pathway for autocatalytic BTTN decomposition. The three possible hydrogen atom abstraction reactions are all exothermic. The rate constants for autocatalytic BTTN decomposition are 3 to 10 40 times greater than the rate constants for the two unimolecular decomposition reactions (O-NO 2 cleavage and HONO elimination). The process of BTTN decomposition can be divided into two stages according to whether the NO 2 concentration is above a threshold value. HONO elimination is the main reaction channel during the first stage because autocatalytic decomposition requires NO 2 and the concentration of NO 2 is initially low. As the reaction proceeds, the concentration of NO 2 gradually increases; when it exceeds the threshold value, the second stage begins, with autocatalytic decomposition becoming the main reaction channel.

Ab initio kinetics of gas phase decomposition reactions.

PubMed

Sharia, Onise; Kuklja, Maija M

2010-12-09

The thermal and kinetic aspects of gas phase decomposition reactions can be extremely complex due to a large number of parameters, a variety of possible intermediates, and an overlap in thermal decomposition traces. The experimental determination of the activation energies is particularly difficult when several possible reaction pathways coexist in the thermal decomposition. Ab initio calculations intended to provide an interpretation of the experiment are often of little help if they produce only the activation barriers and ignore the kinetics of the decomposition process. To overcome this ambiguity, a theoretical study of a complete picture of gas phase thermo-decomposition, including reaction energies, activation barriers, and reaction rates, is illustrated with the example of the β-octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) molecule by means of quantum-chemical calculations. We study three types of major decomposition reactions characteristic of nitramines: the HONO elimination, the NONO rearrangement, and the N-NO(2) homolysis. The reaction rates were determined using the conventional transition state theory for the HONO and NONO decompositions and the variational transition state theory for the N-NO(2) homolysis. Our calculations show that the HMX decomposition process is more complex than it was previously believed to be and is defined by a combination of reactions at any given temperature. At all temperatures, the direct N-NO(2) homolysis prevails with the activation barrier at 38.1 kcal/mol. The nitro-nitrite isomerization and the HONO elimination, with the activation barriers at 46.3 and 39.4 kcal/mol, respectively, are slow reactions at all temperatures. The obtained conclusions provide a consistent interpretation for the reported experimental data.

Reducing Projection Calculation in Quantum Teleportation by Virtue of the IWOP Technique and Schmidt Decomposition of |η〉 State

NASA Astrophysics Data System (ADS)

Fan, Hong-Yi; Fan, Yue

2002-01-01

By virtue of the technique of integration within an ordered product of operators and the Schmidt decomposition of the entangled state |η〉, we reduce the general projection calculation in the theory of quantum teleportation to a as simple as possible form and present a general formalism for teleportating quantum states of continuous variable. The project supported by National Natural Science Foundation of China and Educational Ministry Foundation of China

Solid state green synthesis and catalytic activity of CuO nanorods in thermal decomposition of potassium periodate

NASA Astrophysics Data System (ADS)

Patel, Vinay Kumar; Bhattacharya, Shantanu

2017-09-01

The present study reports a facile solid state green synthesis process using the leaf extracts of Hibiscus rosa-sinensis to synthesize CuO nanorods with average diameters of 15-20 nm and lengths up to 100 nm. The as-synthesized CuO nanorods were characterized by x-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and selected area electron diffraction. The formation mechanism of CuO nanorods has been explained by involving the individual role of amide I (amino groups) and carboxylate groups under excess hydroxyl ions released from NaOH. The catalytic activity of CuO nanorods in thermal decomposition of potassium periodate microparticles (µ-KIO4) microparticles was studied by thermo gravimetric analysis measurement. The original size (~100 µm) of commercially procured potassium periodate was reduced to microscale length scale to about one-tenth by PEG200 assisted emulsion process. The CuO nanorods prepared by solid state green route were found to catalyze the thermal decomposition of µ-KIO4 with a reduction of 18 °C in the final thermal decomposition temperature of potassium periodate.

A control technology evaluation of state-of-the-art, perchloroethylene dry-cleaning machines.

PubMed

Earnest, G Scott

2002-05-01

NIOSH researchers evaluated the ability of fifth-generation dry-cleaning machines to control occupational exposure to perchloroethylene (PERC). Use of these machines is mandated in some countries; however, less than 1 percent of all U.S. shops have them. A study was conducted at a U.S. dry-cleaning shop where two fifth-generation machines were used. Both machines had a refrigerated condenser as a primary control and a carbon adsorber as a secondary control to recover PERC vapors during the dry cycle. These machines were designed to lower the PERC concentration in the cylinder at the end of the dry cycle to below 290 ppm. A single-beam infrared photometer continuously monitors the PERC concentration in the machine cylinder, and a door interlock prevents opening until the concentration is below 290 ppm. Personal breathing zone air samples were measured for the machine operator and presser. The operator had time-weighted average (TWA) PERC exposures that were less than 2 ppm. Highest exposures occurred during loading and unloading the machine and when performing routine machine maintenance. All presser samples were below the limit of detection. Real-time video exposure monitoring showed that the operator had peak exposures near 160 ppm during loading and unloading the machine (below the OSHA maximum of 300 ppm). This exposure (160 ppm) is an order of magnitude lower than exposures with more traditional machines that are widely used in the United States. The evaluated machines were very effective at reducing TWA PERC exposures as well as peak exposures that occur during machine loading and unloading. State-of-the-art dry-cleaning machines equipped with refrigerated condensers, carbon adsorbers, drum monitors, and door interlocks can provide substantially better protection than more traditional machines that are widely used in the United States.

Stress-induced activation of decomposition of organic explosives: a simple way to understand.

PubMed

Zhang, Chaoyang

2013-01-01

We provide a very simply way to understand the stress-induced activation of decomposition of organic explosives by taking the simplest explosive molecule nitromethane (NM) as a prototype and constraining one or two NM molecules in a shell to represent the condensed phrase of NM against the stress caused by tension and compression, sliding and rotational shear, and imperfection. The results show that the stress loaded on NM molecule can always reduce the barriers of its decomposition. We think the origin of this stress-induced activation is due to the increased repulsive intra- and/or inter- molecular interaction potentials in explosives resulted from the stress, whose release is positive to accelerate the decomposition. Besides, by these models, we can understand that the explosives in gaseous state are easier to analyze than those in condensed state and the voids in condensed explosives make them more sensitive to external stimuli relative to the perfect crystals.

Dynamic Mobility via Cellular Decompositions of Coordination Spaces

DTIC Science & Technology

2012-01-01

half-bound, canter, and gallop gaits that actively recruit motion by the animal’s spine. It is posited that the increased performance of a cheetah ...machines [3] [1] P.E. Hudson, S.A. Corr, R.C. Payne-Davis, S.N. Clancy, E. Lane, and A.M. Wilson. Functional anatomy of the cheetah (acinonyx jubatus...vertical position of COM, y •angle of body , Front Leg Stance (3 DOF): Upon impact, we assume the toe acts as a unconstrained pin joint with the

Distributed intelligence for supervisory control

NASA Technical Reports Server (NTRS)

Wolfe, W. J.; Raney, S. D.

1987-01-01

Supervisory control systems must deal with various types of intelligence distributed throughout the layers of control. Typical layers are real-time servo control, off-line planning and reasoning subsystems and finally, the human operator. Design methodologies must account for the fact that the majority of the intelligence will reside with the human operator. Hierarchical decompositions and feedback loops as conceptual building blocks that provide a common ground for man-machine interaction are discussed. Examples of types of parallelism and parallel implementation on several classes of computer architecture are also discussed.

User’s Guide for the Coupled Ocean/Atmospheric Mesoscale Prediction System (COAMPS) Version 5.0

DTIC Science & Technology

2010-03-30

provides tools for common modeling functions, as well as regridding, data decomposition, and communication on parallel computers. NRL/MR/7320...specified gncomDir. If running COAMPS at the DSRC (e.g. BABBAGE, DAVINCI , or EINSTEIN), the global NCOM files will be copied to /scr/[user]/COAMPS/data...the site (DSRC or local) and the platform (BABBAGE. DAVINCI , EINSTEIN, or local machine) on which COAMPS is being run. site=navy_dsrc (for DSRC

Algebraic multigrid domain and range decomposition (AMG-DD / AMG-RD)*

DOE PAGES

Bank, R.; Falgout, R. D.; Jones, T.; ...

2015-10-29

In modern large-scale supercomputing applications, algebraic multigrid (AMG) is a leading choice for solving matrix equations. However, the high cost of communication relative to that of computation is a concern for the scalability of traditional implementations of AMG on emerging architectures. This paper introduces two new algebraic multilevel algorithms, algebraic multigrid domain decomposition (AMG-DD) and algebraic multigrid range decomposition (AMG-RD), that replace traditional AMG V-cycles with a fully overlapping domain decomposition approach. While the methods introduced here are similar in spirit to the geometric methods developed by Brandt and Diskin [Multigrid solvers on decomposed domains, in Domain Decomposition Methods inmore » Science and Engineering, Contemp. Math. 157, AMS, Providence, RI, 1994, pp. 135--155], Mitchell [Electron. Trans. Numer. Anal., 6 (1997), pp. 224--233], and Bank and Holst [SIAM J. Sci. Comput., 22 (2000), pp. 1411--1443], they differ primarily in that they are purely algebraic: AMG-RD and AMG-DD trade communication for computation by forming global composite “grids” based only on the matrix, not the geometry. (As is the usual AMG convention, “grids” here should be taken only in the algebraic sense, regardless of whether or not it corresponds to any geometry.) Another important distinguishing feature of AMG-RD and AMG-DD is their novel residual communication process that enables effective parallel computation on composite grids, avoiding the all-to-all communication costs of the geometric methods. The main purpose of this paper is to study the potential of these two algebraic methods as possible alternatives to existing AMG approaches for future parallel machines. As a result, this paper develops some theoretical properties of these methods and reports on serial numerical tests of their convergence properties over a spectrum of problem parameters.« less

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

A Simple Universal Turing Machine for the Game of Life Turing Machine

NASA Astrophysics Data System (ADS)

Rendell, Paul

In this chapter we present a simple universal Turing machine which is small enough to fit into the design limits of the Turing machine build in Conway's Game of Life by the author. That limit is 8 symbols and 16 states. By way of comparison we also describe one of the smallest known universal Turing machines due to Rogozhin which has 6 symbols and 4 states.

Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples

NASA Astrophysics Data System (ADS)

Zhang, Meijun; Tang, Jian; Zhang, Xiaoming; Zhang, Jiaojiao

2016-03-01

The high accurate classification ability of an intelligent diagnosis method often needs a large amount of training samples with high-dimensional eigenvectors, however the characteristics of the signal need to be extracted accurately. Although the existing EMD(empirical mode decomposition) and EEMD(ensemble empirical mode decomposition) are suitable for processing non-stationary and non-linear signals, but when a short signal, such as a hydraulic impact signal, is concerned, their decomposition accuracy become very poor. An improve EEMD is proposed specifically for short hydraulic impact signals. The improvements of this new EEMD are mainly reflected in four aspects, including self-adaptive de-noising based on EEMD, signal extension based on SVM(support vector machine), extreme center fitting based on cubic spline interpolation, and pseudo component exclusion based on cross-correlation analysis. After the energy eigenvector is extracted from the result of the improved EEMD, the fault pattern recognition based on SVM with small amount of low-dimensional training samples is studied. At last, the diagnosis ability of improved EEMD+SVM method is compared with the EEMD+SVM and EMD+SVM methods, and its diagnosis accuracy is distinctly higher than the other two methods no matter the dimension of the eigenvectors are low or high. The improved EEMD is very propitious for the decomposition of short signal, such as hydraulic impact signal, and its combination with SVM has high ability for the diagnosis of hydraulic impact faults.

Some aspects of the thermodynamic behaviour of the lead-doped Bi-2223 system

NASA Astrophysics Data System (ADS)

Tetenbaum, M.; Maroni, V. A.

1996-02-01

A thermodynamic assessment of lead-doped Bi-2223 with emphasis on compositions and oxygen partial pressures within the homogeneity region prior to solid-state decomposition is presented. Equations for the variation of oxygen partial pressure with composition and temperature have been derived from our EMF measurements. Long-term metastability was indicated during cycling over a temperature range of ∼ 700-815°C of a lead-doped Bi-2223 sample having an oxygen-deficient stoichiometry of 9.64 prior to solid-state decomposition corresponding to the diphasic CuOCu 2O system. A trend of increasing negative values of the partial molar enthalpy Δ overlineH( O 2) and entropy Δ overlineS( O2 with increasing oxygen deficiency of the condensed phase indicated an increase in ordering of the cuprate structure prior to solid-state decomposition.

Research in Parallel Algorithms and Software for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Domel, Neal D.

1996-01-01

Phase I is complete for the development of a Computational Fluid Dynamics parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.

Research in Parallel Algorithms and Software for Computational Aerosciences

NASA Technical Reports Server (NTRS)

Domel, Neal D.

1996-01-01

Phase 1 is complete for the development of a computational fluid dynamics CFD) parallel code with automatic grid generation and adaptation for the Euler analysis of flow over complex geometries. SPLITFLOW, an unstructured Cartesian grid code developed at Lockheed Martin Tactical Aircraft Systems, has been modified for a distributed memory/massively parallel computing environment. The parallel code is operational on an SGI network, Cray J90 and C90 vector machines, SGI Power Challenge, and Cray T3D and IBM SP2 massively parallel machines. Parallel Virtual Machine (PVM) is the message passing protocol for portability to various architectures. A domain decomposition technique was developed which enforces dynamic load balancing to improve solution speed and memory requirements. A host/node algorithm distributes the tasks. The solver parallelizes very well, and scales with the number of processors. Partially parallelized and non-parallelized tasks consume most of the wall clock time in a very fine grain environment. Timing comparisons on a Cray C90 demonstrate that Parallel SPLITFLOW runs 2.4 times faster on 8 processors than its non-parallel counterpart autotasked over 8 processors.

MapReduce SVM Game

DOE PAGES

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.; ...

2015-08-10

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

PubMed

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

MapReduce SVM Game

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

Vineyard, Craig M.; Verzi, Stephen J.; James, Conrad D.

Despite technological advances making computing devices faster, smaller, and more prevalent in today's age, data generation and collection has outpaced data processing capabilities. Simply having more compute platforms does not provide a means of addressing challenging problems in the big data era. Rather, alternative processing approaches are needed and the application of machine learning to big data is hugely important. The MapReduce programming paradigm is an alternative to conventional supercomputing approaches, and requires less stringent data passing constrained problem decompositions. Rather, MapReduce relies upon defining a means of partitioning the desired problem so that subsets may be computed independently andmore » recom- bined to yield the net desired result. However, not all machine learning algorithms are amenable to such an approach. Game-theoretic algorithms are often innately distributed, consisting of local interactions between players without requiring a central authority and are iterative by nature rather than requiring extensive retraining. Effectively, a game-theoretic approach to machine learning is well suited for the MapReduce paradigm and provides a novel, alternative new perspective to addressing the big data problem. In this paper we present a variant of our Support Vector Machine (SVM) Game classifier which may be used in a distributed manner, and show an illustrative example of applying this algorithm.« less

Perfluoropolyalkylether decomposition on catalytic aluminas

NASA Technical Reports Server (NTRS)

Morales, Wilfredo

1994-01-01

The decomposition of Fomblin Z25, a commercial perfluoropolyalkylether liquid lubricant, was studied using the Penn State Micro-oxidation Test, and a thermal gravimetric/differential scanning calorimetry unit. The micro-oxidation test was conducted using 440C stainless steel and pure iron metal catalyst specimens, whereas the thermal gravimetric/differential scanning calorimetry tests were conducted using catalytic alumina pellets. Analysis of the thermal data, high pressure liquid chromatography data, and x-ray photoelectron spectroscopy data support evidence that there are two different decomposition mechanisms for Fomblin Z25, and that reductive sites on the catalytic surfaces are responsible for the decomposition of Fomblin Z25.

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

Energy decomposition analysis for exciplexes using absolutely localized molecular orbitals

NASA Astrophysics Data System (ADS)

Ge, Qinghui; Mao, Yuezhi; Head-Gordon, Martin

2018-02-01

An energy decomposition analysis (EDA) scheme is developed for understanding the intermolecular interaction involving molecules in their excited states. The EDA utilizes absolutely localized molecular orbitals to define intermediate states and is compatible with excited state methods based on linear response theory such as configuration interaction singles and time-dependent density functional theory. The shift in excitation energy when an excited molecule interacts with the environment is decomposed into frozen, polarization, and charge transfer contributions, and the frozen term can be further separated into Pauli repulsion and electrostatics. These terms can be added to their counterparts obtained from the ground state EDA to form a decomposition of the total interaction energy. The EDA scheme is applied to study a variety of systems, including some model systems to demonstrate the correct behavior of all the proposed energy components as well as more realistic systems such as hydrogen-bonding complexes (e.g., formamide-water, pyridine/pyrimidine-water) and halide (F-, Cl-)-water clusters that involve charge-transfer-to-solvent excitations.

Phase decomposition and ordering in Ni-11.3 at.% Ti studied with atom probe tomography.

PubMed

Al-Kassab, T; Kompatscher, M; Kirchheim, R; Kostorz, G; Schönfeld, B

2014-09-01

The decomposition behavior of Ni-rich Ni-Ti was reassessed using Tomographic Atom Probe (TAP) and Laser Assisted Wide Angle Tomographic Atom Probe. Single crystalline specimens of Ni-11.3 at.% Ti were investigated, the states selected from the decomposition path were the metastable γ″ and γ' states introduced on the basis of small-angle neutron scattering (SANS) and the two-phase model for evaluation. The composition values of the precipitates in these states could not be confirmed by APT data as the interface of the ordered precipitates may not be neglected. The present results rather suggest to apply a three-phase model for the interpretation of SANS measurements, in which the width of the interface remains nearly unchanged and the L12 structure close to 3:1 stoichiometry is maintained in the core of the precipitates from the γ″ to the γ' state. Copyright © 2014 Elsevier Ltd. All rights reserved.

Profiling physicochemical and planktonic features from discretely/continuously sampled surface water.

PubMed

Oita, Azusa; Tsuboi, Yuuri; Date, Yasuhiro; Oshima, Takahiro; Sakata, Kenji; Yokoyama, Akiko; Moriya, Shigeharu; Kikuchi, Jun

2018-04-24

There is an increasing need for assessing aquatic ecosystems that are globally endangered. Since aquatic ecosystems are complex, integrated consideration of multiple factors utilizing omics technologies can help us better understand aquatic ecosystems. An integrated strategy linking three analytical (machine learning, factor mapping, and forecast-error-variance decomposition) approaches for extracting the features of surface water from datasets comprising ions, metabolites, and microorganisms is proposed herein. The three developed approaches can be employed for diverse datasets of sample sizes and experimentally analyzed factors. The three approaches are applied to explore the features of bay water surrounding Odaiba, Tokyo, Japan, as a case study. Firstly, the machine learning approach separated 681 surface water samples within Japan into three clusters, categorizing Odaiba water into seawater with relatively low inorganic ions, including Mg, Ba, and B. Secondly, the factor mapping approach illustrated Odaiba water samples from the summer as rich in multiple amino acids and some other metabolites and poor in inorganic ions relative to other seasons based on their seasonal dynamics. Finally, forecast-error-variance decomposition using vector autoregressive models indicated that a type of microalgae (Raphidophyceae) grows in close correlation with alanine, succinic acid, and valine on filters and with isobutyric acid and 4-hydroxybenzoic acid in filtrate, Ba, and average wind speed. Our integrated strategy can be used to examine many biological, chemical, and environmental physical factors to analyze surface water. Copyright © 2018. Published by Elsevier B.V.

Preliminary Development of Real Time Usage-Phase Monitoring System for CNC Machine Tools with a Case Study on CNC Machine VMC 250

NASA Astrophysics Data System (ADS)

Budi Harja, Herman; Prakosa, Tri; Raharno, Sri; Yuwana Martawirya, Yatna; Nurhadi, Indra; Setyo Nogroho, Alamsyah

2018-03-01

The production characteristic of job-shop industry at which products have wide variety but small amounts causes every machine tool will be shared to conduct production process with dynamic load. Its dynamic condition operation directly affects machine tools component reliability. Hence, determination of maintenance schedule for every component should be calculated based on actual usage of machine tools component. This paper describes study on development of monitoring system to obtaining information about each CNC machine tool component usage in real time approached by component grouping based on its operation phase. A special device has been developed for monitoring machine tool component usage by utilizing usage phase activity data taken from certain electronics components within CNC machine. The components are adaptor, servo driver and spindle driver, as well as some additional components such as microcontroller and relays. The obtained data are utilized for detecting machine utilization phases such as power on state, machine ready state or spindle running state. Experimental result have shown that the developed CNC machine tool monitoring system is capable of obtaining phase information of machine tool usage as well as its duration and displays the information at the user interface application.

Hardware support for software controlled fast multiplexing of performance counters

DOEpatents

Salapura, Valentina; Wisniewski, Robert W

2013-10-01

Performance counters may be operable to collect one or more counts of one or more selected activities, and registers may be operable to store a set of performance counter configurations. A state machine may be operable to automatically select a register from the registers for reconfiguring the one or more performance counters in response to receiving a first signal. The state machine may be further operable to reconfigure the one or more performance counters based on a configuration specified in the selected register. The state machine yet further may be operable to copy data in selected one or more of the performance counters to a memory location, or to copy data from the memory location to the counters, in response to receiving a second signal. The state machine may be operable to store or restore the counter values and state machine configuration in response to a context switch event.

Hardware support for software controlled fast multiplexing of performance counters

DOEpatents

Salapura, Valentina; Wisniewski, Robert W.

2013-01-01

Performance counters may be operable to collect one or more counts of one or more selected activities, and registers may be operable to store a set of performance counter configurations. A state machine may be operable to automatically select a register from the registers for reconfiguring the one or more performance counters in response to receiving a first signal. The state machine may be further operable to reconfigure the one or more performance counters based on a configuration specified in the selected register. The state machine yet further may be operable to copy data in selected one or more of the performance counters to a memory location, or to copy data from the memory location to the counters, in response to receiving a second signal. The state machine may be operable to store or restore the counter values and state machine configuration in response to a context switch event.

Allocating dissipation across a molecular machine cycle to maximize flux

PubMed Central

Brown, Aidan I.; Sivak, David A.

2017-01-01

Biomolecular machines consume free energy to break symmetry and make directed progress. Nonequilibrium ATP concentrations are the typical free energy source, with one cycle of a molecular machine consuming a certain number of ATP, providing a fixed free energy budget. Since evolution is expected to favor rapid-turnover machines that operate efficiently, we investigate how this free energy budget can be allocated to maximize flux. Unconstrained optimization eliminates intermediate metastable states, indicating that flux is enhanced in molecular machines with fewer states. When maintaining a set number of states, we show that—in contrast to previous findings—the flux-maximizing allocation of dissipation is not even. This result is consistent with the coexistence of both “irreversible” and reversible transitions in molecular machine models that successfully describe experimental data, which suggests that, in evolved machines, different transitions differ significantly in their dissipation. PMID:29073016

Microbial interactions during carrion decomposition

USDA-ARS?s Scientific Manuscript database

This addresses the microbial ecology of carrion decomposition in the age of metagenomics. It describes what is known about the microbial communities on carrion, including a brief synopsis about the communities on other organic matter sources. It provides a description of studies using state-of-the...

A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System.

PubMed

Frohlich, Holger; Claes, Kasper; De Wolf, Catherine; Van Damme, Xavier; Michel, Anne

2018-05-01

Gait analysis of animal disease models can provide valuable insights into in vivo compound effects and thus help in preclinical drug development. The purpose of this paper is to establish a computational gait analysis approach for the Noldus Catwalk system, in which footprints are automatically captured and stored. We present a - to our knowledge - first machine learning based approach for the Catwalk system, which comprises a step decomposition, definition and extraction of meaningful features, multivariate step sequence alignment, feature selection, and training of different classifiers (gradient boosting machine, random forest, and elastic net). Using animal-wise leave-one-out cross validation we demonstrate that with our method we can reliable separate movement patterns of a putative Parkinson's disease animal model and several control groups. Furthermore, we show that we can predict the time point after and the type of different brain lesions and can even forecast the brain region, where the intervention was applied. We provide an in-depth analysis of the features involved into our classifiers via statistical techniques for model interpretation. A machine learning method for automated analysis of data from the Noldus Catwalk system was established. Our works shows the ability of machine learning to discriminate pharmacologically relevant animal groups based on their walking behavior in a multivariate manner. Further interesting aspects of the approach include the ability to learn from past experiments, improve with more data arriving and to make predictions for single animals in future studies.

Multicopy programmable discrimination of general qubit states

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

Sentis, G.; Bagan, E.; Calsamiglia, J.

2010-10-15

Quantum state discrimination is a fundamental primitive in quantum statistics where one has to correctly identify the state of a system that is in one of two possible known states. A programmable discrimination machine performs this task when the pair of possible states is not a priori known but instead the two possible states are provided through two respective program ports. We study optimal programmable discrimination machines for general qubit states when several copies of states are available in the data or program ports. Two scenarios are considered: One in which the purity of the possible states is a priorimore » known, and the fully universal one where the machine operates over generic mixed states of unknown purity. We find analytical results for both the unambiguous and minimum error discrimination strategies. This allows us to calculate the asymptotic performance of programmable discrimination machines when a large number of copies are provided and to recover the standard state discrimination and state comparison values as different limiting cases.« less

Theoretical evidence of the observed kinetic order dependence on temperature during the N(2)O decomposition over Fe-ZSM-5.

PubMed

Guesmi, Hazar; Berthomieu, Dorothee; Bromley, Bryan; Coq, Bernard; Kiwi-Minsker, Lioubov

2010-03-28

The characterization of Fe/ZSM5 zeolite materials, the nature of Fe-sites active in N(2)O direct decomposition, as well as the rate limiting step are still a matter of debate. The mechanism of N(2)O decomposition on the binuclear oxo-hydroxo bridged extraframework iron core site [Fe(II)(mu-O)(mu-OH)Fe(II)](+) inside the ZSM-5 zeolite has been studied by combining theoretical and experimental approaches. The overall calculated path of N(2)O decomposition involves the oxidation of binuclear Fe(II) core sites by N(2)O (atomic alpha-oxygen formation) and the recombination of two surface alpha-oxygen atoms leading to the formation of molecular oxygen. Rate parameters computed using standard statistical mechanics and transition state theory reveal that elementary catalytic steps involved into N(2)O decomposition are strongly dependent on the temperature. This theoretical result was compared to the experimentally observed steady state kinetics of the N(2)O decomposition and temperature-programmed desorption (TPD) experiments. A switch of the reaction order with respect to N(2)O pressure from zero to one occurs at around 800 K suggesting a change of the rate determining step from the alpha-oxygen recombination to alpha-oxygen formation. The TPD results on the molecular oxygen desorption confirmed the mechanism proposed.

Decomposition of toluene in a steady-state atmospheric-pressure glow discharge

NASA Astrophysics Data System (ADS)

Trushkin, A. N.; Grushin, M. E.; Kochetov, I. V.; Trushkin, N. I.; Akishev, Yu. S.

2013-02-01

Results are presented from experimental studies of decomposition of toluene (C6H5CH3) in a polluted air flow by means of a steady-state atmospheric pressure glow discharge at different water vapor contents in the working gas. The experimental results on the degree of C6H5CH3 removal are compared with the results of computer simulations conducted in the framework of the developed kinetic model of plasma chemical decomposition of toluene in the N2: O2: H2O gas mixture. A substantial influence of the gas flow humidity on toluene decomposition in the atmospheric pressure glow discharge is demonstrated. The main mechanisms of the influence of humidity on C6H5CH3 decomposition are determined. The existence of two stages in the process of toluene removal, which differ in their duration and the intensity of plasma chemical decomposition of C6H5CH3 is established. Based on the results of computer simulations, the composition of the products of plasma chemical reactions at the output of the reactor is analyzed as a function of the specific energy deposition and gas flow humidity. The existence of a catalytic cycle in which hydroxyl radical OH acts a catalyst and which substantially accelerates the recombination of oxygen atoms and suppression of ozone generation when the plasma-forming gas contains water vapor is established.

Oxidative decomposition of propylene carbonate in lithium ion batteries: a DFT study.

PubMed

Leggesse, Ermias Girma; Lin, Rao Tung; Teng, Tsung-Fan; Chen, Chi-Liang; Jiang, Jyh-Chiang

2013-08-22

This paper reports an in-depth mechanistic study on the oxidative decomposition of propylene carbonate in the presence of lithium salts (LiClO4, LiBF4, LiPF6, and LiAsF6) with the aid of density functional theory calculations at the B3LYP/6-311++G(d,p) level of theory. The solvent effect is accounted for by using the implicit solvation model with density method. Moreover, the rate constants for the decompositions of propylene carbonate have been investigated by using transition-state theory. The shortening of the original carbonyl C-O bond and a lengthening of the adjacent ethereal C-O bonds of propylene carbonate, which occurs as a result of oxidation, leads to the formation of acetone radical and CO2 as a primary oxidative decomposition product. The termination of the primary radical generates polycarbonate, acetone, diketone, 2-(ethan-1-ylium-1-yl)-4-methyl-1,3-dioxolan-4-ylium, and CO2. The thermodynamic and kinetic data show that the major oxidative decomposition products of propylene carbonate are independent of the type of lithium salt. However, the decomposition rate constants of propylene carbonate are highly affected by the lithium salt type. On the basis of the rate constant calculations using transition-state theory, the order of gas volume generation is: [PC-ClO4](-) > [PC-BF4](-) > [PC-AsF6](-) > [PC-PF6](-).

Evaluation of global horizontal irradiance to plane-of-array irradiance models at locations across the United States

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

Lave, Matthew; Hayes, William; Pohl, Andrew

2015-02-02

We report an evaluation of the accuracy of combinations of models that estimate plane-of-array (POA) irradiance from measured global horizontal irradiance (GHI). This estimation involves two steps: 1) decomposition of GHI into direct and diffuse horizontal components and 2) transposition of direct and diffuse horizontal irradiance (DHI) to POA irradiance. Measured GHI and coincident measured POA irradiance from a variety of climates within the United States were used to evaluate combinations of decomposition and transposition models. A few locations also had DHI measurements, allowing for decoupled analysis of either the decomposition or the transposition models alone. Results suggest that decompositionmore » models had mean bias differences (modeled versus measured) that vary with climate. Transposition model mean bias differences depended more on the model than the location. Lastly, when only GHI measurements were available and combinations of decomposition and transposition models were considered, the smallest mean bias differences were typically found for combinations which included the Hay/Davies transposition model.« less

Mapping to Irregular Torus Topologies and Other Techniques for Petascale Biomolecular Simulation

PubMed Central

Phillips, James C.; Sun, Yanhua; Jain, Nikhil; Bohm, Eric J.; Kalé, Laxmikant V.

2014-01-01

Currently deployed petascale supercomputers typically use toroidal network topologies in three or more dimensions. While these networks perform well for topology-agnostic codes on a few thousand nodes, leadership machines with 20,000 nodes require topology awareness to avoid network contention for communication-intensive codes. Topology adaptation is complicated by irregular node allocation shapes and holes due to dedicated input/output nodes or hardware failure. In the context of the popular molecular dynamics program NAMD, we present methods for mapping a periodic 3-D grid of fixed-size spatial decomposition domains to 3-D Cray Gemini and 5-D IBM Blue Gene/Q toroidal networks to enable hundred-million atom full machine simulations, and to similarly partition node allocations into compact domains for smaller simulations using multiple-copy algorithms. Additional enabling techniques are discussed and performance is reported for NCSA Blue Waters, ORNL Titan, ANL Mira, TACC Stampede, and NERSC Edison. PMID:25594075

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

Wang Yinan; Shi Handuo; Xiong Zhaoxi

We present a unified universal quantum cloning machine, which combines several different existing universal cloning machines together, including the asymmetric case. In this unified framework, the identical pure states are projected equally into each copy initially constituted by input and one half of the maximally entangled states. We show explicitly that the output states of those universal cloning machines are the same. One importance of this unified cloning machine is that the cloning procession is always the symmetric projection, which reduces dramatically the difficulties for implementation. Also, it is found that this unified cloning machine can be directly modified tomore » the general asymmetric case. Besides the global fidelity and the single-copy fidelity, we also present all possible arbitrary-copy fidelities.« less

A New Approach of evaluating the damage in simply-supported reinforced concrete beam by Local mean decomposition (LMD)

NASA Astrophysics Data System (ADS)

Zhang, Xuebing; Liu, Ning; Xi, Jiaxin; Zhang, Yunqi; Zhang, Wenchun; Yang, Peipei

2017-08-01

How to analyze the nonstationary response signals and obtain vibration characters is extremely important in the vibration-based structural diagnosis methods. In this work, we introduce a more reasonable time-frequency decomposition method termed local mean decomposition (LMD) to instead the widely-used empirical mode decomposition (EMD). By employing the LMD method, one can derive a group of component signals, each of which is more stationary, and then analyze the vibration state and make the assessment of structural damage of a construction or building. We illustrated the effectiveness of LMD by a synthetic data and an experimental data recorded in a simply-supported reinforced concrete beam. Then based on the decomposition results, an elementary method of damage diagnosis was proposed.

Using Microwave Sample Decomposition in Undergraduate Analytical Chemistry

NASA Astrophysics Data System (ADS)

Griff Freeman, R.; McCurdy, David L.

1998-08-01

A shortcoming of many undergraduate classes in analytical chemistry is that students receive little exposure to sample preparation in chemical analysis. This paper reports the progress made in introducing microwave sample decomposition into several quantitative analysis experiments at Truman State University. Two experiments being performed in our current laboratory rotation include closed vessel microwave decomposition applied to the classical gravimetric determination of nickel and the determination of sodium in snack foods by flame atomic emission spectrometry. A third lab, using open-vessel microwave decomposition for the Kjeldahl nitrogen determination is now ready for student trial. Microwave decomposition reduces the time needed to complete these experiments and significantly increases the student awareness of the importance of sample preparation in quantitative chemical analyses, providing greater breadth and realism in the experiments.

Initial mechanisms for the decomposition of electronically excited energetic materials: 1,5′-BT, 5,5′-BT, and AzTT

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

Yuan, Bing; Yu, Zijun; Bernstein, Elliot R., E-mail: erb@lamar.Colostate.edu

2015-03-28

Decomposition of nitrogen-rich energetic materials 1,5′-BT, 5,5′-BT, and AzTT (1,5′-Bistetrazole, 5,5′-Bistetrazole, and 5-(5-azido-(1 or 4)H-1,2,4-triazol-3-yl)tetrazole, respectively), following electronic state excitation, is investigated both experimentally and theoretically. The N{sub 2} molecule is observed as an initial decomposition product from the three materials, subsequent to UV excitation, with a cold rotational temperature (<30 K). Initial decomposition mechanisms for these three electronically excited materials are explored at the complete active space self-consistent field (CASSCF) level. Potential energy surface calculations at the CASSCF(12,8)/6-31G(d) level illustrate that conical intersections play an essential role in the decomposition mechanism. Electronically excited S{sub 1} molecules can non-adiabatically relaxmore » to their ground electronic states through (S{sub 1}/S{sub 0}){sub CI} conical intersections. 1,5′-BT and 5,5′-BT materials have several (S{sub 1}/S{sub 0}){sub CI} conical intersections between S{sub 1} and S{sub 0} states, related to different tetrazole ring opening positions, all of which lead to N{sub 2} product formation. The N{sub 2} product for AzTT is formed primarily by N–N bond rupture of the –N{sub 3} group. The observed rotational energy distributions for the N{sub 2} products are consistent with the final structures of the respective transition states for each molecule on its S{sub 0} potential energy surface. The theoretically derived vibrational temperature of the N{sub 2} product is high, which is similar to that found for energetic salts and molecules studied previously.« less

Effect of Hydration State of Martian Perchlorate Salts on their Decomposition Temperatures during Thermal Extraction

NASA Astrophysics Data System (ADS)

Royle, S. H.; Montgomery, W.; Kounaves, S. P.; Sephton, M. A.

2017-12-01

A number of missions to Mars have analyzed the composition of surface samples using thermal extraction techniques. The temperatures of decomposition have been used as diagnostic information for the materials present. One material of great current interest is perchlorate, a relatively recently discovered component of Mars surface geochemistry that leads to deleterious effects on organic matter during thermal extraction. Knowledge of the thermal decomposition behavior of perchlorate salts is essential for mineral identification and possible avoidance of confounding interactions with organic matter. We have performed a series of stepped pyrolysis experiments on samples of magnesium perchlorate hydrate which were dehydrated to various extents - as confirmed by XRD and FTIR analysis. Our data reveal that the hydration state of magnesium perchlorate has a significant effect on decomposition temperature, with differing temperature releases of oxygen corresponding to different perchlorate hydration states. We find that the peak temperature of oxygen release increases from 500 to 600°C as the proportion of the tetrahydrate form in the sample increases and the hexahydrate form decreases. It was known previously that cation chemistry can affect the temperature of oxygen release and now our work shows that the hydration state of these salts can lead to similar variations. Consequently, incorrect identification of perchlorate species may occur if hydration state is not taken into account and a mixture of metastable hydration states (of one type of perchlorate) may be mistaken for a mixture of perchlorate salts. Our findings are important for Mars as the hydration state of salts in the regolith may change throughout the Martian year due to large variations in humidity and temperature.

The development of a scalable parallel 3-D CFD algorithm for turbomachinery. M.S. Thesis Final Report

NASA Technical Reports Server (NTRS)

Luke, Edward Allen

1993-01-01

Two algorithms capable of computing a transonic 3-D inviscid flow field about rotating machines are considered for parallel implementation. During the study of these algorithms, a significant new method of measuring the performance of parallel algorithms is developed. The theory that supports this new method creates an empirical definition of scalable parallel algorithms that is used to produce quantifiable evidence that a scalable parallel application was developed. The implementation of the parallel application and an automated domain decomposition tool are also discussed.

Proceedings of the Seminar on the DoD Computer Security Initiative Program, National Bureau of Standards, Gaithersburg, Maryland, July 17-18, 1979.

DTIC Science & Technology

1979-01-01

specifications have been prepared for a DoD communications processor on an IBM minicomputer, a minicomputer time sharing system for the DEC PDP-11 and...the Honeywell Level 6. a virtual machine monitor for the IBM 370, and Multics [10] for the Honeywell Level 68. MECHANISMS FOR KERNEL IMPLEMENTATION...HOL INA ZJO : ANERIONS g PROCESSORn , c ...THEOREMS 1 ITP I-THEOREMS PROOF EVIDENCE - p II KV./370 FORMAL DESIGN PROCESS M4ODULAR DECOMPOSITION * NON

Planning paths through a spatial hierarchy - Eliminating stair-stepping effects

NASA Technical Reports Server (NTRS)

Slack, Marc G.

1989-01-01

Stair-stepping effects are a result of the loss of spatial continuity resulting from the decomposition of space into a grid. This paper presents a path planning algorithm which eliminates stair-stepping effects induced by the grid-based spatial representation. The algorithm exploits a hierarchical spatial model to efficiently plan paths for a mobile robot operating in dynamic domains. The spatial model and path planning algorithm map to a parallel machine, allowing the system to operate incrementally, thereby accounting for unexpected events in the operating space.

AN EIGHT WEEK SEMINAR IN AN INTRODUCTION TO NUMERICAL CONTROL ON TWO- AND THREE-AXIS MACHINE TOOLS FOR VOCATIONAL AND TECHNICAL MACHINE TOOL INSTRUCTORS. FINAL REPORT.

ERIC Educational Resources Information Center

BOLDT, MILTON; POKORNY, HARRY

THIRTY-THREE MACHINE SHOP INSTRUCTORS FROM 17 STATES PARTICIPATED IN AN 8-WEEK SEMINAR TO DEVELOP THE SKILLS AND KNOWLEDGE ESSENTIAL FOR TEACHING THE OPERATION OF NUMERICALLY CONTROLLED MACHINE TOOLS. THE SEMINAR WAS GIVEN FROM JUNE 20 TO AUGUST 12, 1966, WITH COLLEGE CREDIT AVAILABLE THROUGH STOUT STATE UNIVERSITY. THE PARTICIPANTS COMPLETED AN…

Towards a molecular logic machine

NASA Astrophysics Data System (ADS)

Remacle, F.; Levine, R. D.

2001-06-01

Finite state logic machines can be realized by pump-probe spectroscopic experiments on an isolated molecule. The most elaborate setup, a Turing machine, can be programmed to carry out a specific computation. We argue that a molecule can be similarly programmed, and provide examples using two photon spectroscopies. The states of the molecule serve as the possible states of the head of the Turing machine and the physics of the problem determines the possible instructions of the program. The tape is written in an alphabet that allows the listing of the different pump and probe signals that are applied in a given experiment. Different experiments using the same set of molecular levels correspond to different tapes that can be read and processed by the same head and program. The analogy to a Turing machine is not a mechanical one and is not completely molecular because the tape is not part of the molecular machine. We therefore also discuss molecular finite state machines, such as sequential devices, for which the tape is not part of the machine. Nonmolecular tapes allow for quite long input sequences with a rich alphabet (at the level of 7 bits) and laser pulse shaping experiments provide concrete examples. Single molecule spectroscopies show that a single molecule can be repeatedly cycled through a logical operation.

Empirical Mode Decomposition and k-Nearest Embedding Vectors for Timely Analyses of Antibiotic Resistance Trends

PubMed Central

Teodoro, Douglas; Lovis, Christian

2013-01-01

Background Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available. Objective To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems. Methods We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels. Results The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends. Conclusion The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends. PMID:23637796

Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

NASA Technical Reports Server (NTRS)

Mandra, Salvatore

2017-01-01

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

Plant Species Rather Than Climate Greatly Alters the Temporal Pattern of Litter Chemical Composition During Long-Term Decomposition

NASA Astrophysics Data System (ADS)

Li, Yongfu; Chen, Na; Harmon, Mark E.; Li, Yuan; Cao, Xiaoyan; Chappell, Mark A.; Mao, Jingdong

2015-10-01

A feedback between decomposition and litter chemical composition occurs with decomposition altering composition that in turn influences the decomposition rate. Elucidating the temporal pattern of chemical composition is vital to understand this feedback, but the effects of plant species and climate on chemical changes remain poorly understood, especially over multiple years. In a 10-year decomposition experiment with litter of four species (Acer saccharum, Drypetes glauca, Pinus resinosa, and Thuja plicata) from four sites that range from the arctic to tropics, we determined the abundance of 11 litter chemical constituents that were grouped into waxes, carbohydrates, lignin/tannins, and proteins/peptides using advanced 13C solid-state NMR techniques. Decomposition generally led to an enrichment of waxes and a depletion of carbohydrates, whereas the changes of other chemical constituents were inconsistent. Inconsistent convergence in chemical compositions during decomposition was observed among different litter species across a range of site conditions, whereas one litter species converged under different climate conditions. Our data clearly demonstrate that plant species rather than climate greatly alters the temporal pattern of litter chemical composition, suggesting the decomposition-chemistry feedback varies among different plant species.

Plant Species Rather Than Climate Greatly Alters the Temporal Pattern of Litter Chemical Composition During Long-Term Decomposition

PubMed Central

Li, Yongfu; Chen, Na; Harmon, Mark E.; Li, Yuan; Cao, Xiaoyan; Chappell, Mark A.; Mao, Jingdong

2015-01-01

A feedback between decomposition and litter chemical composition occurs with decomposition altering composition that in turn influences the decomposition rate. Elucidating the temporal pattern of chemical composition is vital to understand this feedback, but the effects of plant species and climate on chemical changes remain poorly understood, especially over multiple years. In a 10-year decomposition experiment with litter of four species (Acer saccharum, Drypetes glauca, Pinus resinosa, and Thuja plicata) from four sites that range from the arctic to tropics, we determined the abundance of 11 litter chemical constituents that were grouped into waxes, carbohydrates, lignin/tannins, and proteins/peptides using advanced 13C solid-state NMR techniques. Decomposition generally led to an enrichment of waxes and a depletion of carbohydrates, whereas the changes of other chemical constituents were inconsistent. Inconsistent convergence in chemical compositions during decomposition was observed among different litter species across a range of site conditions, whereas one litter species converged under different climate conditions. Our data clearly demonstrate that plant species rather than climate greatly alters the temporal pattern of litter chemical composition, suggesting the decomposition-chemistry feedback varies among different plant species. PMID:26515033

Mapping current fluctuations of stochastic pumps to nonequilibrium steady states.

PubMed

Rotskoff, Grant M

2017-03-01

We show that current fluctuations in a stochastic pump can be robustly mapped to fluctuations in a corresponding time-independent nonequilibrium steady state. We thus refine a recently proposed mapping so that it ensures equivalence of not only the averages, but also optimal representation of fluctuations in currents and density. Our mapping leads to a natural decomposition of the entropy production in stochastic pumps similar to the "housekeeping" heat. As a consequence of the decomposition of entropy production, the current fluctuations in weakly perturbed stochastic pumps are shown to satisfy a universal bound determined by the steady state entropy production.

A fast identification algorithm for Box-Cox transformation based radial basis function neural network.

PubMed

Hong, Xia

2006-07-01

In this letter, a Box-Cox transformation-based radial basis function (RBF) neural network is introduced using the RBF neural network to represent the transformed system output. Initially a fixed and moderate sized RBF model base is derived based on a rank revealing orthogonal matrix triangularization (QR decomposition). Then a new fast identification algorithm is introduced using Gauss-Newton algorithm to derive the required Box-Cox transformation, based on a maximum likelihood estimator. The main contribution of this letter is to explore the special structure of the proposed RBF neural network for computational efficiency by utilizing the inverse of matrix block decomposition lemma. Finally, the Box-Cox transformation-based RBF neural network, with good generalization and sparsity, is identified based on the derived optimal Box-Cox transformation and a D-optimality-based orthogonal forward regression algorithm. The proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with support vector machine regression.

Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data

PubMed Central

Young, Alistair A.; Li, Xiaosong

2014-01-01

Public health surveillance systems provide valuable data for reliable predication of future epidemic events. This paper describes a study that used nine types of infectious disease data collected through a national public health surveillance system in mainland China to evaluate and compare the performances of four time series methods, namely, two decomposition methods (regression and exponential smoothing), autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The data obtained from 2005 to 2011 and in 2012 were used as modeling and forecasting samples, respectively. The performances were evaluated based on three metrics: mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE). The accuracy of the statistical models in forecasting future epidemic disease proved their effectiveness in epidemiological surveillance. Although the comparisons found that no single method is completely superior to the others, the present study indeed highlighted that the SVMs outperforms the ARIMA model and decomposition methods in most cases. PMID:24505382

AUTONOMOUS GAUSSIAN DECOMPOSITION

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

Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.

2015-04-15

We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocitymore » width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.« less

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.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Gray, A.

2014-04-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors. This is likely of particular interest to the radio astronomy community given, for example, that survey projects contain groups dedicated to this topic. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

Scalable Machine Learning for Massive Astronomical Datasets

NASA Astrophysics Data System (ADS)

Ball, Nicholas M.; Astronomy Data Centre, Canadian

2014-01-01

We present the ability to perform data mining and machine learning operations on a catalog of half a billion astronomical objects. This is the result of the combination of robust, highly accurate machine learning algorithms with linear scalability that renders the applications of these algorithms to massive astronomical data tractable. We demonstrate the core algorithms kernel density estimation, K-means clustering, linear regression, nearest neighbors, random forest and gradient-boosted decision tree, singular value decomposition, support vector machine, and two-point correlation function. Each of these is relevant for astronomical applications such as finding novel astrophysical objects, characterizing artifacts in data, object classification (including for rare objects), object distances, finding the important features describing objects, density estimation of distributions, probabilistic quantities, and exploring the unknown structure of new data. The software, Skytree Server, runs on any UNIX-based machine, a virtual machine, or cloud-based and distributed systems including Hadoop. We have integrated it on the cloud computing system of the Canadian Astronomical Data Centre, the Canadian Advanced Network for Astronomical Research (CANFAR), creating the world's first cloud computing data mining system for astronomy. We demonstrate results showing the scaling of each of our major algorithms on large astronomical datasets, including the full 470,992,970 objects of the 2 Micron All-Sky Survey (2MASS) Point Source Catalog. We demonstrate the ability to find outliers in the full 2MASS dataset utilizing multiple methods, e.g., nearest neighbors, and the local outlier factor. 2MASS is used as a proof-of-concept dataset due to its convenience and availability. These results are of interest to any astronomical project with large and/or complex datasets that wishes to extract the full scientific value from its data.

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Forgings, castings and machined bodies. 121.10... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings...

Effect of Copper Oxide, Titanium Dioxide, and Lithium Fluoride on the Thermal Behavior and Decomposition Kinetics of Ammonium Nitrate

NASA Astrophysics Data System (ADS)

Vargeese, Anuj A.; Mija, S. J.; Muralidharan, Krishnamurthi

2014-07-01

Ammonium nitrate (AN) is crystallized along with copper oxide, titanium dioxide, and lithium fluoride. Thermal kinetic constants for the decomposition reaction of the samples were calculated by model-free (Friedman's differential and Vyzovkins nonlinear integral) and model-fitting (Coats-Redfern) methods. To determine the decomposition mechanisms, 12 solid-state mechanisms were tested using the Coats-Redfern method. The results of the Coats-Redfern method show that the decomposition mechanism for all samples is the contracting cylinder mechanism. The phase behavior of the obtained samples was evaluated by differential scanning calorimetry (DSC), and structural properties were determined by X-ray powder diffraction (XRPD). The results indicate that copper oxide modifies the phase transition behavior and can catalyze AN decomposition, whereas LiF inhibits AN decomposition, and TiO2 shows no influence on the rate of decomposition. Possible explanations for these results are discussed. Supplementary materials are available for this article. Go to the publisher's online edition of the Journal of Energetic Materials to view the free supplemental file.

Direct and Indirect Effects of UV-B Exposure on Litter Decomposition: A Meta-Analysis

PubMed Central

Song, Xinzhang; Peng, Changhui; Jiang, Hong; Zhu, Qiuan; Wang, Weifeng

2013-01-01

Ultraviolet-B (UV-B) exposure in the course of litter decomposition may have a direct effect on decomposition rates via changing states of photodegradation or decomposer constitution in litter while UV-B exposure during growth periods may alter chemical compositions and physical properties of plants. Consequently, these changes will indirectly affect subsequent litter decomposition processes in soil. Although studies are available on both the positive and negative effects (including no observable effects) of UV-B exposure on litter decomposition, a comprehensive analysis leading to an adequate understanding remains unresolved. Using data from 93 studies across six biomes, this introductory meta-analysis found that elevated UV-B directly increased litter decomposition rates by 7% and indirectly by 12% while attenuated UV-B directly decreased litter decomposition rates by 23% and indirectly increased litter decomposition rates by 7%. However, neither positive nor negative effects were statistically significant. Woody plant litter decomposition seemed more sensitive to UV-B than herbaceous plant litter except under conditions of indirect effects of elevated UV-B. Furthermore, levels of UV-B intensity significantly affected litter decomposition response to UV-B (P<0.05). UV-B effects on litter decomposition were to a large degree compounded by climatic factors (e.g., MAP and MAT) (P<0.05) and litter chemistry (e.g., lignin content) (P<0.01). Results suggest these factors likely have a bearing on masking the important role of UV-B on litter decomposition. No significant differences in UV-B effects on litter decomposition were found between study types (field experiment vs. laboratory incubation), litter forms (leaf vs. needle), and decay duration. Indirect effects of elevated UV-B on litter decomposition significantly increased with decay duration (P<0.001). Additionally, relatively small changes in UV-B exposure intensity (30%) had significant direct effects on litter decomposition (P<0.05). The intent of this meta-analysis was to improve our understanding of the overall effects of UV-B on litter decomposition. PMID:23818993

Robust one-step catalytic machine for high fidelity anticloning and W-state generation in a multiqubit system.

PubMed

Olaya-Castro, Alexandra; Johnson, Neil F; Quiroga, Luis

2005-03-25

We propose a physically realizable machine which can either generate multiparticle W-like states, or implement high-fidelity 1-->M (M=1,2,...infinity) anticloning of an arbitrary qubit state, in a single step. This universal machine acts as a catalyst in that it is unchanged after either procedure, effectively resetting itself for its next operation. It possesses an inherent immunity to decoherence. Most importantly in terms of practical multiparty quantum communication, the machine's robustness in the presence of decoherence actually increases as the number of qubits M increases.

Evaluation of DeNitrification DeComposition model for estimating ammonia fluxes from chemical fertilizer application

USDA-ARS?s Scientific Manuscript database

DeNitrification DeComposition (DNDC) model predictions of NH3 fluxes following chemical fertilizer application were evaluated by comparison to relaxed eddy accumulation (REA) measurements, in Central Illinois, United States, over the 2014 growing season of corn. Practical issues for evaluating closu...

Physico-Geometrical Kinetics of Solid-State Reactions in an Undergraduate Thermal Analysis Laboratory

ERIC Educational Resources Information Center

Koga, Nobuyoshi; Goshi, Yuri; Yoshikawa, Masahiro; Tatsuoka, Tomoyuki

2014-01-01

An undergraduate kinetic experiment of the thermal decomposition of solids by microscopic observation and thermal analysis was developed by investigating a suitable reaction, applicable techniques of thermal analysis and microscopic observation, and a reliable kinetic calculation method. The thermal decomposition of sodium hydrogen carbonate is…

Development of a ReaxFF reactive force field for ammonium nitrate and application to shock compression and thermal decomposition.

PubMed

Shan, Tzu-Ray; van Duin, Adri C T; Thompson, Aidan P

2014-02-27

We have developed a new ReaxFF reactive force field parametrization for ammonium nitrate. Starting with an existing nitramine/TATB ReaxFF parametrization, we optimized it to reproduce electronic structure calculations for dissociation barriers, heats of formation, and crystal structure properties of ammonium nitrate phases. We have used it to predict the isothermal pressure-volume curve and the unreacted principal Hugoniot states. The predicted isothermal pressure-volume curve for phase IV solid ammonium nitrate agreed with electronic structure calculations and experimental data within 10% error for the considered range of compression. The predicted unreacted principal Hugoniot states were approximately 17% stiffer than experimental measurements. We then simulated thermal decomposition during heating to 2500 K. Thermal decomposition pathways agreed with experimental findings.

A density functional theory study of the decomposition mechanism of nitroglycerin.

PubMed

Pei, Liguan; Dong, Kehai; Tang, Yanhui; Zhang, Bo; Yu, Chang; Li, Wenzuo

2017-08-21

The detailed decomposition mechanism of nitroglycerin (NG) in the gas phase was studied by examining reaction pathways using density functional theory (DFT) and canonical variational transition state theory combined with a small-curvature tunneling correction (CVT/SCT). The mechanism of NG autocatalytic decomposition was investigated at the B3LYP/6-31G(d,p) level of theory. Five possible decomposition pathways involving NG were identified and the rate constants for the pathways at temperatures ranging from 200 to 1000 K were calculated using CVT/SCT. There was found to be a lower energy barrier to the β-H abstraction reaction than to the α-H abstraction reaction during the initial step in the autocatalytic decomposition of NG. The decomposition pathways for CHOCOCHONO 2 (a product obtained following the abstraction of three H atoms from NG by NO 2 ) include O-NO 2 cleavage or isomer production, meaning that the autocatalytic decomposition of NG has two reaction pathways, both of which are exothermic. The rate constants for these two reaction pathways are greater than the rate constants for the three pathways corresponding to unimolecular NG decomposition. The overall process of NG decomposition can be divided into two stages based on the NO 2 concentration, which affects the decomposition products and reactions. In the first stage, the reaction pathway corresponding to O-NO 2 cleavage is the main pathway, but the rates of the two autocatalytic decomposition pathways increase with increasing NO 2 concentration. However, when a threshold NO 2 concentration is reached, the NG decomposition process enters its second stage, with the two pathways for NG autocatalytic decomposition becoming the main and secondary reaction pathways.

Detection of low level benzene exposure in supermarket wrappers by urinary muconic acid.

PubMed

E S Johnson S Halabi G Netto G Lucier W Bechtold R Henderson

1999-01-01

Women who use the 'hot wire' and 'cool rod' machines to wrap meat in supermarkets are potentially exposed to low levels of benzene and polycyclic aromatic hydrocarbons present in fumes emitted during the thermal decomposition of the plastic used to wrap meat. In order to evaluate whether the benzene metabolite trans, trans-muconic acid (MA) can be used to monitor these low levels, we collected urine samples from supermarket workers, and assayed the urine for MA. Geometric mean after-shift MA levels were highest for subjects who used the 'hot wire' machine, i.e. > 300 ng mg-1 creatinine (Cr). The corresponding levels for subjects who used the 'cool rod' machine were similar to those for subjects who did not use either type of machine, and were much lower. These results indicate that urinary muconic acid has some potential for use in monitoring benzene exposures of less than 1 part per million (ppm). The study detected very high background MA levels (exceeding 2000 ng mg-1 Cr) in some subjects, suggesting that individuals in the general population without occupational exposure to benzene may have urinary MA levels equivalent to exposure to up to 2 ppm benzene in ambient air. However, since non-benzene sources of the metabolite cannot be completely ruled out as partially responsible for these high levels, the public health significance of this finding is not known at the moment.

Decomposition of carbon dioxide by recombining hydrogen plasma with ultralow electron temperature

NASA Astrophysics Data System (ADS)

Yamazaki, Masahiro; Nishiyama, Shusuke; Sasaki, Koichi

2018-06-01

We examined the rate coefficient for the decomposition of CO2 in low-pressure recombining hydrogen plasmas with electron temperatures between 0.15 and 0.45 eV, where the electron-impact dissociation was negligible. By using this ultralow-temperature plasma, we clearly observed decomposition processes via vibrational excited states. The rate coefficient of the overall reaction, CO2 + e → products, was 1.5 × 10‑17 m3/s in the ultralow-temperature plasma, which was 10 times larger than the decomposition rate coefficient of 2 × 10‑18 m3/s in an ionizing plasma with an electron temperature of 4 eV.

Theoretical study of the decomposition pathways and products of C5- perfluorinated ketone (C5 PFK)

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

Fu, Yuwei; Wang, Xiaohua, E-mail: xhw@mail.xjtu.edu.cn, E-mail: mzrong@mail.xjtu.edu.cn; Li, Xi

Due to the high global warming potential (GWP) and increasing environmental concerns, efforts on searching the alternative gases to SF{sub 6}, which is predominantly used as insulating and interrupting medium in high-voltage equipment, have become a hot topic in recent decades. Overcoming the drawbacks of the existing candidate gases, C5- perfluorinated ketone (C5 PFK) was reported as a promising gas with remarkable insulation capacity and the low GWP of approximately 1. Experimental measurements of the dielectric strength of this novel gas and its mixtures have been carried out, but the chemical decomposition pathways and products of C5 PFK during breakdownmore » are still unknown, which are the essential factors in evaluating the electric strength of this gas in high-voltage equipment. Therefore, this paper is devoted to exploring all the possible decomposition pathways and species of C5 PFK by density functional theory (DFT). The structural optimizations, vibrational frequency calculations and energy calculations of the species involved in a considered pathway were carried out with DFT-(U)B3LYP/6-311G(d,p) method. Detailed potential energy surface was then investigated thoroughly by the same method. Lastly, six decomposition pathways of C5 PFK decomposition involving fission reactions and the reactions with a transition states were obtained. Important intermediate products were also determined. Among all the pathways studied, the favorable decomposition reactions of C5 PFK were found, involving C-C bond ruptures producing Ia and Ib in pathway I, followed by subsequent C-C bond ruptures and internal F atom transfers in the decomposition of Ia and Ib presented in pathways II + III and IV + V, respectively. Possible routes were pointed out in pathway III and lead to the decomposition of IIa, which is the main intermediate product found in pathway II of Ia decomposition. We also investigated the decomposition of Ib, which can undergo unimolecular reactions to give the formation of IV a, IV b and products of CF{sub 3} + CF-CF{sub 3} in pathway IV. Although IV a is dominant to a lesser extent due to its relative high energy barrier, its complicated decomposition pathway V was also studied and CF{sub 3}, C = CF{sub 2} as well as C-CF{sub 3} species were found as the ultimate products. To complete the decomposition of C5 PFK, pathway V I of Ic decomposition was fully explored and the final products were obtained. Therefore, the integrate decomposition scheme of C5 PFK was proposed, which contains six pathways and forty-eight species (including all the reactants, products and transition states). This work is hopeful to lay a theoretical basis for the insulating properties of C5 PFK.« less

Mössbauer study of the thermal decomposition of alkali tris(oxalato)ferrates(III)

NASA Astrophysics Data System (ADS)

Brar, A. S.; Randhawa, B. S.

1985-07-01

The thermal decomposition of alkali (Li,Na,K,Cs,NH 4) tris(oxalato)ferrates(III) has been studied at different temperatures up to 700°C using Mössbauer, infrared spectroscopy, and thermogravimetric techniques. The formation of different intermediates has been observed during thermal decomposition. The decomposition in these complexes starts at different temperatures, i.e., at 200°C in the case of lithium, cesium, and ammonium ferrate(III), 250°C in the case of sodium, and 270°C in the case of potassium tris(oxalato)ferrate(III). The intermediates, i.e., Fe 11C 2O 4, K 6Fe 112(ox) 5. and Cs 2Fe 11 (ox) 2(H 2O) 2, are formed during thermal decomposition of lithium, potassium, and cesium tris(oxalato)ferrates(III), respectively. In the case of sodium and ammonium tris(oxalato)ferrates(III), the decomposition occurs without reduction to the iron(II) state and leads directly to α-Fe 2O 3.

Machine fault feature extraction based on intrinsic mode functions

NASA Astrophysics Data System (ADS)

Fan, Xianfeng; Zuo, Ming J.

2008-04-01

This work employs empirical mode decomposition (EMD) to decompose raw vibration signals into intrinsic mode functions (IMFs) that represent the oscillatory modes generated by the components that make up the mechanical systems generating the vibration signals. The motivation here is to develop vibration signal analysis programs that are self-adaptive and that can detect machine faults at the earliest onset of deterioration. The change in velocity of the amplitude of some IMFs over a particular unit time will increase when the vibration is stimulated by a component fault. Therefore, the amplitude acceleration energy in the intrinsic mode functions is proposed as an indicator of the impulsive features that are often associated with mechanical component faults. The periodicity of the amplitude acceleration energy for each IMF is extracted by spectrum analysis. A spectrum amplitude index is introduced as a method to select the optimal result. A comparison study of the method proposed here and some well-established techniques for detecting machinery faults is conducted through the analysis of both gear and bearing vibration signals. The results indicate that the proposed method has superior capability to extract machine fault features from vibration signals.

Decomposition of toluene in a steady-state atmospheric-pressure glow discharge

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

Trushkin, A. N.; Grushin, M. E.; Kochetov, I. V.

Results are presented from experimental studies of decomposition of toluene (C{sub 6}H{sub 5}CH{sub 3}) in a polluted air flow by means of a steady-state atmospheric pressure glow discharge at different water vapor contents in the working gas. The experimental results on the degree of C{sub 6}H{sub 5}CH{sub 3} removal are compared with the results of computer simulations conducted in the framework of the developed kinetic model of plasma chemical decomposition of toluene in the N{sub 2}: O{sub 2}: H{sub 2}O gas mixture. A substantial influence of the gas flow humidity on toluene decomposition in the atmospheric pressure glow discharge ismore » demonstrated. The main mechanisms of the influence of humidity on C{sub 6}H{sub 5}CH{sub 3} decomposition are determined. The existence of two stages in the process of toluene removal, which differ in their duration and the intensity of plasma chemical decomposition of C{sub 6}H{sub 5}CH{sub 3} is established. Based on the results of computer simulations, the composition of the products of plasma chemical reactions at the output of the reactor is analyzed as a function of the specific energy deposition and gas flow humidity. The existence of a catalytic cycle in which hydroxyl radical OH acts a catalyst and which substantially accelerates the recombination of oxygen atoms and suppression of ozone generation when the plasma-forming gas contains water vapor is established.« less

Monodisperse Iron Oxide Nanoparticles by Thermal Decomposition: Elucidating Particle Formation by Second-Resolved in Situ Small-Angle X-ray Scattering

PubMed Central

2017-01-01

The synthesis of iron oxide nanoparticles (NPs) by thermal decomposition of iron precursors using oleic acid as surfactant has evolved to a state-of-the-art method to produce monodisperse, spherical NPs. The principles behind such monodisperse syntheses are well-known: the key is a separation between burst nucleation and growth phase, whereas the size of the population is set by the precursor-to-surfactant ratio. Here we follow the thermal decomposition of iron pentacarbonyl in the presence of oleic acid via in situ X-ray scattering. This method allows reaction kinetics and precursor states to be followed with high time resolution and statistical significance. Our investigation demonstrates that the final particle size is directly related to a phase of inorganic cluster formation that takes place between precursor decomposition and particle nucleation. The size and concentration of clusters were shown to be dependent on precursor-to-surfactant ratio and heating rate, which in turn led to differences in the onset of nucleation and concentration of nuclei after the burst nucleation phase. This first direct observation of prenucleation formation of inorganic and micellar structures in iron oxide nanoparticle synthesis by thermal decomposition likely has implications for synthesis of other NPs by similar routes. PMID:28572705

34 CFR 395.17 - Suspension of designation as State licensing agency.

Code of Federal Regulations, 2013 CFR

2013-07-01

... lapse of a reasonable time, the Secretary is of the opinion that such failure to comply still continues... protection of Federal property on which vending machines subject to the requirements of § 395.32 are located in the State. Upon the suspension of such designation, vending machine income from vending machines...

34 CFR 395.17 - Suspension of designation as State licensing agency.

Code of Federal Regulations, 2012 CFR

2012-07-01

... lapse of a reasonable time, the Secretary is of the opinion that such failure to comply still continues... protection of Federal property on which vending machines subject to the requirements of § 395.32 are located in the State. Upon the suspension of such designation, vending machine income from vending machines...

34 CFR 395.17 - Suspension of designation as State licensing agency.

Code of Federal Regulations, 2014 CFR

2014-07-01

... lapse of a reasonable time, the Secretary is of the opinion that such failure to comply still continues... protection of Federal property on which vending machines subject to the requirements of § 395.32 are located in the State. Upon the suspension of such designation, vending machine income from vending machines...

34 CFR 395.17 - Suspension of designation as State licensing agency.

Code of Federal Regulations, 2010 CFR

2010-07-01

... lapse of a reasonable time, the Secretary is of the opinion that such failure to comply still continues... protection of Federal property on which vending machines subject to the requirements of § 395.32 are located in the State. Upon the suspension of such designation, vending machine income from vending machines...

34 CFR 395.17 - Suspension of designation as State licensing agency.

Code of Federal Regulations, 2011 CFR

2011-07-01

... lapse of a reasonable time, the Secretary is of the opinion that such failure to comply still continues... protection of Federal property on which vending machines subject to the requirements of § 395.32 are located in the State. Upon the suspension of such designation, vending machine income from vending machines...

Solid-state resistor for pulsed power machines

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

Stoltzfus, Brian; Savage, Mark E.; Hutsel, Brian Thomas

2016-12-06

A flexible solid-state resistor comprises a string of ceramic resistors that can be used to charge the capacitors of a linear transformer driver (LTD) used in a pulsed power machine. The solid-state resistor is able to absorb the energy of a switch prefire, thereby limiting LTD cavity damage, yet has a sufficiently low RC charge time to allow the capacitor to be recharged without disrupting the operation of the pulsed power machine.

Development of techniques to enhance man/machine communication

NASA Technical Reports Server (NTRS)

Targ, R.; Cole, P.; Puthoff, H.

1974-01-01

A four-state random stimulus generator, considered to function as an ESP teaching machine was used to investigate an approach to facilitating interactions between man and machines. A subject tries to guess in which of four states the machine is. The machine offers the user feedback and reinforcement as to the correctness of his choice. Using this machine, 148 volunteer subjects were screened under various protocols. Several whose learning slope and/or mean score departed significantly from chance expectation were identified. Direct physiological evidence of perception of remote stimuli not presented to any known sense of the percipient using electroencephalographic (EEG) output when a light was flashed in a distant room was also studied.

Method and system for controlling a synchronous machine over full operating range

DOEpatents

Walters, James E.; Gunawan, Fani S.; Xue, Yanhong

2002-01-01

System and method for controlling a synchronous machine are provided. The method allows for calculating a stator voltage index. The method further allows for relating the magnitude of the stator voltage index against a threshold voltage value. An offset signal is generated based on the results of the relating step. A respective state of operation of the machine is determined. The offset signal is processed based on the respective state of the machine.

Experimental validation on the effect of material geometries and processing methodology of Polyoxymethylene (POM)

NASA Astrophysics Data System (ADS)

Hafizzal, Y.; Nurulhuda, A.; Izman, S.; Khadir, AZA

2017-08-01

POM-copolymer bond breaking leads to change depending with respect to processing methodology and material geometries. This paper present the oversights effect on the material integrity due to different geometries and processing methodology. Thermo-analytical methods with reference were used to examine the degradation of thermomechanical while Thermogravimetric Analysis (TGA) was used to judge the thermal stability of sample from its major decomposition temperature. Differential Scanning Calorimetry (DSC) investigation performed to identify the thermal behaviour and thermal properties of materials. The result shown that plastic gear geometries with injection molding at higher tonnage machine more stable thermally rather than resin geometries. Injection plastic gear geometries at low tonnage machine faced major decomposition temperatures at 313.61°C, 305.76 °C and 307.91 °C while higher tonnage processing method are fully decomposed at 890°C, significantly higher compared to low tonnage condition and resin geometries specimen at 398°C. Chemical composition of plastic gear geometries with injection molding at higher and lower tonnage are compare based on their moisture and Volatile Organic Compound (VOC) content, polymeric material content and the absence of filler. Results of higher moisture and Volatile Organic Compound (VOC) content are report in resin geometries (0.120%) compared to higher tonnage of injection plastic gear geometries which is 1.264%. The higher tonnage of injection plastic gear geometry are less sensitive to thermo-mechanical degradation due to polymer chain length and molecular weight of material properties such as tensile strength, flexural strength, fatigue strength and creep resistance.

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.

The Design of Finite State Machine for Asynchronous Replication Protocol

NASA Astrophysics Data System (ADS)

Wang, Yanlong; Li, Zhanhuai; Lin, Wei; Hei, Minglei; Hao, Jianhua

Data replication is a key way to design a disaster tolerance system and to achieve reliability and availability. It is difficult for a replication protocol to deal with the diverse and complex environment. This means that data is less well replicated than it ought to be. To reduce data loss and to optimize replication protocols, we (1) present a finite state machine, (2) run it to manage an asynchronous replication protocol and (3) report a simple evaluation of the asynchronous replication protocol based on our state machine. It's proved that our state machine is applicable to guarantee the asynchronous replication protocol running in the proper state to the largest extent in the event of various possible events. It also can helpful to build up replication-based disaster tolerance systems to ensure the business continuity.

State Machine Modeling of the Space Launch System Solid Rocket Boosters

NASA Technical Reports Server (NTRS)

Harris, Joshua A.; Patterson-Hine, Ann

2013-01-01

The Space Launch System is a Shuttle-derived heavy-lift vehicle currently in development to serve as NASA's premiere launch vehicle for space exploration. The Space Launch System is a multistage rocket with two Solid Rocket Boosters and multiple payloads, including the Multi-Purpose Crew Vehicle. Planned Space Launch System destinations include near-Earth asteroids, the Moon, Mars, and Lagrange points. The Space Launch System is a complex system with many subsystems, requiring considerable systems engineering and integration. To this end, state machine analysis offers a method to support engineering and operational e orts, identify and avert undesirable or potentially hazardous system states, and evaluate system requirements. Finite State Machines model a system as a finite number of states, with transitions between states controlled by state-based and event-based logic. State machines are a useful tool for understanding complex system behaviors and evaluating "what-if" scenarios. This work contributes to a state machine model of the Space Launch System developed at NASA Ames Research Center. The Space Launch System Solid Rocket Booster avionics and ignition subsystems are modeled using MATLAB/Stateflow software. This model is integrated into a larger model of Space Launch System avionics used for verification and validation of Space Launch System operating procedures and design requirements. This includes testing both nominal and o -nominal system states and command sequences.

On bipartite pure-state entanglement structure in terms of disentanglement

NASA Astrophysics Data System (ADS)

Herbut, Fedor

2006-12-01

Schrödinger's disentanglement [E. Schrödinger, Proc. Cambridge Philos. Soc. 31, 555 (1935)], i.e., remote state decomposition, as a physical way to study entanglement, is carried one step further with respect to previous work in investigating the qualitative side of entanglement in any bipartite state vector. Remote measurement (or, equivalently, remote orthogonal state decomposition) from previous work is generalized to remote linearly independent complete state decomposition both in the nonselective and the selective versions. The results are displayed in terms of commutative square diagrams, which show the power and beauty of the physical meaning of the (antiunitary) correlation operator inherent in the given bipartite state vector. This operator, together with the subsystem states (reduced density operators), constitutes the so-called correlated subsystem picture. It is the central part of the antilinear representation of a bipartite state vector, and it is a kind of core of its entanglement structure. The generalization of previously elaborated disentanglement expounded in this article is a synthesis of the antilinear representation of bipartite state vectors, which is reviewed, and the relevant results of [Cassinelli et al., J. Math. Anal. Appl. 210, 472 (1997)] in mathematical analysis, which are summed up. Linearly independent bases (finite or infinite) are shown to be almost as useful in some quantum mechanical studies as orthonormal ones. Finally, it is shown that linearly independent remote pure-state preparation carries the highest probability of occurrence. This singles out linearly independent remote influence from all possible ones.

Ethanol-Glycerin Fixation with Thymol Conservation: A Potential Alternative to Formaldehyde and Phenol Embalming

ERIC Educational Resources Information Center

Hammer, Niels; Loffler, Sabine; Feja, Christine; Sandrock, Mara; Schmidt, Wolfgang; Bechmann, Ingo; Steinke, Hanno

2012-01-01

Anatomical fixation and conservation are required to prevent specimens from undergoing autolysis and decomposition. While fixation is the primary arrest of the structures responsible for autolysis and decomposition, conservation preserves the state of fixation. Although commonly used, formaldehyde has been classified as carcinogenic to humans. For…

Neural-Network Quantum States, String-Bond States, and Chiral Topological States

NASA Astrophysics Data System (ADS)

Glasser, Ivan; Pancotti, Nicola; August, Moritz; Rodriguez, Ivan D.; Cirac, J. Ignacio

2018-01-01

Neural-network quantum states have recently been introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between neural-network quantum states in the form of restricted Boltzmann machines and some classes of tensor-network states in arbitrary dimensions. In particular, we demonstrate that short-range restricted Boltzmann machines are entangled plaquette states, while fully connected restricted Boltzmann machines are string-bond states with a nonlocal geometry and low bond dimension. These results shed light on the underlying architecture of restricted Boltzmann machines and their efficiency at representing many-body quantum states. String-bond states also provide a generic way of enhancing the power of neural-network quantum states and a natural generalization to systems with larger local Hilbert space. We compare the advantages and drawbacks of these different classes of states and present a method to combine them together. This allows us to benefit from both the entanglement structure of tensor networks and the efficiency of neural-network quantum states into a single Ansatz capable of targeting the wave function of strongly correlated systems. While it remains a challenge to describe states with chiral topological order using traditional tensor networks, we show that, because of their nonlocal geometry, neural-network quantum states and their string-bond-state extension can describe a lattice fractional quantum Hall state exactly. In addition, we provide numerical evidence that neural-network quantum states can approximate a chiral spin liquid with better accuracy than entangled plaquette states and local string-bond states. Our results demonstrate the efficiency of neural networks to describe complex quantum wave functions and pave the way towards the use of string-bond states as a tool in more traditional machine-learning applications.

Gibbsian Stationary Non-equilibrium States

NASA Astrophysics Data System (ADS)

De Carlo, Leonardo; Gabrielli, Davide

2017-09-01

We study the structure of stationary non-equilibrium states for interacting particle systems from a microscopic viewpoint. In particular we discuss two different discrete geometric constructions. We apply both of them to determine non reversible transition rates corresponding to a fixed invariant measure. The first one uses the equivalence of this problem with the construction of divergence free flows on the transition graph. Since divergence free flows are characterized by cyclic decompositions we can generate families of models from elementary cycles on the configuration space. The second construction is a functional discrete Hodge decomposition for translational covariant discrete vector fields. According to this, for example, the instantaneous current of any interacting particle system on a finite torus can be canonically decomposed in a gradient part, a circulation term and an harmonic component. All the three components are associated with functions on the configuration space. This decomposition is unique and constructive. The stationary condition can be interpreted as an orthogonality condition with respect to an harmonic discrete vector field and we use this decomposition to construct models having a fixed invariant measure.

The effect of body size on the rate of decomposition in a temperate region of South Africa.

PubMed

Sutherland, A; Myburgh, J; Steyn, M; Becker, P J

2013-09-10

Forensic anthropologists rely on the state of decomposition of a body to estimate the post-mortem-interval (PMI) which provides information about the natural events and environmental forces that could have affected the remains after death. Various factors are known to influence the rate of decomposition, among them temperature, rainfall and exposure of the body. However, conflicting reports appear in the literature on the effect of body size on the rate of decay. The aim of this project was to compare decomposition rates of large pigs (Sus scrofa; 60-90 kg), with that of small pigs (<35 kg), to assess the influence of body size on decomposition rates. For the decomposition rates of small pigs, 15 piglets were assessed three times per week over a period of three months during spring and early summer. Data collection was conducted until complete skeletonization occurred. Stages of decomposition were scored according to separate categories for each anatomical region, and the point values for each region were added to determine the total body score (TBS), which represents the overall stage of decomposition for each pig. For the large pigs, data of 15 pigs were used. Scatter plots illustrating the relationships between TBS and PMI as well as TBS and accumulated degree days (ADD) were used to assess the pattern of decomposition and to compare decomposition rates between small and large pigs. Results indicated that rapid decomposition occurs during the early stages of decomposition for both samples. Large pigs showed a plateau phase in the course of advanced stages of decomposition, during which decomposition was minimal. A similar, but much shorter plateau was reached by small pigs of >20 kg at a PMI of 20-25 days, after which decomposition commenced swiftly. This was in contrast to the small pigs of <20 kg, which showed no plateau phase and their decomposition rates were swift throughout the duration of the study. Overall, small pigs decomposed 2.82 times faster than large pigs, indicating that body size does have an effect on the rate of decomposition. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Mapping current fluctuations of stochastic pumps to nonequilibrium steady states.

NASA Astrophysics Data System (ADS)

Rotskoff, Grant

We show that current fluctuations in stochastic pumps can be robustly mapped to fluctuations in a corresponding time-independent non-equilibrium steady state. We thus refine a recently proposed mapping so that it ensures equivalence of not only the averages, but also the optimal representation of fluctuations in currents and density. Our mapping leads to a natural decomposition of the entropy production in stochastic pumps, similar to the ``housekeeping'' heat. As a consequence of the decomposition of entropy production, the current fluctuations in weakly perturbed stochastic pumps satisfy a universal bound determined by the steady state entropy production. National Science Foundation Graduate Research Fellowship.

Virtual Machine Language 2.1

NASA Technical Reports Server (NTRS)

Riedel, Joseph E.; Grasso, Christopher A.

2012-01-01

VML (Virtual Machine Language) is an advanced computing environment that allows spacecraft to operate using mechanisms ranging from simple, time-oriented sequencing to advanced, multicomponent reactive systems. VML has developed in four evolutionary stages. VML 0 is a core execution capability providing multi-threaded command execution, integer data types, and rudimentary branching. VML 1 added named parameterized procedures, extensive polymorphism, data typing, branching, looping issuance of commands using run-time parameters, and named global variables. VML 2 added for loops, data verification, telemetry reaction, and an open flight adaptation architecture. VML 2.1 contains major advances in control flow capabilities for executable state machines. On the resource requirements front, VML 2.1 features a reduced memory footprint in order to fit more capability into modestly sized flight processors, and endian-neutral data access for compatibility with Intel little-endian processors. Sequence packaging has been improved with object-oriented programming constructs and the use of implicit (rather than explicit) time tags on statements. Sequence event detection has been significantly enhanced with multi-variable waiting, which allows a sequence to detect and react to conditions defined by complex expressions with multiple global variables. This multi-variable waiting serves as the basis for implementing parallel rule checking, which in turn, makes possible executable state machines. The new state machine feature in VML 2.1 allows the creation of sophisticated autonomous reactive systems without the need to develop expensive flight software. Users specify named states and transitions, along with the truth conditions required, before taking transitions. Transitions with the same signal name allow separate state machines to coordinate actions: the conditions distributed across all state machines necessary to arm a particular signal are evaluated, and once found true, that signal is raised. The selected signal then causes all identically named transitions in all present state machines to be taken simultaneously. VML 2.1 has relevance to all potential space missions, both manned and unmanned. It was under consideration for use on Orion.

Kinetics of the cellular decomposition of supersaturated solid solutions

NASA Astrophysics Data System (ADS)

Ivanov, M. A.; Naumuk, A. Yu.

2014-09-01

A consistent description of the kinetics of the cellular decomposition of supersaturated solid solutions with the development of a spatially periodic structure of lamellar (platelike) type, which consists of alternating phases of precipitates on the basis of the impurity component and depleted initial solid solution, is given. One of the equations, which determines the relationship between the parameters that describe the process of decomposition, has been obtained from a comparison of two approaches in order to determine the rate of change in the free energy of the system. The other kinetic parameters can be described with the use of a variational method, namely, by the maximum velocity of motion of the decomposition boundary at a given temperature. It is shown that the mutual directions of growth of the lamellae of different phases are determined by the minimum value of the interphase surface energy. To determine the parameters of the decomposition, a simple thermodynamic model of states with a parabolic dependence of the free energy on the concentrations has been used. As a result, expressions that describe the decomposition rate, interlamellar distance, and the concentration of impurities in the phase that remain after the decomposition have been derived. This concentration proves to be equal to the half-sum of the initial concentration and the equilibrium concentration corresponding to the decomposition temperature.

Studies on seasonal arthropod succession on carrion in the southeastern Iberian Peninsula.

PubMed

Arnaldos, M I; Romera, E; Presa, J J; Luna, A; García, M D

2004-08-01

A global study of the sarcosaprophagous community that occurs in the southeastern Iberian Peninsula during all four seasons is made for the first time, and its diversity is described with reference to biological indices. A total of 18,179 adults and, additionally, a number of preimaginal states were collected. The results for the main arthropod groups, and their diversity are discussed in relation to the season and decompositional stages. The results provide an extensive inventory of carrion-associated arthropods. An association between decomposition stages and more representative arthropod groups is established. With respect to the biological indices applied, Margalef's index shows that the diversity of the community increases as the state of decomposition advances, while Sorenson's quantitative index shows that the greatest similarities are between spring and summer on the one hand, and fall and winter, on the other.

Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Shangbin; Lu, Siliang; He, Qingbo; Kong, Fanrang

2016-09-01

For rotating machines, the defective faults of bearings generally are represented as periodic transient impulses in acquired signals. The extraction of transient features from signals has been a key issue for fault diagnosis. However, the background noise reduces identification performance of periodic faults in practice. This paper proposes a time-varying singular value decomposition (TSVD) method to enhance the identification of periodic faults. The proposed method is inspired by the sliding window method. By applying singular value decomposition (SVD) to the signal under a sliding window, we can obtain a time-varying singular value matrix (TSVM). Each column in the TSVM is occupied by the singular values of the corresponding sliding window, and each row represents the intrinsic structure of the raw signal, namely time-singular-value-sequence (TSVS). Theoretical and experimental analyses show that the frequency of TSVS is exactly twice that of the corresponding intrinsic structure. Moreover, the signal-to-noise ratio (SNR) of TSVS is improved significantly in comparison with the raw signal. The proposed method takes advantages of the TSVS in noise suppression and feature extraction to enhance fault frequency for diagnosis. The effectiveness of the TSVD is verified by means of simulation studies and applications to diagnosis of bearing faults. Results indicate that the proposed method is superior to traditional methods for bearing fault diagnosis.

LP and NLP decomposition without a master problem

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

Fuller, D.; Lan, B.

We describe a new algorithm for decomposition of linear programs and a class of convex nonlinear programs, together with theoretical properties and some test results. Its most striking feature is the absence of a master problem; the subproblems pass primal and dual proposals directly to one another. The algorithm is defined for multi-stage LPs or NLPs, in which the constraints link the current stage`s variables to earlier stages` variables. This problem class is general enough to include many problem structures that do not immediately suggest stages, such as block diagonal problems. The basic algorithmis derived for two-stage problems and extendedmore » to more than two stages through nested decomposition. The main theoretical result assures convergence, to within any preset tolerance of the optimal value, in a finite number of iterations. This asymptotic convergence result contrasts with the results of limited tests on LPs, in which the optimal solution is apparently found exactly, i.e., to machine accuracy, in a small number of iterations. The tests further suggest that for LPs, the new algorithm is faster than the simplex method applied to the whole problem, as long as the stages are linked loosely; that the speedup over the simpex method improves as the number of stages increases; and that the algorithm is more reliable than nested Dantzig-Wolfe or Benders` methods in its improvement over the simplex method.« less

Quantum machine learning for quantum anomaly detection

NASA Astrophysics Data System (ADS)

Liu, Nana; Rebentrost, Patrick

2018-04-01

Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.

On some methods of discrete systems behaviour simulation

NASA Astrophysics Data System (ADS)

Sytnik, Alexander A.; Posohina, Natalia I.

1998-07-01

The project is solving one of the fundamental problems of mathematical cybernetics and discrete mathematics, the one connected with synthesis and analysis of managing systems, depending on the research of their functional opportunities and reliable behaviour. This work deals with the case of finite-state machine behaviour restoration when the structural redundancy is not available and the direct updating of current behaviour is impossible. The described below method, uses number theory to build a special model of finite-state machine, it is simulating the transition between the states of the finite-state machine using specially defined functions of exponential type with the help of several methods of number theory and algebra it is easy to determine, whether there is an opportunity to restore the behaviour (with the help of this method) in the given case or not and also derive the class of finite-state machines, admitting such restoration.

Sequence-invariant state machines

NASA Technical Reports Server (NTRS)

Whitaker, Sterling R.; Manjunath, Shamanna K.; Maki, Gary K.

1991-01-01

A synthesis method and an MOS VLSI architecture are presented to realize sequential circuits that have the ability to implement any state machine having N states and m inputs, regardless of the actual sequence specified in the flow table. The design method utilizes binary tree structured (BTS) logic to implement regular and dense circuits. The desired state sequence can be hardwired with power supply connections or can be dynamically reallocated if stored in a register. This allows programmable VLSI controllers to be designed with a compact size and performance approaching that of dedicated logic. Results of ICV implementations are reported and an example sequence-invariant state machine is contrasted with implementations based on traditional methods.

Enter the machine

NASA Astrophysics Data System (ADS)

Palittapongarnpim, Pantita; Sanders, Barry C.

2018-05-01

Quantum tomography infers quantum states from measurement data, but it becomes infeasible for large systems. Machine learning enables tomography of highly entangled many-body states and suggests a new powerful approach to this problem.

A transient semimetallic layer in detonating nitromethane

NASA Astrophysics Data System (ADS)

Reed, Evan J.; Riad Manaa, M.; Fried, Laurence E.; Glaesemann, Kurt R.; Joannopoulos, J. D.

2008-01-01

Despite decades of research, the microscopic details and extreme states of matter found within a detonating high explosive have yet to be elucidated. Here we present the first quantum molecular-dynamics simulation of a shocked explosive near detonation conditions. We discover that the wide-bandgap insulator nitromethane (CH3NO2) undergoes chemical decomposition and a transformation into a semimetallic state for a limited distance behind the detonation front. We find that this transformation is associated with the production of charged decomposition species and provides a mechanism to explain recent experimental observations.

34 CFR 395.32 - Collection and distribution of vending machine income from vending machines on Federal property.

Code of Federal Regulations, 2010 CFR

2010-07-01

... department, agency, or instrumentality of the United States, in accordance with established procedures of... each property managing department, agency or instrumentality of the United States, subject to the..., agencies, or instrumentalities of the United States, under which blind vendors or State licensing agencies...

Scalability and Portability of Two Parallel Implementations of ADI

NASA Technical Reports Server (NTRS)

Phung, Thanh; VanderWijngaart, Rob F.

1994-01-01

Two domain decompositions for the implementation of the NAS Scalar Penta-diagonal Parallel Benchmark on MIMD systems are investigated, namely transposition and multi-partitioning. Hardware platforms considered are the Intel iPSC/860 and Paragon XP/S-15, and clusters of SGI workstations on ethernet, communicating through PVM. It is found that the multi-partitioning strategy offers the kind of coarse granularity that allows scaling up to hundreds of processors on a massively parallel machine. Moreover, efficiency is retained when the code is ported verbatim (save message passing syntax) to a PVM environment on a modest size cluster of workstations.

Decomposition mechanism of chromite in sulfuric acid-dichromic acid solution

NASA Astrophysics Data System (ADS)

Zhao, Qing; Liu, Cheng-jun; Li, Bao-kuan; Jiang, Mao-fa

2017-12-01

The sulfuric acid leaching process is regarded as a promising, cleaner method to prepare trivalent chromium products from chromite; however, the decomposition mechanism of the ore is poorly understood. In this work, binary spinels of Mg-Al, Mg-Fe, and Mg-Cr in the powdered and lump states were synthesized and used as raw materials to investigate the decomposition mechanism of chromite in sulfuric acid-dichromic acid solution. The leaching yields of metallic elements and the changes in morphology of the spinel were studied. The experimental results showed that the three spinels were stable in sulfuric acid solution and that dichromic acid had little influence on the decomposition behavior of the Mg-Al spinel and Mg-Fe spinel because Mg2+, Al3+, and Fe3+ in spinels cannot be oxidized by Cr6+. However, in the case of the Mg-Cr spinel, dichromic acid substantially promoted the decomposition efficiency and functioned as a catalyst. The decomposition mechanism of chromite in sulfuric acid-dichromic acid solution was illustrated on the basis of the findings of this study.

S-matrix decomposition, natural reaction channels, and the quantum transition state approach to reactive scattering

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

Manthe, Uwe, E-mail: uwe.manthe@uni-bielefeld.de; Ellerbrock, Roman, E-mail: roman.ellerbrock@uni-bielefeld.de

2016-05-28

A new approach for the quantum-state resolved analysis of polyatomic reactions is introduced. Based on the singular value decomposition of the S-matrix, energy-dependent natural reaction channels and natural reaction probabilities are defined. It is shown that the natural reaction probabilities are equal to the eigenvalues of the reaction probability operator [U. Manthe and W. H. Miller, J. Chem. Phys. 99, 3411 (1993)]. Consequently, the natural reaction channels can be interpreted as uniquely defined pathways through the transition state of the reaction. The analysis can efficiently be combined with reactive scattering calculations based on the propagation of thermal flux eigenstates. Inmore » contrast to a decomposition based straightforwardly on thermal flux eigenstates, it does not depend on the choice of the dividing surface separating reactants from products. The new approach is illustrated studying a prototypical example, the H + CH{sub 4} → H{sub 2} + CH{sub 3} reaction. The natural reaction probabilities and the contributions of the different vibrational states of the methyl product to the natural reaction channels are calculated and discussed. The relation between the thermal flux eigenstates and the natural reaction channels is studied in detail.« less

Reconstruction of Complex Network based on the Noise via QR Decomposition and Compressed Sensing.

PubMed

Li, Lixiang; Xu, Dafei; Peng, Haipeng; Kurths, Jürgen; Yang, Yixian

2017-11-08

It is generally known that the states of network nodes are stable and have strong correlations in a linear network system. We find that without the control input, the method of compressed sensing can not succeed in reconstructing complex networks in which the states of nodes are generated through the linear network system. However, noise can drive the dynamics between nodes to break the stability of the system state. Therefore, a new method integrating QR decomposition and compressed sensing is proposed to solve the reconstruction problem of complex networks under the assistance of the input noise. The state matrix of the system is decomposed by QR decomposition. We construct the measurement matrix with the aid of Gaussian noise so that the sparse input matrix can be reconstructed by compressed sensing. We also discover that noise can build a bridge between the dynamics and the topological structure. Experiments are presented to show that the proposed method is more accurate and more efficient to reconstruct four model networks and six real networks by the comparisons between the proposed method and only compressed sensing. In addition, the proposed method can reconstruct not only the sparse complex networks, but also the dense complex networks.

The secondary supernova machine: Gravitational compression, stored Coulomb energy, and SNII displays

NASA Astrophysics Data System (ADS)

Clayton, Donald D.; Meyer, Bradley S.

2016-04-01

Radioactive power for several delayed optical displays of core-collapse supernovae is commonly described as having been provided by decays of 56Ni nuclei. This review analyses the provenance of that energy more deeply: the form in which that energy is stored; what mechanical work causes its storage; what conservation laws demand that it be stored; and why its release is fortuitously delayed for about 106 s into a greatly expanded supernova envelope. We call the unifying picture of those energy transfers the secondary supernova machine owing to its machine-like properties; namely, mechanical work forces storage of large increases of nuclear Coulomb energy, a positive energy component within new nuclei synthesized by the secondary machine. That positive-energy increase occurs despite the fusion decreasing negative total energy within nuclei. The excess of the Coulomb energy can later be radiated, accounting for the intense radioactivity in supernovae. Detailed familiarity with this machine is the focus of this review. The stored positive-energy component created by the machine will not be reduced until roughly 106 s later by radioactive emissions (EC and β +) owing to the slowness of weak decays. The delayed energy provided by the secondary supernova machine is a few × 1049 erg, much smaller than the one percent of the 1053 erg collapse that causes the prompt ejection of matter; however, that relatively small stored energy is vital for activation of the late displays. The conceptual basis of the secondary supernova machine provides a new framework for understanding the energy source for late SNII displays. We demonstrate the nuclear dynamics with nuclear network abundance calculations, with a model of sudden compression and reexpansion of the nuclear gas, and with nuclear energy decompositions of a nuclear-mass law. These tools identify excess Coulomb energy, a positive-energy component of the total negative nuclear energy, as the late activation energy. If the value of fundamental charge e were smaller, SNII would not be so profoundly radioactive. Excess Coulomb energy has been carried within nuclei radially for roughly 109 km before being radiated into greatly expanded supernova remnants. The Coulomb force claims heretofore unacknowledged significance for supernova physics.

Application of Numerical Simulation for the Analysis of the Processes of Rotary Ultrasonic Drilling

NASA Astrophysics Data System (ADS)

Naď, Milan; Čičmancová, Lenka; Hajdu, Štefan

2016-12-01

Rotary ultrasonic machining (RUM) is a hybrid process that combines diamond grinding with ultrasonic machining. It is most suitable to machine hard brittle materials such as ceramics and composites. Due to its excellent machining performance, RUM is very often applied for drilling of hard machinable materials. In the final phase of drilling, the edge deterioration of the drilled hole can occur, which results in a phenomenon called edge chipping. During hole drilling, a change in the thickness of the bottom of the drilled hole occurs. Consequently, the bottom of the hole as a plate structure is exposed to the transfer through the resonance state. This resonance state can be considered as one of the important aspects leading to edge chipping. Effects of changes in the bottom thickness and as well as the fillet radius between the wall and bottom of the borehole on the stress-strain states during RUM are analyzed.

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Code of Federal Regulations, 2012 CFR

2012-07-01

... 34 Education 2 2012-07-01 2012-07-01 false Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending machines on Federal property which has been disbursed to the State licensing agency by a property managing...

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 34 Education 2 2013-07-01 2013-07-01 false Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending machines on Federal property which has been disbursed to the State licensing agency by a property managing...

34 CFR 395.8 - Distribution and use of income from vending machines on Federal property.

Code of Federal Regulations, 2014 CFR

2014-07-01

... 34 Education 2 2014-07-01 2013-07-01 true Distribution and use of income from vending machines on... use of income from vending machines on Federal property. (a) Vending machine income from vending machines on Federal property which has been disbursed to the State licensing agency by a property managing...

Technical Report on Occupations in Numerically Controlled Metal-Cutting Machining.

ERIC Educational Resources Information Center

Manpower Administration (DOL), Washington, DC. U.S. Employment Service.

At the present time, only 5 percent of the short-run metal-cutting machining in the United States is done by numerically controlled machined tools, but within the next decade it is expected to increase by 50 percent. Numerically controlled machines use taped data which is changed into instructions and directs the machine to do certain steps…

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.

Support Vector Machine algorithm for regression and classification

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

Yu, Chenggang; Zavaljevski, Nela

2001-08-01

The software is an implementation of the Support Vector Machine (SVM) algorithm that was invented and developed by Vladimir Vapnik and his co-workers at AT&T Bell Laboratories. The specific implementation reported here is an Active Set method for solving a quadratic optimization problem that forms the major part of any SVM program. The implementation is tuned to specific constraints generated in the SVM learning. Thus, it is more efficient than general-purpose quadratic optimization programs. A decomposition method has been implemented in the software that enables processing large data sets. The size of the learning data is virtually unlimited by themore » capacity of the computer physical memory. The software is flexible and extensible. Two upper bounds are implemented to regulate the SVM learning for classification, which allow users to adjust the false positive and false negative rates. The software can be used either as a standalone, general-purpose SVM regression or classification program, or be embedded into a larger software system.« less

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm.

PubMed

Jinnouchi, Ryosuke; Asahi, Ryoji

2017-09-07

Catalytic activities are often dominated by a few specific surface sites, and designing active sites is the key to realize high-performance heterogeneous catalysts. The great triumphs of modern surface science lead to reproduce catalytic reaction rates by modeling the arrangement of surface atoms with well-defined single-crystal surfaces. However, this method has limitations in the case for highly inhomogeneous atomic configurations such as on alloy nanoparticles with atomic-scale defects, where the arrangement cannot be decomposed into single crystals. Here, we propose a universal machine-learning scheme using a local similarity kernel, which allows interrogation of catalytic activities based on local atomic configurations. We then apply it to direct NO decomposition on RhAu alloy nanoparticles. The proposed method can efficiently predict energetics of catalytic reactions on nanoparticles using DFT data on single crystals, and its combination with kinetic analysis can provide detailed information on structures of active sites and size- and composition-dependent catalytic activities.

Catalytically enhanced thermal decomposition of chemically grown silicon oxide layers on Si(001)

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

Leroy, F., E-mail: leroy@cinam.univ-mrs.fr; Passanante, T.; Cheynis, F.

2016-03-14

The thermal decomposition of Si dioxide layers formed by wet chemical treatment on Si(001) has been studied by low-energy electron microscopy. Independent nucleations of voids occur into the Si oxide layers that open by reaction at the void periphery. Depending on the voids, the reaction rates exhibit large differences via the occurrence of a nonlinear growth of the void radius. This non-steady state regime is attributed to the accumulation of defects and silicon hydroxyl species at the SiO{sub 2}/Si interface that enhances the silicon oxide decomposition at the void periphery.

Surface Characteristics of Machined NiTi Shape Memory Alloy: The Effects of Cryogenic Cooling and Preheating Conditions

NASA Astrophysics Data System (ADS)

Kaynak, Y.; Huang, B.; Karaca, H. E.; Jawahir, I. S.

2017-07-01

This experimental study focuses on the phase state and phase transformation response of the surface and subsurface of machined NiTi alloys. X-ray diffraction (XRD) analysis and differential scanning calorimeter techniques were utilized to measure the phase state and the transformation response of machined specimens, respectively. Specimens were machined under dry machining at ambient temperature, preheated conditions, and cryogenic cooling conditions at various cutting speeds. The findings from this research demonstrate that cryogenic machining substantially alters austenite finish temperature of martensitic NiTi alloy. Austenite finish ( A f) temperature shows more than 25 percent increase resulting from cryogenic machining compared with austenite finish temperature of as-received NiTi. Dry and preheated conditions do not substantially alter austenite finish temperature. XRD analysis shows that distinctive transformation from martensite to austenite occurs during machining process in all three conditions. Complete transformation from martensite to austenite is observed in dry cutting at all selected cutting speeds.

A novel ECG data compression method based on adaptive Fourier decomposition

NASA Astrophysics Data System (ADS)

Tan, Chunyu; Zhang, Liming

2017-12-01

This paper presents a novel electrocardiogram (ECG) compression method based on adaptive Fourier decomposition (AFD). AFD is a newly developed signal decomposition approach, which can decompose a signal with fast convergence, and hence reconstruct ECG signals with high fidelity. Unlike most of the high performance algorithms, our method does not make use of any preprocessing operation before compression. Huffman coding is employed for further compression. Validated with 48 ECG recordings of MIT-BIH arrhythmia database, the proposed method achieves the compression ratio (CR) of 35.53 and the percentage root mean square difference (PRD) of 1.47% on average with N = 8 decomposition times and a robust PRD-CR relationship. The results demonstrate that the proposed method has a good performance compared with the state-of-the-art ECG compressors.

Infrared spectroscopy of radiation-chemical transformation of n-hexane on a beryllium surface

NASA Astrophysics Data System (ADS)

Gadzhieva, N. N.

2017-07-01

The radiation-chemical decomposition of n-hexane in a Be- n-hexane system under the effect of γ-irradiation at room temperature is studied by infrared reflection-absorption spectroscopy. In the absorbed dose range 5 kGy ≤ Vγ ≤ 50 kGy, intermediate surface products of radiation-heterogeneous decomposition of n-hexane (beryllium alkyls, π-olefin complexes, and beryllium hydrides) are detected. It is shown that complete radiolysis occurs at Vγ = 30 kGy; below this dose, decomposition of n-hexane occurs only partially, while higher doses lead to steady-state saturation. The radiation-chemical yield of the final decomposition product—molecular hydrogen—is determined to be G ads(H2) = 24.8 molecules/100 eV. A possible mechanism of this process is discussed.

Limitations Of The Current State Space Modelling Approach In Multistage Machining Processes Due To Operation Variations

NASA Astrophysics Data System (ADS)

Abellán-Nebot, J. V.; Liu, J.; Romero, F.

2009-11-01

The State Space modelling approach has been recently proposed as an engineering-driven technique for part quality prediction in Multistage Machining Processes (MMP). Current State Space models incorporate fixture and datum variations in the multi-stage variation propagation, without explicitly considering common operation variations such as machine-tool thermal distortions, cutting-tool wear, cutting-tool deflections, etc. This paper shows the limitations of the current State Space model through an experimental case study where the effect of the spindle thermal expansion, cutting-tool flank wear and locator errors are introduced. The paper also discusses the extension of the current State Space model to include operation variations and its potential benefits.

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

Jimenez, O.; Roa, Luis; Delgado, A.

We study the probabilistic cloning of equidistant states. These states are such that the inner product between them is a complex constant or its conjugate. Thereby, it is possible to study their cloning in a simple way. In particular, we are interested in the behavior of the cloning probability as a function of the phase of the overlap among the involved states. We show that for certain families of equidistant states Duan and Guo's cloning machine leads to cloning probabilities lower than the optimal unambiguous discrimination probability of equidistant states. We propose an alternative cloning machine whose cloning probability ismore » higher than or equal to the optimal unambiguous discrimination probability for any family of equidistant states. Both machines achieve the same probability for equidistant states whose inner product is a positive real number.« less

Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands.

PubMed

Segil, Jacob L; Controzzi, Marco; Weir, Richard F ff; Cipriani, Christian

2014-01-01

A myoelectric controller should provide an intuitive and effective human-machine interface that deciphers user intent in real-time and is robust enough to operate in daily life. Many myoelectric control architectures have been developed, including pattern recognition systems, finite state machines, and more recently, postural control schemes. Here, we present a comparative study of two types of finite state machines and a postural control scheme using both virtual and physical assessment procedures with seven nondisabled subjects. The Southampton Hand Assessment Procedure (SHAP) was used in order to compare the effectiveness of the controllers during activities of daily living using a multigrasp artificial hand. Also, a virtual hand posture matching task was used to compare the controllers when reproducing six target postures. The performance when using the postural control scheme was significantly better (p < 0.05) than the finite state machines during the physical assessment when comparing within-subject averages using the SHAP percent difference metric. The virtual assessment results described significantly greater completion rates (97% and 99%) for the finite state machines, but the movement time tended to be faster (2.7 s) for the postural control scheme. Our results substantiate that postural control schemes rival other state-of-the-art myoelectric controllers.

Origin of Outstanding Stability in the Lithium Solid Electrolyte Materials: Insights from Thermodynamic Analyses Based on First-Principles Calculations

DOE PAGES

Zhu, Yizhou; He, Xingfeng; Mo, Yifei

2015-10-06

First-principles calculations were performed to investigate the electrochemical stability of lithium solid electrolyte materials in all-solid-state Li-ion batteries. The common solid electrolytes were found to have a limited electrochemical window. Our results suggest that the outstanding stability of the solid electrolyte materials is not thermodynamically intrinsic but is originated from kinetic stabilizations. The sluggish kinetics of the decomposition reactions cause a high overpotential leading to a nominally wide electrochemical window observed in many experiments. The decomposition products, similar to the solid-electrolyte-interphases, mitigate the extreme chemical potential from the electrodes and protect the solid electrolyte from further decompositions. With the aidmore » of the first-principles calculations, we revealed the passivation mechanism of these decomposition interphases and quantified the extensions of the electrochemical window from the interphases. We also found that the artificial coating layers applied at the solid electrolyte and electrode interfaces have a similar effect of passivating the solid electrolyte. Our newly gained understanding provided general principles for developing solid electrolyte materials with enhanced stability and for engineering interfaces in all-solid-state Li-ion batteries.« less

Sequence invariant state machines

NASA Technical Reports Server (NTRS)

Whitaker, S.; Manjunath, S.

1990-01-01

A synthesis method and new VLSI architecture are introduced to realize sequential circuits that have the ability to implement any state machine having N states and m inputs, regardless of the actual sequence specified in the flow table. A design method is proposed that utilizes BTS logic to implement regular and dense circuits. A given state sequence can be programmed with power supply connections or dynamically reallocated if stored in a register. Arbitrary flow table sequences can be modified or programmed to dynamically alter the function of the machine. This allows VLSI controllers to be designed with the programmability of a general purpose processor but with the compact size and performance of dedicated logic.

Decomposition of PCBs in transformer oil using an electron beam accelerator

NASA Astrophysics Data System (ADS)

Jung, In-Ha; Lee, Myun-Joo; Mah, Yoon-Jung

2012-07-01

Decomposition of PCBs in commercially used transformer oil used for more than 30 years has been carried out at normal temperature and pressure without any additives using an electron beam accelerator. The experiments were carried out in two ways: batch and continuous pilot plant with 1.5 MeV of energy, a 50 mA current, and 75 kW of power in a commercial scale accelerator. The electron beam irradiation seemed to transform large molecular weight compounds into lower ones, but the impact was considered too small on the physical properties of oil. Residual concentrations of PCBs after irradiation depend on the absorption dose of the electron beam energy, but aliphatic chloride compounds were produced at higher doses of irradiation. As the results from FT-NMR, chloride ions decomposed from the PCBs are likely to react with aliphatic hydro carbon compounds rather than existing as free radical ions in the transformer oil. Since this is a dry process, treated oil can be used as cutting oil or machine oil for heavy equipment without any additional treatments.

Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2017-05-01

Thanks to some recent research works, dynamic Bayesian wavelet transform as new methodology for extraction of repetitive transients is proposed in this short communication to reveal fault signatures hidden in rotating machine. The main idea of the dynamic Bayesian wavelet transform is to iteratively estimate posterior parameters of wavelet transform via artificial observations and dynamic Bayesian inference. First, a prior wavelet parameter distribution can be established by one of many fast detection algorithms, such as the fast kurtogram, the improved kurtogram, the enhanced kurtogram, the sparsogram, the infogram, continuous wavelet transform, discrete wavelet transform, wavelet packets, multiwavelets, empirical wavelet transform, empirical mode decomposition, local mean decomposition, etc.. Second, artificial observations can be constructed based on one of many metrics, such as kurtosis, the sparsity measurement, entropy, approximate entropy, the smoothness index, a synthesized criterion, etc., which are able to quantify repetitive transients. Finally, given artificial observations, the prior wavelet parameter distribution can be posteriorly updated over iterations by using dynamic Bayesian inference. More importantly, the proposed new methodology can be extended to establish the optimal parameters required by many other signal processing methods for extraction of repetitive transients.

Positron annihilation lifetime study of polyvinylpyrrolidone for nanoparticle-stabilizing pharmaceuticals.

PubMed

Shpotyuk, O; Bujňáková, Z; Baláž, P; Ingram, A; Shpotyuk, Y

2016-01-05

Positron annihilation lifetime spectroscopy was applied to characterize free-volume structure of polyvinylpyrrolidone used as nonionic stabilizer in the production of many nanocomposite pharmaceuticals. The polymer samples with an average molecular weight of 40,000 g mol(-1) were pelletized in a single-punch tableting machine under an applied pressure of 0.7 GPa. Strong mixing in channels of positron and positronium trapping were revealed in the polyvinylpyrrolidone pellets. The positron lifetime spectra accumulated under normal measuring statistics were analysed in terms of unconstrained three- and four-term decomposition, the latter being also realized under fixed 0.125 ns lifetime proper to para-positronium self-annihilation in a vacuum. It was shown that average positron lifetime extracted from each decomposition was primary defined by long-lived ortho-positronium component. The positron lifetime spectra treated within unconstrained three-term fitting were in obvious preference, giving third positron lifetime dominated by ortho-positronium pick-off annihilation in a polymer matrix. This fitting procedure was most meaningful, when analysing expected positron trapping sites in polyvinylpyrrolidone-stabilized nanocomposite pharmaceuticals. Copyright © 2015 Elsevier B.V. All rights reserved.

Balancing Conflicting Requirements for Grid and Particle Decomposition in Continuum-Lagrangian Solvers

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

Sitaraman, Hariswaran; Grout, Ray

2015-10-30

The load balancing strategies for hybrid solvers that involve grid based partial differential equation solution coupled with particle tracking are presented in this paper. A typical Message Passing Interface (MPI) based parallelization of grid based solves are done using a spatial domain decomposition while particle tracking is primarily done using either of the two techniques. One of the techniques is to distribute the particles to MPI ranks to whose grid they belong to while the other is to share the particles equally among all ranks, irrespective of their spatial location. The former technique provides spatial locality for field interpolation butmore » cannot assure load balance in terms of number of particles, which is achieved by the latter. The two techniques are compared for a case of particle tracking in a homogeneous isotropic turbulence box as well as a turbulent jet case. We performed a strong scaling study for more than 32,000 cores, which results in particle densities representative of anticipated exascale machines. The use of alternative implementations of MPI collectives and efficient load equalization strategies are studied to reduce data communication overheads.« less

Knee Joint Vibration Signal Analysis with Matching Pursuit Decomposition and Dynamic Weighted Classifier Fusion

PubMed Central

Cai, Suxian; Yang, Shanshan; Zheng, Fang; Lu, Meng; Wu, Yunfeng; Krishnan, Sridhar

2013-01-01

Analysis of knee joint vibration (VAG) signals can provide quantitative indices for detection of knee joint pathology at an early stage. In addition to the statistical features developed in the related previous studies, we extracted two separable features, that is, the number of atoms derived from the wavelet matching pursuit decomposition and the number of significant signal turns detected with the fixed threshold in the time domain. To perform a better classification over the data set of 89 VAG signals, we applied a novel classifier fusion system based on the dynamic weighted fusion (DWF) method to ameliorate the classification performance. For comparison, a single leastsquares support vector machine (LS-SVM) and the Bagging ensemble were used for the classification task as well. The results in terms of overall accuracy in percentage and area under the receiver operating characteristic curve obtained with the DWF-based classifier fusion method reached 88.76% and 0.9515, respectively, which demonstrated the effectiveness and superiority of the DWF method with two distinct features for the VAG signal analysis. PMID:23573175

Investigation of KDP crystal surface based on an improved bidimensional empirical mode decomposition method

NASA Astrophysics Data System (ADS)

Lu, Lei; Yan, Jihong; Chen, Wanqun; An, Shi

2018-03-01

This paper proposed a novel spatial frequency analysis method for the investigation of potassium dihydrogen phosphate (KDP) crystal surface based on an improved bidimensional empirical mode decomposition (BEMD) method. Aiming to eliminate end effects of the BEMD method and improve the intrinsic mode functions (IMFs) for the efficient identification of texture features, a denoising process was embedded in the sifting iteration of BEMD method. With removing redundant information in decomposed sub-components of KDP crystal surface, middle spatial frequencies of the cutting and feeding processes were identified. Comparative study with the power spectral density method, two-dimensional wavelet transform (2D-WT), as well as the traditional BEMD method, demonstrated that the method developed in this paper can efficiently extract texture features and reveal gradient development of KDP crystal surface. Furthermore, the proposed method was a self-adaptive data driven technique without prior knowledge, which overcame shortcomings of the 2D-WT model such as the parameters selection. Additionally, the proposed method was a promising tool for the application of online monitoring and optimal control of precision machining process.

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition.

PubMed

Al-Qazzaz, Noor Kamal; Ali, Sawal; Ahmad, Siti Anom; Escudero, Javier

2017-07-01

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI.

The association between state bans on soda only and adolescent substitution with other sugar-sweetened beverages: a cross-sectional study.

PubMed

Taber, Daniel R; Chriqui, Jamie F; Vuillaume, Renee; Kelder, Steven H; Chaloupka, Frank J

2015-07-27

Across the United States, many states have actively banned the sale of soda in high schools, and evidence suggests that students' in-school access to soda has declined as a result. However, schools may be substituting soda with other sugar-sweetened beverages (SSBs), and national trends indicate that adolescents are consuming more sports drinks and energy drinks. This study examined whether students consumed more non-soda SSBs in states that banned the sale of soda in school. Student data on consumption of various SSBs and in-school access to vending machines that sold SSBs were obtained from the National Youth Physical Activity and Nutrition Study (NYPANS), conducted in 2010. Student data were linked to state laws regarding the sale of soda in school in 2010. Students were cross-classified based on their access to vending machines and whether their state banned soda in school, creating 4 comparison groups. Zero-inflated negative binomial models were used to compare these 4 groups with respect to students’ self-reported consumption of diet soda, sports drinks, energy drinks, coffee/tea, or other SSBs. Students who had access to vending machines in a state that did not ban soda were the reference group. Models were adjusted for race/ethnicity, sex, grade, home food access, state median income, and U.S. Census region. Students consumed more servings of sports drinks, energy drinks, coffee/tea, and other SSBs if they resided in a state that banned soda in school but attended a school with vending machines that sold other SSBs. Similar results were observed where schools did not have vending machines but the state allowed soda to be sold in school. Intake was generally not elevated where both states and schools limited SSB availability – i.e., states banned soda and schools did not have SSB vending machines. State laws that ban soda but allow other SSBs may lead students to substitute other non-soda SSBs. Additional longitudinal research is needed to confirm this. Elevated SSB intake was not observed when both states and schools took steps to remove SSBs from school.

The association between state bans on soda only and adolescent substitution with other sugar-sweetened beverages: a cross-sectional study

PubMed Central

2015-01-01

Background Across the United States, many states have actively banned the sale of soda in high schools, and evidence suggests that students’ in-school access to soda has declined as a result. However, schools may be substituting soda with other sugar-sweetened beverages (SSBs), and national trends indicate that adolescents are consuming more sports drinks and energy drinks. This study examined whether students consumed more non-soda SSBs in states that banned the sale of soda in school. Methods Student data on consumption of various SSBs and in-school access to vending machines that sold SSBs were obtained from the National Youth Physical Activity and Nutrition Study (NYPANS), conducted in 2010. Student data were linked to state laws regarding the sale of soda in school in 2010. Students were cross-classified based on their access to vending machines and whether their state banned soda in school, creating 4 comparison groups. Zero-inflated negative binomial models were used to compare these 4 groups with respect to students’ self-reported consumption of diet soda, sports drinks, energy drinks, coffee/tea, or other SSBs. Students who had access to vending machines in a state that did not ban soda were the reference group. Models were adjusted for race/ethnicity, sex, grade, home food access, state median income, and U.S. Census region. Results Students consumed more servings of sports drinks, energy drinks, coffee/tea, and other SSBs if they resided in a state that banned soda in school but attended a school with vending machines that sold other SSBs. Similar results were observed where schools did not have vending machines but the state allowed soda to be sold in school. Intake was generally not elevated where both states and schools limited SSB availability – i.e., states banned soda and schools did not have SSB vending machines. Conclusion State laws that ban soda but allow other SSBs may lead students to substitute other non-soda SSBs. Additional longitudinal research is needed to confirm this. Elevated SSB intake was not observed when both states and schools took steps to remove SSBs from school. PMID:26221969

Skipping the real world: Classification of PolSAR images without explicit feature extraction

NASA Astrophysics Data System (ADS)

Hänsch, Ronny; Hellwich, Olaf

2018-06-01

The typical processing chain for pixel-wise classification from PolSAR images starts with an optional preprocessing step (e.g. speckle reduction), continues with extracting features projecting the complex-valued data into the real domain (e.g. by polarimetric decompositions) which are then used as input for a machine-learning based classifier, and ends in an optional postprocessing (e.g. label smoothing). The extracted features are usually hand-crafted as well as preselected and represent (a somewhat arbitrary) projection from the complex to the real domain in order to fit the requirements of standard machine-learning approaches such as Support Vector Machines or Artificial Neural Networks. This paper proposes to adapt the internal node tests of Random Forests to work directly on the complex-valued PolSAR data, which makes any explicit feature extraction obsolete. This approach leads to a classification framework with a significantly decreased computation time and memory footprint since no image features have to be computed and stored beforehand. The experimental results on one fully-polarimetric and one dual-polarimetric dataset show that, despite the simpler approach, accuracy can be maintained (decreased by only less than 2 % for the fully-polarimetric dataset) or even improved (increased by roughly 9 % for the dual-polarimetric dataset).

Performance of an MPI-only semiconductor device simulator on a quad socket/quad core InfiniBand platform.

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

Shadid, John Nicolas; Lin, Paul Tinphone

2009-01-01

This preliminary study considers the scaling and performance of a finite element (FE) semiconductor device simulator on a capacity cluster with 272 compute nodes based on a homogeneous multicore node architecture utilizing 16 cores. The inter-node communication backbone for this Tri-Lab Linux Capacity Cluster (TLCC) machine is comprised of an InfiniBand interconnect. The nonuniform memory access (NUMA) nodes consist of 2.2 GHz quad socket/quad core AMD Opteron processors. The performance results for this study are obtained with a FE semiconductor device simulation code (Charon) that is based on a fully-coupled Newton-Krylov solver with domain decomposition and multilevel preconditioners. Scaling andmore » multicore performance results are presented for large-scale problems of 100+ million unknowns on up to 4096 cores. A parallel scaling comparison is also presented with the Cray XT3/4 Red Storm capability platform. The results indicate that an MPI-only programming model for utilizing the multicore nodes is reasonably efficient on all 16 cores per compute node. However, the results also indicated that the multilevel preconditioner, which is critical for large-scale capability type simulations, scales better on the Red Storm machine than the TLCC machine.« less

Quantifying matrix product state

NASA Astrophysics Data System (ADS)

Bhatia, Amandeep Singh; Kumar, Ajay

2018-03-01

Motivated by the concept of quantum finite-state machines, we have investigated their relation with matrix product state of quantum spin systems. Matrix product states play a crucial role in the context of quantum information processing and are considered as a valuable asset for quantum information and communication purpose. It is an effective way to represent states of entangled systems. In this paper, we have designed quantum finite-state machines of one-dimensional matrix product state representations for quantum spin systems.

Litter decomposition over broad spatial and long time scales investigated by advanced solid-state NMR: insight into effects of climate, litter quality, and time

NASA Astrophysics Data System (ADS)

Mao, J.; Chen, N.; Harmon, M. E.; Li, Y.; Cao, X.; Chappell, M.

2012-12-01

Advanced 13C solid-state NMR techniques were employed to study the chemical structural changes of litter decomposition across broad spatial and long time scales. The fresh and decomposed litter samples of four species (Acer saccharum (ACSA), Drypetes glauca (DRGL), Pinus resinosa (PIRE), and Thuja plicata (THPL)) incubated for up to 10 years at four sites under different climatic conditions (from Arctic to tropical forest) were examined. Decomposition generally led to an enrichment of cutin and surface wax materials, and a depletion of carbohydrates causing overall composition to become more similar compared with original litters. However, the changes of main constituents in the four litters were inconsistent with the four litters following different pathways of decomposition at the same site. As decomposition proceeded, waxy materials decreased at the early stage and then gradually increased in PIRE; DRGL showed a significant depletion of lignin and tannin while the changes of lignin and tannin were relative small and inconsistent for ACSA and THPL. In addition, the NCH groups, which could be associated with either fungal cell wall chitin or bacterial wall petidoglycan, were enriched in all litters except THPL. Contrary to the classic lignin-enrichment hypothesis, DRGL with low-quality C substrate had the highest degree of composition changes. Furthermore, some samples had more "advanced" compositional changes in the intermediate stage of decomposition than in the highly-decomposed stage. This pattern might be attributed to the formation of new cross-linking structures, that rendered substrates more complex and difficult for enzymes to attack. Finally, litter quality overrode climate and time factors as a control of long-term changes of chemical composition.

Thermal decomposition pathways of ethane

NASA Astrophysics Data System (ADS)

Gordon, Mark S.; Truong, Thanh N.; Pople, John A.

1986-10-01

The alternate thermal decomposition pathways for ethane in its ground state have been investigated, using ab initio electronic structure calculations. Single-point energies were obtained at the full MP4/6-311 G ∗∗ level, using 6-31 G ∗ geometries for reactant, products, and transition states. The thermodynamically favored products are ethylene and molecular hydrogen, but a very large barrier (130 kcal/mol) is found for the direct 1,2-elimination of hydrogen. When calculated barriers are taken into account, the lowest-energy process is the homolytic cleavage of the C-C bond to form two methyl radicals.

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.

Research on intelligent machine self-perception method based on LSTM

NASA Astrophysics Data System (ADS)

Wang, Qiang; Cheng, Tao

2018-05-01

In this paper, we use the advantages of LSTM in feature extraction and processing high-dimensional and complex nonlinear data, and apply it to the autonomous perception of intelligent machines. Compared with the traditional multi-layer neural network, this model has memory, can handle time series information of any length. Since the multi-physical domain signals of processing machines have a certain timing relationship, and there is a contextual relationship between states and states, using this deep learning method to realize the self-perception of intelligent processing machines has strong versatility and adaptability. The experiment results show that the method proposed in this paper can obviously improve the sensing accuracy under various working conditions of the intelligent machine, and also shows that the algorithm can well support the intelligent processing machine to realize self-perception.

Tomography and generative training with quantum Boltzmann machines

NASA Astrophysics Data System (ADS)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has made their development an aspirational goal for quantum machine learning and quantum computing in general. Here we provide methods of training quantum Boltzmann machines. Our work generalizes existing methods and provides additional approaches for training quantum neural networks that compare favorably to existing methods. We further demonstrate that quantum Boltzmann machines enable a form of partial quantum state tomography that further provides a generative model for the input quantum state. Classical Boltzmann machines are incapable of this. This verifies the long-conjectured connection between tomography and quantum machine learning. Finally, we prove that classical computers cannot simulate our training process in general unless BQP=BPP , provide lower bounds on the complexity of the training procedures and numerically investigate training for small nonstoquastic Hamiltonians.

A comparison between decomposition rates of buried and surface remains in a temperate region of South Africa.

PubMed

Marais-Werner, Anátulie; Myburgh, J; Becker, P J; Steyn, M

2018-01-01

Several studies have been conducted on decomposition patterns and rates of surface remains; however, much less are known about this process for buried remains. Understanding the process of decomposition in buried remains is extremely important and aids in criminal investigations, especially when attempting to estimate the post mortem interval (PMI). The aim of this study was to compare the rates of decomposition between buried and surface remains. For this purpose, 25 pigs (Sus scrofa; 45-80 kg) were buried and excavated at different post mortem intervals (7, 14, 33, 92, and 183 days). The observed total body scores were then compared to those of surface remains decomposing at the same location. Stages of decomposition were scored according to separate categories for different anatomical regions based on standardised methods. Variation in the degree of decomposition was considerable especially with the buried 7-day interval pigs that displayed different degrees of discolouration in the lower abdomen and trunk. At 14 and 33 days, buried pigs displayed features commonly associated with the early stages of decomposition, but with less variation. A state of advanced decomposition was reached where little change was observed in the next ±90-183 days after interment. Although the patterns of decomposition for buried and surface remains were very similar, the rates differed considerably. Based on the observations made in this study, guidelines for the estimation of PMI are proposed. This pertains to buried remains found at a depth of approximately 0.75 m in the Central Highveld of South Africa.

Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle

NASA Astrophysics Data System (ADS)

Hu, Xiaogang; Rymer, William Z.; Suresh, Nina L.

2014-04-01

Objective. The aim of this study is to assess the accuracy of a surface electromyogram (sEMG) motor unit (MU) decomposition algorithm during low levels of muscle contraction. Approach. A two-source method was used to verify the accuracy of the sEMG decomposition system, by utilizing simultaneous intramuscular and surface EMG recordings from the human first dorsal interosseous muscle recorded during isometric trapezoidal force contractions. Spike trains from each recording type were decomposed independently utilizing two different algorithms, EMGlab and dEMG decomposition algorithms. The degree of agreement of the decomposed spike timings was assessed for three different segments of the EMG signals, corresponding to specified regions in the force task. A regression analysis was performed to examine whether certain properties of the sEMG and force signal can predict the decomposition accuracy. Main results. The average accuracy of successful decomposition among the 119 MUs that were common to both intramuscular and surface records was approximately 95%, and the accuracy was comparable between the different segments of the sEMG signals (i.e., force ramp-up versus steady state force versus combined). The regression function between the accuracy and properties of sEMG and force signals revealed that the signal-to-noise ratio of the action potential and stability in the action potential records were significant predictors of the surface decomposition accuracy. Significance. The outcomes of our study confirm the accuracy of the sEMG decomposition algorithm during low muscle contraction levels and provide confidence in the overall validity of the surface dEMG decomposition algorithm.

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

Olivares, Stefano

We investigate the performance of a selective cloning machine based on linear optical elements and Gaussian measurements, which allows one to clone at will one of the two incoming input states. This machine is a complete generalization of a 1{yields}2 cloning scheme demonstrated by Andersen et al. [Phys. Rev. Lett. 94, 240503 (2005)]. The input-output fidelity is studied for a generic Gaussian input state, and the effect of nonunit quantum efficiency is also taken into account. We show that, if the states to be cloned are squeezed states with known squeezing parameter, then the fidelity can be enhanced using amore » third suitable squeezed state during the final stage of the cloning process. A binary communication protocol based on the selective cloning machine is also discussed.« less

Ab initio kinetics and thermal decomposition mechanism of mononitrobiuret and 1,5-dinitrobiuret

NASA Astrophysics Data System (ADS)

Sun, Hongyan; Vaghjiani, Ghanshyam L.

2015-05-01

Mononitrobiuret (MNB) and 1,5-dinitrobiuret (DNB) are tetrazole-free, nitrogen-rich, energetic compounds. For the first time, a comprehensive ab initio kinetics study on the thermal decomposition mechanisms of MNB and DNB is reported here. In particular, the intramolecular interactions of amine H-atom with electronegative nitro O-atom and carbonyl O-atom have been analyzed for biuret, MNB, and DNB at the M06-2X/aug-cc-pVTZ level of theory. The results show that the MNB and DNB molecules are stabilized through six-member-ring moieties via intramolecular H-bonding with interatomic distances between 1.8 and 2.0 Å, due to electrostatic as well as polarization and dispersion interactions. Furthermore, it was found that the stable molecules in the solid state have the smallest dipole moment amongst all the conformers in the nitrobiuret series of compounds, thus revealing a simple way for evaluating reactivity of fuel conformers. The potential energy surface for thermal decomposition of MNB was characterized by spin restricted coupled cluster theory at the RCCSD(T)/cc-pV∞ Z//M06-2X/aug-cc-pVTZ level. It was found that the thermal decomposition of MNB is initiated by the elimination of HNCO and HNN(O)OH intermediates. Intramolecular transfer of a H-atom, respectively, from the terminal NH2 group to the adjacent carbonyl O-atom via a six-member-ring transition state eliminates HNCO with an energy barrier of 35 kcal/mol and from the central NH group to the adjacent nitro O-atom eliminates HNN(O)OH with an energy barrier of 34 kcal/mol. Elimination of HNN(O)OH is also the primary process involved in the thermal decomposition of DNB, which processes C2v symmetry. The rate coefficients for the primary decomposition channels for MNB and DNB were quantified as functions of temperature and pressure. In addition, the thermal decomposition of HNN(O)OH was analyzed via Rice-Ramsperger-Kassel-Marcus/multi-well master equation simulations, the results of which reveal the formation of (NO2 + H2O) to be the major decomposition path. Furthermore, we provide fundamental interpretations for the experimental results of Klapötke et al. [Combust. Flame 139, 358-366 (2004)] regarding the thermal stability of MNB and DNB, and their decomposition products. Notably, a fundamental understanding of fuel stability, decomposition mechanism, and key reactions leading to ignition is essential in the design and manipulation of molecular systems for the development of new energetic materials for advanced propulsion applications.

Ab Initio Kinetics and Thermal Decomposition Mechanism of Mononitrobiuret and 1,5- Dinitrobiuret

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

Sun, Hongyan; Vaghjiani, Ghanshyam G.

2015-05-26

Mononitrobiuret (MNB) and 1,5-dinitrobiuret (DNB) are tetrazole-free, nitrogen-rich, energetic compounds. For the first time, a comprehensive ab initio kinetics study on the thermal decomposition mechanisms of MNB and DNB is reported here. In particular, the intramolecular interactions of amine H-atom with electronegative nitro O-atom and carbonyl O-atom have been analyzed for biuret, MNB, and DNB at the M06-2X/aug-cc-pVTZ level of theory. The results show that the MNB and DNB molecules are stabilized through six-member-ring moieties via intramolecular H-bonding with interatomic distances between 1.8 and 2.0 Å, due to electrostatic as well as polarization and dispersion interactions. Furthermore, it was foundmore » that the stable molecules in the solid state have the smallest dipole moment amongst all the conformers in the nitrobiuret series of compounds, thus revealing a simple way for evaluating reactivity of fuel conformers. The potential energy surface for thermal decomposition of MNB was characterized by spin restricted coupled cluster theory at the RCCSD(T)/cc-pV∞ Z//M06-2X/aug-cc-pVTZ level. It was found that the thermal decomposition of MNB is initiated by the elimination of HNCO and HNN(O)OH intermediates. Intramolecular transfer of a H-atom, respectively, from the terminal NH2 group to the adjacent carbonyl O-atom via a six-member-ring transition state eliminates HNCO with an energy barrier of 35 kcal/mol and from the central NH group to the adjacent nitro O-atom eliminates HNN(O)OH with an energy barrier of 34 kcal/mol. Elimination of HNN(O)OH is also the primary process involved in the thermal decomposition of DNB, which processes C2v symmetry. The rate coefficients for the primary decomposition channels for MNB and DNB were quantified as functions of temperature and pressure. In addition, the thermal decomposition of HNN(O)OH was analyzed via Rice–Ramsperger–Kassel–Marcus/multi-well master equation simulations, the results of which reveal the formation of (NO2 + H2O) to be the major decomposition path. Furthermore, we provide fundamental interpretations for the experimental results of Klapötke et al. [Combust. Flame 139, 358–366 (2004)] regarding the thermal stability of MNB and DNB, and their decomposition products. Notably, a fundamental understanding of fuel stability, decomposition mechanism, and key reactions leading to ignition is essential in the design and manipulation of molecular systems for the development of new energetic materials for advanced propulsion applications.« less

Ab initio kinetics and thermal decomposition mechanism of mononitrobiuret and 1,5-dinitrobiuret.

PubMed

Sun, Hongyan; Vaghjiani, Ghanshyam L

2015-05-28

Mononitrobiuret (MNB) and 1,5-dinitrobiuret (DNB) are tetrazole-free, nitrogen-rich, energetic compounds. For the first time, a comprehensive ab initio kinetics study on the thermal decomposition mechanisms of MNB and DNB is reported here. In particular, the intramolecular interactions of amine H-atom with electronegative nitro O-atom and carbonyl O-atom have been analyzed for biuret, MNB, and DNB at the M06-2X/aug-cc-pVTZ level of theory. The results show that the MNB and DNB molecules are stabilized through six-member-ring moieties via intramolecular H-bonding with interatomic distances between 1.8 and 2.0 Å, due to electrostatic as well as polarization and dispersion interactions. Furthermore, it was found that the stable molecules in the solid state have the smallest dipole moment amongst all the conformers in the nitrobiuret series of compounds, thus revealing a simple way for evaluating reactivity of fuel conformers. The potential energy surface for thermal decomposition of MNB was characterized by spin restricted coupled cluster theory at the RCCSD(T)/cc-pV∞ Z//M06-2X/aug-cc-pVTZ level. It was found that the thermal decomposition of MNB is initiated by the elimination of HNCO and HNN(O)OH intermediates. Intramolecular transfer of a H-atom, respectively, from the terminal NH2 group to the adjacent carbonyl O-atom via a six-member-ring transition state eliminates HNCO with an energy barrier of 35 kcal/mol and from the central NH group to the adjacent nitro O-atom eliminates HNN(O)OH with an energy barrier of 34 kcal/mol. Elimination of HNN(O)OH is also the primary process involved in the thermal decomposition of DNB, which processes C2v symmetry. The rate coefficients for the primary decomposition channels for MNB and DNB were quantified as functions of temperature and pressure. In addition, the thermal decomposition of HNN(O)OH was analyzed via Rice-Ramsperger-Kassel-Marcus/multi-well master equation simulations, the results of which reveal the formation of (NO2 + H2O) to be the major decomposition path. Furthermore, we provide fundamental interpretations for the experimental results of Klapötke et al. [Combust. Flame 139, 358-366 (2004)] regarding the thermal stability of MNB and DNB, and their decomposition products. Notably, a fundamental understanding of fuel stability, decomposition mechanism, and key reactions leading to ignition is essential in the design and manipulation of molecular systems for the development of new energetic materials for advanced propulsion applications.

Distributed Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

2014-01-01

Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

Applications of singular value analysis and partial-step algorithm for nonlinear orbit determination

NASA Technical Reports Server (NTRS)

Ryne, Mark S.; Wang, Tseng-Chan

1991-01-01

An adaptive method in which cruise and nonlinear orbit determination problems can be solved using a single program is presented. It involves singular value decomposition augmented with an extended partial step algorithm. The extended partial step algorithm constrains the size of the correction to the spacecraft state and other solve-for parameters. The correction is controlled by an a priori covariance and a user-supplied bounds parameter. The extended partial step method is an extension of the update portion of the singular value decomposition algorithm. It thus preserves the numerical stability of the singular value decomposition method, while extending the region over which it converges. In linear cases, this method reduces to the singular value decomposition algorithm with the full rank solution. Two examples are presented to illustrate the method's utility.

Divergence-free approach for obtaining decompositions of quantum-optical processes

NASA Astrophysics Data System (ADS)

Sabapathy, K. K.; Ivan, J. S.; García-Patrón, R.; Simon, R.

2018-02-01

Operator-sum representations of quantum channels can be obtained by applying the channel to one subsystem of a maximally entangled state and deploying the channel-state isomorphism. However, for continuous-variable systems, such schemes contain natural divergences since the maximally entangled state is ill defined. We introduce a method that avoids such divergences by utilizing finitely entangled (squeezed) states and then taking the limit of arbitrary large squeezing. Using this method, we derive an operator-sum representation for all single-mode bosonic Gaussian channels where a unique feature is that both quantum-limited and noisy channels are treated on an equal footing. This technique facilitates a proof that the rank-1 Kraus decomposition for Gaussian channels at its respective entanglement-breaking thresholds, obtained in the overcomplete coherent-state basis, is unique. The methods could have applications to simulation of continuous-variable channels.

Modeling the Car Crash Crisis Management System Using HiLA

NASA Astrophysics Data System (ADS)

Hölzl, Matthias; Knapp, Alexander; Zhang, Gefei

An aspect-oriented modeling approach to the Car Crash Crisis Management System (CCCMS) using the High-Level Aspect (HiLA) language is described. HiLA is a language for expressing aspects for UML static structures and UML state machines. In particular, HiLA supports both a static graph transformational and a dynamic approach of applying aspects. Furthermore, it facilitates methodologically turning use case descriptions into state machines: for each main success scenario, a base state machine is developed; all extensions to this main success scenario are covered by aspects. Overall, the static structure of the CCCMS is modeled in 43 classes, the main success scenarios in 13 base machines, the use case extensions in 47 static and 31 dynamic aspects, most of which are instantiations of simple aspect templates.

A Subspace Approach to the Structural Decomposition and Identification of Ankle Joint Dynamic Stiffness.

PubMed

Jalaleddini, Kian; Tehrani, Ehsan Sobhani; Kearney, Robert E

2017-06-01

The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone.

A Novel Bearing Multi-Fault Diagnosis Approach Based on Weighted Permutation Entropy and an Improved SVM Ensemble Classifier.

PubMed

Zhou, Shenghan; Qian, Silin; Chang, Wenbing; Xiao, Yiyong; Cheng, Yang

2018-06-14

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Bearing diagnostics: A method based on differential geometry

NASA Astrophysics Data System (ADS)

Tian, Ye; Wang, Zili; Lu, Chen; Wang, Zhipeng

2016-12-01

The structures around bearings are complex, and the working environment is variable. These conditions cause the collected vibration signals to become nonlinear, non-stationary, and chaotic characteristics that make noise reduction, feature extraction, fault diagnosis, and health assessment significantly challenging. Thus, a set of differential geometry-based methods with superiorities in nonlinear analysis is presented in this study. For noise reduction, the Local Projection method is modified by both selecting the neighborhood radius based on empirical mode decomposition and determining noise subspace constrained by neighborhood distribution information. For feature extraction, Hessian locally linear embedding is introduced to acquire manifold features from the manifold topological structures, and singular values of eigenmatrices as well as several specific frequency amplitudes in spectrograms are extracted subsequently to reduce the complexity of the manifold features. For fault diagnosis, information geometry-based support vector machine is applied to classify the fault states. For health assessment, the manifold distance is employed to represent the health information; the Gaussian mixture model is utilized to calculate the confidence values, which directly reflect the health status. Case studies on Lorenz signals and vibration datasets of bearings demonstrate the effectiveness of the proposed methods.

Identifying patients with poststroke mild cognitive impairment by pattern recognition of working memory load-related ERP.

PubMed

Li, Xiaoou; Yan, Yuning; Wei, Wenshi

2013-01-01

The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.

Decomposition of Time Scales in Linear Systems and Markovian Decision Processes.

DTIC Science & Technology

1980-11-01

this research. I, 3 iv U TABLE OF CONTENTS *Chapter Page *-1. INTRODUCTION .................................................. 1 2. EIGENSTRUCTTJRE...Components ..... o....... 16 2.4. Ordering of State Variables.. ......... ........ 20 2.5. Example - 8th Order Power System Model................ 22 3 ...results. In Chapter 3 we consider the time scale decomposition of singularly perturbed systems. For this problem (1.1) takes the form 12 + u (1.4) 2

a Transient Semi-Metallic Layer in Detonating Nitromethane

NASA Astrophysics Data System (ADS)

Reed, Evan J.; Manaa, M. Riad; Fried, Laurence E.; Glaesemann, Kurt; Joannopoulos, John D.

2007-12-01

We present the first ever glimpse behind a detonation shock front in a chemically reactive quantum molecular dynamics simulation of the explosive nitromethane (CH3NO2). We discover that the wide-bandgap insulator nitromethane undergoes chemical decomposition and a transformation into a semi-metallic state for a limited distance behind the detonation front. We find this transformation is associated with the production of charged decomposition species.

Ensemble Feature Learning of Genomic Data Using Support Vector Machine

PubMed Central

Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R.; Braytee, Ali; Kennedy, Paul J.

2016-01-01

The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data. PMID:27304923

Augmentation of machine structure to improve its diagnosability

NASA Technical Reports Server (NTRS)

Hsieh, L.

1973-01-01

Two methods of augmenting the structure of a sequential machine so that it is diagnosable are presented. The checkable (checking sequences) and repeated symbol distinguishing sequences (RDS) are discussed. It was found that as few as twice the number of outputs of the given machine is sufficient for constructing a state-output augmentation with RDS. Techniques for minimizing the number of states in resolving convergences and in resolving equivalent and nonreduced cycles are developed.

Theoretical study of the decomposition mechanism of environmentally friendly insulating medium C3F7CN in the presence of H2O in a discharge

NASA Astrophysics Data System (ADS)

Zhang, Xiaoxing; Li, Yi; Xiao, Song; Tian, Shuangshuang; Deng, Zaitao; Tang, Ju

2017-08-01

C3F7CN has been the focus of the alternative gas research field over the past two years because of its excellent insulation properties and environmental characteristics. Experimental studies on its insulation performance have made many achievements. However, few studies on the formation mechanism of the decomposition components exist. A discussion of the decomposition characteristics of insulating media will provide guidance for scientific experimental research and the work that must be completed before further engineering application. In this study, the decomposition mechanism of C3F7CN in the presence of trace H2O under discharge was calculated based on the density functional theory and transition state theory. The reaction heat, Gibbs free energy, and activation energy of different decomposition pathways were investigated. The ionization parameters and toxicity of C3F7CN and various decomposition products were analyzed from the molecular structure perspective. The formation mechanism of the C3F7CN discharge decomposition components and the influence of trace water were evaluated. This paper confirms that C3F7CN has excellent decomposition characteristics, which provide theoretical support for later experiments and related engineering applications. However, the existence of trace water has a negative impact on C3F7CN’s insulation performance. Thus, strict trace water content standards should be developed to ensure dielectric insulation and the safety of maintenance personnel.

Calculating utilization rates for rubber tired grapple skidders in the Southern United States

Treesearch

Jason D. Thompson

2001-01-01

Utilization rate is an important factor in calculating machine rates for forest harvesting machines. Machine rates allow an evaluation of harvesting system costs and facilitate comparisons between different systems and machines. There are many factors that affect utilization rate. These include mechanical delays, non-mechanical delays, operational lost time, and...

Modelling machine ensembles with discrete event dynamical system theory

NASA Technical Reports Server (NTRS)

Hunter, Dan

1990-01-01

Discrete Event Dynamical System (DEDS) theory can be utilized as a control strategy for future complex machine ensembles that will be required for in-space construction. The control strategy involves orchestrating a set of interactive submachines to perform a set of tasks for a given set of constraints such as minimum time, minimum energy, or maximum machine utilization. Machine ensembles can be hierarchically modeled as a global model that combines the operations of the individual submachines. These submachines are represented in the global model as local models. Local models, from the perspective of DEDS theory , are described by the following: a set of system and transition states, an event alphabet that portrays actions that takes a submachine from one state to another, an initial system state, a partial function that maps the current state and event alphabet to the next state, and the time required for the event to occur. Each submachine in the machine ensemble is presented by a unique local model. The global model combines the local models such that the local models can operate in parallel under the additional logistic and physical constraints due to submachine interactions. The global model is constructed from the states, events, event functions, and timing requirements of the local models. Supervisory control can be implemented in the global model by various methods such as task scheduling (open-loop control) or implementing a feedback DEDS controller (closed-loop control).

In vitro analysis of rifampicin and its effect on quality control tests of rifampicin containing dosage forms.

PubMed

Agrawal, S; Panchagnula, R

2004-10-01

The chemical stability of rifampicin both in solid state and various media has widely been investigated. While rifampicin is appreciably stable in solid-state, its decomposition rate is very high in acidic as well as in alkaline medium and a variety of decomposition products were identified. The literature reports on highly variable rifampicin decomposition in acidic medium. Hence, the objective of this investigation was to study possible reasons responsible for this variability. For this purpose, filter validation and correlation between rifampicin and its degradation products were developed to account for the loss of rifampicin in acidic media. For analysis of rifampicin with or without the presence of isoniazid, a simple and accurate method was developed using high performance chromatography recommended in FDC monographs of the United States Pharmacopoeia. Using the equations developed in this investigation, the amount of rifampicin degraded in the acidic media was calculated from the area under curve of the degradation products. Further, it was proved that in a dissolution study, the colorimetric method of analysis recommended in the United States Pharmacopoeia provides accurate results regarding rifampicin release. Filter type, time of injection as well as interpretation of data are important factors that affect analysis results of rifampicin in in vitro studies and quality control.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

Ideology of a multiparametric system for estimating the insulation system of electric machines on the basis of absorption testing methods

NASA Astrophysics Data System (ADS)

Kislyakov, M. A.; Chernov, V. A.; Maksimkin, V. L.; Bozhin, Yu. M.

2017-12-01

The article deals with modern methods of monitoring the state and predicting the life of electric machines. In 50% of the cases of failure in the performance of electric machines is associated with insulation damage. As promising, nondestructive methods of control, methods based on the investigation of the processes of polarization occurring in insulating materials are proposed. To improve the accuracy of determining the state of insulation, a multiparametric approach is considered, which is a basis for the development of an expert system for estimating the state of health.

Control of discrete event systems modeled as hierarchical state machines

NASA Technical Reports Server (NTRS)

Brave, Y.; Heymann, M.

1991-01-01

The authors examine a class of discrete event systems (DESs) modeled as asynchronous hierarchical state machines (AHSMs). For this class of DESs, they provide an efficient method for testing reachability, which is an essential step in many control synthesis procedures. This method utilizes the asynchronous nature and hierarchical structure of AHSMs, thereby illustrating the advantage of the AHSM representation as compared with its equivalent (flat) state machine representation. An application of the method is presented where an online minimally restrictive solution is proposed for the problem of maintaining a controlled AHSM within prescribed legal bounds.

Programmable Pulse-Position-Modulation Encoder

NASA Technical Reports Server (NTRS)

Zhu, David; Farr, William

2006-01-01

A programmable pulse-position-modulation (PPM) encoder has been designed for use in testing an optical communication link. The encoder includes a programmable state machine and an electronic code book that can be updated to accommodate different PPM coding schemes. The encoder includes a field-programmable gate array (FPGA) that is programmed to step through the stored state machine and code book and that drives a custom high-speed serializer circuit board that is capable of generating subnanosecond pulses. The stored state machine and code book can be updated by means of a simple text interface through the serial port of a personal computer.

Distributed Damage Estimation for Prognostics based on Structural Model Decomposition

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Bregon, Anibal; Roychoudhury, Indranil

2011-01-01

Model-based prognostics approaches capture system knowledge in the form of physics-based models of components, and how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.

An MPI + $X$ implementation of contact global search using Kokkos

DOE PAGES

Hansen, Glen A.; Xavier, Patrick G.; Mish, Sam P.; ...

2015-10-05

This paper describes an approach that seeks to parallelize the spatial search associated with computational contact mechanics. In contact mechanics, the purpose of the spatial search is to find “nearest neighbors,” which is the prelude to an imprinting search that resolves the interactions between the external surfaces of contacting bodies. In particular, we are interested in the contact global search portion of the spatial search associated with this operation on domain-decomposition-based meshes. Specifically, we describe an implementation that combines standard domain-decomposition-based MPI-parallel spatial search with thread-level parallelism (MPI-X) available on advanced computer architectures (those with GPU coprocessors). Our goal ismore » to demonstrate the efficacy of the MPI-X paradigm in the overall contact search. Standard MPI-parallel implementations typically use a domain decomposition of the external surfaces of bodies within the domain in an attempt to efficiently distribute computational work. This decomposition may or may not be the same as the volume decomposition associated with the host physics. The parallel contact global search phase is then employed to find and distribute surface entities (nodes and faces) that are needed to compute contact constraints between entities owned by different MPI ranks without further inter-rank communication. Key steps of the contact global search include computing bounding boxes, building surface entity (node and face) search trees and finding and distributing entities required to complete on-rank (local) spatial searches. To enable source-code portability and performance across a variety of different computer architectures, we implemented the algorithm using the Kokkos hardware abstraction library. While we targeted development towards machines with a GPU accelerator per MPI rank, we also report performance results for OpenMP with a conventional multi-core compute node per rank. Results here demonstrate a 47 % decrease in the time spent within the global search algorithm, comparing the reference ACME algorithm with the GPU implementation, on an 18M face problem using four MPI ranks. As a result, while further work remains to maximize performance on the GPU, this result illustrates the potential of the proposed implementation.« less

Semiconductor cooling apparatus

NASA Technical Reports Server (NTRS)

Banks, Bruce A. (Inventor); Gaier, James R. (Inventor)

1993-01-01

Gas derived graphite fibers generated by the decomposition of an organic gas are joined with a suitable binder. This produces a high thermal conductivity composite material which passively conducts heat from a source, such as a semiconductor, to a heat sink. The fibers may be intercalated. The intercalate can be halogen or halide salt, alkaline metal, or any other species which contributes to the electrical conductivity improvement of the graphite fiber. The fibers are bundled and joined with a suitable binder to form a high thermal conductivity composite material device. The heat transfer device may also be made of intercalated highly oriented pyrolytic graphite and machined, rather than made of fibers.

Program Helps Decompose Complex Design Systems

NASA Technical Reports Server (NTRS)

Rogers, James L., Jr.; Hall, Laura E.

1995-01-01

DeMAID (Design Manager's Aid for Intelligent Decomposition) computer program is knowledge-based software system for ordering sequence of modules and identifying possible multilevel structure for design problems such as large platforms in outer space. Groups modular subsystems on basis of interactions among them. Saves considerable amount of money and time in total design process, particularly in new design problem in which order of modules has not been defined. Originally written for design problems, also applicable to problems containing modules (processes) that take inputs and generate outputs. Available in three machine versions: Macintosh written in Symantec's Think C 3.01, Sun, and SGI IRIS in C language.

A partitioning strategy for nonuniform problems on multiprocessors

NASA Technical Reports Server (NTRS)

Berger, M. J.; Bokhari, S.

1985-01-01

The partitioning of a problem on a domain with unequal work estimates in different subddomains is considered in a way that balances the work load across multiple processors. Such a problem arises for example in solving partial differential equations using an adaptive method that places extra grid points in certain subregions of the domain. A binary decomposition of the domain is used to partition it into rectangles requiring equal computational effort. The communication costs of mapping this partitioning onto different microprocessors: a mesh-connected array, a tree machine and a hypercube is then studied. The communication cost expressions can be used to determine the optimal depth of the above partitioning.

The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.

PubMed

Casey, M

1996-08-15

Recurrent neural networks (RNNs) can learn to perform finite state computations. It is shown that an RNN performing a finite state computation must organize its state space to mimic the states in the minimal deterministic finite state machine that can perform that computation, and a precise description of the attractor structure of such systems is given. This knowledge effectively predicts activation space dynamics, which allows one to understand RNN computation dynamics in spite of complexity in activation dynamics. This theory provides a theoretical framework for understanding finite state machine (FSM) extraction techniques and can be used to improve training methods for RNNs performing FSM computations. This provides an example of a successful approach to understanding a general class of complex systems that has not been explicitly designed, e.g., systems that have evolved or learned their internal structure.

High speed operation of permanent magnet machines

NASA Astrophysics Data System (ADS)

El-Refaie, Ayman M.

This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been investigated. A 6kW, 36slot/30pole prototype SPM machine has been designed and built. Experimental measurements have been used to verify the analytical and FEA results. These test results have demonstrated that wide constant-power speed range can be achieved. Other important machine features such as the near-sinusoidal back-emf, high efficiency, and low cogging torque have also been demonstrated.

An easy-to-use calculating machine to simulate steady state and non-steady-state preparative separations by multiple dual mode counter-current chromatography with semi-continuous loading of feed mixtures.

PubMed

Kostanyan, Artak E; Shishilov, Oleg N

2018-06-01

Multiple dual mode counter-current chromatography (MDM CCC) separation processes with semi-continuous large sample loading consist of a succession of two counter-current steps: with "x" phase (first step) and "y" phase (second step) flow periods. A feed mixture dissolved in the "x" phase is continuously loaded into a CCC machine at the beginning of the first step of each cycle over a constant time with the volumetric rate equal to the flow rate of the pure "x" phase. An easy-to-use calculating machine is developed to simulate the chromatograms and the amounts of solutes eluted with the phases at each cycle for steady-state (the duration of the flow periods of the phases is kept constant for all the cycles) and non-steady-state (with variable duration of alternating phase elution steps) separations. Using the calculating machine, the separation of mixtures containing up to five components can be simulated and designed. Examples of the application of the calculating machine for the simulation of MDM CCC processes are discussed. Copyright © 2018 Elsevier B.V. All rights reserved.

Method of Individual Forecasting of Technical State of Logging Machines

NASA Astrophysics Data System (ADS)

Kozlov, V. G.; Gulevsky, V. A.; Skrypnikov, A. V.; Logoyda, V. S.; Menzhulova, A. S.

2018-03-01

Development of the model that evaluates the possibility of failure requires the knowledge of changes’ regularities of technical condition parameters of the machines in use. To study the regularities, the need to develop stochastic models that take into account physical essence of the processes of destruction of structural elements of the machines, the technology of their production, degradation and the stochastic properties of the parameters of the technical state and the conditions and modes of operation arose.

Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

NASA Astrophysics Data System (ADS)

Yu, Jianbo

2017-01-01

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.

Young Children's Thinking About Decomposition: Early Modeling Entrees to Complex Ideas in Science

NASA Astrophysics Data System (ADS)

Ero-Tolliver, Isi; Lucas, Deborah; Schauble, Leona

2013-10-01

This study was part of a multi-year project on the development of elementary students' modeling approaches to understanding the life sciences. Twenty-three first grade students conducted a series of coordinated observations and investigations on decomposition, a topic that is rarely addressed in the early grades. The instruction included in-class observations of different types of soil and soil profiling, visits to the school's compost bin, structured observations of decaying organic matter of various kinds, study of organisms that live in the soil, and models of environmental conditions that affect rates of decomposition. Both before and after instruction, students completed a written performance assessment that asked them to reason about the process of decomposition. Additional information was gathered through one-on-one interviews with six focus students who represented variability of performance across the class. During instruction, researchers collected video of classroom activity, student science journal entries, and charts and illustrations produced by the teacher. After instruction, the first-grade students showed a more nuanced understanding of the composition and variability of soils, the role of visible organisms in decomposition, and environmental factors that influence rates of decomposition. Through a variety of representational devices, including drawings, narrative records, and physical models, students came to regard decomposition as a process, rather than simply as an end state that does not require explanation.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part.

PubMed

Ahmed, Yassmin Seid; Fox-Rabinovich, German; Paiva, Jose Mario; Wagg, Terry; Veldhuis, Stephen Clarence

2017-10-25

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool-chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear.

Effect of Built-Up Edge Formation during Stable State of Wear in AISI 304 Stainless Steel on Machining Performance and Surface Integrity of the Machined Part

PubMed Central

Fox-Rabinovich, German; Wagg, Terry

2017-01-01

During machining of stainless steels at low cutting -speeds, workpiece material tends to adhere to the cutting tool at the tool–chip interface, forming built-up edge (BUE). BUE has a great importance in machining processes; it can significantly modify the phenomenon in the cutting zone, directly affecting the workpiece surface integrity, cutting tool forces, and chip formation. The American Iron and Steel Institute (AISI) 304 stainless steel has a high tendency to form an unstable BUE, leading to deterioration of the surface quality. Therefore, it is necessary to understand the nature of the surface integrity induced during machining operations. Although many reports have been published on the effect of tool wear during machining of AISI 304 stainless steel on surface integrity, studies on the influence of the BUE phenomenon in the stable state of wear have not been investigated so far. The main goal of the present work is to investigate the close link between the BUE formation, surface integrity and cutting forces in the stable sate of wear for uncoated cutting tool during the cutting tests of AISI 304 stainless steel. The cutting parameters were chosen to induce BUE formation during machining. X-ray diffraction (XRD) method was used for measuring superficial residual stresses of the machined surface through the stable state of wear in the cutting and feed directions. In addition, surface roughness of the machined surface was investigated using the Alicona microscope and Scanning Electron Microscopy (SEM) was used to reveal the surface distortions created during the cutting process, combined with chip undersurface analyses. The investigated BUE formation during the stable state of wear showed that the BUE can cause a significant improvement in the surface integrity and cutting forces. Moreover, it can be used to compensate for tool wear through changing the tool geometry, leading to the protection of the cutting tool from wear. PMID:29068405

High-throughput state-machine replication using software transactional memory.

PubMed

Zhao, Wenbing; Yang, William; Zhang, Honglei; Yang, Jack; Luo, Xiong; Zhu, Yueqin; Yang, Mary; Luo, Chaomin

2016-11-01

State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could severely limit the system throughput. This issue has been partially addressed by identifying non-conflicting requests based on application semantics and executing these requests concurrently. However, identifying and tracking non-conflicting requests require intimate knowledge of application design and implementation, and a custom fault tolerance solution developed for one application cannot be easily adopted by other applications. Software transactional memory offers a new way of constructing concurrent programs. In this article, we present the mechanisms needed to retrofit existing concurrency control algorithms designed for software transactional memory for state-machine replication. The main benefit for using software transactional memory in state-machine replication is that general purpose concurrency control mechanisms can be designed without deep knowledge of application semantics. As such, new fault tolerance systems based on state-machine replications with excellent throughput can be easily designed and maintained. In this article, we introduce three different concurrency control mechanisms for state-machine replication using software transactional memory, namely, ordered strong strict two-phase locking, conventional timestamp-based multiversion concurrency control, and speculative timestamp-based multiversion concurrency control. Our experiments show that speculative timestamp-based multiversion concurrency control mechanism has the best performance in all types of workload, the conventional timestamp-based multiversion concurrency control offers the worst performance due to high abort rate in the presence of even moderate contention between transactions. The ordered strong strict two-phase locking mechanism offers the simplest solution with excellent performance in low contention workload, and fairly good performance in high contention workload.

High-throughput state-machine replication using software transactional memory

PubMed Central

Yang, William; Zhang, Honglei; Yang, Jack; Luo, Xiong; Zhu, Yueqin; Yang, Mary; Luo, Chaomin

2017-01-01

State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could severely limit the system throughput. This issue has been partially addressed by identifying non-conflicting requests based on application semantics and executing these requests concurrently. However, identifying and tracking non-conflicting requests require intimate knowledge of application design and implementation, and a custom fault tolerance solution developed for one application cannot be easily adopted by other applications. Software transactional memory offers a new way of constructing concurrent programs. In this article, we present the mechanisms needed to retrofit existing concurrency control algorithms designed for software transactional memory for state-machine replication. The main benefit for using software transactional memory in state-machine replication is that general purpose concurrency control mechanisms can be designed without deep knowledge of application semantics. As such, new fault tolerance systems based on state-machine replications with excellent throughput can be easily designed and maintained. In this article, we introduce three different concurrency control mechanisms for state-machine replication using software transactional memory, namely, ordered strong strict two-phase locking, conventional timestamp-based multiversion concurrency control, and speculative timestamp-based multiversion concurrency control. Our experiments show that speculative timestamp-based multiversion concurrency control mechanism has the best performance in all types of workload, the conventional timestamp-based multiversion concurrency control offers the worst performance due to high abort rate in the presence of even moderate contention between transactions. The ordered strong strict two-phase locking mechanism offers the simplest solution with excellent performance in low contention workload, and fairly good performance in high contention workload. PMID:29075049

75 FR 34673 - Approval of the Clean Air Act, Section 112(l), Authority for Hazardous Air Pollutants: Air...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-18

... Halogenated Solvent Cleaning Machines: State of Rhode Island Department of Environmental Management AGENCY... machines in Rhode Island, except for continuous web cleaning machines. This approval would grant RI DEM the... Halogenated Solvent NESHAP for organic solvent cleaning machines and would make the Rhode Island Department of...

Technology of machine tools. Volume 4. Machine tool controls

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

Not Available

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 3. Machine tool mechanics

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

Tlusty, J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Technology of machine tools. Volume 5. Machine tool accuracy

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

Hocken, R.J.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Machine learning phases of matter

NASA Astrophysics Data System (ADS)

Carrasquilla, Juan; Melko, Roger G.

2017-02-01

Condensed-matter physics is the study of the collective behaviour of infinitely complex assemblies of electrons, nuclei, magnetic moments, atoms or qubits. This complexity is reflected in the size of the state space, which grows exponentially with the number of particles, reminiscent of the `curse of dimensionality' commonly encountered in machine learning. Despite this curse, the machine learning community has developed techniques with remarkable abilities to recognize, classify, and characterize complex sets of data. Here, we show that modern machine learning architectures, such as fully connected and convolutional neural networks, can identify phases and phase transitions in a variety of condensed-matter Hamiltonians. Readily programmable through modern software libraries, neural networks can be trained to detect multiple types of order parameter, as well as highly non-trivial states with no conventional order, directly from raw state configurations sampled with Monte Carlo.

Irrelevance of the Power Stroke for the Directionality, Stopping Force, and Optimal Efficiency of Chemically Driven Molecular Machines

PubMed Central

Astumian, R. Dean

2015-01-01

A simple model for a chemically driven molecular walker shows that the elastic energy stored by the molecule and released during the conformational change known as the power-stroke (i.e., the free-energy difference between the pre- and post-power-stroke states) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Further, the apportionment of the dependence on the externally applied force between the forward and reverse rate constants of the power-stroke (or indeed among all rate constants) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Arguments based on the principle of microscopic reversibility demonstrate that this result is general for all chemically driven molecular machines, and even more broadly that the relative energies of the states of the motor have no role in determining the directionality, stopping force, or optimal efficiency of the machine. Instead, the directionality, stopping force, and optimal efficiency are determined solely by the relative heights of the energy barriers between the states. Molecular recognition—the ability of a molecular machine to discriminate between substrate and product depending on the state of the machine—is far more important for determining the intrinsic directionality and thermodynamics of chemo-mechanical coupling than are the details of the internal mechanical conformational motions of the machine. In contrast to the conclusions for chemical driving, a power-stroke is very important for the directionality and efficiency of light-driven molecular machines and for molecular machines driven by external modulation of thermodynamic parameters. PMID:25606678

Metal-porphyrin: a potential catalyst for direct decomposition of N(2)O by theoretical reaction mechanism investigation.

PubMed

Maitarad, Phornphimon; Namuangruk, Supawadee; Zhang, Dengsong; Shi, Liyi; Li, Hongrui; Huang, Lei; Boekfa, Bundet; Ehara, Masahiro

2014-06-17

The adsorption of nitrous oxide (N2O) on metal-porphyrins (metal: Ti, Cr, Fe, Co, Ni, Cu, or Zn) has been theoretically investigated using density functional theory with the M06L functional to explore their use as potential catalysts for the direct decomposition of N2O. Among these metal-porphyrins, Ti-porphyrin is the most active for N2O adsorption in the triplet ground state with the strongest adsorption energy (-13.32 kcal/mol). Ti-porphyrin was then assessed for the direct decomposition of N2O. For the overall reaction mechanism of three N2O molecules on Ti-porphyrin, two plausible catalytic cycles are proposed. Cycle 1 involves the consecutive decomposition of the first two N2O molecules, while cycle 2 is the decomposition of the third N2O molecule. For cycle 1, the activation energies of the first and second N2O decompositions are computed to be 3.77 and 49.99 kcal/mol, respectively. The activation energy for the third N2O decomposition in cycle 2 is 47.79 kcal/mol, which is slightly lower than that of the second activation energy of the first cycle. O2 molecules are released in cycles 1 and 2 as the products of the reaction, which requires endothermic energies of 102.96 and 3.63 kcal/mol, respectively. Therefore, the O2 desorption is mainly released in catalytic cycle 2 of a TiO3-porphyrin intermediate catalyst. In conclusion, regarding the O2 desorption step for the direct decomposition of N2O, the findings would be very useful to guide the search for potential N2O decomposition catalysts in new directions.

Experimental and theoretical study of the sec-C[sub 4]H[sub 9] [r reversible] CH[sub 3] + C[sub 3]H[sub 6] reaction

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

Knyazev, V.D.; Dubinsky, I.A.; Slagle, I.R.

1994-10-27

The kinetics of the unimolecular decomposition of the sec-C[sub 4]H[sub 9] radical has been studied experimentally in a heated tubular flow reactor coupled to a photoionization mass spectrometer. Rate constants for the decomposition were determined in time-resolved experiments as a function of temperature (598-680 K) and bath gas density (3-18) [times] 10[sup 16] molecules cm[sup [minus]3] in three bath gases: He, Ar, and N[sub 2]. The rate constants are in the falloff region under the conditions of the experiments. The results of earlier studies of the reverse reaction were reanalyzed and used to create a transition state model of themore » reaction. This transition state model was used to obtain values of the microcanonical rate constants, k (E). Falloff behavior was reproduced using master equation modeling with the energy barrier height for decomposition (necessary to calculate k(E)) obtained from optimization of the agreement between experimental and calculated rate constants. The resulting model of the reaction provides the high-pressure limit rate constants for the decomposition reaction and the reverse reaction. 52 refs., 7 figs., 3 tabs.« less

Universality of Schmidt decomposition and particle identity

NASA Astrophysics Data System (ADS)

Sciara, Stefania; Lo Franco, Rosario; Compagno, Giuseppe

2017-03-01

Schmidt decomposition is a widely employed tool of quantum theory which plays a key role for distinguishable particles in scenarios such as entanglement characterization, theory of measurement and state purification. Yet, its formulation for identical particles remains controversial, jeopardizing its application to analyze general many-body quantum systems. Here we prove, using a newly developed approach, a universal Schmidt decomposition which allows faithful quantification of the physical entanglement due to the identity of particles. We find that it is affected by single-particle measurement localization and state overlap. We study paradigmatic two-particle systems where identical qubits and qutrits are located in the same place or in separated places. For the case of two qutrits in the same place, we show that their entanglement behavior, whose physical interpretation is given, differs from that obtained before by different methods. Our results are generalizable to multiparticle systems and open the way for further developments in quantum information processing exploiting particle identity as a resource.

Universality of Schmidt decomposition and particle identity

PubMed Central

Sciara, Stefania; Lo Franco, Rosario; Compagno, Giuseppe

2017-01-01

Schmidt decomposition is a widely employed tool of quantum theory which plays a key role for distinguishable particles in scenarios such as entanglement characterization, theory of measurement and state purification. Yet, its formulation for identical particles remains controversial, jeopardizing its application to analyze general many-body quantum systems. Here we prove, using a newly developed approach, a universal Schmidt decomposition which allows faithful quantification of the physical entanglement due to the identity of particles. We find that it is affected by single-particle measurement localization and state overlap. We study paradigmatic two-particle systems where identical qubits and qutrits are located in the same place or in separated places. For the case of two qutrits in the same place, we show that their entanglement behavior, whose physical interpretation is given, differs from that obtained before by different methods. Our results are generalizable to multiparticle systems and open the way for further developments in quantum information processing exploiting particle identity as a resource. PMID:28333163

Universality of Schmidt decomposition and particle identity.

PubMed

Sciara, Stefania; Lo Franco, Rosario; Compagno, Giuseppe

2017-03-23

Schmidt decomposition is a widely employed tool of quantum theory which plays a key role for distinguishable particles in scenarios such as entanglement characterization, theory of measurement and state purification. Yet, its formulation for identical particles remains controversial, jeopardizing its application to analyze general many-body quantum systems. Here we prove, using a newly developed approach, a universal Schmidt decomposition which allows faithful quantification of the physical entanglement due to the identity of particles. We find that it is affected by single-particle measurement localization and state overlap. We study paradigmatic two-particle systems where identical qubits and qutrits are located in the same place or in separated places. For the case of two qutrits in the same place, we show that their entanglement behavior, whose physical interpretation is given, differs from that obtained before by different methods. Our results are generalizable to multiparticle systems and open the way for further developments in quantum information processing exploiting particle identity as a resource.

Solid State Multinuclear Magnetic Resonance Investigation of Electrolyte Decomposition Products on Lithium Ion Electrodes

NASA Technical Reports Server (NTRS)

DeSilva, J .H. S. R.; Udinwe, V.; Sideris, P. J.; Smart, M. C.; Krause, F. C.; Hwang, C.; Smith, K. A.; Greenbaum, S. G.

2012-01-01

Solid electrolyte interphase (SEI) formation in lithium ion cells prepared with advanced electrolytes is investigated by solid state multinuclear (7Li, 19F, 31P) magnetic resonance (NMR) measurements of electrode materials harvested from cycled cells subjected to an accelerated aging protocol. The electrolyte composition is varied to include the addition of fluorinated carbonates and triphenyl phosphate (TPP, a flame retardant). In addition to species associated with LiPF6 decomposition, cathode NMR spectra are characterized by the presence of compounds originating from the TPP additive. Substantial amounts of LiF are observed in the anodes as well as compounds originating from the fluorinated carbonates.

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

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

Garrett, C. Kristopher; Hauck, Cory D.

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

A Fast Solver for Implicit Integration of the Vlasov--Poisson System in the Eulerian Framework

DOE PAGES

Garrett, C. Kristopher; Hauck, Cory D.

2018-04-05

In this paper, we present a domain decomposition algorithm to accelerate the solution of Eulerian-type discretizations of the linear, steady-state Vlasov equation. The steady-state solver then forms a key component in the implementation of fully implicit or nearly fully implicit temporal integrators for the nonlinear Vlasov--Poisson system. The solver relies on a particular decomposition of phase space that enables the use of sweeping techniques commonly used in radiation transport applications. The original linear system for the phase space unknowns is then replaced by a smaller linear system involving only unknowns on the boundary between subdomains, which can then be solvedmore » efficiently with Krylov methods such as GMRES. Steady-state solves are combined to form an implicit Runge--Kutta time integrator, and the Vlasov equation is coupled self-consistently to the Poisson equation via a linearized procedure or a nonlinear fixed-point method for the electric field. Finally, numerical results for standard test problems demonstrate the efficiency of the domain decomposition approach when compared to the direct application of an iterative solver to the original linear system.« less

Design and FPGA Implementation of a Universal Chaotic Signal Generator Based on the Verilog HDL Fixed-Point Algorithm and State Machine Control

NASA Astrophysics Data System (ADS)

Qiu, Mo; Yu, Simin; Wen, Yuqiong; Lü, Jinhu; He, Jianbin; Lin, Zhuosheng

In this paper, a novel design methodology and its FPGA hardware implementation for a universal chaotic signal generator is proposed via the Verilog HDL fixed-point algorithm and state machine control. According to continuous-time or discrete-time chaotic equations, a Verilog HDL fixed-point algorithm and its corresponding digital system are first designed. In the FPGA hardware platform, each operation step of Verilog HDL fixed-point algorithm is then controlled by a state machine. The generality of this method is that, for any given chaotic equation, it can be decomposed into four basic operation procedures, i.e. nonlinear function calculation, iterative sequence operation, iterative values right shifting and ceiling, and chaotic iterative sequences output, each of which corresponds to only a state via state machine control. Compared with the Verilog HDL floating-point algorithm, the Verilog HDL fixed-point algorithm can save the FPGA hardware resources and improve the operation efficiency. FPGA-based hardware experimental results validate the feasibility and reliability of the proposed approach.

Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories

NASA Astrophysics Data System (ADS)

Matsunaga, Y.; Sugita, Y.

2018-06-01

A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.

Identification of Tool Wear when Machining of Austenitic Steels and Titatium by Miniature Machining

NASA Astrophysics Data System (ADS)

Pilc, Jozef; Kameník, Roman; Varga, Daniel; Martinček, Juraj; Sadilek, Marek

2016-12-01

Application of miniature machining is currently rapidly increasing mainly in biomedical industry and machining of hard-to-machine materials. Machinability of materials with increased level of toughness depends on factors that are important in the final state of surface integrity. Because of this, it is necessary to achieve high precision (varying in microns) in miniature machining. If we want to guarantee machining high precision, it is necessary to analyse tool wear intensity in direct interaction with given machined materials. During long-term cutting process, different cutting wedge deformations occur, leading in most cases to a rapid wear and destruction of the cutting wedge. This article deal with experimental monitoring of tool wear intensity during miniature machining.

Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

PubMed

Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich

2005-10-01

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

Testing the monogamy relations via rank-2 mixtures

NASA Astrophysics Data System (ADS)

Jung, Eylee; Park, DaeKil

2016-10-01

We introduce two tangle-based four-party entanglement measures t1 and t2, and two negativity-based measures n1 and n2, which are derived from the monogamy relations. These measures are computed for three four-qubit maximally entangled and W states explicitly. We also compute these measures for the rank-2 mixture ρ4=p | GHZ4>< GHZ4|+(1 -p ) | W4>< W4| by finding the corresponding optimal decompositions. It turns out that t1(ρ4) is trivial and the corresponding optimal decomposition is equal to the spectral decomposition. Probably, this triviality is a sign of the fact that the corresponding monogamy inequality is not sufficiently tight. We fail to compute t2(ρ4) due to the difficulty in the calculation of the residual entanglement. The negativity-based measures n1(ρ4) and n2(ρ4) are explicitly computed and the corresponding optimal decompositions are also derived explicitly.

Distributed state machine supervision for long-baseline gravitational-wave detectors

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

Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org

The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitatemore » the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.« less

Automating CPM-GOMS

NASA Technical Reports Server (NTRS)

John, Bonnie; Vera, Alonso; Matessa, Michael; Freed, Michael; Remington, Roger

2002-01-01

CPM-GOMS is a modeling method that combines the task decomposition of a GOMS analysis with a model of human resource usage at the level of cognitive, perceptual, and motor operations. CPM-GOMS models have made accurate predictions about skilled user behavior in routine tasks, but developing such models is tedious and error-prone. We describe a process for automatically generating CPM-GOMS models from a hierarchical task decomposition expressed in a cognitive modeling tool called Apex. Resource scheduling in Apex automates the difficult task of interleaving the cognitive, perceptual, and motor resources underlying common task operators (e.g. mouse move-and-click). Apex's UI automatically generates PERT charts, which allow modelers to visualize a model's complex parallel behavior. Because interleaving and visualization is now automated, it is feasible to construct arbitrarily long sequences of behavior. To demonstrate the process, we present a model of automated teller interactions in Apex and discuss implications for user modeling. available to model human users, the Goals, Operators, Methods, and Selection (GOMS) method [6, 21] has been the most widely used, providing accurate, often zero-parameter, predictions of the routine performance of skilled users in a wide range of procedural tasks [6, 13, 15, 27, 28]. GOMS is meant to model routine behavior. The user is assumed to have methods that apply sequences of operators and to achieve a goal. Selection rules are applied when there is more than one method to achieve a goal. Many routine tasks lend themselves well to such decomposition. Decomposition produces a representation of the task as a set of nested goal states that include an initial state and a final state. The iterative decomposition into goals and nested subgoals can terminate in primitives of any desired granularity, the choice of level of detail dependent on the predictions required. Although GOMS has proven useful in HCI, tools to support the construction of GOMS models have not yet come into general use.

Ab initio calculations of the effects of H+ and NH4+ on the initial decomposition of HMX.

PubMed

Wang, Luoxin; Tuo, Xinlin; Yi, Changhai; Wang, Xiaogong

2008-10-01

In this work, the effects of H(+) and NH(4)(+) on the initial decomposition of HMX were investigated on the basis of the B3P86/6-31G** and B3LYP/6-31G* calculations. Three initial decomposition pathways including the N-NO(2) bond fission, HONO elimination and C-N bond dissociation were considered for the complexes formed by HMX with H(+) (PHMX1 and PHMX2) or with NH(4)(+) (AHMX). We found that H(+) and NH(4)(+) did not evidently induce the HMX to trigger the N-NO(2) heterolysis because the energy barrier of N-NO(2) heterolysis was found to be higher than the bond dissociation energy of N-NO(2) homolytic cleavage. Meanwhile, the transition state barriers of the HONO elimination from the complexes were found to be similar to that from the isolated HMX, which means that the HONO elimination reaction of HMX was not affected by the H(+) and NH(4)(+). As for the ring-opening reaction of HMX due to the C-N bond dissociation, the calculated potential energy profile showed that the energy of the complex (AHMX) went uphill along the C-N bond length and no transition state existed on the curve. However, the transition state energy barriers of C-N bond dissociation were calculated to be only 5.0 kcal/mol and 5.5 kcal/mol for the PHMX1 and PHMX2 complexes, respectively, which were much lower than the C-N bond dissociation energy of isolated HMX. Moreover, among the three initial decomposition reactions, the C-N bond dissociation was also the most energetically favorable pathway for the PHMX1 and PHMX2. Our calculation results showed that the H(+) can significantly promote the initial thermal decomposition of C-N bond of HMX, which, however, is influenced by NH(4)(+) slightly.

On the Stability of Jump-Linear Systems Driven by Finite-State Machines with Markovian Inputs

NASA Technical Reports Server (NTRS)

Patilkulkarni, Sudarshan; Herencia-Zapana, Heber; Gray, W. Steven; Gonzalez, Oscar R.

2004-01-01

This paper presents two mean-square stability tests for a jump-linear system driven by a finite-state machine with a first-order Markovian input process. The first test is based on conventional Markov jump-linear theory and avoids the use of any higher-order statistics. The second test is developed directly using the higher-order statistics of the machine s output process. The two approaches are illustrated with a simple model for a recoverable computer control system.

A rule-based approach to model checking of UML state machines

NASA Astrophysics Data System (ADS)

Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

2016-12-01

In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

Unitary irreducible representations of SL(2,C) in discrete and continuous SU(1,1) bases

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

Conrady, Florian; Hnybida, Jeff; Department of Physics, University of Waterloo, Waterloo, Ontario

2011-01-15

We derive the matrix elements of generators of unitary irreducible representations of SL(2,C) with respect to basis states arising from a decomposition into irreducible representations of SU(1,1). This is done with regard to a discrete basis diagonalized by J{sup 3} and a continuous basis diagonalized by K{sup 1}, and for both the discrete and continuous series of SU(1,1). For completeness, we also treat the more conventional SU(2) decomposition as a fifth case. The derivation proceeds in a functional/differential framework and exploits the fact that state functions and differential operators have a similar structure in all five cases. The states aremore » defined explicitly and related to SU(1,1) and SU(2) matrix elements.« less

Shock chemistry in SX358 foams

NASA Astrophysics Data System (ADS)

Maerzke, Katie; Coe, Joshua; Fredenburg, Anthony; Lang, John; Dattelbaum, Dana

2017-06-01

We have developed new equation of state models for SX358, a cross-linked PDMS polymer. Recent experiments on SX358 over a range of initial densities (0-65% porous) have yielded new data that allow for a more thorough calibration of the equations of state. SX358 chemically decomposes under shock compression, as evidenced by a cusp in the shock locus. We therefore treat this material using two equations of state, specifically a SESAME model for the unreacted material and a free energy minimization assuming full chemical and thermodynamic equilibrium for the decomposition products. The shock locus of porous SX358 is found to be ``anomalous'' in that the decomposition reaction causes a volume expansion, rather than a volume collapse. Similar behavior has been observed in other polymer foams, notably polyurethane.

Nanowire nanocomputer as a finite-state machine.

PubMed

Yao, Jun; Yan, Hao; Das, Shamik; Klemic, James F; Ellenbogen, James C; Lieber, Charles M

2014-02-18

Implementation of complex computer circuits assembled from the bottom up and integrated on the nanometer scale has long been a goal of electronics research. It requires a design and fabrication strategy that can address individual nanometer-scale electronic devices, while enabling large-scale assembly of those devices into highly organized, integrated computational circuits. We describe how such a strategy has led to the design, construction, and demonstration of a nanoelectronic finite-state machine. The system was fabricated using a design-oriented approach enabled by a deterministic, bottom-up assembly process that does not require individual nanowire registration. This methodology allowed construction of the nanoelectronic finite-state machine through modular design using a multitile architecture. Each tile/module consists of two interconnected crossbar nanowire arrays, with each cross-point consisting of a programmable nanowire transistor node. The nanoelectronic finite-state machine integrates 180 programmable nanowire transistor nodes in three tiles or six total crossbar arrays, and incorporates both sequential and arithmetic logic, with extensive intertile and intratile communication that exhibits rigorous input/output matching. Our system realizes the complete 2-bit logic flow and clocked control over state registration that are required for a finite-state machine or computer. The programmable multitile circuit was also reprogrammed to a functionally distinct 2-bit full adder with 32-set matched and complete logic output. These steps forward and the ability of our unique design-oriented deterministic methodology to yield more extensive multitile systems suggest that proposed general-purpose nanocomputers can be realized in the near future.

Nanowire nanocomputer as a finite-state machine

PubMed Central

Yao, Jun; Yan, Hao; Das, Shamik; Klemic, James F.; Ellenbogen, James C.; Lieber, Charles M.

2014-01-01

Implementation of complex computer circuits assembled from the bottom up and integrated on the nanometer scale has long been a goal of electronics research. It requires a design and fabrication strategy that can address individual nanometer-scale electronic devices, while enabling large-scale assembly of those devices into highly organized, integrated computational circuits. We describe how such a strategy has led to the design, construction, and demonstration of a nanoelectronic finite-state machine. The system was fabricated using a design-oriented approach enabled by a deterministic, bottom–up assembly process that does not require individual nanowire registration. This methodology allowed construction of the nanoelectronic finite-state machine through modular design using a multitile architecture. Each tile/module consists of two interconnected crossbar nanowire arrays, with each cross-point consisting of a programmable nanowire transistor node. The nanoelectronic finite-state machine integrates 180 programmable nanowire transistor nodes in three tiles or six total crossbar arrays, and incorporates both sequential and arithmetic logic, with extensive intertile and intratile communication that exhibits rigorous input/output matching. Our system realizes the complete 2-bit logic flow and clocked control over state registration that are required for a finite-state machine or computer. The programmable multitile circuit was also reprogrammed to a functionally distinct 2-bit full adder with 32-set matched and complete logic output. These steps forward and the ability of our unique design-oriented deterministic methodology to yield more extensive multitile systems suggest that proposed general-purpose nanocomputers can be realized in the near future. PMID:24469812

Isoconversional approach for non-isothermal decomposition of un-irradiated and photon-irradiated 5-fluorouracil.

PubMed

Mohamed, Hala Sh; Dahy, AbdelRahman A; Mahfouz, Refaat M

2017-10-25

Kinetic analysis for the non-isothermal decomposition of un-irradiated and photon-beam-irradiated 5-fluorouracil (5-FU) as anti-cancer drug, was carried out in static air. Thermal decomposition of 5-FU proceeds in two steps. One minor step in the temperature range of (270-283°C) followed by the major step in the temperature range of (285-360°C). The non-isothermal data for un-irradiated and photon-irradiated 5-FU were analyzed using linear (Tang) and non-linear (Vyazovkin) isoconversional methods. The results of the application of these free models on the present kinetic data showed quite a dependence of the activation energy on the extent of conversion. For un-irradiated 5-FU, the non-isothermal data analysis indicates that the decomposition is generally described by A3 and A4 modeles for the minor and major decomposition steps, respectively. For a photon-irradiated sample of 5-FU with total absorbed dose of 10Gy, the decomposition is controlled by A2 model throughout the coversion range. The activation energies calculated in case of photon-irradiated 5-FU were found to be lower compared to the values obtained from the thermal decomposition of the un-irradiated sample probably due to the formation of additional nucleation sites created by a photon-irradiation. The decomposition path was investigated by intrinsic reaction coordinate (IRC) at the B3LYP/6-311++G(d,p) level of DFT. Two transition states were involved in the process by homolytic rupture of NH bond and ring secession, respectively. Published by Elsevier B.V.

Solid-state reaction kinetics of neodymium doped magnesium hydrogen phosphate system

NASA Astrophysics Data System (ADS)

Gupta, Rashmi; Slathia, Goldy; Bamzai, K. K.

2018-05-01

Neodymium doped magnesium hydrogen phosphate (NdMHP) crystals were grown by using gel encapsulation technique. Structural characterization of the grown crystals has been carried out by single crystal X-ray diffraction (XRD) and it revealed that NdMHP crystals crystallize in orthorhombic crystal system with space group Pbca. Kinetics of the decomposition of the grown crystals has been studied by non-isothermal analysis. The estimation of decomposition temperatures and weight loss has been made from the thermogravimetric/differential thermo analytical (TG/DTA) in conjuncture with DSC studies. The various steps involved in the thermal decomposition of the material have been analysed using Horowitz-Metzger, Coats-Redfern and Piloyan-Novikova equations for evaluating various kinetic parameters.

Thermodynamic Changes in the Coal Matrix - Gas - Moisture System Under Pressure Release and Phase Transformations of Gas Hydrates

NASA Astrophysics Data System (ADS)

Dyrdin, V. V.; Smirnov, V. G.; Kim, T. L.; Manakov, A. Yu.; Fofanov, A. A.; Kartopolova, I. S.

2017-06-01

The physical processes occurring in the coal - natural gas system under the gas pressure release were studied experimentally. The possibility of gas hydrates presence in the inner space of natural coal was shown, which decomposition leads to an increase in the amount of gas passing into the free state. The decomposition of gas hydrates can be caused either by the seam temperature increase or the pressure decrease to lower than the gas hydrates equilibrium curve. The contribution of methane released during gas hydrates decomposition should be taken into account in the design of safe mining technologies for coal seams prone to gas dynamic phenomena.

Technology of machine tools. Volume 2. Machine tool systems management and utilization

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

Thomson, A.R.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

31 CFR 11.2 - Policy.

Code of Federal Regulations, 2010 CFR

2010-07-01

... vending facilities, including vending machines, on property controlled by the Department of the Treasury... States. Treasury bureaus shall ensure that the collection and distribution of vending machine income from vending machines on Treasury-controlled property shall be in compliance with the regulations set forth in...

Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD.

PubMed

Atasoy, Selen; Roseman, Leor; Kaelen, Mendel; Kringelbach, Morten L; Deco, Gustavo; Carhart-Harris, Robin L

2017-12-15

Recent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used 'connectome-harmonic decomposition', a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.

Ab initio kinetics and thermal decomposition mechanism of mononitrobiuret and 1,5-dinitrobiuret

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

Sun, Hongyan, E-mail: hongyan.sun1@gmail.com, E-mail: ghanshyam.vaghjiani@us.af.mil; Vaghjiani, Ghanshyam L., E-mail: hongyan.sun1@gmail.com, E-mail: ghanshyam.vaghjiani@us.af.mil

2015-05-28

Mononitrobiuret (MNB) and 1,5-dinitrobiuret (DNB) are tetrazole-free, nitrogen-rich, energetic compounds. For the first time, a comprehensive ab initio kinetics study on the thermal decomposition mechanisms of MNB and DNB is reported here. In particular, the intramolecular interactions of amine H-atom with electronegative nitro O-atom and carbonyl O-atom have been analyzed for biuret, MNB, and DNB at the M06-2X/aug-cc-pVTZ level of theory. The results show that the MNB and DNB molecules are stabilized through six-member-ring moieties via intramolecular H-bonding with interatomic distances between 1.8 and 2.0 Å, due to electrostatic as well as polarization and dispersion interactions. Furthermore, it was foundmore » that the stable molecules in the solid state have the smallest dipole moment amongst all the conformers in the nitrobiuret series of compounds, thus revealing a simple way for evaluating reactivity of fuel conformers. The potential energy surface for thermal decomposition of MNB was characterized by spin restricted coupled cluster theory at the RCCSD(T)/cc-pV∞ Z//M06-2X/aug-cc-pVTZ level. It was found that the thermal decomposition of MNB is initiated by the elimination of HNCO and HNN(O)OH intermediates. Intramolecular transfer of a H-atom, respectively, from the terminal NH{sub 2} group to the adjacent carbonyl O-atom via a six-member-ring transition state eliminates HNCO with an energy barrier of 35 kcal/mol and from the central NH group to the adjacent nitro O-atom eliminates HNN(O)OH with an energy barrier of 34 kcal/mol. Elimination of HNN(O)OH is also the primary process involved in the thermal decomposition of DNB, which processes C{sub 2v} symmetry. The rate coefficients for the primary decomposition channels for MNB and DNB were quantified as functions of temperature and pressure. In addition, the thermal decomposition of HNN(O)OH was analyzed via Rice–Ramsperger–Kassel–Marcus/multi-well master equation simulations, the results of which reveal the formation of (NO{sub 2} + H{sub 2}O) to be the major decomposition path. Furthermore, we provide fundamental interpretations for the experimental results of Klapötke et al. [Combust. Flame 139, 358–366 (2004)] regarding the thermal stability of MNB and DNB, and their decomposition products. Notably, a fundamental understanding of fuel stability, decomposition mechanism, and key reactions leading to ignition is essential in the design and manipulation of molecular systems for the development of new energetic materials for advanced propulsion applications.« less

How state taxes and policies targeting soda consumption modify the association between school vending machines and student dietary behaviors: a cross-sectional analysis.

PubMed

Taber, Daniel R; Chriqui, Jamie F; Vuillaume, Renee; Chaloupka, Frank J

2014-01-01

Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.). Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference  =  -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption.

How State Taxes and Policies Targeting Soda Consumption Modify the Association between School Vending Machines and Student Dietary Behaviors: A Cross-Sectional Analysis

PubMed Central

Taber, Daniel R.; Chriqui, Jamie F.; Vuillaume, Renee; Chaloupka, Frank J.

2014-01-01

Background Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods Data on school vending machine access and student diet were obtained as part of the National Youth Physical Activity and Nutrition Study (NYPANS) and linked to state-level data on soda taxes, restaurant taxes, and state laws governing the sale of soda in schools. Regression models were used to: 1) estimate associations between vending machine access and soda consumption, fast food consumption, and lunch source, and 2) determine if associations were modified by state soda taxes, restaurant taxes, laws banning in-school soda sales, or student characteristics (race/ethnicity, sex, home food access, weight loss behaviors.) Results Contrary to the hypothesis, students tended to consume 0.53 fewer servings of soda/week (95% CI: -1.17, 0.11) and consume fast food on 0.24 fewer days/week (95% CI: -0.44, -0.05) if they had in-school access to vending machines. They were also less likely to consume soda daily (23.9% vs. 27.9%, average difference = -4.02, 95% CI: -7.28, -0.76). However, these inverse associations were observed primarily among states with lower soda and restaurant tax rates (relative to general food tax rates) and states that did not ban in-school soda sales. Associations did not vary by any student characteristics except for weight loss behaviors. Conclusion Isolated changes to the school food environment may have unintended consequences unless policymakers incorporate other initiatives designed to discourage overall soda consumption. PMID:25083906

Gambling with stimulus payments: feeding gaming machines with federal dollars.

PubMed

Lye, Jenny; Hirschberg, Joe

2014-09-01

In late 2008 and early 2009 the Australian Federal Government introduced a series of economic stimulus packages designed to maintain consumer spending in the early days of the Great Recession. When these packages were initiated the media suggested that the wide-spread availability of electronic gaming machines (EGMs, e.g. slot machines, poker machines, video lottery terminals) in Australia would result in stimulating the EGMs. Using state level monthly data we estimate that the stimulus packages led to an increase of 26 % in EGM revenues. This also resulted in over $60 million in additional tax revenue for State Governments. We also estimate a short-run aggregate income demand elasticity for EGMs to be approximately 2.

Implementing a quantum cloning machine in separate cavities via the optical coherent pulse as a quantum communication bus

NASA Astrophysics Data System (ADS)

Zhu, Meng-Zheng; Ye, Liu

2015-04-01

An efficient scheme is proposed to implement a quantum cloning machine in separate cavities based on a hybrid interaction between electron-spin systems placed in the cavities and an optical coherent pulse. The coefficient of the output state for the present cloning machine is just the direct product of two trigonometric functions, which ensures that different types of quantum cloning machine can be achieved readily in the same framework by appropriately adjusting the rotated angles. The present scheme can implement optimal one-to-two symmetric (asymmetric) universal quantum cloning, optimal symmetric (asymmetric) phase-covariant cloning, optimal symmetric (asymmetric) real-state cloning, optimal one-to-three symmetric economical real-state cloning, and optimal symmetric cloning of qubits given by an arbitrary axisymmetric distribution. In addition, photon loss of the qubus beams during the transmission and decoherence effects caused by such a photon loss are investigated.

Thermal Decomposition of 1,5-Dinitrobiuret (DNB): Direct Dynamics Trajectory Simulations and Statistical Modeling

DTIC Science & Technology

2011-05-03

18 . NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON Dr. Tommy W. Hawkins a. REPORT Unclassified b. ABSTRACT Unclassified c. THIS PAGE...branching using Rice-Ramsperger-Kassel-Marcus (RRKM) theory, 18 and finally to the analysis of inter-conversions of primary decomposition products...theory, 18 was employed to examine the properties of the reactant, intermediate complex and transition states as a function of the total internal energy

On the decomposition of a dynamical system into non-interacting subsystems.

NASA Technical Reports Server (NTRS)

Rosen, R.

1972-01-01

It is shown that, under rather general conditions, it is possible to formally decompose the dynamics of an n-dimensional dynamical system into a number of non-interacting subsystems. It is shown that these decompositions are in general not simply related to the kinds of observational procedures in terms of which the original state variables of the system are defined. Some consequences of this construction for reductionism in biology are discussed.

A Framework to Guide the Assessment of Human-Machine Systems.

PubMed

Stowers, Kimberly; Oglesby, James; Sonesh, Shirley; Leyva, Kevin; Iwig, Chelsea; Salas, Eduardo

2017-03-01

We have developed a framework for guiding measurement in human-machine systems. The assessment of safety and performance in human-machine systems often relies on direct measurement, such as tracking reaction time and accidents. However, safety and performance emerge from the combination of several variables. The assessment of precursors to safety and performance are thus an important part of predicting and improving outcomes in human-machine systems. As part of an in-depth literature analysis involving peer-reviewed, empirical articles, we located and classified variables important to human-machine systems, giving a snapshot of the state of science on human-machine system safety and performance. Using this information, we created a framework of safety and performance in human-machine systems. This framework details several inputs and processes that collectively influence safety and performance. Inputs are divided according to human, machine, and environmental inputs. Processes are divided into attitudes, behaviors, and cognitive variables. Each class of inputs influences the processes and, subsequently, outcomes that emerge in human-machine systems. This framework offers a useful starting point for understanding the current state of the science and measuring many of the complex variables relating to safety and performance in human-machine systems. This framework can be applied to the design, development, and implementation of automated machines in spaceflight, military, and health care settings. We present a hypothetical example in our write-up of how it can be used to aid in project success.

Energy: Machines, Science (Experimental): 5311.03.

ERIC Educational Resources Information Center

Castaldi, June P.

This unit of instruction was designed as an introductory course in energy involving six simple machines, electricity, magnetism, and motion. The booklet lists the relevant state-adopted texts and states the performance objectives for the unit. It provides an outline of the course content and suggests experiments, demonstrations, field trips, and…

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2013 CFR

2013-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings... identifiable as defense articles. If the end-item is an article on the U.S. Munitions List (including...

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2011 CFR

2011-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings... identifiable as defense articles. If the end-item is an article on the U.S. Munitions List (including...

22 CFR 121.10 - Forgings, castings and machined bodies.

Code of Federal Regulations, 2012 CFR

2012-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings... identifiable as defense articles. If the end-item is an article on the U.S. Munitions List (including...

Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

PubMed Central

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

PubMed

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

Implementing Molecular Dynamics for Hybrid High Performance Computers - 1. Short Range Forces

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

Brown, W Michael; Wang, Peng; Plimpton, Steven J

The use of accelerators such as general-purpose graphics processing units (GPGPUs) have become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high performance computers, machines with more than one type of floating-point processor, are now becoming more prevalent due to these advantages. In this work, we discuss several important issues in porting a large molecular dynamics code for use on parallel hybrid machines - 1) choosing a hybrid parallel decomposition that works on central processing units (CPUs) with distributed memory and accelerator cores with shared memory,more » 2) minimizing the amount of code that must be ported for efficient acceleration, 3) utilizing the available processing power from both many-core CPUs and accelerators, and 4) choosing a programming model for acceleration. We present our solution to each of these issues for short-range force calculation in the molecular dynamics package LAMMPS. We describe algorithms for efficient short range force calculation on hybrid high performance machines. We describe a new approach for dynamic load balancing of work between CPU and accelerator cores. We describe the Geryon library that allows a single code to compile with both CUDA and OpenCL for use on a variety of accelerators. Finally, we present results on a parallel test cluster containing 32 Fermi GPGPUs and 180 CPU cores.« less

Analysis of programming properties and the row-column generation method for 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper deals with fast methods for training a 1-norm support vector machine (SVM). First, we define a specific class of linear programming with many sparse constraints, i.e., row-column sparse constraint linear programming (RCSC-LP). In nature, the 1-norm SVM is a sort of RCSC-LP. In order to construct subproblems for RCSC-LP and solve them, a family of row-column generation (RCG) methods is introduced. RCG methods belong to a category of decomposition techniques, and perform row and column generations in a parallel fashion. Specially, for the 1-norm SVM, the maximum size of subproblems of RCG is identical with the number of Support Vectors (SVs). We also introduce a semi-deleting rule for RCG methods and prove the convergence of RCG methods when using the semi-deleting rule. Experimental results on toy data and real-world datasets illustrate that it is efficient to use RCG to train the 1-norm SVM, especially in the case of small SVs. Copyright © 2013 Elsevier Ltd. All rights reserved.

Joint properties of a tool machining process to guarantee fluid-proof abilities

NASA Astrophysics Data System (ADS)

Bataille, C.; Deltombe, R.; Jourani, A.; Bigerelle, M.

2017-12-01

This study addressed the impact of rod surface topography in contact with reciprocating seals. Rods were tooled with and without centreless grinding. All rods tooled with centreless grinding were fluid-proof, in contrast to rods tooled without centreless grinding that either had leaks or were fluid-proof. A method was developed to analyse the machining signature, and the software Mesrug™ was used in order to discriminate roughness parameters that can be used to characterize the sealing functionality. According to this surface roughness analysis, a fluid-proof rod tooled without centreless grinding presents aperiodic large plateaus, and the relevant roughness parameter for characterizing the sealing functionality is the density of summits S DS. Increasing the density of summits counteracts leakage, which may be because motif decomposition integrates three topographical components: circularity (perpendicular long-wave roughness), longitudinal waviness, and roughness thanks to the Wolf pruning algorithm. A 3D analytical contact model was applied to analyse the contact area of each type of sample with the seal surface. This model provides a leakage probability, and the results were consistent with the interpretation of the topographical analysis.

Amino acid "little Big Bang": representing amino acid substitution matrices as dot products of Euclidian vectors.

PubMed

Zimmermann, Karel; Gibrat, Jean-François

2010-01-04

Sequence comparisons make use of a one-letter representation for amino acids, the necessary quantitative information being supplied by the substitution matrices. This paper deals with the problem of finding a representation that provides a comprehensive description of amino acid intrinsic properties consistent with the substitution matrices. We present a Euclidian vector representation of the amino acids, obtained by the singular value decomposition of the substitution matrices. The substitution matrix entries correspond to the dot product of amino acid vectors. We apply this vector encoding to the study of the relative importance of various amino acid physicochemical properties upon the substitution matrices. We also characterize and compare the PAM and BLOSUM series substitution matrices. This vector encoding introduces a Euclidian metric in the amino acid space, consistent with substitution matrices. Such a numerical description of the amino acid is useful when intrinsic properties of amino acids are necessary, for instance, building sequence profiles or finding consensus sequences, using machine learning algorithms such as Support Vector Machine and Neural Networks algorithms.

A Qualitative Model of Human Interaction with Complex Dynamic Systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

A qualitative model of human interaction with complex dynamic systems

NASA Technical Reports Server (NTRS)

Hess, Ronald A.

1987-01-01

A qualitative model describing human interaction with complex dynamic systems is developed. The model is hierarchical in nature and consists of three parts: a behavior generator, an internal model, and a sensory information processor. The behavior generator is responsible for action decomposition, turning higher level goals or missions into physical action at the human-machine interface. The internal model is an internal representation of the environment which the human is assumed to possess and is divided into four submodel categories. The sensory information processor is responsible for sensory composition. All three parts of the model act in consort to allow anticipatory behavior on the part of the human in goal-directed interaction with dynamic systems. Human workload and error are interpreted in this framework, and the familiar example of an automobile commute is used to illustrate the nature of the activity in the three model elements. Finally, with the qualitative model as a guide, verbal protocols from a manned simulation study of a helicopter instrument landing task are analyzed with particular emphasis on the effect of automation on human-machine performance.

Long-term litter decomposition controlled by manganese redox cycling

PubMed Central

Keiluweit, Marco; Nico, Peter; Harmon, Mark E.; Mao, Jingdong; Pett-Ridge, Jennifer; Kleber, Markus

2015-01-01

Litter decomposition is a keystone ecosystem process impacting nutrient cycling and productivity, soil properties, and the terrestrial carbon (C) balance, but the factors regulating decomposition rate are still poorly understood. Traditional models assume that the rate is controlled by litter quality, relying on parameters such as lignin content as predictors. However, a strong correlation has been observed between the manganese (Mn) content of litter and decomposition rates across a variety of forest ecosystems. Here, we show that long-term litter decomposition in forest ecosystems is tightly coupled to Mn redox cycling. Over 7 years of litter decomposition, microbial transformation of litter was paralleled by variations in Mn oxidation state and concentration. A detailed chemical imaging analysis of the litter revealed that fungi recruit and redistribute unreactive Mn2+ provided by fresh plant litter to produce oxidative Mn3+ species at sites of active decay, with Mn eventually accumulating as insoluble Mn3+/4+ oxides. Formation of reactive Mn3+ species coincided with the generation of aromatic oxidation products, providing direct proof of the previously posited role of Mn3+-based oxidizers in the breakdown of litter. Our results suggest that the litter-decomposing machinery at our coniferous forest site depends on the ability of plants and microbes to supply, accumulate, and regenerate short-lived Mn3+ species in the litter layer. This observation indicates that biogeochemical constraints on bioavailability, mobility, and reactivity of Mn in the plant–soil system may have a profound impact on litter decomposition rates. PMID:26372954

A comparison of reduced-order modelling techniques for application in hyperthermia control and estimation.

PubMed

Bailey, E A; Dutton, A W; Mattingly, M; Devasia, S; Roemer, R B

1998-01-01

Reduced-order modelling techniques can make important contributions in the control and state estimation of large systems. In hyperthermia, reduced-order modelling can provide a useful tool by which a large thermal model can be reduced to the most significant subset of its full-order modes, making real-time control and estimation possible. Two such reduction methods, one based on modal decomposition and the other on balanced realization, are compared in the context of simulated hyperthermia heat transfer problems. The results show that the modal decomposition reduction method has three significant advantages over that of balanced realization. First, modal decomposition reduced models result in less error, when compared to the full-order model, than balanced realization reduced models of similar order in problems with low or moderate advective heat transfer. Second, because the balanced realization based methods require a priori knowledge of the sensor and actuator placements, the reduced-order model is not robust to changes in sensor or actuator locations, a limitation not present in modal decomposition. Third, the modal decomposition transformation is less demanding computationally. On the other hand, in thermal problems dominated by advective heat transfer, numerical instabilities make modal decomposition based reduction problematic. Modal decomposition methods are therefore recommended for reduction of models in which advection is not dominant and research continues into methods to render balanced realization based reduction more suitable for real-time clinical hyperthermia control and estimation.

Long-term litter decomposition controlled by manganese redox cycling.

PubMed

Keiluweit, Marco; Nico, Peter; Harmon, Mark E; Mao, Jingdong; Pett-Ridge, Jennifer; Kleber, Markus

2015-09-22

Litter decomposition is a keystone ecosystem process impacting nutrient cycling and productivity, soil properties, and the terrestrial carbon (C) balance, but the factors regulating decomposition rate are still poorly understood. Traditional models assume that the rate is controlled by litter quality, relying on parameters such as lignin content as predictors. However, a strong correlation has been observed between the manganese (Mn) content of litter and decomposition rates across a variety of forest ecosystems. Here, we show that long-term litter decomposition in forest ecosystems is tightly coupled to Mn redox cycling. Over 7 years of litter decomposition, microbial transformation of litter was paralleled by variations in Mn oxidation state and concentration. A detailed chemical imaging analysis of the litter revealed that fungi recruit and redistribute unreactive Mn(2+) provided by fresh plant litter to produce oxidative Mn(3+) species at sites of active decay, with Mn eventually accumulating as insoluble Mn(3+/4+) oxides. Formation of reactive Mn(3+) species coincided with the generation of aromatic oxidation products, providing direct proof of the previously posited role of Mn(3+)-based oxidizers in the breakdown of litter. Our results suggest that the litter-decomposing machinery at our coniferous forest site depends on the ability of plants and microbes to supply, accumulate, and regenerate short-lived Mn(3+) species in the litter layer. This observation indicates that biogeochemical constraints on bioavailability, mobility, and reactivity of Mn in the plant-soil system may have a profound impact on litter decomposition rates.

High-speed machining of Space Shuttle External Tank (ET) panels

NASA Technical Reports Server (NTRS)

Miller, J. A.

1983-01-01

Potential production rates and project cost savings achieved by converting the conventional machining process in manufacturing shuttle external tank panels to high speed machining (HSM) techniques were studied. Savings were projected from the comparison of current production rates with HSM rates and with rates attainable on new conventional machines. The HSM estimates were also based on rates attainable by retrofitting existing conventional equipment with high speed spindle motors and rates attainable using new state of the art machines designed and built for HSM.

Overview of the Machine-Tool Task Force

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

Sutton, G.P.

1981-06-08

The Machine Tool Task Force, (MTTF) surveyed the state of the art of machine tool technology for material removal for two and one-half years. This overview gives a brief summary of the approach, specific subjects covered, principal conclusions and some of the key recommendations aimed at improving the technology and advancing the productivity of machine tools. The Task Force consisted of 123 experts from the US and other countries. Their findings are documented in a five-volume report, Technology of Machine Tools.

VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling

NASA Technical Reports Server (NTRS)

Grasso, Christopher A.; Riedel, Joseph E.

2012-01-01

VML (Virtual Machine Language) has been used as the sequencing flight software on over a dozen JPL deep-space missions, most recently flying on GRAIL and JUNO. In conjunction with the NASA SBIR entitled "Reactive Rendezvous and Docking Sequencer", VML version 3.0 has been enhanced to include object-oriented element organization, built-in queuing operations, and sophisticated matrix / vector operations. These improvements allow VML scripts to easily perform much of the work that formerly would have required a great deal of expensive flight software development to realize. Autonomous turning and tracking makes considerable use of new VML features. Profiles generated by flight software are managed using object-oriented VML data constructs executed in discrete time by the VML flight software. VML vector and matrix operations provide the ability to calculate and supply quaternions to the attitude controller flight software which produces torque requests. Using VML-based attitude planning components eliminates flight software development effort, and reduces corresponding costs. In addition, the direct management of the quaternions allows turning and tracking to be tied in with sophisticated high-level VML state machines. These state machines provide autonomous management of spacecraft operations during critical tasks like a hypothetic Mars sample return rendezvous and docking. State machines created for autonomous science observations can also use this sort of attitude planning system, allowing heightened autonomy levels to reduce operations costs. VML state machines cannot be considered merely sequences - they are reactive logic constructs capable of autonomous decision making within a well-defined domain. The state machine approach enabled by VML 3.0 is progressing toward flight capability with a wide array of applicable mission activities.

DIFFRACTION STUDY ON THE THERMAL STABILITY OF Ti{sub 3}SiC{sub 2}/TiC/TiSi{sub 2} COMPOSITES IN VACUUM

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

Pang, W. K.; Low, I. M.; O'Connor, B. H.

2010-01-05

Titanium silicon carbide (Ti{sub 3}SiC{sub 2}) possesses a unique combination of properties of both metals and ceramics, for it is thermally shock resistant, thermally and electrically conductive, damage tolerant, lightweight, highly oxidation resistant, elastically stiff, and mechanically machinable. In this paper, the effect of high vacuum annealing on the phase stability and phase transitions of Ti{sub 3}SiC{sub 2}/TiC/TiSi{sub 2} composites at up to 1550 deg. C was studied using in-situ neutron diffraction. The role of TiC and TiSi{sub 2} on the thermal stability of Ti{sub 3}SiC{sub 2} during vacuum annealing is discussed. TiC reacts with TiSi{sub 2} between 1400-1450 deg.more » C to form Ti{sub 3}SiC{sub 2}. Above 1400 deg. C, decomposition of Ti{sub 3}SiC{sub 2} into TiC commenced and the rate increased with increased temperature and dwell time. Furthermore, the activation energy for the formation and decomposition of Ti{sub 3}SiC{sub 2} was determined.« less

Multispectral image fusion for illumination-invariant palmprint recognition

PubMed Central

Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied. PMID:28558064

Multispectral image fusion for illumination-invariant palmprint recognition.

PubMed

Lu, Longbin; Zhang, Xinman; Xu, Xuebin; Shang, Dongpeng

2017-01-01

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy

NASA Astrophysics Data System (ADS)

Li, Yongbo; Xu, Minqiang; Wang, Rixin; Huang, Wenhu

2016-01-01

This paper presents a new rolling bearing fault diagnosis method based on local mean decomposition (LMD), improved multiscale fuzzy entropy (IMFE), Laplacian score (LS) and improved support vector machine based binary tree (ISVM-BT). When the fault occurs in rolling bearings, the measured vibration signal is a multi-component amplitude-modulated and frequency-modulated (AM-FM) signal. LMD, a new self-adaptive time-frequency analysis method can decompose any complicated signal into a series of product functions (PFs), each of which is exactly a mono-component AM-FM signal. Hence, LMD is introduced to preprocess the vibration signal. Furthermore, IMFE that is designed to avoid the inaccurate estimation of fuzzy entropy can be utilized to quantify the complexity and self-similarity of time series for a range of scales based on fuzzy entropy. Besides, the LS approach is introduced to refine the fault features by sorting the scale factors. Subsequently, the obtained features are fed into the multi-fault classifier ISVM-BT to automatically fulfill the fault pattern identifications. The experimental results validate the effectiveness of the methodology and demonstrate that proposed algorithm can be applied to recognize the different categories and severities of rolling bearings.

Approximate analytical solutions in the analysis of elastic structures of complex geometry

NASA Astrophysics Data System (ADS)

Goloskokov, Dmitriy P.; Matrosov, Alexander V.

2018-05-01

A method of analytical decomposition for analysis plane structures of a complex configuration is presented. For each part of the structure in the form of a rectangle all the components of the stress-strain state are constructed by the superposition method. The method is based on two solutions derived in the form of trigonometric series with unknown coefficients using the method of initial functions. The coefficients are determined from the system of linear algebraic equations obtained while satisfying the boundary conditions and the conditions for joining the structure parts. The components of the stress-strain state of a bent plate with holes are calculated using the analytical decomposition method.

Equation of State and Shock-Driven Decomposition of 'Soft' Materials

DOE PAGES

Coe, Joshua Damon; Dattelbaum, Dana Mcgraw

2017-12-01

Equation of state (EOS) efforts at National Nuclear Security Administration (NNSA) national laboratories tend to focus heavily on metals, and rightly so given their obvious primacy in nuclear weapons. Our focus here, however, is on the EOS of 'soft' matter such as polymers and their derived foams, which present a number of challenges distinct from those of other material classes. This brief description will cover only one aspect of polymer EOS modeling: treatment of shock-driven decomposition. Here, these interesting (and sometimes neglected) materials exhibit a number of other challenging features— glass transitions, complex thermal behavior, response that is both viscousmore » and elastic—each warranting additional discussions of their own.« less

Equation of State and Shock-Driven Decomposition of 'Soft' Materials

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

Coe, Joshua Damon; Dattelbaum, Dana Mcgraw

Equation of state (EOS) efforts at National Nuclear Security Administration (NNSA) national laboratories tend to focus heavily on metals, and rightly so given their obvious primacy in nuclear weapons. Our focus here, however, is on the EOS of 'soft' matter such as polymers and their derived foams, which present a number of challenges distinct from those of other material classes. This brief description will cover only one aspect of polymer EOS modeling: treatment of shock-driven decomposition. Here, these interesting (and sometimes neglected) materials exhibit a number of other challenging features— glass transitions, complex thermal behavior, response that is both viscousmore » and elastic—each warranting additional discussions of their own.« less

Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator

NASA Astrophysics Data System (ADS)

Li, Qianxiao; Dietrich, Felix; Bollt, Erik M.; Kevrekidis, Ioannis G.

2017-10-01

Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD)51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator.

PubMed

Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G

2017-10-01

Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

Machinability of Green Powder Metallurgy Components: Part I. Characterization of the Influence of Tool Wear

NASA Astrophysics Data System (ADS)

Robert-Perron, Etienne; Blais, Carl; Pelletier, Sylvain; Thomas, Yannig

2007-06-01

The green machining process is an interesting approach for solving the mediocre machining behavior of high-performance powder metallurgy (PM) steels. This process appears as a promising method for extending tool life and reducing machining costs. Recent improvements in binder/lubricant technologies have led to high green strength systems that enable green machining. So far, tool wear has been considered negligible when characterizing the machinability of green PM specimens. This inaccurate assumption may lead to the selection of suboptimum cutting conditions. The first part of this study involves the optimization of the machining parameters to minimize the effects of tool wear on the machinability in turning of green PM components. The second part of our work compares the sintered mechanical properties of components machined in green state with other machined after sintering.

Runtime Verification of C Programs

NASA Technical Reports Server (NTRS)

Havelund, Klaus

2008-01-01

We present in this paper a framework, RMOR, for monitoring the execution of C programs against state machines, expressed in a textual (nongraphical) format in files separate from the program. The state machine language has been inspired by a graphical state machine language RCAT recently developed at the Jet Propulsion Laboratory, as an alternative to using Linear Temporal Logic (LTL) for requirements capture. Transitions between states are labeled with abstract event names and Boolean expressions over such. The abstract events are connected to code fragments using an aspect-oriented pointcut language similar to ASPECTJ's or ASPECTC's pointcut language. The system is implemented in the C analysis and transformation package CIL, and is programmed in OCAML, the implementation language of CIL. The work is closely related to the notion of stateful aspects within aspect-oriented programming, where pointcut languages are extended with temporal assertions over the execution trace.

Implementing finite state machines in a computer-based teaching system

NASA Astrophysics Data System (ADS)

Hacker, Charles H.; Sitte, Renate

1999-09-01

Finite State Machines (FSM) are models for functions commonly implemented in digital circuits such as timers, remote controls, and vending machines. Teaching FSM is core in the curriculum of many university digital electronic or discrete mathematics subjects. Students often have difficulties grasping the theoretical concepts in the design and analysis of FSM. This has prompted the author to develop an MS-WindowsTM compatible software, WinState, that provides a tutorial style teaching aid for understanding the mechanisms of FSM. The animated computer screen is ideal for visually conveying the required design and analysis procedures. WinState complements other software for combinatorial logic previously developed by the author, and enhances the existing teaching package by adding sequential logic circuits. WinState enables the construction of a students own FSM, which can be simulated, to test the design for functionality and possible errors.

Multi-category micro-milling tool wear monitoring with continuous hidden Markov models

NASA Astrophysics Data System (ADS)

Zhu, Kunpeng; Wong, Yoke San; Hong, Geok Soon

2009-02-01

In-process monitoring of tool conditions is important in micro-machining due to the high precision requirement and high tool wear rate. Tool condition monitoring in micro-machining poses new challenges compared to conventional machining. In this paper, a multi-category classification approach is proposed for tool flank wear state identification in micro-milling. Continuous Hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micro-milling, and estimation of the tool wear state given the cutting force features. For a noise-robust approach, the HMM outputs are connected via a medium filter to minimize the tool state before entry into the next state due to high noise level. A detailed study on the selection of HMM structures for tool condition monitoring (TCM) is presented. Case studies on the tool state estimation in the micro-milling of pure copper and steel demonstrate the effectiveness and potential of these methods.

State sales tax rates for soft drinks and snacks sold through grocery stores and vending machines, 2007.

PubMed

Chriqui, Jamie F; Eidson, Shelby S; Bates, Hannalori; Kowalczyk, Shelly; Chaloupka, Frank J

2008-07-01

Junk food consumption is associated with rising obesity rates in the United States. While a "junk food" specific tax is a potential public health intervention, a majority of states already impose sales taxes on certain junk food and soft drinks. This study reviews the state sales tax variance for soft drinks and selected snack products sold through grocery stores and vending machines as of January 2007. Sales taxes vary by state, intended retail location (grocery store vs. vending machine), and product. Vended snacks and soft drinks are taxed at a higher rate than grocery items and other food products, generally, indicative of a "disfavored" tax status attributed to vended items. Soft drinks, candy, and gum are taxed at higher rates than are other items examined. Similar tax schemes in other countries and the potential implications of these findings relative to the relationship between price and consumption are discussed.

Research Results Of Stress-Strain State Of Cutting Tool When Aviation Materials Turning

NASA Astrophysics Data System (ADS)

Serebrennikova, A. G.; Nikolaeva, E. P.; Savilov, A. V.; Timofeev, S. A.; Pyatykh, A. S.

2018-01-01

Titanium alloys and stainless steels are hard-to-machine of all the machining types. Cutting edge state of turning tool after machining titanium and high-strength aluminium alloys and corrosion-resistant high-alloy steel has been studied. Cutting forces and chip contact arears with the rake surface of cutter has been measured. The relationship of cutting forces and residual stresses are shown. Cutting forces and residual stresses vs value of cutting tool rake angle relation were obtained. Measurements of residual stresses were performed by x-ray diffraction.

Cue-Reactive Altered State of Consciousness Mediates the Relationship Between Problem-Gambling Severity and Cue-Reactive Urge in Poker-Machine Gamblers.

PubMed

Tricker, Christopher; Rock, Adam J; Clark, Gavin I

2016-06-01

In order to enhance our understanding of the nature of poker-machine problem-gambling, a community sample of 37 poker-machine gamblers (M age = 32 years, M PGSI = 5; PGSI = Problem Gambling Severity Index) were assessed for urge to gamble (responses on a visual analogue scale) and altered state of consciousness (assessed by the Altered State of Awareness dimension of the Phenomenology of Consciousness Inventory) at baseline, after a neutral cue, and after a gambling cue. It was found that (a) problem-gambling severity (PGSI score) predicted increase in urge (from neutral cue to gambling cue, controlling for baseline; sr (2) = .19, p = .006) and increase in altered state of consciousness (from neutral cue to gambling cue, controlling for baseline; sr (2) = .57, p < .001), and (b) increase in altered state of consciousness (from neutral cue to gambling cue) mediated the relationship between problem-gambling severity and increase in urge (from neutral cue to gambling cue; κ(2) = .40, 99 % CI [.08, .71]). These findings suggest that cue-reactive altered state of consciousness is an important component of cue-reactive urge in poker-machine problem-gamblers.

Acceleration of saddle-point searches with machine learning.

PubMed

Peterson, Andrew A

2016-08-21

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the number of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.

Acceleration of saddle-point searches with machine learning

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

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu

In atomistic simulations, the location of the saddle point on the potential-energy surface (PES) gives important information on transitions between local minima, for example, via transition-state theory. However, the search for saddle points often involves hundreds or thousands of ab initio force calls, which are typically all done at full accuracy. This results in the vast majority of the computational effort being spent calculating the electronic structure of states not important to the researcher, and very little time performing the calculation of the saddle point state itself. In this work, we describe how machine learning (ML) can reduce the numbermore » of intermediate ab initio calculations needed to locate saddle points. Since machine-learning models can learn from, and thus mimic, atomistic simulations, the saddle-point search can be conducted rapidly in the machine-learning representation. The saddle-point prediction can then be verified by an ab initio calculation; if it is incorrect, this strategically has identified regions of the PES where the machine-learning representation has insufficient training data. When these training data are used to improve the machine-learning model, the estimates greatly improve. This approach can be systematized, and in two simple example problems we demonstrate a dramatic reduction in the number of ab initio force calls. We expect that this approach and future refinements will greatly accelerate searches for saddle points, as well as other searches on the potential energy surface, as machine-learning methods see greater adoption by the atomistics community.« less

Nanoindentation testing as a powerful screening tool for assessing phase stability of nanocrystalline high-entropy alloys

DOE PAGES

Maier-Kiener, Verena; Schuh, Benjamin; George, Easo P.; ...

2016-11-19

The equiatomic high-entropy alloy (HEA), CrMnFeCoNi, has recently been shown to be microstructurally unstable, resulting in a multi-phase microstructure after intermediate-temperature annealing treatments. The decomposition occurs rapidly in the nanocrystalline (NC) state and after longer annealing times in coarse-grained states. To characterize the mechanical properties of differently annealed NC states containing multiple phases, nanoindentation was used in this paper. The results revealed besides drastic changes in hardness, also for the first time significant changes in the Young's modulus and strain rate sensitivity. Finally, nanoindentation of NC HEAs is, therefore, a useful complementary screening tool with high potential as a highmore » throughput approach to detect phase decomposition, which can also be used to qualitatively predict the long-term stability of single-phase HEAs.« less

Transition mechanism of the reaction interface of the thermal decomposition of calcite

NASA Astrophysics Data System (ADS)

Li, Zhi; Zhao, Zhen; Wang, Qi; Wang, Guocheng

2018-06-01

Even the reaction layer (excited state CaCO3) is so thin that it is difficult to detect, it is significantly restrict the orientation of the solid product (excited state CaO) of the thermal decomposition of calcite. Quantum chemical calculation with GGA-PW91 functional reveals that the ground-state (CaCO3)m clusters are more stable than the hybrid objects (CaCO3)m-(CaO)n clusters. The lowest-energy (CaCO3)m clusters are more kinetically stable than that of (CaCO3)m-n(CaO)n clusters and then than that of (CaO)n clusters except (CaCO3)(CaO)3 clusters from the HOMO-LUMO gaps. (CaCO3)2 clusters should co-exist at room temperature and they prefer to decompose with the temperature increasing.

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations

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

Peng, Bo; Kowalski, Karol

The representation and storage of two-electron integral tensors are vital in large- scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this paper, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition ofmore » the two-electron integral tensor in our implementation. For the size of atomic basis set N_b ranging from ~ 100 up to ~ 2, 000, the observed numerical scaling of our implementation shows O(N_b^{2.5~3}) versus O(N_b^{3~4}) of single CD in most of other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic-orbital (AO) two-electron integral tensor from O(N_b^4) to O(N_b^2 log_{10}(N_b)) with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled- cluster formalism employing single and double excitations (CCSD) on several bench- mark systems including the C_{60} molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10^{-4} to 10^{-3} to give acceptable compromise between efficiency and accuracy.« less

Highly Efficient and Scalable Compound Decomposition of Two-Electron Integral Tensor and Its Application in Coupled Cluster Calculations.

PubMed

Peng, Bo; Kowalski, Karol

2017-09-12

The representation and storage of two-electron integral tensors are vital in large-scale applications of accurate electronic structure methods. Low-rank representation and efficient storage strategy of integral tensors can significantly reduce the numerical overhead and consequently time-to-solution of these methods. In this work, by combining pivoted incomplete Cholesky decomposition (CD) with a follow-up truncated singular vector decomposition (SVD), we develop a decomposition strategy to approximately represent the two-electron integral tensor in terms of low-rank vectors. A systematic benchmark test on a series of 1-D, 2-D, and 3-D carbon-hydrogen systems demonstrates high efficiency and scalability of the compound two-step decomposition of the two-electron integral tensor in our implementation. For the size of the atomic basis set, N b , ranging from ∼100 up to ∼2,000, the observed numerical scaling of our implementation shows [Formula: see text] versus [Formula: see text] cost of performing single CD on the two-electron integral tensor in most of the other implementations. More importantly, this decomposition strategy can significantly reduce the storage requirement of the atomic orbital (AO) two-electron integral tensor from [Formula: see text] to [Formula: see text] with moderate decomposition thresholds. The accuracy tests have been performed using ground- and excited-state formulations of coupled cluster formalism employing single and double excitations (CCSD) on several benchmark systems including the C 60 molecule described by nearly 1,400 basis functions. The results show that the decomposition thresholds can be generally set to 10 -4 to 10 -3 to give acceptable compromise between efficiency and accuracy.

Pulse Generator

NASA Technical Reports Server (NTRS)

Greer, Lawrence (Inventor)

2017-01-01

An apparatus and a computer-implemented method for generating pulses synchronized to a rising edge of a tachometer signal from rotating machinery are disclosed. For example, in one embodiment, a pulse state machine may be configured to generate a plurality of pulses, and a period state machine may be configured to determine a period for each of the plurality of pulses.

TRS-80 at the Maine State Library.

ERIC Educational Resources Information Center

Wismer, Donald

This report describes the applications and work flow of a TRS-80 microcomputer at the Maine State Library, and provides sample computer-generated records and programs used with the TRS-80. The machine was chosen for its price, availability, and compatibility with machines already in Maine's schools. It is used for mailing list management (with…

An Analysis of Heavy-Ion Single Event Effects for a Variety of Finite State-Machine Mitigation Strategies

NASA Technical Reports Server (NTRS)

Berg, Melanie D.; Label, Kenneth A.; Kim, Hak; Phan, Anthony; Seidleck, Christina

2014-01-01

Finite state-machines (FSMs) are used to control operational flow in application specific integrated circuits (ASICs) and field programmable gate array (FPGA) devices. Because of their ease of interpretation, FSMs simplify the design and verification process and consequently are significant components in a synchronous design.

Synthesis of Ag-doped TiO2 nanoparticles by combining laser decomposition of titanium isopropoxide and ablation of Ag for dye-sensitized solar cells

NASA Astrophysics Data System (ADS)

Al-Kamal, Ahmed Kamal

Nanostructured powders of TiO2 and Ag-doped TiO2 are synthesized by a novel pulsed-laser process that combines laser ablation of a silver (Ag) disc with laser decomposition of a titanium tetra-isopropoxide (TTIP) solution. Nanoparticles are formed by rapid condensation of vaporized species in the plasma plume generated by the high power laser, resulting in the formation of rapidly quenched Ag-doped TiO2 nanoparticles that have far-from-equilibrium or metastable structures. The uniqueness of the new ablation process is that it is a one-step process, in contrast to the two-step process developed by previous researchers in the field. Moreover, its ability to synthesize an extended-solid solution phase of Ag in TiO 2 may also be unique. The present work implies that other oxide phases, such as Al2O3, MgO and MgAl2O4, can be doped with normally insoluble metals, such as Pt and Ir, thus opening new opportunities for catalytic applications. Again, there is the prospect of being able to synthesize nanopowders of diamond, c-BN, and mixtures thereof, which are of interest for applications in machine tools, rock-drill bits, and lightweight armor. A wet-chemistry method is also investigated, which has much in common with that adopted by previous workers in the field. However, photo-voltaic properties do not measure up to expectations based on published data. A possible explanation is that the selected Ag concentrations are too high, so that recombination of holes and electrons occurs via a quantum-tunneling mechanism reduces photo-activity. Future work, therefore, will investigate lower concentrations of Ag dopant in TiO2, while also examining the effects of metastable states, including extended solid solution, amorphous, and semi-crystalline structures.

Approximation methods for stochastic petri nets

NASA Technical Reports Server (NTRS)

Jungnitz, Hauke Joerg

1992-01-01

Stochastic Marked Graphs are a concurrent decision free formalism provided with a powerful synchronization mechanism generalizing conventional Fork Join Queueing Networks. In some particular cases the analysis of the throughput can be done analytically. Otherwise the analysis suffers from the classical state explosion problem. Embedded in the divide and conquer paradigm, approximation techniques are introduced for the analysis of stochastic marked graphs and Macroplace/Macrotransition-nets (MPMT-nets), a new subclass introduced herein. MPMT-nets are a subclass of Petri nets that allow limited choice, concurrency and sharing of resources. The modeling power of MPMT is much larger than that of marked graphs, e.g., MPMT-nets can model manufacturing flow lines with unreliable machines and dataflow graphs where choice and synchronization occur. The basic idea leads to the notion of a cut to split the original net system into two subnets. The cuts lead to two aggregated net systems where one of the subnets is reduced to a single transition. A further reduction leads to a basic skeleton. The generalization of the idea leads to multiple cuts, where single cuts can be applied recursively leading to a hierarchical decomposition. Based on the decomposition, a response time approximation technique for the performance analysis is introduced. Also, delay equivalence, which has previously been introduced in the context of marked graphs by Woodside et al., Marie's method and flow equivalent aggregation are applied to the aggregated net systems. The experimental results show that response time approximation converges quickly and shows reasonable accuracy in most cases. The convergence of Marie's method and flow equivalent aggregation are applied to the aggregated net systems. The experimental results show that response time approximation converges quickly and shows reasonable accuracy in most cases. The convergence of Marie's is slower, but the accuracy is generally better. Delay equivalence often fails to converge, while flow equivalent aggregation can lead to potentially bad results if a strong dependence of the mean completion time on the interarrival process exists.

People counting in classroom based on video surveillance

NASA Astrophysics Data System (ADS)

Zhang, Quanbin; Huang, Xiang; Su, Juan

2014-11-01

Currently, the switches of the lights and other electronic devices in the classroom are mainly relied on manual control, as a result, many lights are on while no one or only few people in the classroom. It is important to change the current situation and control the electronic devices intelligently according to the number and the distribution of the students in the classroom, so as to reduce the considerable waste of electronic resources. This paper studies the problem of people counting in classroom based on video surveillance. As the camera in the classroom can not get the full shape contour information of bodies and the clear features information of faces, most of the classical algorithms such as the pedestrian detection method based on HOG (histograms of oriented gradient) feature and the face detection method based on machine learning are unable to obtain a satisfied result. A new kind of dual background updating model based on sparse and low-rank matrix decomposition is proposed in this paper, according to the fact that most of the students in the classroom are almost in stationary state and there are body movement occasionally. Firstly, combining the frame difference with the sparse and low-rank matrix decomposition to predict the moving areas, and updating the background model with different parameters according to the positional relationship between the pixels of current video frame and the predicted motion regions. Secondly, the regions of moving objects are determined based on the updated background using the background subtraction method. Finally, some operations including binarization, median filtering and morphology processing, connected component detection, etc. are performed on the regions acquired by the background subtraction, in order to induce the effects of the noise and obtain the number of people in the classroom. The experiment results show the validity of the algorithm of people counting.

14 CFR 382.3 - What do the terms in this rule mean?

Code of Federal Regulations, 2014 CFR

2014-01-01

... devices and medications. Automated airport kiosk means a self-service transaction machine that a carrier... machine means a continuous positive airway pressure machine. Department or DOT means the United States..., emotional or mental illness, and specific learning disabilities. The term physical or mental impairment...

Use of Direct Dynamics Simulations to Determine Unimolecular Reaction Paths and Arrhenius Parameters for Large Molecules.

PubMed

Yang, Li; Sun, Rui; Hase, William L

2011-11-08

In a previous study (J. Chem. Phys.2008, 129, 094701) it was shown that for a large molecule, with a total energy much greater than its barrier for decomposition and whose vibrational modes are harmonic oscillators, the expressions for the classical Rice-Ramsperger-Kassel-Marcus (RRKM) (i.e., RRK) and classical transition-state theory (TST) rate constants become equivalent. Using this relationship, a molecule's unimolecular rate constants versus temperature may be determined from chemical dynamics simulations of microcanonical ensembles for the molecule at different total energies. The simulation identifies the molecule's unimolecular pathways and their Arrhenius parameters. In the work presented here, this approach is used to study the thermal decomposition of CH3-NH-CH═CH-CH3, an important constituent in the polymer of cross-linked epoxy resins. Direct dynamics simulations, at the MP2/6-31+G* level of theory, were used to investigate the decomposition of microcanonical ensembles for this molecule. The Arrhenius A and Ea parameters determined from the direct dynamics simulation are in very good agreement with the TST Arrhenius parameters for the MP2/6-31+G* potential energy surface. The simulation method applied here may be particularly useful for large molecules with a multitude of decomposition pathways and whose transition states may be difficult to determine and have structures that are not readily obvious.

The Effect of Specific Surface Area of Chitin-Metal Silicate Coprocessed Excipient on the Chemical Decomposition of Cefotaxime Sodium.

PubMed

Al-Nimry, Suhair S; Alkhamis, Khouloud A; Alzarieni, Kawthar Z

2017-02-01

Chitin-metal silicates are multifunctional excipients used in tablets. Previously, a correlation between the surface acidity of chitin-calcium and chitin-magnesium silicate and the chemical decomposition of cefotaxime sodium was found but not with chitin-aluminum silicate. This lack of correlation could be due to the catalytic effect of silica alumina or the difference in surface area of the excipients. The objective of this study was to investigate the effect of the specific surface area of the excipient on the chemical decomposition of cefotaxime sodium in the solid state. Chitin was purified and coprocessed with different metal silicates to prepare the excipients. The specific surface area was determined using gas adsorption. The chemical decomposition was studied at constant temperature and relative humidity. Also, the degradation in solution was studied. A correlation was found between the degradation rate constant and the surface area of chitin-aluminum and chitin-calcium silicate but not with chitin-magnesium silicate. This was due to the small average pore diameter of this excipient. Also, the degradation in solution was slower than in solid state. In conclusion, the stability of cefotaxime sodium was dependent on the surface area of the excipient in contact with the drug. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Predicting responses from Rasch measures.

PubMed

Linacre, John M

2010-01-01

There is a growing family of Rasch models for polytomous observations. Selecting a suitable model for an existing dataset, estimating its parameters and evaluating its fit is now routine. Problems arise when the model parameters are to be estimated from the current data, but used to predict future data. In particular, ambiguities in the nature of the current data, or overfit of the model to the current dataset, may mean that better fit to the current data may lead to worse fit to future data. The predictive power of several Rasch and Rasch-related models are discussed in the context of the Netflix Prize. Rasch-related models are proposed based on Singular Value Decomposition (SVD) and Boltzmann Machines.

Parallel processing in finite element structural analysis

NASA Technical Reports Server (NTRS)

Noor, Ahmed K.

1987-01-01

A brief review is made of the fundamental concepts and basic issues of parallel processing. Discussion focuses on parallel numerical algorithms, performance evaluation of machines and algorithms, and parallelism in finite element computations. A computational strategy is proposed for maximizing the degree of parallelism at different levels of the finite element analysis process including: 1) formulation level (through the use of mixed finite element models); 2) analysis level (through additive decomposition of the different arrays in the governing equations into the contributions to a symmetrized response plus correction terms); 3) numerical algorithm level (through the use of operator splitting techniques and application of iterative processes); and 4) implementation level (through the effective combination of vectorization, multitasking and microtasking, whenever available).

Investigation of induced unimolecular decomposition for development of visible chemical lasers. Quarterly progress report, 1 August 1976--30 October 1976

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

Piper, L G; Taylor, R L

This report summarizes progress during the second quarterly period of the subject contract. The methods available for the production of excited electronic states following azide decomposition are summarized. It is concluded that an experiment designed to study the kinetics of and branching ratios for electronically excited products from azide radicals reactions will be most productive in elucidating excitation mechanisms for potential chemical lasers. A flow reactor is described in which these studies may be undertaken. The major feature of this apparatus is a clean azide radical source based upon the thermal decomposition of solid, ionic azides. The contruction of themore » experimental apparatus has been started.« less

Theoretical Studies of Some HEDM Species: Cyclic O4, Cyclic O3 and Cubane

NASA Technical Reports Server (NTRS)

Walch, Stephen P.; Langhoff, Steve R. (Technical Monitor)

1996-01-01

Calculations have been carried out for the HEDM species (cyclic O4, cyclic O3, and cubane) using CASSCF/derivative and CASSCF/ICCI methods. Cyclic O4 is of interest both as a potential HEDM species and because of its possible role in the ozone deficit problem in atmospheric chemistry. We have studied the pathway for decomposition from the D(2d) minimum and also have found the approximate location of the singlet triplet crossing. The barrier to decomposition is found to be about 9 kcal/mol and is not limited by the singlet triplet crossing. For cyclic O3 we have focused on the crossings between the lowest five surfaces (X(1)A(1), s(1)A(1), (1)A(2), (1)B(1), and (1)B(2)) to provide some insight into ways to form cyclic O3 photochemically. The crossing region between the X(1)A(1) and 2(1)A(1) surfaces is in agreement with the work of Xantheas et al. The calculations show that vertical excitation from the ground state to the (1)A(2) state leads to a crossing with the (1)A(1) manifold near the crossing region of the X(1)A(1) and 2(1)A(1) surfaces. We have studied the decomposition pathways for cubane to benzene plus acetylene and to cyclooctatetraene. We have also studied the ground and excited states for the photochemical ring closure step. The state which closes to cubane can be described as a double triplet pi to pi* excitation with respect to the ground state. Thus, this state has only a small oscillator strength with respect to the ground state. However, there is a singlet pi to pi* state at nearly the same energy and excitation to this state followed by intersystem crossing could lead to the triplet pi to pi* state.

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

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Verbs in the lexicon: Why is hitting easier than breaking?

PubMed

McKoon, Gail; Love, Jessica

2011-11-01

Adult speakers use verbs in syntactically appropriate ways. For example, they know implicitly that the boy hit at the fence is acceptable but the boy broke at the fence is not. We suggest that this knowledge is lexically encoded in semantic decompositions. The decomposition for break verbs (e.g. crack, smash) is hypothesized to be more complex than that for hit verbs (e.g. kick, kiss). Specifically, the decomposition of a break verb denotes that "an entity changes state as the result of some external force" whereas the decomposition for a hit verb denotes only that "an entity potentially comes in contact with another entity." In this article, verbs of the two types were compared in a lexical decision experiment - Experiment 1 - and they were compared in sentence comprehension experiments with transitive sentences (e.g. the car hit the bicycle and the car broke the bicycle) - Experiments 2 and 3. In Experiment 1, processing times were shorter for the hit than the break verbs and in Experiments 2 and 3, processing times were shorter for the hit sentences than the break sentences, results that are in accord with the complexities of the postulated semantic decompositions.

Vending machine policies and practices in Delaware.

PubMed

Gemmill, Erin; Cotugna, Nancy

2005-04-01

Overweight has reached alarming proportions among America's youth. Although the cause of the rise in overweight rates in children and adolescents is certainly the result of the interaction of a variety of factors, the presence of vending machines in schools is one issue that has recently come to the forefront. Many states have passed or proposed legislation that limits student access to vending machines in schools or require that vending machines in schools offer healthier choices. The purposes of this study were (a) to assess the food and beverage vending machine offerings in the public school districts in the state of Delaware and (b) to determine whether there are any district vending policies in place other than the current U.S. Department of Agriculture regulations. The results of this study indicate the most commonly sold food and drink items in school vending machines are of minimal nutritional value. School administrators are most frequently in charge of the vending contract, as well as setting and enforcing vending machine policies. Suggestions are offered to assist school nurses, often the only health professional in the school, in becoming advocates for changes in school vending practices and policies that promote the health and well-being of children and adolescents.

Laurdan spectrum decomposition as a tool for the analysis of surface bilayer structure and polarity: a study with DMPG, peptides and cholesterol.

PubMed

Lúcio, Aline D; Vequi-Suplicy, Cíntia C; Fernandez, Roberto M; Lamy, M Teresa

2010-03-01

The highly hydrophobic fluorophore Laurdan (6-dodecanoyl-2-(dimethylaminonaphthalene)) has been widely used as a fluorescent probe to monitor lipid membranes. Actually, it monitors the structure and polarity of the bilayer surface, where its fluorescent moiety is supposed to reside. The present paper discusses the high sensitivity of Laurdan fluorescence through the decomposition of its emission spectrum into two Gaussian bands, which correspond to emissions from two different excited states, one more solvent relaxed than the other. It will be shown that the analysis of the area fraction of each band is more sensitive to bilayer structural changes than the largely used parameter called Generalized Polarization, possibly because the latter does not completely separate the fluorescence emission from the two different excited states of Laurdan. Moreover, it will be shown that this decomposition should be done with the spectrum as a function of energy, and not wavelength. Due to the presence of the two emission bands in Laurdan spectrum, fluorescence anisotropy should be measured around 480 nm, to be able to monitor the fluorescence emission from one excited state only, the solvent relaxed state. Laurdan will be used to monitor the complex structure of the anionic phospholipid DMPG (dimyristoyl phosphatidylglycerol) at different ionic strengths, and the alterations caused on gel and fluid membranes due to the interaction of cationic peptides and cholesterol. Analyzing both the emission spectrum decomposition and anisotropy it was possible to distinguish between effects on the packing and on the hydration of the lipid membrane surface. It could be clearly detected that a more potent analog of the melanotropic hormone alpha-MSH (Ac-Ser(1)-Tyr(2)-Ser(3)-Met(4)-Glu(5)-His(6)-Phe(7)-Arg(8)-Trp(9)-Gly(10)-Lys(11)-Pro(12)-Val(13)-NH(2)) was more effective in rigidifying the bilayer surface of fluid membranes than the hormone, though the hormone significantly decreases the bilayer surface hydration.

A state-based approach to trend recognition and failure prediction for the Space Station Freedom

NASA Technical Reports Server (NTRS)

Nelson, Kyle S.; Hadden, George D.

1992-01-01

A state-based reasoning approach to trend recognition and failure prediction for the Altitude Determination, and Control System (ADCS) of the Space Station Freedom (SSF) is described. The problem domain is characterized by features (e.g., trends and impending failures) that develop over a variety of time spans, anywhere from several minutes to several years. Our state-based reasoning approach, coupled with intelligent data screening, allows features to be tracked as they develop in a time-dependent manner. That is, each state machine has the ability to encode a time frame for the feature it detects. As features are detected, they are recorded and can be used as input to other state machines, creating a hierarchical feature recognition scheme. Furthermore, each machine can operate independently of the others, allowing simultaneous tracking of features. State-based reasoning was implemented in the trend recognition and the prognostic modules of a prototype Space Station Freedom Maintenance and Diagnostic System (SSFMDS) developed at Honeywell's Systems and Research Center.

Rational design of the column of a heavy multipurpose machining center

NASA Astrophysics Data System (ADS)

Atapin, V. V.; Kurlaev, N. V.

2016-04-01

The main purpose in the design of supporting constructions of heavy multipurpose machining center is the reduction of mass at the given precision and productivity of machining. Accomplish these ends the technology of rational design of supporting constructions is offered. This technology is based on the decomposition method and the finite elements method in the combination with optimization methods. The technology has four stages: 1) calculation of external forces and loads, 2) as a result the boundary conditions (force, kinematics) for individual supporting constructions are formed, 3) a problem about final optimal distribution of a material by the individual supporting constructions with the real cross-section is solved; 4) dynamic analysis. By the example of design of the column of a heavy multipurpose machining center the main stages of rational design of the individual supporting constructions are shown. At a design stage of the carrying system consisting of load-bearing structures with simplified geometry, optimum overall dimensions of the column are identified. For the admitted system of preferences, it is necessary to accept the fact that the carrying system with the column with the sizes of cross section of 1.8 m (along x axis) and 2.6 m (along y axis) is the best. The analysis of the work of the column under the torsion condition with the use of method of mechanics shows that the column with square cross sections = 2.46·2.46 m which rigidity on torsion is 26 % higher in comparison with a production version is the best. The results of calculation show that a production-release design of the column with longitudinal and transverse edges of rigidity is 24 % heavier than the column with the edges located on a diagonally at equal rigidity.

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Implementation and Characterization of Three-Dimensional Particle-in-Cell Codes on Multiple-Instruction-Multiple-Data Massively Parallel Supercomputers

NASA Technical Reports Server (NTRS)

Lyster, P. M.; Liewer, P. C.; Decyk, V. K.; Ferraro, R. D.

1995-01-01

A three-dimensional electrostatic particle-in-cell (PIC) plasma simulation code has been developed on coarse-grain distributed-memory massively parallel computers with message passing communications. Our implementation is the generalization to three-dimensions of the general concurrent particle-in-cell (GCPIC) algorithm. In the GCPIC algorithm, the particle computation is divided among the processors using a domain decomposition of the simulation domain. In a three-dimensional simulation, the domain can be partitioned into one-, two-, or three-dimensional subdomains ("slabs," "rods," or "cubes") and we investigate the efficiency of the parallel implementation of the push for all three choices. The present implementation runs on the Intel Touchstone Delta machine at Caltech; a multiple-instruction-multiple-data (MIMD) parallel computer with 512 nodes. We find that the parallel efficiency of the push is very high, with the ratio of communication to computation time in the range 0.3%-10.0%. The highest efficiency (> 99%) occurs for a large, scaled problem with 64(sup 3) particles per processing node (approximately 134 million particles of 512 nodes) which has a push time of about 250 ns per particle per time step. We have also developed expressions for the timing of the code which are a function of both code parameters (number of grid points, particles, etc.) and machine-dependent parameters (effective FLOP rate, and the effective interprocessor bandwidths for the communication of particles and grid points). These expressions can be used to estimate the performance of scaled problems--including those with inhomogeneous plasmas--to other parallel machines once the machine-dependent parameters are known.

Circular Mixture Modeling of Color Distribution for Blind Stain Separation in Pathology Images.

PubMed

Li, Xingyu; Plataniotis, Konstantinos N

2017-01-01

In digital pathology, to address color variation and histological component colocalization in pathology images, stain decomposition is usually performed preceding spectral normalization and tissue component segmentation. This paper examines the problem of stain decomposition, which is a naturally nonnegative matrix factorization (NMF) problem in algebra, and introduces a systematical and analytical solution consisting of a circular color analysis module and an NMF-based computation module. Unlike the paradigm of existing stain decomposition algorithms where stain proportions are computed from estimated stain spectra using a matrix inverse operation directly, the introduced solution estimates stain spectra and stain depths via probabilistic reasoning individually. Since the proposed method pays extra attentions to achromatic pixels in color analysis and stain co-occurrence in pixel clustering, it achieves consistent and reliable stain decomposition with minimum decomposition residue. Particularly, aware of the periodic and angular nature of hue, we propose the use of a circular von Mises mixture model to analyze the hue distribution, and provide a complete color-based pixel soft-clustering solution to address color mixing introduced by stain overlap. This innovation combined with saturation-weighted computation makes our study effective for weak stains and broad-spectrum stains. Extensive experimentation on multiple public pathology datasets suggests that our approach outperforms state-of-the-art blind stain separation methods in terms of decomposition effectiveness.

Influence of gamma-irradiation on the non-isothermal decomposition of calcium-gadolinium oxalate

NASA Astrophysics Data System (ADS)

Moharana, S. C.; Praharaj, J.; Bhatta, D.

Thermal decomposition of co-precipitated unirradiated and irradiated Ca-Gd oxalate has been studied by adopting differential thermal analysis (DTA) and thermogravimetric (TG) techniques. The reaction occurs through two stages corresponding to the decomposition of gadolinium oxalate (Gd-Ox) followed by that of calcium oxalate (Ca-Ox). The kinetic parameters for both the stages are calculated by using solid state reaction models and Coats-Redfern's equation. The co-precipitation as well as irradiation alter the DTA peak temperatures and the kinetic parameters of Ca-Ox. The decomposition of Gd-Ox follows the two dimensional Contracting area (R-2) mechanism, while that of Ca-Ox follows the Avrami-Erofeev (A(2)) mechanism (n =2), which are also exhibited by the co-precipitated and irradiated samples. Co-precipitation decreases the energy of activation and the pre-exponential factor of the individual components but the reverse phenomenon takes place upon irradiation of the co-precipitate. The mechanisms underlying the phenomena are explored.

A DFT study of ethanol adsorption and decomposition on α-Al2O3(0 0 0 1) surface

NASA Astrophysics Data System (ADS)

Chiang, Hsin-Ni; Nachimuthu, Santhanamoorthi; Cheng, Ya-Chin; Damayanti, Nur Pradani; Jiang, Jyh-Chiang

2016-02-01

Ethanol adsorption and decomposition on the clean α-Al2O3(0 0 0 1) surface have been systematically investigated by density functional theory calculations. The nature of the surface-ethanol bonding has studied through the density of states (DOS) and the electron density difference (EDD) contour plots. The DOS patterns confirm that the lone pair electrons of EtOH are involved in the formation of a surface Alsbnd O dative bond and the EDD plots provide evidences for the bond weakening/forming, which are consistent with the DOS analysis. Our ethanol decomposition results indicate that ethanol dehydration to ethylene (CH3CH2OH(a) → C2H4(g) + OH(a) + H(a)), is the main reaction pathway with the energy barrier of 1.46 eV. Although the cleavage of the hydroxyl group of ethanol has lower energy barrier, the further decomposition of ethoxy owns much higher energy barrier.

Sewage sludge effects on mesofauna and cork oak (Quercus suber L.) leaves decomposition in a Mediterranean forest firebreak.

PubMed

Pernin, Céline; Cortet, Jérôme; Joffre, Richard; Le Petit, Jean; Torre, Franck

2006-01-01

Effects of sewage sludge on litter mesofauna communities (Collembola and Acari) and cork oak (Quercus suber L.) leaf litter decomposition have been studied during 18 mo using litterbags in an in situ experimental forest firebreak in southeastern France. The sludge (2.74 t DM ha(-1) yr(-1)) was applied to fertilize and maintain a pasture created on the firebreak. Litterbag colonization had similar dynamics on both the control and fertilized plots and followed a typical Mediterranean pattern showing a greater abundance in spring and autumn and a lower abundance in summer. After 9 mo of litter colonization, Collembola and Acari, but mainly Oribatida, were more abundant on the sludge-fertilized plot. Leaf litter decomposition showed a similar pattern on both plots, but it was faster on the control plot. Furthermore, leaves from the fertilized plot were characterized by greater nitrogen content. Both chemical composition of leaves and sludges and the decomposition state of leaves have significantly affected the mesofauna community composition from each plot.

Modal identification of spindle-tool unit in high-speed machining

NASA Astrophysics Data System (ADS)

Gagnol, Vincent; Le, Thien-Phu; Ray, Pascal

2011-10-01

The accurate knowledge of high-speed motorised spindle dynamic behaviour during machining is important in order to ensure the reliability of machine tools in service and the quality of machined parts. More specifically, the prediction of stable cutting regions, which is a critical requirement for high-speed milling operations, requires the accurate estimation of tool/holder/spindle set dynamic modal parameters. These estimations are generally obtained through Frequency Response Function (FRF) measurements of the non-rotating spindle. However, significant changes in modal parameters are expected to occur during operation, due to high-speed spindle rotation. The spindle's modal variations are highlighted through an integrated finite element model of the dynamic high-speed spindle-bearing system, taking into account rotor dynamics effects. The dependency of dynamic behaviour on speed range is then investigated and determined with accuracy. The objective of the proposed paper is to validate these numerical results through an experiment-based approach. Hence, an experimental setup is elaborated to measure rotating tool vibration during the machining operation in order to determine the spindle's modal frequency variation with respect to spindle speed in an industrial environment. The identification of natural frequencies of the spindle under rotating conditions is challenging, due to the low number of sensors and the presence of many harmonics in the measured signals. In order to overcome these issues and to extract the characteristics of the system, the spindle modes are determined through a 3-step procedure. First, spindle modes are highlighted using the Frequency Domain Decomposition (FDD) technique, with a new formulation at the considered rotating speed. These extracted modes are then analysed through the value of their respective damping ratios in order to separate the harmonics component from structural spindle natural frequencies. Finally, the stochastic properties of the modes are also investigated by considering the probability density of the retained modes. Results show a good correlation between numerical and experiment-based identified frequencies. The identified spindle-tool modal properties during machining allow the numerical model to be considered as representative of the real dynamic properties of the system.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

PubMed

N Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

A Web-Based Visualization and Animation Platform for Digital Logic Design

ERIC Educational Resources Information Center

Shoufan, Abdulhadi; Lu, Zheng; Huss, Sorin A.

2015-01-01

This paper presents a web-based education platform for the visualization and animation of the digital logic design process. This includes the design of combinatorial circuits using logic gates, multiplexers, decoders, and look-up-tables as well as the design of finite state machines. Various configurations of finite state machines can be selected…

Classifying Black Hole States with Machine Learning

NASA Astrophysics Data System (ADS)

Huppenkothen, Daniela

2018-01-01

Galactic black hole binaries are known to go through different states with apparent signatures in both X-ray light curves and spectra, leading to important implications for accretion physics as well as our knowledge of General Relativity. Existing frameworks of classification are usually based on human interpretation of low-dimensional representations of the data, and generally only apply to fairly small data sets. Machine learning, in contrast, allows for rapid classification of large, high-dimensional data sets. In this talk, I will report on advances made in classification of states observed in Black Hole X-ray Binaries, focusing on the two sources GRS 1915+105 and Cygnus X-1, and show both the successes and limitations of using machine learning to derive physical constraints on these systems.

2011 International Infantry and Joint Services Small Arms Systems Symposium, Exhibition and Firing Demonstration

DTIC Science & Technology

2011-05-26

Machine Gun 24 12.7mm NATO Nominated Weapon United States – General Dynamics M2 Heavy Barrel Machine Gun 25...Explosively-Clad Refractory Barrel Liners for Small Caliber Machine Guns , Dr. Douglas Taylor, TPL, Inc. 12299 - The HAMR Project, Mr. Xavier Gavage, FN Herstal... Barrel Liners for Small Caliber Machine Guns Dr. Douglas Taylor, TPL, Inc. 12330 - 40mm Low Velocity Air-Burst Munitions System Mr.

Can the biomass-ratio hypothesis predict mixed-species litter decomposition along a climatic gradient?

PubMed Central

Tardif, Antoine; Shipley, Bill; Bloor, Juliette M. G.; Soussana, Jean-François

2014-01-01

Background and Aims The biomass-ratio hypothesis states that ecosystem properties are driven by the characteristics of dominant species in the community. In this study, the hypothesis was operationalized as community-weighted means (CWMs) of monoculture values and tested for predicting the decomposition of multispecies litter mixtures along an abiotic gradient in the field. Methods Decomposition rates (mg g−1 d−1) of litter from four herb species were measured using litter-bed experiments with the same soil at three sites in central France along a correlated climatic gradient of temperature and precipitation. All possible combinations from one to four species mixtures were tested over 28 weeks of incubation. Observed mixture decomposition rates were compared with those predicted by the biomass-ratio hypothesis. Variability of the prediction errors was compared with the species richness of the mixtures, across sites, and within sites over time. Key Results Both positive and negative prediction errors occurred. Despite this, the biomass-ratio hypothesis was true as an average claim for all sites (r = 0·91) and for each site separately, except for the climatically intermediate site, which showed mainly synergistic deviations. Variability decreased with increasing species richness and in less favourable climatic conditions for decomposition. Conclusions Community-weighted mean values provided good predictions of mixed-species litter decomposition, converging to the predicted values with increasing species richness and in climates less favourable to decomposition. Under a context of climate change, abiotic variability would be important to take into account when predicting ecosystem processes. PMID:24482152

Long-term litter decomposition controlled by manganese redox cycling

DOE PAGES

Keiluweit, Marco; Nico, Peter S.; Harmon, Mark; ...

2015-09-08

Litter decomposition is a keystone ecosystem process impacting nutrient cycling and productivity, soil properties, and the terrestrial carbon (C) balance, but the factors regulating decomposition rate are still poorly understood. Traditional models assume that the rate is controlled by litter quality, relying on parameters such as lignin content as predictors. However, a strong correlation has been observed between the manganese (Mn) content of litter and decomposition rates across a variety of forest ecosystems. Here, we show that long-term litter decomposition in forest ecosystems is tightly coupled to Mn redox cycling. Over 7 years of litter decomposition, microbial transformation of littermore » was paralleled by variations in Mn oxidation state and concentration. A detailed chemical imaging analysis of the litter revealed that fungi recruit and redistribute unreactive Mn 2+ provided by fresh plant litter to produce oxidative Mn 3+ species at sites of active decay, with Mn eventually accumulating as insoluble Mn 3+/4+ oxides. Formation of reactive Mn 3+ species coincided with the generation of aromatic oxidation products, providing direct proof of the previously posited role of Mn 3+-based oxidizers in the breakdown of litter. Our results suggest that the litter-decomposing machinery at our coniferous forest site depends on the ability of plants and microbes to supply, accumulate, and regenerate short-lived Mn 3+ species in the litter layer. As a result, this observation indicates that biogeochemical constraints on bioavailability, mobility, and reactivity of Mn in the plant–soil system may have a profound impact on litter decomposition rates.« less

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…

The War in Man; Media and Machines.

ERIC Educational Resources Information Center

Wilhelmsen, Frederick D.; Bret, Jane

The authors present a picture of contemporary man torn by conflicting forces, caught in a psychic house divided against itself, a victim of war between media and machines. Machines, they state, represent the rationalistic tradition which has brought man to the brink of psychic and social disaster. The media they see as offering hope--true…

6 CFR 37.19 - Machine readable technology on the driver's license or identification card.

Code of Federal Regulations, 2011 CFR

2011-01-01

..., States must use the ISO/IEC 15438:2006(E) Information Technology—Automatic identification and data... 6 Domestic Security 1 2011-01-01 2011-01-01 false Machine readable technology on the driver's..., Verification, and Card Issuance Requirements § 37.19 Machine readable technology on the driver's license or...

6 CFR 37.19 - Machine readable technology on the driver's license or identification card.

Code of Federal Regulations, 2010 CFR

2010-01-01

..., States must use the ISO/IEC 15438:2006(E) Information Technology—Automatic identification and data... 6 Domestic Security 1 2010-01-01 2010-01-01 false Machine readable technology on the driver's..., Verification, and Card Issuance Requirements § 37.19 Machine readable technology on the driver's license or...

Electrochemical Protection of Thin Film Electrodes in Solid State Nanopores

PubMed Central

Harrer, Stefan; Waggoner, Philip S.; Luan, Binquan; Afzali-Ardakani, Ali; Goldfarb, Dario L.; Peng, Hongbo; Martyna, Glenn; Rossnagel, Stephen M.; Stolovitzky, Gustavo A.

2011-01-01

We have eliminated electrochemical surface oxidation and reduction as well as water decomposition inside sub-5-nm wide nanopores in conducting TiN membranes using a surface passivation technique. Nanopore ionic conductances, and therefore pore diameters, were unchanged in passivated pores after applying potentials of ±4.5 V for as long as 24 h. Water decomposition was eliminated by using aqueous 90% glycerol solvent. The use of a protective self-assembled monolayer of hexadecylphosphonic acid was also investigated. PMID:21597142

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme

PubMed Central

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI. PMID:26880873

Automated detection of microaneurysms using robust blob descriptors

NASA Astrophysics Data System (ADS)

Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

2013-03-01

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

PubMed

Ji, Hongfei; Li, Jie; Lu, Rongrong; Gu, Rong; Cao, Lei; Gong, Xiaoliang

2016-01-01

Electroencephalogram- (EEG-) based brain-computer interface (BCI) systems usually utilize one type of changes in the dynamics of brain oscillations for control, such as event-related desynchronization/synchronization (ERD/ERS), steady state visual evoked potential (SSVEP), and P300 evoked potentials. There is a recent trend to detect more than one of these signals in one system to create a hybrid BCI. However, in this case, EEG data were always divided into groups and analyzed by the separate processing procedures. As a result, the interactive effects were ignored when different types of BCI tasks were executed simultaneously. In this work, we propose an improved tensor based multiclass multimodal scheme especially for hybrid BCI, in which EEG signals are denoted as multiway tensors, a nonredundant rank-one tensor decomposition model is proposed to obtain nonredundant tensor components, a weighted fisher criterion is designed to select multimodal discriminative patterns without ignoring the interactive effects, and support vector machine (SVM) is extended to multiclass classification. Experiment results suggest that the proposed scheme can not only identify the different changes in the dynamics of brain oscillations induced by different types of tasks but also capture the interactive effects of simultaneous tasks properly. Therefore, it has great potential use for hybrid BCI.

Mode Shape Estimation Algorithms Under Ambient Conditions: A Comparative Review

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

Dosiek, Luke; Zhou, Ning; Pierre, John W.

Abstract—This paper provides a comparative review of five existing ambient electromechanical mode shape estimation algorithms, i.e., the Transfer Function (TF), Spectral, Frequency Domain Decomposition (FDD), Channel Matching, and Subspace Methods. It is also shown that the TF Method is a general approach to estimating mode shape and that the Spectral, FDD, and Channel Matching Methods are actually special cases of it. Additionally, some of the variations of the Subspace Method are reviewed and the Numerical algorithm for Subspace State Space System IDentification (N4SID) is implemented. The five algorithms are then compared using data simulated from a 17-machine model of themore » Western Electricity Coordinating Council (WECC) under ambient conditions with both low and high damping, as well as during the case where ambient data is disrupted by an oscillatory ringdown. The performance of the algorithms is compared using the statistics from Monte Carlo Simulations and results from measured WECC data, and a discussion of the practical issues surrounding their implementation, including cases where power system probing is an option, is provided. The paper concludes with some recommendations as to the appropriate use of the various techniques. Index Terms—Electromechanical mode shape, small-signal stability, phasor measurement units (PMU), system identification, N4SID, subspace.« less

Exploring an optimal wavelet-based filter for cryo-ET imaging.

PubMed

Huang, Xinrui; Li, Sha; Gao, Song

2018-02-07

Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.

Human decomposition and the reliability of a 'Universal' model for post mortem interval estimations.

PubMed

Cockle, Diane L; Bell, Lynne S

2015-08-01

Human decomposition is a complex biological process driven by an array of variables which are not clearly understood. The medico-legal community have long been searching for a reliable method to establish the post-mortem interval (PMI) for those whose deaths have either been hidden, or gone un-noticed. To date, attempts to develop a PMI estimation method based on the state of the body either at the scene or at autopsy have been unsuccessful. One recent study has proposed that two simple formulae, based on the level of decomposition humidity and temperature, could be used to accurately calculate the PMI for bodies outside, on or under the surface worldwide. This study attempted to validate 'Formula I' [1] (for bodies on the surface) using 42 Canadian cases with known PMIs. The results indicated that bodies exposed to warm temperatures consistently overestimated the known PMI by a large and inconsistent margin for Formula I estimations. And for bodies exposed to cold and freezing temperatures (less than 4°C), then the PMI was dramatically under estimated. The ability of 'Formulae II' to estimate the PMI for buried bodies was also examined using a set of 22 known Canadian burial cases. As these cases used in this study are retrospective, some of the data needed for Formula II was not available. The 4.6 value used in Formula II to represent the standard ratio of time that burial decelerates the rate of decomposition was examined. The average time taken to achieve each stage of decomposition both on, and under the surface was compared for the 118 known cases. It was found that the rate of decomposition was not consistent throughout all stages of decomposition. The rates of autolysis above and below the ground were equivalent with the buried cases staying in a state of putrefaction for a prolonged period of time. It is suggested that differences in temperature extremes and humidity levels between geographic regions may make it impractical to apply formulas developed in one region to any other region. These results also suggest that there are other variables, apart from temperature and humidity that may impact the rate of human decomposition. These variables, or complex of variables, are considered regionally specific. Neither of the Universal Formulae performed well, and our results do not support the proposition of Universality for PMI estimation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Gelcasting compositions having improved drying characteristics and machinability

DOEpatents

Janney, Mark A.; Walls, Claudia A. H.

2001-01-01

A gelcasting composition has improved drying behavior, machinability and shelf life in the dried and unfired state. The composition includes an inorganic powder, solvent, monomer system soluble in the solvent, an initiator system for polymerizing the monomer system, and a plasticizer soluble in the solvent. Dispersants and other processing aides to control slurry properties can be added. The plasticizer imparts an ability to dry thick section parts, to store samples in the dried state without cracking under conditions of varying relative humidity, and to machine dry gelcast parts without cracking or chipping. A method of making gelcast parts is also disclosed.

Sequential behavior and its inherent tolerance to memory faults.

NASA Technical Reports Server (NTRS)

Meyer, J. F.

1972-01-01

Representation of a memory fault of a sequential machine M by a function mu on the states of M and the result of the fault by an appropriately determined machine M(mu). Given some sequential behavior B, its inherent tolerance to memory faults can then be measured in terms of the minimum memory redundancy required to realize B with a state-assigned machine having fault tolerance type tau and fault tolerance level t. A behavior having maximum inherent tolerance is exhibited, and it is shown that behaviors of the same size can have different inherent tolerance.

Finding and defining the natural automata acting in living plants: Toward the synthetic biology for robotics and informatics in vivo.

PubMed

Kawano, Tomonori; Bouteau, François; Mancuso, Stefano

2012-11-01

The automata theory is the mathematical study of abstract machines commonly studied in the theoretical computer science and highly interdisciplinary fields that combine the natural sciences and the theoretical computer science. In the present review article, as the chemical and biological basis for natural computing or informatics, some plants, plant cells or plant-derived molecules involved in signaling are listed and classified as natural sequential machines (namely, the Mealy machines or Moore machines) or finite state automata. By defining the actions (states and transition functions) of these natural automata, the similarity between the computational data processing and plant decision-making processes became obvious. Finally, their putative roles as the parts for plant-based computing or robotic systems are discussed.

Finding and defining the natural automata acting in living plants: Toward the synthetic biology for robotics and informatics in vivo

PubMed Central

Kawano, Tomonori; Bouteau, François; Mancuso, Stefano

2012-01-01

The automata theory is the mathematical study of abstract machines commonly studied in the theoretical computer science and highly interdisciplinary fields that combine the natural sciences and the theoretical computer science. In the present review article, as the chemical and biological basis for natural computing or informatics, some plants, plant cells or plant-derived molecules involved in signaling are listed and classified as natural sequential machines (namely, the Mealy machines or Moore machines) or finite state automata. By defining the actions (states and transition functions) of these natural automata, the similarity between the computational data processing and plant decision-making processes became obvious. Finally, their putative roles as the parts for plant-based computing or robotic systems are discussed. PMID:23336016

Snack food as a modulator of human resting-state functional connectivity.

PubMed

Mendez-Torrijos, Andrea; Kreitz, Silke; Ivan, Claudiu; Konerth, Laura; Rösch, Julie; Pischetsrieder, Monika; Moll, Gunther; Kratz, Oliver; Dörfler, Arnd; Horndasch, Stefanie; Hess, Andreas

2018-04-04

To elucidate the mechanisms of how snack foods may induce non-homeostatic food intake, we used resting state functional magnetic resonance imaging (fMRI), as resting state networks can individually adapt to experience after short time exposures. In addition, we used graph theoretical analysis together with machine learning techniques (support vector machine) to identifying biomarkers that can categorize between high-caloric (potato chips) vs. low-caloric (zucchini) food stimulation. Seventeen healthy human subjects with body mass index (BMI) 19 to 27 underwent 2 different fMRI sessions where an initial resting state scan was acquired, followed by visual presentation of different images of potato chips and zucchini. There was then a 5-minute pause to ingest food (day 1=potato chips, day 3=zucchini), followed by a second resting state scan. fMRI data were further analyzed using graph theory analysis and support vector machine techniques. Potato chips vs. zucchini stimulation led to significant connectivity changes. The support vector machine was able to accurately categorize the 2 types of food stimuli with 100% accuracy. Visual, auditory, and somatosensory structures, as well as thalamus, insula, and basal ganglia were found to be important for food classification. After potato chips consumption, the BMI was associated with the path length and degree in nucleus accumbens, middle temporal gyrus, and thalamus. The results suggest that high vs. low caloric food stimulation in healthy individuals can induce significant changes in resting state networks. These changes can be detected using graph theory measures in conjunction with support vector machine. Additionally, we found that the BMI affects the response of the nucleus accumbens when high caloric food is consumed.

Optical realization of optimal symmetric real state quantum cloning machine

NASA Astrophysics Data System (ADS)

Hu, Gui-Yu; Zhang, Wen-Hai; Ye, Liu

2010-01-01

We present an experimentally uniform linear optical scheme to implement the optimal 1→2 symmetric and optimal 1→3 symmetric economical real state quantum cloning machine of the polarization state of the single photon. This scheme requires single-photon sources and two-photon polarization entangled state as input states. It also involves linear optical elements and three-photon coincidence. Then we consider the realistic realization of the scheme by using the parametric down-conversion as photon resources. It is shown that under certain condition, the scheme is feasible by current experimental technology.

Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech.

NASA Astrophysics Data System (ADS)

Campo, D.; Quintero, O. L.; Bastidas, M.

2016-04-01

We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis- discrete wavelet transform - was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.

Hugoniot curve calculation of nitromethane decomposition mixtures: A reactive force field molecular dynamics approach

NASA Astrophysics Data System (ADS)

Guo, Feng; Zhang, Hong; Hu, Hai-Quan; Cheng, Xin-Lu; Zhang, Li-Yan

2015-11-01

We investigate the Hugoniot curve, shock-particle velocity relations, and Chapman-Jouguet conditions of the hot dense system through molecular dynamics (MD) simulations. The detailed pathways from crystal nitromethane to reacted state by shock compression are simulated. The phase transition of N2 and CO mixture is found at about 10 GPa, and the main reason is that the dissociation of the C-O bond and the formation of C-C bond start at 10.0-11.0 GPa. The unreacted state simulations of nitromethane are consistent with shock Hugoniot data. The complete pathway from unreacted to reacted state is discussed. Through chemical species analysis, we find that the C-N bond breaking is the main event of the shock-induced nitromethane decomposition. Project supported by the National Natural Science Foundation of China (Grant No. 11374217) and the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2014BQ008).

Entanglement branching operator

NASA Astrophysics Data System (ADS)

Harada, Kenji

2018-01-01

We introduce an entanglement branching operator to split a composite entanglement flow in a tensor network which is a promising theoretical tool for many-body systems. We can optimize an entanglement branching operator by solving a minimization problem based on squeezing operators. The entanglement branching is a new useful operation to manipulate a tensor network. For example, finding a particular entanglement structure by an entanglement branching operator, we can improve a higher-order tensor renormalization group method to catch a proper renormalization flow in a tensor network space. This new method yields a new type of tensor network states. The second example is a many-body decomposition of a tensor by using an entanglement branching operator. We can use it for a perfect disentangling among tensors. Applying a many-body decomposition recursively, we conceptually derive projected entangled pair states from quantum states that satisfy the area law of entanglement entropy.

Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients

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

Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.

We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numericalmore » experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.« less

Laser assisted machining: a state of art review

NASA Astrophysics Data System (ADS)

Punugupati, Gurabvaiah; Kandi, Kishore Kumar; Bose, P. S. C.; Rao, C. S. P.

2016-09-01

Difficult-to-cut materials have increasing demand in aerospace and automobile industries due to their high yield stress, high strength to weight ratio, high toughness, high wear resistance, high creep, high corrosion resistivity, ability to retain high strength at high temperature, etc. The machinability of these advanced materials, using conventional methods of machining is typical due to the high temperature and pressure at the cutting zone and tool and properties such as low thermal conductivity, high cutting forces and cutting temperatures makes the materials difficult to machine. Laser assisted machining (LAM) is a new and innovative technique for machining the difficult-to-cut materials. This paper deals with a review on the advances in lasers, tools and the mechanism of machining using LAM and their effects.

Ab Initio Density Fitting: Accuracy Assessment of Auxiliary Basis Sets from Cholesky Decompositions.

PubMed

Boström, Jonas; Aquilante, Francesco; Pedersen, Thomas Bondo; Lindh, Roland

2009-06-09

The accuracy of auxiliary basis sets derived by Cholesky decompositions of the electron repulsion integrals is assessed in a series of benchmarks on total ground state energies and dipole moments of a large test set of molecules. The test set includes molecules composed of atoms from the first three rows of the periodic table as well as transition metals. The accuracy of the auxiliary basis sets are tested for the 6-31G**, correlation consistent, and atomic natural orbital basis sets at the Hartree-Fock, density functional theory, and second-order Møller-Plesset levels of theory. By decreasing the decomposition threshold, a hierarchy of auxiliary basis sets is obtained with accuracies ranging from that of standard auxiliary basis sets to that of conventional integral treatments.

A biorthogonal decomposition for the identification and simulation of non-stationary and non-Gaussian random fields

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

Zentner, I.; Ferré, G., E-mail: gregoire.ferre@ponts.org; Poirion, F.

2016-06-01

In this paper, a new method for the identification and simulation of non-Gaussian and non-stationary stochastic fields given a database is proposed. It is based on two successive biorthogonal decompositions aiming at representing spatio–temporal stochastic fields. The proposed double expansion allows to build the model even in the case of large-size problems by separating the time, space and random parts of the field. A Gaussian kernel estimator is used to simulate the high dimensional set of random variables appearing in the decomposition. The capability of the method to reproduce the non-stationary and non-Gaussian features of random phenomena is illustrated bymore » applications to earthquakes (seismic ground motion) and sea states (wave heights).« less

Machine Tool Advanced Skills Technology Program (MAST). Overview and Methodology.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

The Machine Tool Advanced Skills Technology Program (MAST) is a geographical partnership of six of the nation's best two-year colleges located in the six states that have about one-third of the density of metals-related industries in the United States. The purpose of the MAST grant is to develop and implement a national training model to overcome…

Using Multiple Indicators of Cognitive State in Logistic Models that Predict Individual Performance in Machine-Mediated Learning Environments.

ERIC Educational Resources Information Center

Hancock, Thomas E.; And Others

1995-01-01

In machine-mediated learning environments, there is a need for more reliable methods of calculating the probability that a learner's response will be correct in future trials. A combination of domain-independent response-state measures of cognition along with two instructional variables for maximum predictive ability are demonstrated. (Author/LRW)

Parameterizing Phrase Based Statistical Machine Translation Models: An Analytic Study

ERIC Educational Resources Information Center

Cer, Daniel

2011-01-01

The goal of this dissertation is to determine the best way to train a statistical machine translation system. I first develop a state-of-the-art machine translation system called Phrasal and then use it to examine a wide variety of potential learning algorithms and optimization criteria and arrive at two very surprising results. First, despite the…

Contrasting State-of-the-Art in the Machine Scoring of Short-Form Constructed Responses

ERIC Educational Resources Information Center

Shermis, Mark D.

2015-01-01

This study compared short-form constructed responses evaluated by both human raters and machine scoring algorithms. The context was a public competition on which both public competitors and commercial vendors vied to develop machine scoring algorithms that would match or exceed the performance of operational human raters in a summative high-stakes…

The Machine Scoring of Writing

ERIC Educational Resources Information Center

McCurry, Doug

2010-01-01

This article provides an introduction to the kind of computer software that is used to score student writing in some high stakes testing programs, and that is being promoted as a teaching and learning tool to schools. It sketches the state of play with machines for the scoring of writing, and describes how these machines work and what they do.…

Technology of machine tools. Volume 1. Executive summary

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

Sutton, G.P.

1980-10-01

The Machine Tool Task Force (MTTF) was formed to characterize the state of the art of machine tool technology and to identify promising future directions of this technology. This volume is one of a five-volume series that presents the MTTF findings; reports on various areas of the technology were contributed by experts in those areas.

Reversibility in Quantum Models of Stochastic Processes

NASA Astrophysics Data System (ADS)

Gier, David; Crutchfield, James; Mahoney, John; James, Ryan

Natural phenomena such as time series of neural firing, orientation of layers in crystal stacking and successive measurements in spin-systems are inherently probabilistic. The provably minimal classical models of such stochastic processes are ɛ-machines, which consist of internal states, transition probabilities between states and output values. The topological properties of the ɛ-machine for a given process characterize the structure, memory and patterns of that process. However ɛ-machines are often not ideal because their statistical complexity (Cμ) is demonstrably greater than the excess entropy (E) of the processes they represent. Quantum models (q-machines) of the same processes can do better in that their statistical complexity (Cq) obeys the relation Cμ >= Cq >= E. q-machines can be constructed to consider longer lengths of strings, resulting in greater compression. With code-words of sufficiently long length, the statistical complexity becomes time-symmetric - a feature apparently novel to this quantum representation. This result has ramifications for compression of classical information in quantum computing and quantum communication technology.

[Probabilistic calculations of biomolecule charge states that generate mass spectra of multiply charged ions].

PubMed

Raznikova, M O; Raznikov, V V

2015-01-01

In this work, information relating to charge states of biomolecule ions in solution obtained using the electrospray ionization mass spectrometry of different biopolymers is analyzed. The data analyses have mainly been carried out by solving an inverse problem of calculating the probabilities of retention of protons and other charge carriers by ionogenic groups of biomolecules with known primary structures. The approach is a new one and has no known to us analogues. A program titled "Decomposition" was developed and used to analyze the charge distribution of ions of native and denatured cytochrome c mass spectra. The possibility of splitting of the charge-state distribution of albumin into normal components, which likely corresponds to various conformational states of the biomolecule, has been demonstrated. The applicability criterion for using previously described method of decomposition of multidimensional charge-state distributions with two charge carriers, e.g., a proton and a sodium ion, to characterize the spatial structure of biopolymers in solution has been formulated. In contrast to known mass-spectrometric approaches, this method does not require the use of enzymatic hydrolysis or collision-induced dissociation of the biopolymers.

State but not district nutrition policies are associated with less junk food in vending machines and school stores in US public schools.

PubMed

Kubik, Martha Y; Wall, Melanie; Shen, Lijuan; Nanney, Marilyn S; Nelson, Toben F; Laska, Melissa N; Story, Mary

2010-07-01

Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. A nationally representative sample (n=563) of public elementary, middle, and high schools was studied. Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food in school vending machines and school stores offered less junk food than elementary schools that neither required nor recommended prohibiting junk food (13% vs 37%; P=0.006). Middle schools that required prohibiting junk food in vending machines and school stores offered less junk food than middle schools that recommended prohibiting junk food (71% vs 87%; P=0.07). Similar associations were not evident for district-level polices or high schools. Policy may be an effective tool to decrease junk food in schools, particularly in elementary and middle schools. Copyright 2010 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

State but not District Nutrition Policies Are Associated with Less Junk Food in Vending Machines and School Stores in US Public Schools

PubMed Central

KUBIK, MARTHA Y.; WALL, MELANIE; SHEN, LIJUAN; NANNEY, MARILYN S.; NELSON, TOBEN F.; LASKA, MELISSA N.; STORY, MARY

2012-01-01

Background Policy that targets the school food environment has been advanced as one way to increase the availability of healthy food at schools and healthy food choice by students. Although both state- and district-level policy initiatives have focused on school nutrition standards, it remains to be seen whether these policies translate into healthy food practices at the school level, where student behavior will be impacted. Objective To examine whether state- and district-level nutrition policies addressing junk food in school vending machines and school stores were associated with less junk food in school vending machines and school stores. Junk food was defined as foods and beverages with low nutrient density that provide calories primarily through fats and added sugars. Design A cross-sectional study design was used to assess self-report data collected by computer-assisted telephone interviews or self-administered mail questionnaires from state-, district-, and school-level respondents participating in the School Health Policies and Programs Study 2006. The School Health Policies and Programs Study, administered every 6 years since 1994 by the Centers for Disease Control and Prevention, is considered the largest, most comprehensive assessment of school health policies and programs in the United States. Subjects/setting A nationally representative sample (n = 563) of public elementary, middle, and high schools was studied. Statistical analysis Logistic regression adjusted for school characteristics, sampling weights, and clustering was used to analyze data. Policies were assessed for strength (required, recommended, neither required nor recommended prohibiting junk food) and whether strength was similar for school vending machines and school stores. Results School vending machines and school stores were more prevalent in high schools (93%) than middle (84%) and elementary (30%) schools. For state policies, elementary schools that required prohibiting junk food in school vending machines and school stores offered less junk food than elementary schools that neither required nor recommended prohibiting junk food (13% vs 37%; P = 0.006). Middle schools that required prohibiting junk food in vending machines and school stores offered less junk food than middle schools that recommended prohibiting junk food (71% vs 87%; P = 0.07). Similar associations were not evident for district-level polices or high schools. Conclusions Policy may be an effective tool to decrease junk food in schools, particularly in elementary and middle schools. PMID:20630161

Decision support system for diabetic retinopathy using discrete wavelet transform.

PubMed

Noronha, K; Acharya, U R; Nayak, K P; Kamath, S; Bhandary, S V

2013-03-01

Prolonged duration of the diabetes may affect the tiny blood vessels of the retina causing diabetic retinopathy. Routine eye screening of patients with diabetes helps to detect diabetic retinopathy at the early stage. It is very laborious and time-consuming for the doctors to go through many fundus images continuously. Therefore, decision support system for diabetic retinopathy detection can reduce the burden of the ophthalmologists. In this work, we have used discrete wavelet transform and support vector machine classifier for automated detection of normal and diabetic retinopathy classes. The wavelet-based decomposition was performed up to the second level, and eight energy features were extracted. Two energy features from the approximation coefficients of two levels and six energy values from the details in three orientations (horizontal, vertical and diagonal) were evaluated. These features were fed to the support vector machine classifier with various kernel functions (linear, radial basis function, polynomial of orders 2 and 3) to evaluate the highest classification accuracy. We obtained the highest average classification accuracy, sensitivity and specificity of more than 99% with support vector machine classifier (polynomial kernel of order 3) using three discrete wavelet transform features. We have also proposed an integrated index called Diabetic Retinopathy Risk Index using clinically significant wavelet energy features to identify normal and diabetic retinopathy classes using just one number. We believe that this (Diabetic Retinopathy Risk Index) can be used as an adjunct tool by the doctors during the eye screening to cross-check their diagnosis.

Synthesis and characterization of nanoscale molybdenum sulfide catalysts by controlled gas phase decomposition of Mo(CO){sub 6} and H{sub 2}S

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

Close, M.R.; Petersen, J.L.; Kugler, E.L.

1999-04-05

Molybdenum sulfide catalysts with surface areas ranging from 16 to 120 m{sup 2}/g were prepared by the thermal decomposition of Mo(CO){sub 6} and H{sub 2}S vapors in a specially designed tubular reactor system. The gas phase decomposition (GPD) reactions performed at 500--1100 C produced only MoS{sub 2} when excess H{sub 2}S was used. The optimum temperature range for the high-yield production of MoS{sub 2} was from 500 to 700 C. By controlling the decomposition temperature, the Mo(CO){sub 6} partial pressure, or the inert gas flow rate, the surface area, oxidation state, chemical composition, and the grain size of the molybdenummore » sulfide product(s) were modified. At reactor temperatures between 300 and 400 C, lower valent molybdenum sulfide materials, which were sulfur deficient relative to MoS{sub 2}, were obtained with formal molybdenum oxidation states intermediate to those found for Chevrel phase compounds, M{prime}Mo{sub 6}S{sub 8} (M{prime} = Fe, Ni, Co) and MoS{sub 2}. By lowering the H{sub 2}S flow rate used for the GPD reaction at 1000 C, mixtures containing variable amounts of MoS{sub 2} and Mo{sub 2}S{sub 3} were produced. Thus, through the modification of critical reactor parameters used for these GPD reactions, fundamental material properties were controlled.« less

Advanced Aircraft Interfaces: The Machine Side of the Man-Machine Interface (Les Interfaces sur les Avions de Pointe: L’Aspect Machine de l’Interface Homme-Machine)

DTIC Science & Technology

1992-10-01

Manager , Advanced Transport Operating Systems Program Office Langley Research Center Mail Stop 265 Hampton, VA 23665-5225 United States Programme Committee...J.H.Lind, and C.G.Burge Advanced Cockpit - Mission and Image Management 4 by J. Struck Aircrew Acceptance of Automation in the Cockpit 5 by M. Hicks and I...DESIGN CONCEPTS AND TOOLS A Systems Approach to the Advanced Aircraft Man-Machine Interface 23 by F. Armogida Management of Avionics Data in the Cockpit

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

Bartkiewicz, Karol; Miranowicz, Adam

We find an optimal quantum cloning machine, which clones qubits of arbitrary symmetrical distribution around the Bloch vector with the highest fidelity. The process is referred to as phase-independent cloning in contrast to the standard phase-covariant cloning for which an input qubit state is a priori better known. We assume that the information about the input state is encoded in an arbitrary axisymmetric distribution (phase function) on the Bloch sphere of the cloned qubits. We find analytical expressions describing the optimal cloning transformation and fidelity of the clones. As an illustration, we analyze cloning of qubit state described by themore » von Mises-Fisher and Brosseau distributions. Moreover, we show that the optimal phase-independent cloning machine can be implemented by modifying the mirror phase-covariant cloning machine for which quantum circuits are known.« less

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET

PubMed Central

N. Ahmed, Malik; Abdullah, Abdul Hanan; Kaiwartya, Omprakash

2016-01-01

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks. PMID:27285146

Synthesis and characterization of nickel and zinc ferrite nanocatalysts for decomposition of CO2 greenhouse effect gas.

PubMed

Lin, Kuen-Song; Adhikari, Abhijit Krishna; Wang, Chi-Yu; Hsu, Pei-Ju; Chan, Ho-Yang

2013-04-01

The decomposition of CO2 over oxygen deficient nickel ferrite nanoparticles (NFNs) and zinc ferrite nanoparticles (ZFNs) at 573 K was studied. The oxidation states with fine structure of Fe/Ni or Fe/Zn species were also measured in NFNs and ZFNs catalysts, respectively. Oxygen deficiency of catalysts was obtained by reduction in hydrogen. Decomposition of CO2 into carbon and oxygen has been carried out within few minutes when it comes into contact with oxygen deficient catalysts through incorporation of oxygen into ferrite nanoparticles. Oxygen and carbon rather than CO were produced in the decomposition process. The complete decomposition of CO2 was possible because of higher degree of oxygen deficiency andsurface-to-volume ratio of the catalysts. The pre-edge XANES spectra of Fe species in both catalysts exhibit an absorbance feature at 7114 eV for the 1s to 3d transition which is forbidden by the selection rule in case of perfect octahedral symmetry. The EXAFS data showed that the NFNs had two central Fe atoms coordinated by primarily Fe-O and Fe-Fe with bond distances of 1.871 and 3.051 angstroms, respectively. In case of ZFNs these values are 1.889 and 3.062 A, respectively. Methane gas was produced during the reactivation of NFNs by flowing hydrogen gas. Decomposition of CO2, moreover, recovery of valuable methane using heat energy of offgas produced from power generation plant or steel industry is an appealing alternative for energy recovery.

On the Structure Sensitivity of Formic Acid Decomposition on Cu Catalysts

DOE PAGES

Li, Sha; Scaranto, Jessica; Mavrikakis, Manos

2016-08-03

Catalytic decomposition of formic acid (HCOOH) has attracted substantial attention since HCOOH is a major by-product in biomass reforming, a promising hydrogen carrier, and also a potential low temperature fuel cell feed. Despite the abundance of experimental studies for vapor-phase HCOOH decomposition on Cu catalysts, the reaction mechanism and its structure sensitivity is still under debate. In this work, self-consistent, periodic density functional theory calculations were performed on three model surfaces of copper—Cu(111), Cu(100) and Cu(211), and both the HCOO (formate)-mediated and COOH (carboxyl)-mediated pathways were investigated for HCOOH decomposition. The energetics of both pathways suggest that the HCOO-mediated routemore » is more favorable than the COOH-mediated route on all three surfaces, and that HCOOH decomposition proceeds through two consecutive dehydrogenation steps via the HCOO intermediate followed by the recombinative desorption of H 2. On all three surfaces, HCOO dehydrogenation is the likely rate determining step since it has the highest transition state energy and also the highest activation energy among the three catalytic steps in the HCOO pathway. The reaction is structure sensitive on Cu catalysts since the examined three Cu facets have dramatically different binding strengths for the key intermediate HCOO and varied barriers for the likely rate determining step—HCOO dehydrogenation. Cu(100) and Cu(211) bind HCOO much more strongly than Cu(111), and they are also characterized by potential energy surfaces that are lower in energy than that for the Cu(111) facet. Coadsorbed HCOO and H represents the most stable state along the reaction coordinate, indicating that, under reaction conditions, there might be a substantial surface coverage of the HCOO intermediate, especially at under-coordinated step, corner or defect sites. Therefore, under reaction conditions, HCOOH decomposition is predicted to occur most readily on the terrace sites of Cu nanoparticles. Finally, as a result, we hereby present an example of a fundamentally structure-sensitive reaction, which may present itself as structure-insensitive in typical varied particle-size experiments.« less

Thermal decomposition of the amino acids glycine, cysteine, aspartic acid, asparagine, glutamic acid, glutamine, arginine and histidine.

PubMed

Weiss, Ingrid M; Muth, Christina; Drumm, Robert; Kirchner, Helmut O K

2018-01-01

The pathways of thermal instability of amino acids have been unknown. New mass spectrometric data allow unequivocal quantitative identification of the decomposition products. Calorimetry, thermogravimetry and mass spectrometry were used to follow the thermal decomposition of the eight amino acids G, C, D, N, E, Q, R and H between 185 °C and 280 °C. Endothermic heats of decomposition between 72 and 151 kJ/mol are needed to form 12 to 70% volatile products. This process is neither melting nor sublimation. With exception of cysteine they emit mainly H 2 O, some NH 3 and no CO 2 . Cysteine produces CO 2 and little else. The reactions are described by polynomials, AA→ a NH 3 + b H 2 O+ c CO 2 + d H 2 S+ e residue, with integer or half integer coefficients. The solid monomolecular residues are rich in peptide bonds. Eight of the 20 standard amino acids decompose at well-defined, characteristic temperatures, in contrast to commonly accepted knowledge. Products of decomposition are simple. The novel quantitative results emphasize the impact of water and cyclic condensates with peptide bonds and put constraints on hypotheses of the origin, state and stability of amino acids in the range between 200 °C and 300 °C.

A parallel domain decomposition-based implicit method for the Cahn–Hilliard–Cook phase-field equation in 3D

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

Zheng, Xiang; Yang, Chao; State Key Laboratory of Computer Science, Chinese Academy of Sciences, Beijing 100190

2015-03-15

We present a numerical algorithm for simulating the spinodal decomposition described by the three dimensional Cahn–Hilliard–Cook (CHC) equation, which is a fourth-order stochastic partial differential equation with a noise term. The equation is discretized in space and time based on a fully implicit, cell-centered finite difference scheme, with an adaptive time-stepping strategy designed to accelerate the progress to equilibrium. At each time step, a parallel Newton–Krylov–Schwarz algorithm is used to solve the nonlinear system. We discuss various numerical and computational challenges associated with the method. The numerical scheme is validated by a comparison with an explicit scheme of high accuracymore » (and unreasonably high cost). We present steady state solutions of the CHC equation in two and three dimensions. The effect of the thermal fluctuation on the spinodal decomposition process is studied. We show that the existence of the thermal fluctuation accelerates the spinodal decomposition process and that the final steady morphology is sensitive to the stochastic noise. We also show the evolution of the energies and statistical moments. In terms of the parallel performance, it is found that the implicit domain decomposition approach scales well on supercomputers with a large number of processors.« less

Uncertainty propagation in orbital mechanics via tensor decomposition

NASA Astrophysics Data System (ADS)

Sun, Yifei; Kumar, Mrinal

2016-03-01

Uncertainty forecasting in orbital mechanics is an essential but difficult task, primarily because the underlying Fokker-Planck equation (FPE) is defined on a relatively high dimensional (6-D) state-space and is driven by the nonlinear perturbed Keplerian dynamics. In addition, an enormously large solution domain is required for numerical solution of this FPE (e.g. encompassing the entire orbit in the x-y-z subspace), of which the state probability density function (pdf) occupies a tiny fraction at any given time. This coupling of large size, high dimensionality and nonlinearity makes for a formidable computational task, and has caused the FPE for orbital uncertainty propagation to remain an unsolved problem. To the best of the authors' knowledge, this paper presents the first successful direct solution of the FPE for perturbed Keplerian mechanics. To tackle the dimensionality issue, the time-varying state pdf is approximated in the CANDECOMP/PARAFAC decomposition tensor form where all the six spatial dimensions as well as the time dimension are separated from one other. The pdf approximation for all times is obtained simultaneously via the alternating least squares algorithm. Chebyshev spectral differentiation is employed for discretization on account of its spectral ("super-fast") convergence rate. To facilitate the tensor decomposition and control the solution domain size, system dynamics is expressed using spherical coordinates in a noninertial reference frame. Numerical results obtained on a regular personal computer are compared with Monte Carlo simulations.

International Business Machines (IBM) Corporation Interim Agreement EPA Case No. 08-0113-00

EPA Pesticide Factsheets

On March 27, 2008, the United States Environmental Protection Agency (EPA), suspended International Business Machines (IBM) from receiving Federal Contracts, approved subcontracts, assistance, loans and other benefits.

122. BENCH SHOP, SOUTHWEST CORNER SHOWING WOOD BORING MACHINE. DOOR ...

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

122. BENCH SHOP, SOUTHWEST CORNER SHOWING WOOD BORING MACHINE. DOOR TO WOODSHOP ON RIGHT. - Gruber Wagon Works, Pennsylvania Route 183 & State Hill Road at Red Bridge Park, Bernville, Berks County, PA

22 CFR 121.10 - Forgings, castings, and machined bodies.

Code of Federal Regulations, 2014 CFR

2014-04-01

... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings, and machined bodies. The U.S. Munitions List controls as defense articles those forgings, castings, and other unfinished products, such as...

Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations

NASA Astrophysics Data System (ADS)

García Plaza, E.; Núñez López, P. J.

2018-01-01

The wavelet packet transform method decomposes a time signal into several independent time-frequency signals called packets. This enables the temporary location of transient events occurring during the monitoring of the cutting processes, which is advantageous in monitoring condition and fault diagnosis. This paper proposes the monitoring of surface roughness using a single low cost sensor that is easily implemented in numerical control machine tools in order to make on-line decisions on workpiece surface finish quality. Packet feature extraction in vibration signals was applied to correlate the sensor signals to measured surface roughness. For the successful application of the WPT method, mother wavelets, packet decomposition level, and appropriate packet selection methods should be considered, but are poorly understood aspects in the literature. In this novel contribution, forty mother wavelets, optimal decomposition level, and packet reduction methods were analysed, as well as identifying the effective frequency range providing the best packet feature extraction for monitoring surface finish. The results show that mother wavelet biorthogonal 4.4 in decomposition level L3 with the fusion of the orthogonal vibration components (ax + ay + az) were the best option in the vibration signal and surface roughness correlation. The best packets were found in the medium-high frequency DDA (6250-9375 Hz) and high frequency ADA (9375-12500 Hz) ranges, and the feed acceleration component ay was the primary source of information. The packet reduction methods forfeited packets with relevant features to the signal, leading to poor results for the prediction of surface roughness. WPT is a robust vibration signal processing method for the monitoring of surface roughness using a single sensor without other information sources, satisfactory results were obtained in comparison to other processing methods with a low computational cost.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

Resident Space Object Characterization and Behavior Understanding via Machine Learning and Ontology-based Bayesian Networks

NASA Astrophysics Data System (ADS)

Furfaro, R.; Linares, R.; Gaylor, D.; Jah, M.; Walls, R.

2016-09-01

In this paper, we present an end-to-end approach that employs machine learning techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of resident space objects. State-of-the-Art machine learning architectures (e.g. Extreme Learning Machines, Convolutional Deep Networks) are trained on physical models to learn the Resident Space Object (RSO) features in the vectorized energy and momentum states and parameters. The mapping from measurements to vectorized energy and momentum states and parameters enables behavior characterization via clustering in the features space and subsequent RSO classification. Additionally, Space Object Behavioral Ontologies (SOBO) are employed to define and capture the domain knowledge-base (KB) and BNs are constructed from the SOBO in a semi-automatic fashion to execute probabilistic reasoning over conclusions drawn from trained classifiers and/or directly from processed data. Such an approach enables integrating machine learning classifiers and probabilistic reasoning to support higher-level decision making for space domain awareness applications. The innovation here is to use these methods (which have enjoyed great success in other domains) in synergy so that it enables a "from data to discovery" paradigm by facilitating the linkage and fusion of large and disparate sources of information via a Big Data Science and Analytics framework.

Pursuing reliable thermal analysis techniques for energetic materials: decomposition kinetics and thermal stability of dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50).

PubMed

Muravyev, Nikita V; Monogarov, Konstantin A; Asachenko, Andrey F; Nechaev, Mikhail S; Ananyev, Ivan V; Fomenkov, Igor V; Kiselev, Vitaly G; Pivkina, Alla N

2016-12-21

Thermal decomposition of a novel promising high-performance explosive dihydroxylammonium 5,5'-bistetrazole-1,1'-diolate (TKX-50) was studied using a number of thermal analysis techniques (thermogravimetry, differential scanning calorimetry, and accelerating rate calorimetry, ARC). To obtain more comprehensive insight into the kinetics and mechanism of TKX-50 decomposition, a variety of complementary thermoanalytical experiments were performed under various conditions. Non-isothermal and isothermal kinetics were obtained at both atmospheric and low (up to 0.3 Torr) pressures. The gas products of thermolysis were detected in situ using IR spectroscopy, and the structure of solid-state decomposition products was determined by X-ray diffraction and scanning electron microscopy. Diammonium 5,5'-bistetrazole-1,1'-diolate (ABTOX) was directly identified to be the most important intermediate of the decomposition process. The important role of bistetrazole diol (BTO) in the mechanism of TKX-50 decomposition was also rationalized by thermolysis experiments with mixtures of TKX-50 and BTO. Several widely used thermoanalytical data processing techniques (Kissinger, isoconversional, formal kinetic approaches, etc.) were independently benchmarked against the ARC data, which are more germane to the real storage and application conditions of energetic materials. Our study revealed that none of the Arrhenius parameters reported before can properly describe the complex two-stage decomposition process of TKX-50. In contrast, we showed the superior performance of the isoconversional methods combined with isothermal measurements, which yielded the most reliable kinetic parameters of TKX-50 thermolysis. In contrast with the existing reports, the thermal stability of TKX-50 was determined in the ARC experiments to be lower than that of hexogen, but close to that of hexanitrohexaazaisowurtzitane (CL-20).

New mechanism for autocatalytic decomposition of H2CO3 in the vapor phase.

PubMed

Ghoshal, Sourav; Hazra, Montu K

2014-04-03

In this article, we present high level ab initio calculations investigating the energetics of a new autocatalytic decomposition mechanism for carbonic acid (H2CO3) in the vapor phase. The calculation have been performed at the MP2 level of theory in conjunction with aug-cc-pVDZ, aug-cc-pVTZ, and 6-311++G(3df,3pd) basis sets as well as at the CCSD(T)/aug-cc-pVTZ level. The present study suggests that this new decomposition mechanism is effectively a near-barrierless process at room temperature and makes vapor phase of H2CO3 unstable even in the absence of water molecules. Our calculation at the MP2/aug-cc-pVTZ level predicts that the effective barrier, defined as the difference between the zero-point vibrational energy (ZPE) corrected energy of the transition state and the total energy of the isolated starting reactants in terms of bimolecular encounters, is nearly zero for the autocatalytic decomposition mechanism. The results at the CCSD(T)/aug-cc-pVTZ level of calculations suggest that the effective barrier, as defined above, is sensitive to some extent to the levels of calculations used, nevertheless, we find that the effective barrier height predicted at the CCSD(T)/aug-cc-pVTZ level is very small or in other words the autocatalytic decomposition mechanism presented in this work is a near-barrierless process as mentioned above. Thus, we suggest that this new autocatalytic decomposition mechanism has to be considered as the primary mechanism for the decomposition of carbonic acid, especially at its source, where the vapor phase concentration of H2CO3 molecules reaches its highest levels.

An evaluation of soil chemistry in human cadaver decomposition islands: Potential for estimating postmortem interval (PMI).

PubMed

Fancher, J P; Aitkenhead-Peterson, J A; Farris, T; Mix, K; Schwab, A P; Wescott, D J; Hamilton, M D

2017-10-01

Soil samples from the Forensic Anthropology Research Facility (FARF) at Texas State University, San Marcos, TX, were analyzed for multiple soil characteristics from cadaver decomposition islands to a depth of 5centimeters (cm) from 63 human decomposition sites, as well as depths up to 15cm in a subset of 11 of the cadaver decomposition islands plus control soils. Postmortem interval (PMI) of the cadaver decomposition islands ranged from 6 to 1752 days. Some soil chemistry, including nitrate-N (NO 3 -N), ammonium-N (NH 4 -N), and dissolved inorganic carbon (DIC), peaked at early PMI values and their concentrations at 0-5cm returned to near control values over time likely due to translocation down the soil profile. Other soil chemistry, including dissolved organic carbon (DOC), dissolved organic nitrogen (DON), orthophosphate-P (PO 4 -P), sodium (Na + ), and potassium (K + ), remained higher than the control soil up to a PMI of 1752days postmortem. The body mass index (BMI) of the cadaver appeared to have some effect on the cadaver decomposition island chemistry. To estimate PMI using soil chemistry, backward, stepwise multiple regression analysis was used with PMI as the dependent variable and soil chemistry, body mass index (BMI) and physical soil characteristics such as saturated hydraulic conductivity as independent variables. Measures of soil parameters derived from predator and microbial mediated decomposition of human remains shows promise in estimating PMI to within 365days for a period up to nearly five years. This persistent change in soil chemistry extends the ability to estimate PMI beyond the traditionally utilized methods of entomology and taphonomy in support of medical-legal investigations, humanitarian recovery efforts, and criminal and civil cases. Copyright © 2017 Elsevier B.V. All rights reserved.

Surface-Mediated Solvent Decomposition in Li–Air Batteries: Impact of Peroxide and Superoxide Surface Terminations

DOE PAGES

Kumar, Nitin; Radin, Maxwell D.; Wood, Brandon C.; ...

2015-04-13

A viable Li/O 2 battery will require the development of stable electrolytes that do not continuously decompose during cell operation. In some recent experiments it is suggested that reactions occurring at the interface between the liquid electrolyte and the solid lithium peroxide (Li 2O 2) discharge phase are a major contributor to these instabilities. To clarify the mechanisms associated with these reactions, a variety of atomistic simulation techniques, classical Monte Carlo, van der Waals-augmented density functional theory, ab initio molecular dynamics, and various solvation models, are used to study the initial decomposition of the common electrolyte solvent, dimethoxyethane (DME), onmore » surfaces of Li 2O 2. Comparisons are made between the two predominant Li 2O 2 surface charge states by calculating decomposition pathways on peroxide-terminated (O 2 2–) and superoxide-terminated (O 2 1–) facets. For both terminations, DME decomposition proceeds exothermically via a two-step process comprised of hydrogen abstraction (H-abstraction) followed by nucleophilic attack. In the first step, abstracted H dissociates a surface O 2 dimer, and combines with a dissociated oxygen to form a hydroxide ion (OH –). In the remaining surface oxygen then attacks the DME, resulting in a DME fragment that is strongly bound to the Li 2O 2 surface. DME decomposition is predicted to be more exothermic on the peroxide facet; nevertheless, the rate of DME decomposition is faster on the superoxide termination. The impact of solvation (explicit vs implicit) and an applied electric field on the reaction energetics are investigated. Finally, our calculations suggest that surface-mediated electrolyte decomposition should out-pace liquid-phase processes such as solvent auto-oxidation by dissolved O 2.« less

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.

Preparation of poly (arylene ether nitrile)/NzdFeB composite film with excellent thermal properties and tensile strength

NASA Astrophysics Data System (ADS)

Pan, Hai; Xu, Mingzhen; Liu, Xiaobo

2017-12-01

PEN/NdFeB composite films were prepared by the solution casting method. The thermal properties, fracture morphology and tensile strength of the composite films were tested by DSC, TGA, SEM and electromechanical universal testing machine, respectively. The results reveal that the composite film has good thermal properties and tensile strength. Glass-transition temperature and decomposition temperatures at weight loss of 5% ot the composite films retain at 166±1 C and 462±4 C, respectively. The composite film with 5 wt.% NdFeB has the best tensile strength value for 100.5 MPa. In addition, it was found that the NdFeB filler was well dispersed in PEN matrix by SEM analysis.

Tracking and recognition of multiple human targets moving in a wireless pyroelectric infrared sensor network.

PubMed

Xiong, Ji; Li, Fangmin; Zhao, Ning; Jiang, Na

2014-04-22

With characteristics of low-cost and easy deployment, the distributed wireless pyroelectric infrared sensor network has attracted extensive interest, which aims to make it an alternate infrared video sensor in thermal biometric applications for tracking and identifying human targets. In these applications, effectively processing signals collected from sensors and extracting the features of different human targets has become crucial. This paper proposes the application of empirical mode decomposition and the Hilbert-Huang transform to extract features of moving human targets both in the time domain and the frequency domain. Moreover, the support vector machine is selected as the classifier. The experimental results demonstrate that by using this method the identification rates of multiple moving human targets are around 90%.

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

Chen, Chao; Pouransari, Hadi; Rajamanickam, Sivasankaran

We present a parallel hierarchical solver for general sparse linear systems on distributed-memory machines. For large-scale problems, this fully algebraic algorithm is faster and more memory-efficient than sparse direct solvers because it exploits the low-rank structure of fill-in blocks. Depending on the accuracy of low-rank approximations, the hierarchical solver can be used either as a direct solver or as a preconditioner. The parallel algorithm is based on data decomposition and requires only local communication for updating boundary data on every processor. Moreover, the computation-to-communication ratio of the parallel algorithm is approximately the volume-to-surface-area ratio of the subdomain owned by everymore » processor. We also provide various numerical results to demonstrate the versatility and scalability of the parallel algorithm.« less

Double-Resonance Facilitated Decomposion of Emission Spectra

NASA Astrophysics Data System (ADS)

Kato, Ryota; Ishikawa, Haruki

2016-06-01

Emission spectra provide us with rich information about the excited-state processes such as proton-transfer, charge-transfer and so on. In the cases that more than one excited states are involved, emission spectra from different excited states sometimes overlap and a decomposition of the overlapped spectra is desired. One of the methods to perform a decomposition is a time-resolved fluorescence technique. It uses a difference in time evolutions of components involved. However, in the gas-phase, a concentration of the sample is frequently too small to carry out this method. On the other hand, double-resonance technique is a very powerful tool to discriminate or identify a common species in the spectra in the gas-phase. Thus, in the present study, we applied the double-resonance technique to resolve the overlapped emission spectra. When transient IR absorption spectra of the excited state are available, we can label the population of the certain species by the IR excitation with a proper selection of the IR wavenumbers. Thus, we can obtain the emission spectra of labeled species by subtracting the emission spectra with IR labeling from that without IR. In the present study, we chose the charge-transfer emission spectra of cyanophenyldisilane (CPDS) as a test system. One of us reported that two charge-transfer (CT) states are involved in the intramolecular charge-transfer (ICT) process of CPDS-water cluster and recorded the transient IR spectra. As expected, we have succeeded in resolving the CT emission spectra of CPDS-water cluster by the double resonance facilitated decomposion technique. In the present paper, we will report the details of the experimental scheme and the results of the decomposition of the emission spectra. H. Ishikawa, et al., Chem. Phys. Phys. Chem., 9, 117 (2007).

Machine learning for science: state of the art and future prospects.

PubMed

Mjolsness, E; DeCoste, D

2001-09-14

Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learning methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions.

Diverse Effects, Complex Causes: Children Use Information about Machines' Functional Diversity to Infer Internal Complexity

ERIC Educational Resources Information Center

Ahl, Richard E.; Keil, Frank C.

2017-01-01

Four studies explored the abilities of 80 adults and 180 children (4-9 years), from predominantly middle-class families in the Northeastern United States, to use information about machines' observable functional capacities to infer their internal, "hidden" mechanistic complexity. Children as young as 4 and 5 years old used machines'…