HTTR plant dynamic simulation using a hybrid computer

International Nuclear Information System (INIS)

Shimazaki, Junya; Suzuki, Katsuo; Nabeshima, Kunihiko; Watanabe, Koichi; Shinohara, Yoshikuni; Nakagawa, Shigeaki.

1990-01-01

A plant dynamic simulation of High-Temperature Engineering Test Reactor has been made using a new-type hybrid computer. This report describes a dynamic simulation model of HTTR, a hybrid simulation method for SIMSTAR and some results obtained from dynamics analysis of HTTR simulation. It concludes that the hybrid plant simulation is useful for on-line simulation on account of its capability of computation at high speed, compared with that of all digital computer simulation. With sufficient accuracy, 40 times faster computation than real time was reached only by changing an analog time scale for HTTR simulation. (author)

Evaporator modeling - A hybrid approach

International Nuclear Information System (INIS)

Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun

2009-01-01

In this paper, a hybrid modeling approach is proposed to model two-phase flow evaporators. The main procedures for hybrid modeling includes: (1) Based on the energy and material balance, and thermodynamic principles to formulate the process fundamental governing equations; (2) Select input/output (I/O) variables responsible to the system performance which can be measured and controlled; (3) Represent those variables existing in the original equations but are not measurable as simple functions of selected I/Os or constants; (4) Obtaining a single equation which can correlate system inputs and outputs; and (5) Identify unknown parameters by linear or nonlinear least-squares methods. The method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating evaporator with the maximum error of ±8%. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis

An Adaptive and Hybrid Approach for Revisiting the Visibility Pipeline

Directory of Open Access Journals (Sweden)

Ícaro Lins Leitão da Cunha

2016-04-01

Full Text Available We revisit the visibility problem, which is traditionally known in Computer Graphics and Vision fields as the process of computing a (potentially visible set of primitives in the computational model of a scene. We propose a hybrid solution that uses a dry structure (in the sense of data reduction, a triangulation of the type J1a, to accelerate the task of searching for visible primitives. We came up with a solution that is useful for real-time, on-line, interactive applications as 3D visualization. In such applications the main goal is to load the minimum amount of primitives from the scene during the rendering stage, as possible. For this purpose, our algorithm executes the culling by using a hybrid paradigm based on viewing-frustum, back-face culling and occlusion models. Results have shown substantial improvement over these traditional approaches if applied separately. This novel approach can be used in devices with no dedicated processors or with low processing power, as cell phones or embedded displays, or to visualize data through the Internet, as in virtual museums applications.

An energy management approach of hybrid vehicles using traffic preview information for energy saving

International Nuclear Information System (INIS)

Zheng, Chunhua; Xu, Guoqing; Xu, Kun; Pan, Zhongming; Liang, Quan

2015-01-01

Highlights: • Energy management approach of hybrid vehicles using traffic preview information. • Vehicle velocity profile and fuel consumption are optimized at the same time. • It is proved that a further energy saving is achieved by the proposed approach. • The proposed approach is useful especially for autonomous hybrid vehicles. - Abstract: The traffic preview information is very helpful for hybrid vehicles when distributing the power requirement of the vehicle to power sources and when determining the next driving route of the vehicle. In this research, an energy management approach for hybrid vehicles is proposed, which optimizes the vehicle velocity profile while minimizing the fuel consumption with the help of the traffic preview information, so that a further energy saving for hybrid vehicles can be achieved. The Pontryagin’s Minimum Principle (PMP) is adopted on the proposed approach. A fuel cell hybrid vehicle (FCHV) is selected as an example, and the proposed energy management approach is applied to the FCHV in a computer simulation environment for the offline and online cases respectively. Simulation results show that the fuel economy of the FCHV is improved by the proposed energy management approach compared to a benchmark case where the driving cycle is fixed and only the hybrid power split (allocation) ratio is optimized. The proposed energy management approach is useful especially for the autonomous hybrid vehicles.

Hybrid computing - Generalities and bibliography

International Nuclear Information System (INIS)

Neel, Daniele

1970-01-01

This note presents the content of a research thesis. It describes the evolution of hybrid computing systems, discusses the benefits and shortcomings of analogue or hybrid systems, discusses the building up of an hybrid system (requires properties), comments different possible uses, addresses the issues of language and programming, discusses analysis methods and scopes of application. An appendix proposes a bibliography on these issues and notably the different scopes of application (simulation, fluid dynamics, biology, chemistry, electronics, energy, errors, space, programming languages, hardware, mechanics, and optimisation of equations or processes, physics) [fr

Generalised Computability and Applications to Hybrid Systems

DEFF Research Database (Denmark)

Korovina, Margarita V.; Kudinov, Oleg V.

2001-01-01

We investigate the concept of generalised computability of operators and functionals defined on the set of continuous functions, firstly introduced in [9]. By working in the reals, with equality and without equality, we study properties of generalised computable operators and functionals. Also we...... propose an interesting application to formalisation of hybrid systems. We obtain some class of hybrid systems, which trajectories are computable in the sense of computable analysis. This research was supported in part by the RFBR (grants N 99-01-00485, N 00-01- 00810) and by the Siberian Branch of RAS (a...... grant for young researchers, 2000)...

Computer code MLCOSP for multiple-correlation and spectrum analysis with a hybrid computer

International Nuclear Information System (INIS)

Oguma, Ritsuo; Fujii, Yoshio; Usui, Hozumi; Watanabe, Koichi

1975-10-01

Usage of the computer code MLCOSP(Multiple Correlation and Spectrum) developed is described for a hybrid computer installed in JAERI Functions of the hybrid computer and its terminal devices are utilized ingeniously in the code to reduce complexity of the data handling which occurrs in analysis of the multivariable experimental data and to perform the analysis in perspective. Features of the code are as follows; Experimental data can be fed to the digital computer through the analog part of the hybrid computer by connecting with a data recorder. The computed results are displayed in figures, and hardcopies are taken when necessary. Series-messages to the code are shown on the terminal, so man-machine communication is possible. And further the data can be put in through a keyboard, so case study according to the results of analysis is possible. (auth.)

Universal blind quantum computation for hybrid system

Science.gov (United States)

Huang, He-Liang; Bao, Wan-Su; Li, Tan; Li, Feng-Guang; Fu, Xiang-Qun; Zhang, Shuo; Zhang, Hai-Long; Wang, Xiang

2017-08-01

As progress on the development of building quantum computer continues to advance, first-generation practical quantum computers will be available for ordinary users in the cloud style similar to IBM's Quantum Experience nowadays. Clients can remotely access the quantum servers using some simple devices. In such a situation, it is of prime importance to keep the security of the client's information. Blind quantum computation protocols enable a client with limited quantum technology to delegate her quantum computation to a quantum server without leaking any privacy. To date, blind quantum computation has been considered only for an individual quantum system. However, practical universal quantum computer is likely to be a hybrid system. Here, we take the first step to construct a framework of blind quantum computation for the hybrid system, which provides a more feasible way for scalable blind quantum computation.

Hybrid simulation of reactor kinetics in CANDU reactors using a modal approach

International Nuclear Information System (INIS)

Monaghan, B.M.; McDonnell, F.N.; Hinds, H.W.T.; m.

1980-01-01

A hybrid computer model for simulating the behaviour of large CANDU (Canada Deuterium Uranium) reactor cores is presented. The main dynamic variables are expressed in terms of weighted sums of a base set of spatial natural-mode functions with time-varying co-efficients. This technique, known as the modal or synthesis approach, permits good three-dimensional representation of reactor dynamics and is well suited to hybrid simulation. The hybrid model provides improved man-machine interaction and real-time capability. The model was used in two applications. The first studies the transient that follows a loss of primary coolant and reactor shutdown; the second is a simulation of the dynamics of xenon, a fission product which has a high absorption cross-section for neutrons and thus has an important effect on reactor behaviour. Comparison of the results of the hybrid computer simulation with those of an all-digital one is good, within 1% to 2%

A Review of Hybrid Brain-Computer Interface Systems

Directory of Open Access Journals (Sweden)

Setare Amiri

2013-01-01

Full Text Available Increasing number of research activities and different types of studies in brain-computer interface (BCI systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

Hybrid cloud and cluster computing paradigms for life science applications.

Science.gov (United States)

Qiu, Judy; Ekanayake, Jaliya; Gunarathne, Thilina; Choi, Jong Youl; Bae, Seung-Hee; Li, Hui; Zhang, Bingjing; Wu, Tak-Lon; Ruan, Yang; Ekanayake, Saliya; Hughes, Adam; Fox, Geoffrey

2010-12-21

Clouds and MapReduce have shown themselves to be a broadly useful approach to scientific computing especially for parallel data intensive applications. However they have limited applicability to some areas such as data mining because MapReduce has poor performance on problems with an iterative structure present in the linear algebra that underlies much data analysis. Such problems can be run efficiently on clusters using MPI leading to a hybrid cloud and cluster environment. This motivates the design and implementation of an open source Iterative MapReduce system Twister. Comparisons of Amazon, Azure, and traditional Linux and Windows environments on common applications have shown encouraging performance and usability comparisons in several important non iterative cases. These are linked to MPI applications for final stages of the data analysis. Further we have released the open source Twister Iterative MapReduce and benchmarked it against basic MapReduce (Hadoop) and MPI in information retrieval and life sciences applications. The hybrid cloud (MapReduce) and cluster (MPI) approach offers an attractive production environment while Twister promises a uniform programming environment for many Life Sciences applications. We used commercial clouds Amazon and Azure and the NSF resource FutureGrid to perform detailed comparisons and evaluations of different approaches to data intensive computing. Several applications were developed in MPI, MapReduce and Twister in these different environments.

Hybrid biasing approaches for global variance reduction

International Nuclear Information System (INIS)

Wu, Zeyun; Abdel-Khalik, Hany S.

2013-01-01

A new variant of Monte Carlo—deterministic (DT) hybrid variance reduction approach based on Gaussian process theory is presented for accelerating convergence of Monte Carlo simulation and compared with Forward-Weighted Consistent Adjoint Driven Importance Sampling (FW-CADIS) approach implemented in the SCALE package from Oak Ridge National Laboratory. The new approach, denoted the Gaussian process approach, treats the responses of interest as normally distributed random processes. The Gaussian process approach improves the selection of the weight windows of simulated particles by identifying a subspace that captures the dominant sources of statistical response variations. Like the FW-CADIS approach, the Gaussian process approach utilizes particle importance maps obtained from deterministic adjoint models to derive weight window biasing. In contrast to the FW-CADIS approach, the Gaussian process approach identifies the response correlations (via a covariance matrix) and employs them to reduce the computational overhead required for global variance reduction (GVR) purpose. The effective rank of the covariance matrix identifies the minimum number of uncorrelated pseudo responses, which are employed to bias simulated particles. Numerical experiments, serving as a proof of principle, are presented to compare the Gaussian process and FW-CADIS approaches in terms of the global reduction in standard deviation of the estimated responses. - Highlights: ► Hybrid Monte Carlo Deterministic Method based on Gaussian Process Model is introduced. ► Method employs deterministic model to calculate responses correlations. ► Method employs correlations to bias Monte Carlo transport. ► Method compared to FW-CADIS methodology in SCALE code. ► An order of magnitude speed up is achieved for a PWR core model.

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

Directory of Open Access Journals (Sweden)

Antonino Laudani

2015-01-01

Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

Improved signal processing approaches in an offline simulation of a hybrid brain–computer interface

Science.gov (United States)

Brunner, Clemens; Allison, Brendan Z.; Krusienski, Dean J.; Kaiser, Vera; Müller-Putz, Gernot R.; Pfurtscheller, Gert; Neuper, Christa

2012-01-01

In a conventional brain–computer interface (BCI) system, users perform mental tasks that yield specific patterns of brain activity. A pattern recognition system determines which brain activity pattern a user is producing and thereby infers the user’s mental task, allowing users to send messages or commands through brain activity alone. Unfortunately, despite extensive research to improve classification accuracy, BCIs almost always exhibit errors, which are sometimes so severe that effective communication is impossible. We recently introduced a new idea to improve accuracy, especially for users with poor performance. In an offline simulation of a “hybrid” BCI, subjects performed two mental tasks independently and then simultaneously. This hybrid BCI could use two different types of brain signals common in BCIs – event-related desynchronization (ERD) and steady-state evoked potentials (SSEPs). This study suggested that such a hybrid BCI is feasible. Here, we re-analyzed the data from our initial study. We explored eight different signal processing methods that aimed to improve classification and further assess both the causes and the extent of the benefits of the hybrid condition. Most analyses showed that the improved methods described here yielded a statistically significant improvement over our initial study. Some of these improvements could be relevant to conventional BCIs as well. Moreover, the number of illiterates could be reduced with the hybrid condition. Results are also discussed in terms of dual task interference and relevance to protocol design in hybrid BCIs. PMID:20153371

Input/output routines for a hybrid computer

International Nuclear Information System (INIS)

Izume, Akitada; Yodo, Terutaka; Sakama, Iwao; Sakamoto, Akira; Miyake, Osamu

1976-05-01

This report is concerned with data processing programs for a hybrid computer system. Especially pre-data processing of magnetic tapes which are recorded during the dynamic experiment by FACOM 270/25 data logging system in the 50 MW steam generator test facility is described in detail. The magnetic tape is a most effective recording medium for data logging, but recording formats of the magnetic tape are different between data logging systems. In our section, the final data analyses are performed by data in the disk of EAI-690 hybrid computer system, and to transfer all required information in magnetic tapes to the disk, the magnetic tape editing and data transit are necessary by sub-computer NEAC-3200 system. This report is written for users as a manual and reference hand book of pre-data processing between different type computers. (auth.)

A hybrid agent-based computational economics and optimization approach for supplier selection problem

Directory of Open Access Journals (Sweden)

Zahra Pourabdollahi

2017-12-01

Full Text Available Supplier evaluation and selection problem is among the most important of logistics decisions that have been addressed extensively in supply chain management. The same logistics decision is also important in freight transportation since it identifies trade relationships between business establishments and determines commodity flows between production and consumption points. The commodity flows are then used as input to freight transportation models to determine cargo movements and their characteristics including mode choice and shipment size. Various approaches have been proposed to explore this latter problem in previous studies. Traditionally, potential suppliers are evaluated and selected using only price/cost as the influential criteria and the state-of-practice methods. This paper introduces a hybrid agent-based computational economics and optimization approach for supplier selection. The proposed model combines an agent-based multi-criteria supplier evaluation approach with a multi-objective optimization model to capture both behavioral and economical aspects of the supplier selection process. The model uses a system of ordered response models to determine importance weights of the different criteria in supplier evaluation from a buyers’ point of view. The estimated weights are then used to calculate a utility for each potential supplier in the market and rank them. The calculated utilities are then entered into a mathematical programming model in which best suppliers are selected by maximizing the total accrued utility for all buyers and minimizing total shipping costs while balancing the capacity of potential suppliers to ensure market clearing mechanisms. The proposed model, herein, was implemented under an operational agent-based supply chain and freight transportation framework for the Chicago Metropolitan Area.

Adaptation and hybridization in computational intelligence

CERN Document Server

Jr, Iztok

2015-01-01

  This carefully edited book takes a walk through recent advances in adaptation and hybridization in the Computational Intelligence (CI) domain. It consists of ten chapters that are divided into three parts. The first part illustrates background information and provides some theoretical foundation tackling the CI domain, the second part deals with the adaptation in CI algorithms, while the third part focuses on the hybridization in CI. This book can serve as an ideal reference for researchers and students of computer science, electrical and civil engineering, economy, and natural sciences that are confronted with solving the optimization, modeling and simulation problems. It covers the recent advances in CI that encompass Nature-inspired algorithms, like Artificial Neural networks, Evolutionary Algorithms and Swarm Intelligence –based algorithms.  

A Hybrid Approach to Teaching Managerial Economics

Science.gov (United States)

Metzgar, Matthew

2014-01-01

Many institutions in higher education are experimenting with hybrid teaching approaches to undergraduate courses. Online resources may provide a number of advantages to students as compared to in-class approaches. Research regarding the effectiveness of hybrid approaches is mixed and still accumulating. This paper discusses the use of a hybrid…

Hybrid perovskites: Approaches towards light-emitting devices

KAUST Repository

Alias, Mohd Sharizal

2016-10-06

The high optical gain and absorption of organic-inorganic hybrid perovskites have attracted extensive research for photonic device applications. Using the bromide halide as an example, we present key approaches of our work towards realizing efficient perovskites based light-emitters. The approaches involved determination of optical constants for the hybrid perovskites thin films, fabrication of photonic nanostructures in the form of subwavelength grating reflector patterned directly on the hybrid perovskites as light manipulation layer, and enhancing the emission property of the hybrid perovskites by using microcavity structure. Our results provide a platform for realization of hybrid perovskites based light-emitting devices for solid-state lighting and display applications. © 2016 IEEE.

Hybrid perovskites: Approaches towards light-emitting devices

KAUST Repository

Alias, Mohd Sharizal; Dursun, Ibrahim; Priante, Davide; Saidaminov, Makhsud I.; Ng, Tien Khee; Bakr, Osman; Ooi, Boon S.

2016-01-01

The high optical gain and absorption of organic-inorganic hybrid perovskites have attracted extensive research for photonic device applications. Using the bromide halide as an example, we present key approaches of our work towards realizing efficient perovskites based light-emitters. The approaches involved determination of optical constants for the hybrid perovskites thin films, fabrication of photonic nanostructures in the form of subwavelength grating reflector patterned directly on the hybrid perovskites as light manipulation layer, and enhancing the emission property of the hybrid perovskites by using microcavity structure. Our results provide a platform for realization of hybrid perovskites based light-emitting devices for solid-state lighting and display applications. © 2016 IEEE.

A Hybrid Approach to Protect Palmprint Templates

Directory of Open Access Journals (Sweden)

Hailun Liu

2014-01-01

Full Text Available Biometric template protection is indispensable to protect personal privacy in large-scale deployment of biometric systems. Accuracy, changeability, and security are three critical requirements for template protection algorithms. However, existing template protection algorithms cannot satisfy all these requirements well. In this paper, we propose a hybrid approach that combines random projection and fuzzy vault to improve the performances at these three points. Heterogeneous space is designed for combining random projection and fuzzy vault properly in the hybrid scheme. New chaff point generation method is also proposed to enhance the security of the heterogeneous vault. Theoretical analyses of proposed hybrid approach in terms of accuracy, changeability, and security are given in this paper. Palmprint database based experimental results well support the theoretical analyses and demonstrate the effectiveness of proposed hybrid approach.

Technology for Large-Scale Translation of Clinical Practice Guidelines: A Pilot Study of the Performance of a Hybrid Human and Computer-Assisted Approach.

Science.gov (United States)

Van de Velde, Stijn; Macken, Lieve; Vanneste, Koen; Goossens, Martine; Vanschoenbeek, Jan; Aertgeerts, Bert; Vanopstal, Klaar; Vander Stichele, Robert; Buysschaert, Joost

2015-10-09

The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, began in 2011 to optimize quality of care by promoting evidence-based decision making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL MultiTerm terminology databases) from medical terminology databases, and support from online machine translation. This resulted in a validated translation memory, which is now in use for the translation of new and updated guidelines. The objective of this experiment was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. We conducted a pilot study in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (1) by certificated junior translators without medical specialization using the hybrid method, (2) by an experienced medical translator without this support, and (3) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The human translation edit rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of

Hybrid parallel computing architecture for multiview phase shifting

Science.gov (United States)

Zhong, Kai; Li, Zhongwei; Zhou, Xiaohui; Shi, Yusheng; Wang, Congjun

2014-11-01

The multiview phase-shifting method shows its powerful capability in achieving high resolution three-dimensional (3-D) shape measurement. Unfortunately, this ability results in very high computation costs and 3-D computations have to be processed offline. To realize real-time 3-D shape measurement, a hybrid parallel computing architecture is proposed for multiview phase shifting. In this architecture, the central processing unit can co-operate with the graphic processing unit (GPU) to achieve hybrid parallel computing. The high computation cost procedures, including lens distortion rectification, phase computation, correspondence, and 3-D reconstruction, are implemented in GPU, and a three-layer kernel function model is designed to simultaneously realize coarse-grained and fine-grained paralleling computing. Experimental results verify that the developed system can perform 50 fps (frame per second) real-time 3-D measurement with 260 K 3-D points per frame. A speedup of up to 180 times is obtained for the performance of the proposed technique using a NVIDIA GT560Ti graphics card rather than a sequential C in a 3.4 GHZ Inter Core i7 3770.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Solar Flare Prediction

Science.gov (United States)

Benvenuto, Federico; Piana, Michele; Campi, Cristina; Massone, Anna Maria

2018-01-01

This paper introduces a novel method for flare forecasting, combining prediction accuracy with the ability to identify the most relevant predictive variables. This result is obtained by means of a two-step approach: first, a supervised regularization method for regression, namely, LASSO is applied, where a sparsity-enhancing penalty term allows the identification of the significance with which each data feature contributes to the prediction; then, an unsupervised fuzzy clustering technique for classification, namely, Fuzzy C-Means, is applied, where the regression outcome is partitioned through the minimization of a cost function and without focusing on the optimization of a specific skill score. This approach is therefore hybrid, since it combines supervised and unsupervised learning; realizes classification in an automatic, skill-score-independent way; and provides effective prediction performances even in the case of imbalanced data sets. Its prediction power is verified against NOAA Space Weather Prediction Center data, using as a test set, data in the range between 1996 August and 2010 December and as training set, data in the range between 1988 December and 1996 June. To validate the method, we computed several skill scores typically utilized in flare prediction and compared the values provided by the hybrid approach with the ones provided by several standard (non-hybrid) machine learning methods. The results showed that the hybrid approach performs classification better than all other supervised methods and with an effectiveness comparable to the one of clustering methods; but, in addition, it provides a reliable ranking of the weights with which the data properties contribute to the forecast.

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

International Nuclear Information System (INIS)

Brown, W. Michael; Wang, Peng; Plimpton, Steven J.; Tharrington, Arnold N.

2011-01-01

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, (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.

Computational hybrid anthropometric paediatric phantom library for internal radiation dosimetry

Science.gov (United States)

Xie, Tianwu; Kuster, Niels; Zaidi, Habib

2017-04-01

Hybrid computational phantoms combine voxel-based and simplified equation-based modelling approaches to provide unique advantages and more realism for the construction of anthropomorphic models. In this work, a methodology and C++ code are developed to generate hybrid computational phantoms covering statistical distributions of body morphometry in the paediatric population. The paediatric phantoms of the Virtual Population Series (IT’IS Foundation, Switzerland) were modified to match target anthropometric parameters, including body mass, body length, standing height and sitting height/stature ratio, determined from reference databases of the National Centre for Health Statistics and the National Health and Nutrition Examination Survey. The phantoms were selected as representative anchor phantoms for the newborn, 1, 2, 5, 10 and 15 years-old children, and were subsequently remodelled to create 1100 female and male phantoms with 10th, 25th, 50th, 75th and 90th body morphometries. Evaluation was performed qualitatively using 3D visualization and quantitatively by analysing internal organ masses. Overall, the newly generated phantoms appear very reasonable and representative of the main characteristics of the paediatric population at various ages and for different genders, body sizes and sitting stature ratios. The mass of internal organs increases with height and body mass. The comparison of organ masses of the heart, kidney, liver, lung and spleen with published autopsy and ICRP reference data for children demonstrated that they follow the same trend when correlated with age. The constructed hybrid computational phantom library opens up the prospect of comprehensive radiation dosimetry calculations and risk assessment for the paediatric population of different age groups and diverse anthropometric parameters.

A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran

International Nuclear Information System (INIS)

Mostafavi, Elham Sadat; Ramiyani, Sara Saeidi; Sarvar, Rahim; Moud, Hashem Izadi; Mousavi, Seyyed Mohammad

2013-01-01

This paper presents an innovative hybrid approach for the estimation of the solar global radiation. New prediction equations were developed for the global radiation using an integrated search method of genetic programming (GP) and simulated annealing (SA), called GP/SA. The solar radiation was formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years in two cities of Iran were used to develop GP/SA-based models. Separate models were established for each city. The generalization of the models was verified using a separate testing database. A sensitivity analysis was conducted to investigate the contribution of the parameters affecting the solar radiation. The derived models make accurate predictions of the solar global radiation and notably outperform the existing models. -- Highlights: ► A hybrid approach is presented for the estimation of the solar global radiation. ► The proposed method integrates the capabilities of GP and SA. ► Several climatological and meteorological parameters are included in the analysis. ► The GP/SA models make accurate predictions of the solar global radiation.

A hybrid personalized data recommendation approach for geoscience data sharing

Science.gov (United States)

WANG, M.; Wang, J.

2016-12-01

Recommender systems are effective tools helping Internet users overcome information overloading. The two most widely used recommendation algorithms are collaborating filtering (CF) and content-based filtering (CBF). A number of recommender systems based on those two algorithms were developed for multimedia, online sells, and other domains. Each of the two algorithms has its advantages and shortcomings. Hybrid approaches that combine these two algorithms are better choices in many cases. In geoscience data sharing domain, where the items (datasets) are more informative (in space and time) and domain-specific, no recommender system is specialized for data users. This paper reports a dynamic weighted hybrid recommendation algorithm that combines CF and CBF for geoscience data sharing portal. We first derive users' ratings on items with their historical visiting time by Jenks Natural Break. In the CBF part, we incorporate the space, time, and subject information of geoscience datasets to compute item similarity. Predicted ratings were computed with k-NN method separately using CBF and CF, and then combined with weights. With training dataset we attempted to find the best model describing ideal weights and users' co-rating numbers. A logarithmic function was confirmed to be the best model. The model was then used to tune the weights of CF and CBF on user-item basis with test dataset. Evaluation results show that the dynamic weighted approach outperforms either solo CF or CBF approach in terms of Precision and Recall.

A Generalized Hybrid Multiscale Modeling Approach for Flow and Reactive Transport in Porous Media

Science.gov (United States)

Yang, X.; Meng, X.; Tang, Y. H.; Guo, Z.; Karniadakis, G. E.

2017-12-01

Using emerging understanding of biological and environmental processes at fundamental scales to advance predictions of the larger system behavior requires the development of multiscale approaches, and there is strong interest in coupling models at different scales together in a hybrid multiscale simulation framework. A limited number of hybrid multiscale simulation methods have been developed for subsurface applications, mostly using application-specific approaches for model coupling. The proposed generalized hybrid multiscale approach is designed with minimal intrusiveness to the at-scale simulators (pre-selected) and provides a set of lightweight C++ scripts to manage a complex multiscale workflow utilizing a concurrent coupling approach. The workflow includes at-scale simulators (using the lattice-Boltzmann method, LBM, at the pore and Darcy scale, respectively), scripts for boundary treatment (coupling and kriging), and a multiscale universal interface (MUI) for data exchange. The current study aims to apply the generalized hybrid multiscale modeling approach to couple pore- and Darcy-scale models for flow and mixing-controlled reaction with precipitation/dissolution in heterogeneous porous media. The model domain is packed heterogeneously that the mixing front geometry is more complex and not known a priori. To address those challenges, the generalized hybrid multiscale modeling approach is further developed to 1) adaptively define the locations of pore-scale subdomains, 2) provide a suite of physical boundary coupling schemes and 3) consider the dynamic change of the pore structures due to mineral precipitation/dissolution. The results are validated and evaluated by comparing with single-scale simulations in terms of velocities, reactive concentrations and computing cost.

A hybrid, coupled approach for modeling charged fluids from the nano to the mesoscale

Science.gov (United States)

Cheung, James; Frischknecht, Amalie L.; Perego, Mauro; Bochev, Pavel

2017-11-01

We develop and demonstrate a new, hybrid simulation approach for charged fluids, which combines the accuracy of the nonlocal, classical density functional theory (cDFT) with the efficiency of the Poisson-Nernst-Planck (PNP) equations. The approach is motivated by the fact that the more accurate description of the physics in the cDFT model is required only near the charged surfaces, while away from these regions the PNP equations provide an acceptable representation of the ionic system. We formulate the hybrid approach in two stages. The first stage defines a coupled hybrid model in which the PNP and cDFT equations act independently on two overlapping domains, subject to suitable interface coupling conditions. At the second stage we apply the principles of the alternating Schwarz method to the hybrid model by using the interface conditions to define the appropriate boundary conditions and volume constraints exchanged between the PNP and the cDFT subdomains. Numerical examples with two representative examples of ionic systems demonstrate the numerical properties of the method and its potential to reduce the computational cost of a full cDFT calculation, while retaining the accuracy of the latter near the charged surfaces.

A Hybrid Genetic Algorithm Approach for Optimal Power Flow

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Sydulu Maheswarapu

2011-08-01

Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

Photo-Ionization of Noble Gases: A Demonstration of Hybrid Coupled Channels Approach

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Vinay Pramod Majety

2015-01-01

Full Text Available We present here an application of the recently developed hybrid coupled channels approach to study photo-ionization of noble gas atoms: Neon and Argon. We first compute multi-photon ionization rates and cross-sections for these inert gas atoms with our approach and compare them with reliable data available from R-matrix Floquet theory. The good agreement between coupled channels and R-matrix Floquet theory show that our method treats multi-electron systems on par with the well established R-matrix theory. We then apply the time dependent surface flux (tSURFF method with our approach to compute total and angle resolved photo-electron spectra from Argon with linearly and circularly polarized 12 nm wavelength laser fields, a typical wavelength available from Free Electron Lasers (FELs.

An online hybrid brain-computer interface combining multiple physiological signals for webpage browse.

Science.gov (United States)

Long Chen; Zhongpeng Wang; Feng He; Jiajia Yang; Hongzhi Qi; Peng Zhou; Baikun Wan; Dong Ming

2015-08-01

The hybrid brain computer interface (hBCI) could provide higher information transfer rate than did the classical BCIs. It included more than one brain-computer or human-machine interact paradigms, such as the combination of the P300 and SSVEP paradigms. Research firstly constructed independent subsystems of three different paradigms and tested each of them with online experiments. Then we constructed a serial hybrid BCI system which combined these paradigms to achieve the functions of typing letters, moving and clicking cursor, and switching among them for the purpose of browsing webpages. Five subjects were involved in this study. They all successfully realized these functions in the online tests. The subjects could achieve an accuracy above 90% after training, which met the requirement in operating the system efficiently. The results demonstrated that it was an efficient system capable of robustness, which provided an approach for the clinic application.

Solving Problems in Various Domains by Hybrid Models of High Performance Computations

Directory of Open Access Journals (Sweden)

Yurii Rogozhin

2014-03-01

Full Text Available This work presents a hybrid model of high performance computations. The model is based on membrane system (P~system where some membranes may contain quantum device that is triggered by the data entering the membrane. This model is supposed to take advantages of both biomolecular and quantum paradigms and to overcome some of their inherent limitations. The proposed approach is demonstrated through two selected problems: SAT, and image retrieving.

Interactions among biotic and abiotic factors affect the reliability of tungsten microneedles puncturing in vitro and in vivo peripheral nerves: A hybrid computational approach

Energy Technology Data Exchange (ETDEWEB)

Sergi, Pier Nicola, E-mail: p.sergi@sssup.it [Translational Neural Engineering Laboratory, The Biorobotics Institute, Scuola Superiore Sant' Anna, Viale Rinaldo Piaggio 34, Pontedera, 56025 (Italy); Jensen, Winnie [Department of Health Science and Technology, Fredrik Bajers Vej 7, 9220 Aalborg (Denmark); Yoshida, Ken [Department of Biomedical Engineering, Indiana University - Purdue University Indianapolis, 723 W. Michigan St., SL220, Indianapolis, IN 46202 (United States)

2016-02-01

Tungsten is an elective material to produce slender and stiff microneedles able to enter soft tissues and minimize puncture wounds. In particular, tungsten microneedles are used to puncture peripheral nerves and insert neural interfaces, bridging the gap between the nervous system and robotic devices (e.g., hand prostheses). Unfortunately, microneedles fail during the puncture process and this failure is not dependent on stiffness or fracture toughness of the constituent material. In addition, the microneedles' performances decrease during in vivo trials with respect to the in vitro ones. This further effect is independent on internal biotic effects, while it seems to be related to external biotic causes. Since the exact synergy of phenomena decreasing the in vivo reliability is still not known, this work explored the connection between in vitro and in vivo behavior of tungsten microneedles through the study of interactions between biotic and abiotic factors. A hybrid computational approach, simultaneously using theoretical relationships and in silico models of nerves, was implemented to model the change of reliability varying the microneedle diameter, and to predict in vivo performances by using in vitro reliability and local differences between in vivo and in vitro mechanical response of nerves. - Highlights: • We provide phenomenological Finite Element (FE) models of peripheral nerves to study the interactions with W microneedles • We provide a general interaction-based approach to model the reliability of slender microneedles • We evaluate the reliability of W microneedels to puncture in vivo nerves • We provide a novel synergistic hybrid approach (theory + simulations) involving interactions among biotic and abiotic factors • We validate the hybrid approach by using experimental data from literature.

Interactions among biotic and abiotic factors affect the reliability of tungsten microneedles puncturing in vitro and in vivo peripheral nerves: A hybrid computational approach

International Nuclear Information System (INIS)

Sergi, Pier Nicola; Jensen, Winnie; Yoshida, Ken

2016-01-01

Tungsten is an elective material to produce slender and stiff microneedles able to enter soft tissues and minimize puncture wounds. In particular, tungsten microneedles are used to puncture peripheral nerves and insert neural interfaces, bridging the gap between the nervous system and robotic devices (e.g., hand prostheses). Unfortunately, microneedles fail during the puncture process and this failure is not dependent on stiffness or fracture toughness of the constituent material. In addition, the microneedles' performances decrease during in vivo trials with respect to the in vitro ones. This further effect is independent on internal biotic effects, while it seems to be related to external biotic causes. Since the exact synergy of phenomena decreasing the in vivo reliability is still not known, this work explored the connection between in vitro and in vivo behavior of tungsten microneedles through the study of interactions between biotic and abiotic factors. A hybrid computational approach, simultaneously using theoretical relationships and in silico models of nerves, was implemented to model the change of reliability varying the microneedle diameter, and to predict in vivo performances by using in vitro reliability and local differences between in vivo and in vitro mechanical response of nerves. - Highlights: • We provide phenomenological Finite Element (FE) models of peripheral nerves to study the interactions with W microneedles • We provide a general interaction-based approach to model the reliability of slender microneedles • We evaluate the reliability of W microneedels to puncture in vivo nerves • We provide a novel synergistic hybrid approach (theory + simulations) involving interactions among biotic and abiotic factors • We validate the hybrid approach by using experimental data from literature

A hybrid clustering approach to recognition of protein families in 114 microbial genomes

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Gogarten J Peter

2004-04-01

Full Text Available Abstract Background Grouping proteins into sequence-based clusters is a fundamental step in many bioinformatic analyses (e.g., homology-based prediction of structure or function. Standard clustering methods such as single-linkage clustering capture a history of cluster topologies as a function of threshold, but in practice their usefulness is limited because unrelated sequences join clusters before biologically meaningful families are fully constituted, e.g. as the result of matches to so-called promiscuous domains. Use of the Markov Cluster algorithm avoids this non-specificity, but does not preserve topological or threshold information about protein families. Results We describe a hybrid approach to sequence-based clustering of proteins that combines the advantages of standard and Markov clustering. We have implemented this hybrid approach over a relational database environment, and describe its application to clustering a large subset of PDB, and to 328577 proteins from 114 fully sequenced microbial genomes. To demonstrate utility with difficult problems, we show that hybrid clustering allows us to constitute the paralogous family of ATP synthase F1 rotary motor subunits into a single, biologically interpretable hierarchical grouping that was not accessible using either single-linkage or Markov clustering alone. We describe validation of this method by hybrid clustering of PDB and mapping SCOP families and domains onto the resulting clusters. Conclusion Hybrid (Markov followed by single-linkage clustering combines the advantages of the Markov Cluster algorithm (avoidance of non-specific clusters resulting from matches to promiscuous domains and single-linkage clustering (preservation of topological information as a function of threshold. Within the individual Markov clusters, single-linkage clustering is a more-precise instrument, discerning sub-clusters of biological relevance. Our hybrid approach thus provides a computationally efficient

Hybrid computer modelling in plasma physics

International Nuclear Information System (INIS)

Hromadka, J; Ibehej, T; Hrach, R

2016-01-01

Our contribution is devoted to development of hybrid modelling techniques. We investigate sheath structures in the vicinity of solids immersed in low temperature argon plasma of different pressures by means of particle and fluid computer models. We discuss the differences in results obtained by these methods and try to propose a way to improve the results of fluid models in the low pressure area. There is a possibility to employ Chapman-Enskog method to find appropriate closure relations of fluid equations in a case when particle distribution function is not Maxwellian. We try to follow this way to enhance fluid model and to use it in hybrid plasma model further. (paper)

Opposition-Based Memetic Algorithm and Hybrid Approach for Sorting Permutations by Reversals.

Science.gov (United States)

Soncco-Álvarez, José Luis; Muñoz, Daniel M; Ayala-Rincón, Mauricio

2018-02-21

Sorting unsigned permutations by reversals is a difficult problem; indeed, it was proved to be NP-hard by Caprara (1997). Because of its high complexity, many approximation algorithms to compute the minimal reversal distance were proposed until reaching the nowadays best-known theoretical ratio of 1.375. In this article, two memetic algorithms to compute the reversal distance are proposed. The first one uses the technique of opposition-based learning leading to an opposition-based memetic algorithm; the second one improves the previous algorithm by applying the heuristic of two breakpoint elimination leading to a hybrid approach. Several experiments were performed with one-hundred randomly generated permutations, single benchmark permutations, and biological permutations. Results of the experiments showed that the proposed OBMA and Hybrid-OBMA algorithms achieve the best results for practical cases, that is, for permutations of length up to 120. Also, Hybrid-OBMA showed to improve the results of OBMA for permutations greater than or equal to 60. The applicability of our proposed algorithms was checked processing permutations based on biological data, in which case OBMA gave the best average results for all instances.

A hybrid system approach to airspeed, angle of attack and sideslip estimation in Unmanned Aerial Vehicles

KAUST Repository

Shaqura, Mohammad; Claudel, Christian

2015-01-01

, low power autopilots in real-time. The computational method is based on a hybrid decomposition of the modes of operation of the UAV. A Bayesian approach is considered for estimation, in which the estimated airspeed, angle of attack and sideslip

Accelerating Climate and Weather Simulations through Hybrid Computing

Science.gov (United States)

Zhou, Shujia; Cruz, Carlos; Duffy, Daniel; Tucker, Robert; Purcell, Mark

2011-01-01

Unconventional multi- and many-core processors (e.g. IBM (R) Cell B.E.(TM) and NVIDIA (R) GPU) have emerged as effective accelerators in trial climate and weather simulations. Yet these climate and weather models typically run on parallel computers with conventional processors (e.g. Intel, AMD, and IBM) using Message Passing Interface. To address challenges involved in efficiently and easily connecting accelerators to parallel computers, we investigated using IBM's Dynamic Application Virtualization (TM) (IBM DAV) software in a prototype hybrid computing system with representative climate and weather model components. The hybrid system comprises two Intel blades and two IBM QS22 Cell B.E. blades, connected with both InfiniBand(R) (IB) and 1-Gigabit Ethernet. The system significantly accelerates a solar radiation model component by offloading compute-intensive calculations to the Cell blades. Systematic tests show that IBM DAV can seamlessly offload compute-intensive calculations from Intel blades to Cell B.E. blades in a scalable, load-balanced manner. However, noticeable communication overhead was observed, mainly due to IP over the IB protocol. Full utilization of IB Sockets Direct Protocol and the lower latency production version of IBM DAV will reduce this overhead.

A Game-Theoretic approach to Fault Diagnosis of Hybrid Systems

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Davide Bresolin

2011-06-01

Full Text Available Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem for hybrid systems from a game-theoretical point of view. A hybrid system is a system mixing continuous and discrete behaviours that cannot be faithfully modeled neither by using a formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, and we define a Fault Diagnosis Game on them, using two players: the environment and the diagnoser. The environment controls the evolution of the system and chooses whether and when a fault occurs. The diagnoser observes the external behaviour of the system and announces whether a fault has occurred or not. Existence of a winning strategy for the diagnoser implies that faults can be detected correctly, while computing such a winning strategy corresponds to implement a diagnoser for the system. We will show how to determine the existence of a winning strategy, and how to compute it, for some decidable classes of hybrid automata like o-minimal hybrid automata.

Computation of the Lyapunov exponents in the compass-gait model under OGY control via a hybrid Poincaré map

International Nuclear Information System (INIS)

Gritli, Hassène; Belghith, Safya

2015-01-01

Highlights: • A numerical calculation method of the Lyapunov exponents in the compass-gait model under OGY control is proposed. • A new linearization method of the impulsive hybrid dynamics around a one-periodic hybrid limit cycle is achieved. • We develop a simple analytical expression of a controlled hybrid Poincaré map. • A dimension reduction of the hybrid Poincaré map is realized. • We describe the numerical computation procedure of the Lyapunov exponents via the designed hybrid Poincaré map. - Abstract: This paper aims at providing a numerical calculation method of the spectrum of Lyapunov exponents in a four-dimensional impulsive hybrid nonlinear dynamics of a passive compass-gait model under the OGY control approach by means of a controlled hybrid Poincaré map. We present a four-dimensional simplified analytical expression of such hybrid map obtained by linearizing the uncontrolled impulsive hybrid nonlinear dynamics around a desired one-periodic passive hybrid limit cycle. In order to compute the spectrum of Lyapunov exponents, a dimension reduction of the controlled hybrid Poincaré map is realized. The numerical calculation of the spectrum of Lyapunov exponents using the reduced-dimension controlled hybrid Poincaré map is given in detail. In order to show the effectiveness of the developed method, the spectrum of Lyapunov exponents is calculated as the slope (bifurcation) parameter varies and hence used to predict the walking dynamics behavior of the compass-gait model under the OGY control.

A hybrid computational-experimental approach for automated crystal structure solution

Science.gov (United States)

Meredig, Bryce; Wolverton, C.

2013-02-01

Crystal structure solution from diffraction experiments is one of the most fundamental tasks in materials science, chemistry, physics and geology. Unfortunately, numerous factors render this process labour intensive and error prone. Experimental conditions, such as high pressure or structural metastability, often complicate characterization. Furthermore, many materials of great modern interest, such as batteries and hydrogen storage media, contain light elements such as Li and H that only weakly scatter X-rays. Finally, structural refinements generally require significant human input and intuition, as they rely on good initial guesses for the target structure. To address these many challenges, we demonstrate a new hybrid approach, first-principles-assisted structure solution (FPASS), which combines experimental diffraction data, statistical symmetry information and first-principles-based algorithmic optimization to automatically solve crystal structures. We demonstrate the broad utility of FPASS to clarify four important crystal structure debates: the hydrogen storage candidates MgNH and NH3BH3; Li2O2, relevant to Li-air batteries; and high-pressure silane, SiH4.

Attention-level transitory response: a novel hybrid BCI approach

Science.gov (United States)

Diez, Pablo F.; Garcés Correa, Agustina; Orosco, Lorena; Laciar, Eric; Mut, Vicente

2015-10-01

Objective. People with disabilities may control devices such as a computer or a wheelchair by means of a brain-computer interface (BCI). BCI based on steady-state visual evoked potentials (SSVEP) requires visual stimulation of the user. However, this SSVEP-based BCI suffers from the ‘Midas touch effect’, i.e., the BCI can detect an SSVEP even when the user is not gazing at the stimulus. Then, these incorrect detections deteriorate the performance of the system, especially in asynchronous BCI because ongoing EEG is classified. In this paper, a novel transitory response of the attention-level of the user is reported. It was used to develop a hybrid BCI (hBCI). Approach. Three methods are proposed to detect the attention-level of the user. They are based on the alpha rhythm and theta/beta rate. The proposed hBCI scheme is presented along with these methods. Hence, the hBCI sends a command only when the user is at a high-level of attention, or in other words, when the user is really focused on the task being performed. The hBCI was tested over two different EEG datasets. Main results. The performance of the hybrid approach is superior to the standard one. Improvements of 20% in accuracy and 10 bits min-1 are reported. Moreover, the attention-level is extracted from the same EEG channels used in SSVEP detection and this way, no extra hardware is needed. Significance. A transitory response of EEG signal is used to develop the attention-SSVEP hBCI which is capable of reducing the Midas touch effect.

A Hybrid Scheduler for Many Task Computing in Big Data Systems

Directory of Open Access Journals (Sweden)

Vasiliu Laura

2017-06-01

Full Text Available With the rapid evolution of the distributed computing world in the last few years, the amount of data created and processed has fast increased to petabytes or even exabytes scale. Such huge data sets need data-intensive computing applications and impose performance requirements to the infrastructures that support them, such as high scalability, storage, fault tolerance but also efficient scheduling algorithms. This paper focuses on providing a hybrid scheduling algorithm for many task computing that addresses big data environments with few penalties, taking into consideration the deadlines and satisfying a data dependent task model. The hybrid solution consists of several heuristics and algorithms (min-min, min-max and earliest deadline first combined in order to provide a scheduling algorithm that matches our problem. The experimental results are conducted by simulation and prove that the proposed hybrid algorithm behaves very well in terms of meeting deadlines.

A hybrid model for the computationally-efficient simulation of the cerebellar granular layer

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Anna eCattani

2016-04-01

Full Text Available The aim of the present paper is to efficiently describe the membrane potential dynamics of neural populations formed by species having a high density difference in specific brain areas. We propose a hybrid model whose main ingredients are a conductance-based model (ODE system and its continuous counterpart (PDE system obtained through a limit process in which the number of neurons confined in a bounded region of the brain tissue is sent to infinity. Specifically, in the discrete model, each cell is described by a set of time-dependent variables, whereas in the continuum model, cells are grouped into populations that are described by a set of continuous variables.Communications between populations, which translate into interactions among the discrete and the continuous models, are the essence of the hybrid model we present here. The cerebellum and cerebellum-like structures show in their granular layer a large difference in the relative density of neuronal species making them a natural testing ground for our hybrid model. By reconstructing the ensemble activity of the cerebellar granular layer network and by comparing our results to a more realistic computational network, we demonstrate that our description of the network activity, even though it is not biophysically detailed, is still capable of reproducing salient features of neural network dynamics. Our modeling approach yields a significant computational cost reduction by increasing the simulation speed at least $270$ times. The hybrid model reproduces interesting dynamics such as local microcircuit synchronization, traveling waves, center-surround and time-windowing.

Statistical comparison of a hybrid approach with approximate and exact inference models for Fusion 2+

Science.gov (United States)

Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew

2007-04-01

One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.

Non-adaptive and adaptive hybrid approaches for enhancing water quality management

Science.gov (United States)

Kalwij, Ineke M.; Peralta, Richard C.

2008-09-01

parameter values for a new optimization problem can be time consuming. For comparison, AGA, AGCT, and GC are applied to optimize pumping rates for assumed well locations of a complex large-scale contaminant transport and remediation optimization problem at Blaine Naval Ammunition Depot (NAD). Both hybrid approaches converged more closely to the optimal solution than the non-hybrid AGA. GC averaged 18.79% better convergence than AGCT, and 31.9% than AGA, within the same computation time (12.5 days). AGCT averaged 13.1% better convergence than AGA. The GC can significantly reduce the burden of employing computationally intensive hydrologic simulation models within a limited time period and for real-world optimization problems. Although demonstrated for a groundwater quality problem, it is also applicable to other arenas, such as managing salt water intrusion and surface water contaminant loading.

A hybrid approach for integrated healthcare cooperative purchasing and supply chain configuration.

Science.gov (United States)

Rego, Nazaré; Claro, João; Pinho de Sousa, Jorge

2014-12-01

This paper presents an innovative and flexible approach for recommending the number, size and composition of purchasing groups, for a set of hospitals willing to cooperate, while minimising their shared supply chain costs. This approach makes the financial impact of the various cooperation alternatives transparent to the group and the individual participants, opening way to a negotiation process concerning the allocation of the cooperation costs and gains. The approach was developed around a hybrid Variable Neighbourhood Search (VNS)/Tabu Search metaheuristic, resulting in a flexible tool that can be applied to purchasing groups with different characteristics, namely different operative and market circumstances, and to supply chains with different topologies and atypical cost characteristics. Preliminary computational results show the potential of the approach in solving a broad range of problems.

Hybrid computational phantoms of the male and female newborn patient: NURBS-based whole-body models

International Nuclear Information System (INIS)

Lee, Choonsik; Lodwick, Daniel; Hasenauer, Deanna; Williams, Jonathan L; Lee, Choonik; Bolch, Wesley E

2007-01-01

phantom is performed in three steps: polygonization of the voxel phantom, organ modeling via NURBS surfaces and phantom voxelization. Two 3D graphic tools, 3D-DOCTOR(TM) and Rhinoceros(TM), were utilized to polygonize the newborn voxel phantom and generate NURBS surfaces, while an in-house MATLAB(TM) code was used to voxelize the resulting NURBS model into a final computational phantom ready for use in Monte Carlo radiation transport calculations. A total of 126 anatomical organ and tissue models, including 38 skeletal sites and 31 cartilage sites, were described within the hybrid phantom using either NURBS or polygon surfaces. A male hybrid newborn phantom was constructed following the development of the female phantom through the replacement of female-specific organs with male-specific organs. The outer body contour and internal anatomy of the NURBS-based phantoms were adjusted to match anthropometric and reference newborn data reported by the International Commission on Radiological Protection in their Publication 89. The voxelization process was designed to accurately convert NURBS models to a voxel phantom with minimum volumetric change. A sensitivity study was additionally performed to better understand how the meshing tolerance and voxel resolution would affect volumetric changes between the hybrid-NURBS and hybrid-voxel phantoms. The male and female hybrid-NURBS phantoms were constructed in a manner so that all internal organs approached their ICRP reference masses to within 1%, with the exception of the skin (-6.5% relative error) and brain (-15.4% relative error). Both hybrid-voxel phantoms were constructed with an isotropic voxel resolution of 0.663 mm-equivalent to the ICRP 89 reference thickness of the newborn skin (dermis and epidermis). Hybrid-NURBS phantoms used to create their voxel counterpart retain the non-uniform scalability of stylized phantoms, while maintaining the anatomic realism of segmented voxel phantoms with respect to organ shape, depth and

A Study on a Hybrid Approach for Diagnosing Faults in Nuclear Power Plant

International Nuclear Information System (INIS)

Yang, M.; Zhang, Z.J.; Peng, M.J.; Yan, S.Y.; Wang, H.; Ouyang, J.

2006-01-01

Proper and rapid identification of malfunctions is of premier importance for the safe operation of Nuclear Power Plants (NPP). Many monitoring or/and diagnosis methodologies based on artificial and computational intelligence have been proposed to aid operator to understand system problems, perform trouble-shooting action and reduce human error under serious pressure. However, because no single method is adequate to handle all requirements for diagnostic system, hybrid approaches where different methods work in conjunction to solve parts of the problem interest researchers greatly. In this study, Multilevel Flow Models (MFM) and Artificial Neural Network (ANN) are proposed and employed to develop a fault diagnosis system with the intention of improving the success rate of recognition on the one hand, and improving the understandability of diagnostic process and results on the other hand. Several simulation cases were conducted for evaluating the performance of the proposed diagnosis system. The simulation results validated the effectiveness of the proposed hybrid approach. (authors)

Genetic algorithm and neural network hybrid approach for job-shop scheduling

OpenAIRE

Zhao, Kai; Yang, Shengxiang; Wang, Dingwei

1998-01-01

Copyright @ 1998 ACTA Press This paper proposes a genetic algorithm (GA) and constraint satisfaction adaptive neural network (CSANN) hybrid approach for job-shop scheduling problems. In the hybrid approach, GA is used to iterate for searching optimal solutions, CSANN is used to obtain feasible solutions during the iteration of genetic algorithm. Simulations have shown the valid performance of the proposed hybrid approach for job-shop scheduling with respect to the quality of solutions and ...

A Hybrid Autonomic Computing-Based Approach to Distributed Constraint Satisfaction Problems

Directory of Open Access Journals (Sweden)

Abhishek Bhatia

2015-03-01

Full Text Available Distributed constraint satisfaction problems (DisCSPs are among the widely endeavored problems using agent-based simulation. Fernandez et al. formulated sensor and mobile tracking problem as a DisCSP, known as SensorDCSP In this paper, we adopt a customized ERE (environment, reactive rules and entities algorithm for the SensorDCSP, which is otherwise proven as a computationally intractable problem. An amalgamation of the autonomy-oriented computing (AOC-based algorithm (ERE and genetic algorithm (GA provides an early solution of the modeled DisCSP. Incorporation of GA into ERE facilitates auto-tuning of the simulation parameters, thereby leading to an early solution of constraint satisfaction. This study further contributes towards a model, built up in the NetLogo simulation environment, to infer the efficacy of the proposed approach.

A Hybrid Approach to the Optimization of Multiechelon Systems

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Paweł Sitek

2015-01-01

Full Text Available In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP and constraint logic programming (CLP are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in the ECLiPSe system using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets.

HAMDA: Hybrid Approach for MiRNA-Disease Association prediction.

Science.gov (United States)

Chen, Xing; Niu, Ya-Wei; Wang, Guang-Hui; Yan, Gui-Ying

2017-12-01

For decades, enormous experimental researches have collectively indicated that microRNA (miRNA) could play indispensable roles in many critical biological processes and thus also the pathogenesis of human complex diseases. Whereas the resource and time cost required in traditional biology experiments are expensive, more and more attentions have been paid to the development of effective and feasible computational methods for predicting potential associations between disease and miRNA. In this study, we developed a computational model of Hybrid Approach for MiRNA-Disease Association prediction (HAMDA), which involved the hybrid graph-based recommendation algorithm, to reveal novel miRNA-disease associations by integrating experimentally verified miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity into a recommendation algorithm. HAMDA took not only network structure and information propagation but also node attribution into consideration, resulting in a satisfactory prediction performance. Specifically, HAMDA obtained AUCs of 0.9035 and 0.8395 in the frameworks of global and local leave-one-out cross validation, respectively. Meanwhile, HAMDA also achieved good performance with AUC of 0.8965 ± 0.0012 in 5-fold cross validation. Additionally, we conducted case studies about three important human cancers for performance evaluation of HAMDA. As a result, 90% (Lymphoma), 86% (Prostate Cancer) and 92% (Kidney Cancer) of top 50 predicted miRNAs were confirmed by recent experiment literature, which showed the reliable prediction ability of HAMDA. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic modelling of an adsorption storage tank using a hybrid approach combining computational fluid dynamics and process simulation

Science.gov (United States)

Mota, J.P.B.; Esteves, I.A.A.C.; Rostam-Abadi, M.

2004-01-01

A computational fluid dynamics (CFD) software package has been coupled with the dynamic process simulator of an adsorption storage tank for methane fuelled vehicles. The two solvers run as independent processes and handle non-overlapping portions of the computational domain. The codes exchange data on the boundary interface of the two domains to ensure continuity of the solution and of its gradient. A software interface was developed to dynamically suspend and activate each process as necessary, and be responsible for data exchange and process synchronization. This hybrid computational tool has been successfully employed to accurately simulate the discharge of a new tank design and evaluate its performance. The case study presented here shows that CFD and process simulation are highly complementary computational tools, and that there are clear benefits to be gained from a close integration of the two. ?? 2004 Elsevier Ltd. All rights reserved.

Hybrid computer optimization of systems with random parameters

Science.gov (United States)

White, R. C., Jr.

1972-01-01

A hybrid computer Monte Carlo technique for the simulation and optimization of systems with random parameters is presented. The method is applied to the simultaneous optimization of the means and variances of two parameters in the radar-homing missile problem treated by McGhee and Levine.

A hybrid FDTD-Rayleigh integral computational method for the simulation of the ultrasound measurement of proximal femur.

Science.gov (United States)

Cassereau, Didier; Nauleau, Pierre; Bendjoudi, Aniss; Minonzio, Jean-Gabriel; Laugier, Pascal; Bossy, Emmanuel; Grimal, Quentin

2014-07-01

The development of novel quantitative ultrasound (QUS) techniques to measure the hip is critically dependent on the possibility to simulate the ultrasound propagation. One specificity of hip QUS is that ultrasounds propagate through a large thickness of soft tissue, which can be modeled by a homogeneous fluid in a first approach. Finite difference time domain (FDTD) algorithms have been widely used to simulate QUS measurements but they are not adapted to simulate ultrasonic propagation over long distances in homogeneous media. In this paper, an hybrid numerical method is presented to simulate hip QUS measurements. A two-dimensional FDTD simulation in the vicinity of the bone is coupled to the semi-analytic calculation of the Rayleigh integral to compute the wave propagation between the probe and the bone. The method is used to simulate a setup dedicated to the measurement of circumferential guided waves in the cortical compartment of the femoral neck. The proposed approach is validated by comparison with a full FDTD simulation and with an experiment on a bone phantom. For a realistic QUS configuration, the computation time is estimated to be sixty times less with the hybrid method than with a full FDTD approach. Copyright © 2013 Elsevier B.V. All rights reserved.

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2014-01-01

Full Text Available Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP, the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR, artificial neural network (ANN, multivariate adaptive regression splines (MARS, and support vector regression (SVR techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models.

A Hybrid Approach for Supporting Adaptivity in E-Learning Environments

Science.gov (United States)

Al-Omari, Mohammad; Carter, Jenny; Chiclana, Francisco

2016-01-01

Purpose: The purpose of this paper is to identify a framework to support adaptivity in e-learning environments. The framework reflects a novel hybrid approach incorporating the concept of the event-condition-action (ECA) model and intelligent agents. Moreover, a system prototype is developed reflecting the hybrid approach to supporting adaptivity…

Three-dimensional pseudo-random number generator for implementing in hybrid computer systems

International Nuclear Information System (INIS)

Ivanov, M.A.; Vasil'ev, N.P.; Voronin, A.V.; Kravtsov, M.Yu.; Maksutov, A.A.; Spiridonov, A.A.; Khudyakova, V.I.; Chugunkov, I.V.

2012-01-01

The algorithm for generating pseudo-random numbers oriented to implementation by using hybrid computer systems is considered. The proposed solution is characterized by a high degree of parallel computing [ru

Using hybrid expert system approaches for engineering applications

Science.gov (United States)

Allen, R. H.; Boarnet, M. G.; Culbert, C. J.; Savely, R. T.

1987-01-01

In this paper, the use of hybrid expert system shells and hybrid (i.e., algorithmic and heuristic) approaches for solving engineering problems is reported. Aspects of various engineering problem domains are reviewed for a number of examples with specific applications made to recently developed prototype expert systems. Based on this prototyping experience, critical evaluations of and comparisons between commercially available tools, and some research tools, in the United States and Australia, and their underlying problem-solving paradigms are made. Characteristics of the implementation tool and the engineering domain are compared and practical software engineering issues are discussed with respect to hybrid tools and approaches. Finally, guidelines are offered with the hope that expert system development will be less time consuming, more effective, and more cost-effective than it has been in the past.

Energy efficient hybrid computing systems using spin devices

Science.gov (United States)

Sharad, Mrigank

Emerging spin-devices like magnetic tunnel junctions (MTJ's), spin-valves and domain wall magnets (DWM) have opened new avenues for spin-based logic design. This work explored potential computing applications which can exploit such devices for higher energy-efficiency and performance. The proposed applications involve hybrid design schemes, where charge-based devices supplement the spin-devices, to gain large benefits at the system level. As an example, lateral spin valves (LSV) involve switching of nanomagnets using spin-polarized current injection through a metallic channel such as Cu. Such spin-torque based devices possess several interesting properties that can be exploited for ultra-low power computation. Analog characteristic of spin current facilitate non-Boolean computation like majority evaluation that can be used to model a neuron. The magneto-metallic neurons can operate at ultra-low terminal voltage of ˜20mV, thereby resulting in small computation power. Moreover, since nano-magnets inherently act as memory elements, these devices can facilitate integration of logic and memory in interesting ways. The spin based neurons can be integrated with CMOS and other emerging devices leading to different classes of neuromorphic/non-Von-Neumann architectures. The spin-based designs involve `mixed-mode' processing and hence can provide very compact and ultra-low energy solutions for complex computation blocks, both digital as well as analog. Such low-power, hybrid designs can be suitable for various data processing applications like cognitive computing, associative memory, and currentmode on-chip global interconnects. Simulation results for these applications based on device-circuit co-simulation framework predict more than ˜100x improvement in computation energy as compared to state of the art CMOS design, for optimal spin-device parameters.

Resource-Efficient, Hierarchical Auto-Tuning of a Hybrid Lattice Boltzmann Computation on the Cray XT4

International Nuclear Information System (INIS)

Williams, Samuel; Carter, Jonathan; Oliker, Leonid; Shalf, John; Yelick, Katherine

2009-01-01

We apply auto-tuning to a hybrid MPI-pthreads lattice Boltzmann computation running on the Cray XT4 at National Energy Research Scientific Computing Center (NERSC). Previous work showed that multicore-specific auto-tuning can improve the performance of lattice Boltzmann magnetohydrodynamics (LBMHD) by a factor of 4x when running on dual- and quad-core Opteron dual-socket SMPs. We extend these studies to the distributed memory arena via a hybrid MPI/pthreads implementation. In addition to conventional auto-tuning at the local SMP node, we tune at the message-passing level to determine the optimal aspect ratio as well as the correct balance between MPI tasks and threads per MPI task. Our study presents a detailed performance analysis when moving along an isocurve of constant hardware usage: fixed total memory, total cores, and total nodes. Overall, our work points to approaches for improving intra- and inter-node efficiency on large-scale multicore systems for demanding scientific applications

Hybrid Computational Model for High-Altitude Aeroassist Vehicles, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — A hybrid continuum/noncontinuum computational model will be developed for analyzing the aerodynamics and heating on aeroassist vehicles. Unique features of this...

Accelerating Climate Simulations Through Hybrid Computing

Science.gov (United States)

Zhou, Shujia; Sinno, Scott; Cruz, Carlos; Purcell, Mark

2009-01-01

Unconventional multi-core processors (e.g., IBM Cell B/E and NYIDIDA GPU) have emerged as accelerators in climate simulation. However, climate models typically run on parallel computers with conventional processors (e.g., Intel and AMD) using MPI. Connecting accelerators to this architecture efficiently and easily becomes a critical issue. When using MPI for connection, we identified two challenges: (1) identical MPI implementation is required in both systems, and; (2) existing MPI code must be modified to accommodate the accelerators. In response, we have extended and deployed IBM Dynamic Application Virtualization (DAV) in a hybrid computing prototype system (one blade with two Intel quad-core processors, two IBM QS22 Cell blades, connected with Infiniband), allowing for seamlessly offloading compute-intensive functions to remote, heterogeneous accelerators in a scalable, load-balanced manner. Currently, a climate solar radiation model running with multiple MPI processes has been offloaded to multiple Cell blades with approx.10% network overhead.

Preliminary Study on Hybrid Computational Phantom for Radiation Dosimetry Based on Subdivision Surface

International Nuclear Information System (INIS)

Jeong, Jong Hwi; Choi, Sang Hyoun; Cho, Sung Koo; Kim, Chan Hyeong

2007-01-01

The anthropomorphic computational phantoms are classified into two groups. One group is the stylized phantoms, or MIRD phantoms, which are based on mathematical representations of the anatomical structures. The shapes and positions of the organs and tissues in these phantoms can be adjusted by changing the coefficients of the equations in use. The other group is the voxel phantoms, which are based on tomographic images of a real person such as CT, MR and serially sectioned color slice images from a cadaver. Obviously, the voxel phantoms represent the anatomical structures of a human body much more realistically than the stylized phantoms. A realistic representation of anatomical structure is very important for an accurate calculation of radiation dose in the human body. Consequently, the ICRP recently has decided to use the voxel phantoms for the forthcoming update of the dose conversion coefficients. However, the voxel phantoms also have some limitations: (1) The topology and dimensions of the organs and tissues in a voxel model are extremely difficult to change, and (2) The thin organs, such as oral mucosa and skin, cannot be realistically modeled unless the voxel resolution is prohibitively high. Recently, a new approach has been implemented by several investigators. The investigators converted their voxel phantoms to hybrid computational phantoms based on NURBS (Non-Uniform Rational B-Splines) surface, which is smooth and deformable. It is claimed that these new phantoms have the flexibility of the stylized phantom along with the realistic representations of the anatomical structures. The topology and dimensions of the anatomical structures can be easily changed as necessary. Thin organs can be modeled without affecting computational speed or memory requirement. The hybrid phantoms can be also used for 4-D Monte Carlo simulations. In this preliminary study, the external shape of a voxel phantom (i.e., skin), HDRK-Man, was converted to a hybrid computational

Evaluation of a Compact Hybrid Brain-Computer Interface System

Directory of Open Access Journals (Sweden)

Jaeyoung Shin

2017-01-01

Full Text Available We realized a compact hybrid brain-computer interface (BCI system by integrating a portable near-infrared spectroscopy (NIRS device with an economical electroencephalography (EEG system. The NIRS array was located on the subjects’ forehead, covering the prefrontal area. The EEG electrodes were distributed over the frontal, motor/temporal, and parietal areas. The experimental paradigm involved a Stroop word-picture matching test in combination with mental arithmetic (MA and baseline (BL tasks, in which the subjects were asked to perform either MA or BL in response to congruent or incongruent conditions, respectively. We compared the classification accuracies of each of the modalities (NIRS or EEG with that of the hybrid system. We showed that the hybrid system outperforms the unimodal EEG and NIRS systems by 6.2% and 2.5%, respectively. Since the proposed hybrid system is based on portable platforms, it is not confined to a laboratory environment and has the potential to be used in real-life situations, such as in neurorehabilitation.

Reducing the Digital Divide among Children Who Received Desktop or Hybrid Computers for the Home

Directory of Open Access Journals (Sweden)

Gila Cohen Zilka

2016-06-01

Full Text Available Researchers and policy makers have been exploring ways to reduce the digital divide. Parameters commonly used to examine the digital divide worldwide, as well as in this study, are: (a the digital divide in the accessibility and mobility of the ICT infrastructure and of the content infrastructure (e.g., sites used in school; and (b the digital divide in literacy skills. In the present study we examined the degree of effectiveness of receiving a desktop or hybrid computer for the home in reducing the digital divide among children of low socio-economic status aged 8-12 from various localities across Israel. The sample consisted of 1,248 respondents assessed in two measurements. As part of the mixed-method study, 128 children were also interviewed. Findings indicate that after the children received desktop or hybrid computers, changes occurred in their frequency of access, mobility, and computer literacy. Differences were found between the groups: hybrid computers reduce disparities and promote work with the computer and surfing the Internet more than do desktop computers. Narrowing the digital divide for this age group has many implications for the acquisition of skills and study habits, and consequently, for the realization of individual potential. The children spoke about self improvement as a result of exposure to the digital environment, about a sense of empowerment and of improvement in their advantage in the social fabric. Many children expressed a desire to continue their education and expand their knowledge of computer applications, the use of software, of games, and more. Therefore, if there is no computer in the home and it is necessary to decide between a desktop and a hybrid computer, a hybrid computer is preferable.

A Cost–Effective Computer-Based, Hybrid Motorised and Gravity ...

African Journals Online (AJOL)

A Cost–Effective Computer-Based, Hybrid Motorised and Gravity-Driven Material Handling System for the Mauritian Apparel Industry. ... Thus, many companies are investing significantly in a Research & Development department in order to design new techniques to improve worker's efficiency, and to decrease the amount ...

A hybrid approach for biobjective optimization

DEFF Research Database (Denmark)

Stidsen, Thomas Jacob Riis; Andersen, Kim Allan

2018-01-01

to singleobjective problems is that no standard multiobjective solvers exist and specialized algorithms need to be programmed from scratch.In this article we will present a hybrid approach, which operates both in decision space and in objective space. The approach enables massive efficient parallelization and can...... be used to a wide variety of biobjective Mixed Integer Programming models. We test the approach on the biobjective extension of the classic traveling salesman problem, on the standard datasets, and determine the full set of nondominated points. This has only been done once before (Florios and Mavrotas...

Hybrid Approach of Aortic Diseases: Zone 1 Delivery and Volumetric Analysis on the Descending Aorta

Directory of Open Access Journals (Sweden)

José Augusto Duncan

Full Text Available Abstract Introduction: Conventional techniques of surgical correction of arch and descending aortic diseases remains as high-risk procedures. Endovascular treatments of abdominal and descending thoracic aorta have lower surgical risk. Evolution of both techniques - open debranching of the arch and endovascular approach of the descending aorta - may extend a less invasive endovascular treatment for a more extensive disease with necessity of proximal landing zone in the arch. Objective: To evaluate descending thoracic aortic remodeling by means of volumetric analysis after hybrid approach of aortic arch debranching and stenting the descending aorta. Methods: Retrospective review of seven consecutive patients treated between September 2014 and August 2016 for diseases of proximal descending aorta (aneurysms and dissections by hybrid approach to deliver the endograft at zone 1. Computed tomography angiography were analyzed using a specific software to calculate descending thoracic aorta volumes pre- and postoperatively. Results: Follow-up was done in 100% of patients with a median time of 321 days (range, 41-625 days. No deaths or permanent neurological complications were observed. There were no endoleaks or stent migrations. Freedom from reintervention was 100% at 300 days and 66% at 600 days. Median volume reduction was of 45.5 cm3, representing a median volume shrinkage by 9.3%. Conclusion: Hybrid approach of arch and descending thoracic aorta diseases is feasible and leads to a favorable aortic remodeling with significant volume reduction.

Formal verification of dynamic hybrid systems: a NuSMV-based model checking approach

Directory of Open Access Journals (Sweden)

Xu Zhi

2018-01-01

Full Text Available Software security is an important and challenging research topic in developing dynamic hybrid embedded software systems. Ensuring the correct behavior of these systems is particularly difficult due to the interactions between the continuous subsystem and the discrete subsystem. Currently available security analysis methods for system risks have been limited, as they rely on manual inspections of the individual subsystems under simplifying assumptions. To improve this situation, a new approach is proposed that is based on the symbolic model checking tool NuSMV. A dual PID system is used as an example system, for which the logical part and the computational part of the system are modeled in a unified manner. Constraints are constructed on the controlled object, and a counter-example path is ultimately generated, indicating that the hybrid system can be analyzed by the model checking tool.

Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach

Science.gov (United States)

Bregon, Anibal; Daigle, Matthew; Roychoudhury, Indranil

2016-01-01

Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

KAUST Repository

Mora Cordova, Angel

2018-01-30

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite\\'s conductivity based on these parameters.

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

KAUST Repository

Mora Cordova, Angel; Han, Fei; Lubineau, Gilles

2018-01-01

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite's conductivity based on these parameters.

When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System

KAUST Repository

Liu, Xiao

2017-03-21

Privacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice.

Comparing Hybrid Learning with Traditional Approaches on Learning the Microsoft Office Power Point 2003 Program in Tertiary Education

Science.gov (United States)

Vernadakis, Nikolaos; Antoniou, Panagiotis; Giannousi, Maria; Zetou, Eleni; Kioumourtzoglou, Efthimis

2011-01-01

The purpose of this study was to determine the effectiveness of a hybrid learning approach to deliver a computer science course concerning the Microsoft office PowerPoint 2003 program in comparison to delivering the same course content in the form of traditional lectures. A hundred and seventy-two first year university students were randomly…

Hybrid simulation of scatter intensity in industrial cone-beam computed tomography

International Nuclear Information System (INIS)

Thierry, R.; Miceli, A.; Hofmann, J.; Flisch, A.; Sennhauser, U.

2009-01-01

A cone-beam computed tomography (CT) system using a 450 kV X-ray tube has been developed to challenge the three-dimensional imaging of parts of the automotive industry in short acquisition time. Because the probability of detecting scattered photons is high regarding the energy range and the area of detection, a scattering correction becomes mandatory for generating reliable images with enhanced contrast detectability. In this paper, we present a hybrid simulator for the fast and accurate calculation of the scattering intensity distribution. The full acquisition chain, from the generation of a polyenergetic photon beam, its interaction with the scanned object and the energy deposit in the detector is simulated. Object phantoms can be spatially described in form of voxels, mathematical primitives or CAD models. Uncollided radiation is treated with a ray-tracing method and scattered radiation is split into single and multiple scattering. The single scattering is calculated with a deterministic approach accelerated with a forced detection method. The residual noisy signal is subsequently deconvoluted with the iterative Richardson-Lucy method. Finally the multiple scattering is addressed with a coarse Monte Carlo (MC) simulation. The proposed hybrid method has been validated on aluminium phantoms with varying size and object-to-detector distance, and found in good agreement with the MC code Geant4. The acceleration achieved by the hybrid method over the standard MC on a single projection is approximately of three orders of magnitude.

A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Directory of Open Access Journals (Sweden)

Wen-An Yang

2016-01-01

Full Text Available Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs like the conventional backpropagation (BP algorithm and support vector machines (SVMs. In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

Computation-aware algorithm selection approach for interlaced-to-progressive conversion

Science.gov (United States)

Park, Sang-Jun; Jeon, Gwanggil; Jeong, Jechang

2010-05-01

We discuss deinterlacing results in a computationally constrained and varied environment. The proposed computation-aware algorithm selection approach (CASA) for fast interlaced to progressive conversion algorithm consists of three methods: the line-averaging (LA) method for plain regions, the modified edge-based line-averaging (MELA) method for medium regions, and the proposed covariance-based adaptive deinterlacing (CAD) method for complex regions. The proposed CASA uses two criteria, mean-squared error (MSE) and CPU time, for assigning the method. We proposed a CAD method. The principle idea of CAD is based on the correspondence between the high and low-resolution covariances. We estimated the local covariance coefficients from an interlaced image using Wiener filtering theory and then used these optimal minimum MSE interpolation coefficients to obtain a deinterlaced image. The CAD method, though more robust than most known methods, was not found to be very fast compared to the others. To alleviate this issue, we proposed an adaptive selection approach using a fast deinterlacing algorithm rather than using only one CAD algorithm. The proposed hybrid approach of switching between the conventional schemes (LA and MELA) and our CAD was proposed to reduce the overall computational load. A reliable condition to be used for switching the schemes was presented after a wide set of initial training processes. The results of computer simulations showed that the proposed methods outperformed a number of methods presented in the literature.

Hybrid methodological approach to context-dependent speech recognition

Directory of Open Access Journals (Sweden)

Dragiša Mišković

2017-01-01

Full Text Available Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

Hybrid Quantum-Classical Approach to Quantum Optimal Control.

Science.gov (United States)

Li, Jun; Yang, Xiaodong; Peng, Xinhua; Sun, Chang-Pu

2017-04-14

A central challenge in quantum computing is to identify more computational problems for which utilization of quantum resources can offer significant speedup. Here, we propose a hybrid quantum-classical scheme to tackle the quantum optimal control problem. We show that the most computationally demanding part of gradient-based algorithms, namely, computing the fitness function and its gradient for a control input, can be accomplished by the process of evolution and measurement on a quantum simulator. By posing queries to and receiving answers from the quantum simulator, classical computing devices update the control parameters until an optimal control solution is found. To demonstrate the quantum-classical scheme in experiment, we use a seven-qubit nuclear magnetic resonance system, on which we have succeeded in optimizing state preparation without involving classical computation of the large Hilbert space evolution.

Optical hybrid quantum teleportation and its applications

Science.gov (United States)

Takeda, Shuntaro; Okada, Masanori; Furusawa, Akira

2017-08-01

Quantum teleportation, a transfer protocol of quantum states, is the essence of many sophisticated quantum information protocols. There have been two complementary approaches to optical quantum teleportation: discrete variables (DVs) and continuous variables (CVs). However, both approaches have pros and cons. Here we take a "hybrid" approach to overcome the current limitations: CV quantum teleportation of DVs. This approach enabled the first realization of deterministic quantum teleportation of photonic qubits without post-selection. We also applied the hybrid scheme to several experiments, including entanglement swapping between DVs and CVs, conditional CV teleportation of single photons, and CV teleportation of qutrits. We are now aiming at universal, scalable, and fault-tolerant quantum computing based on these hybrid technologies.

Hybrid energy system evaluation in water supply system energy production: neural network approach

Energy Technology Data Exchange (ETDEWEB)

Goncalves, Fabio V.; Ramos, Helena M. [Civil Engineering Department, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001, Lisbon (Portugal); Reis, Luisa Fernanda R. [Universidade de Sao Paulo, EESC/USP, Departamento de Hidraulica e Saneamento., Avenida do Trabalhador Saocarlense, 400, Sao Carlos-SP (Brazil)

2010-07-01

Water supply systems are large consumers of energy and the use of hybrid systems for green energy production is this new proposal. This work presents a computational model based on neural networks to determine the best configuration of a hybrid system to generate energy in water supply systems. In this study the energy sources to make this hybrid system can be the national power grid, micro-hydro and wind turbines. The artificial neural network is composed of six layers, trained to use data generated by a model of hybrid configuration and an economic simulator - CES. The reason for the development of an advanced model of forecasting based on neural networks is to allow rapid simulation and proper interaction with hydraulic and power model simulator - HPS. The results show that this computational model is useful as advanced decision support system in the design of configurations of hybrid power systems applied to water supply systems, improving the solutions in the development of its global energy efficiency.

Computational modeling of electrically conductive networks formed by graphene nanoplatelet-carbon nanotube hybrid particles

Science.gov (United States)

Mora, A.; Han, F.; Lubineau, G.

2018-04-01

One strategy to ensure that nanofiller networks in a polymer composite percolate at low volume fractions is to promote segregation. In a segregated structure, the concentration of nanofillers is kept low in some regions of the sample. In turn, the concentration in the remaining regions is much higher than the average concentration of the sample. This selective placement of the nanofillers ensures percolation at low average concentration. One original strategy to promote segregation is by tuning the shape of the nanofillers. We use a computational approach to study the conductive networks formed by hybrid particles obtained by growing carbon nanotubes (CNTs) on graphene nanoplatelets (GNPs). The objective of this study is (1) to show that the higher electrical conductivity of these composites is due to the hybrid particles forming a segregated structure and (2) to understand which parameters defining the hybrid particles determine the efficiency of the segregation. We construct a microstructure to observe the conducting paths and determine whether a segregated structure has indeed been formed inside the composite. A measure of efficiency is presented based on the fraction of nanofillers that contribute to the conductive network. Then, the efficiency of the hybrid-particle networks is compared to those of three other networks of carbon-based nanofillers in which no hybrid particles are used: only CNTs, only GNPs, and a mix of CNTs and GNPs. Finally, some parameters of the hybrid particle are studied: the CNT density on the GNPs, and the CNT and GNP geometries. We also present recommendations for the further improvement of a composite’s conductivity based on these parameters.

Dry Port Location Problem: A Hybrid Multi-Criteria Approach

Directory of Open Access Journals (Sweden)

BENTALEB Fatimazahra

2016-03-01

Full Text Available Choosing a location for a dry port is a problem which becomes more essential and crucial. This study deals with the problem of locating dry ports. On this matter, a model combining multi-criteria (MACBETH and mono-criteria (BARYCENTER methods to find a solution to dry port location problem has been proposed. In the first phase, a systematic literature review was carried out on dry port location problem and then a methodological classification was presented for this research. In the second phase, a hybrid multi-criteria approach was developed in order to determine the best dry port location taking different criteria into account. A Computational practice and a qualitative analysis from a case study in the Moroccan context have been provided. The results show that the optimal location is very convenient with the geographical region and the government policies.

Adaptive hybrid brain-computer interaction: ask a trainer for assistance!

Science.gov (United States)

Müller-Putz, Gernot R; Steyrl, David; Faller, Josef

2014-01-01

In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based, adaptive, hybrid BCIs. Hence, in a first session we merged the features of a high aptitude BCI user, a trainer, and a novice user, the trainee, in a closed-loop BCI feedback task and automatically adapted the classifier over time. In a second session the trainees operated the system unassisted. Twelve healthy participants ran through this protocol. Along with the trainer, the trainees achieved a very high overall peak accuracy of 95.3 %. In the second session, where users operated the BCI unassisted, they still achieved a high overall peak accuracy of 83.6%. Ten of twelve first time BCI users successfully achieved significantly better than chance accuracy. Concluding, we can say that this trainer-trainee approach is very promising. Future research should investigate, whether this approach is superior to conventional training approaches. This trainer-trainee concept could have potential for future application of BCIs to end users.

Computational hybrid anthropometric paediatric phantom library for internal radiation dosimetry

DEFF Research Database (Denmark)

Xie, Tianwu; Kuster, Niels; Zaidi, Habib

2017-01-01

for children demonstrated that they follow the same trend when correlated with age. The constructed hybrid computational phantom library opens up the prospect of comprehensive radiation dosimetry calculations and risk assessment for the paediatric population of different age groups and diverse anthropometric...

Hybrid Cloud Computing Environment for EarthCube and Geoscience Community

Science.gov (United States)

Yang, C. P.; Qin, H.

2016-12-01

The NSF EarthCube Integration and Test Environment (ECITE) has built a hybrid cloud computing environment to provides cloud resources from private cloud environments by using cloud system software - OpenStack and Eucalyptus, and also manages public cloud - Amazon Web Service that allow resource synchronizing and bursting between private and public cloud. On ECITE hybrid cloud platform, EarthCube and geoscience community can deploy and manage the applications by using base virtual machine images or customized virtual machines, analyze big datasets by using virtual clusters, and real-time monitor the virtual resource usage on the cloud. Currently, a number of EarthCube projects have deployed or started migrating their projects to this platform, such as CHORDS, BCube, CINERGI, OntoSoft, and some other EarthCube building blocks. To accomplish the deployment or migration, administrator of ECITE hybrid cloud platform prepares the specific needs (e.g. images, port numbers, usable cloud capacity, etc.) of each project in advance base on the communications between ECITE and participant projects, and then the scientists or IT technicians in those projects launch one or multiple virtual machines, access the virtual machine(s) to set up computing environment if need be, and migrate their codes, documents or data without caring about the heterogeneity in structure and operations among different cloud platforms.

A Practical Approach to Improve Optical Channel Utilization Period for Hybrid FSO/RF Systems

Directory of Open Access Journals (Sweden)

Ahmet Akbulut

2014-01-01

Full Text Available In hybrid FSO/RF systems, mostly a hard switching mechanism is preferred in case of the FSO signal level falls below to the predefined threshold. In this work, a computationally simple approach is proposed to increase the utilization of the FSO channels bandwidth advantage. For the channel, clear air conditions have been supposed with the atmospheric turbulence. In this approach, FSO bit rate is adaptively changed to achieve desired BER performance. An IM/DD modulation, OOK (NRZ format has been used to show the benefit of the proposed method. Furthermore, to be more realistic with respect to the atmospheric turbulence variations within a day, some experimental observations have been followed up.

Stochastic Computational Approach for Complex Nonlinear Ordinary Differential Equations

International Nuclear Information System (INIS)

Khan, Junaid Ali; Raja, Muhammad Asif Zahoor; Qureshi, Ijaz Mansoor

2011-01-01

We present an evolutionary computational approach for the solution of nonlinear ordinary differential equations (NLODEs). The mathematical modeling is performed by a feed-forward artificial neural network that defines an unsupervised error. The training of these networks is achieved by a hybrid intelligent algorithm, a combination of global search with genetic algorithm and local search by pattern search technique. The applicability of this approach ranges from single order NLODEs, to systems of coupled differential equations. We illustrate the method by solving a variety of model problems and present comparisons with solutions obtained by exact methods and classical numerical methods. The solution is provided on a continuous finite time interval unlike the other numerical techniques with comparable accuracy. With the advent of neuroprocessors and digital signal processors the method becomes particularly interesting due to the expected essential gains in the execution speed. (general)

Hybrid computer simulation of the dynamics of the Hoger Onderwijs Reactor

International Nuclear Information System (INIS)

Moers, J.C.; Vries, J.W. de.

1976-01-01

A distributed parameter model for the dynamics of the Hoger Onderwijs Reactor (HOR) at Delft is presented. The neutronic and the thermodynamic part of this model have been separately implemented on the AD4-IBM1800 Hybrid Computer of the Delft University of Technology Computation Centre. A continuous Space/Discrete Time solution method has been employed. Some test results of the simulation are included

Fatigue of hybrid glass/carbon composites: 3D computational studies

DEFF Research Database (Denmark)

Dai, Gaoming; Mishnaevsky, Leon

2014-01-01

3D computational simulations of fatigue of hybrid carbon/glass fiber reinforced composites is carried out using X-FEM and multifiber unit cell models. A new software code for the automatic generation of unit cell multifiber models of composites with randomly misaligned fibers of various properties...... and geometrical parameters is developed. With the use of this program code and the X-FEM method, systematic investigations of the effect of microstructure of hybrid composites (fraction of carbon versus glass fibers, misalignment, and interface strength) and the loading conditions (tensile versus compression...... cyclic loading effects) on fatigue behavior of the materials are carried out. It was demonstrated that the higher fraction of carbon fibers in hybrid composites is beneficial for the fatigue lifetime of the composites under tension-tension cyclic loading, but might have negative effect on the lifetime...

Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing

Directory of Open Access Journals (Sweden)

Ahmad M. Manasrah

2018-01-01

Full Text Available Cloud computing environment provides several on-demand services and resource sharing for clients. Business processes are managed using the workflow technology over the cloud, which represents one of the challenges in using the resources in an efficient manner due to the dependencies between the tasks. In this paper, a Hybrid GA-PSO algorithm is proposed to allocate tasks to the resources efficiently. The Hybrid GA-PSO algorithm aims to reduce the makespan and the cost and balance the load of the dependent tasks over the heterogonous resources in cloud computing environments. The experiment results show that the GA-PSO algorithm decreases the total execution time of the workflow tasks, in comparison with GA, PSO, HSGA, WSGA, and MTCT algorithms. Furthermore, it reduces the execution cost. In addition, it improves the load balancing of the workflow application over the available resources. Finally, the obtained results also proved that the proposed algorithm converges to optimal solutions faster and with higher quality compared to other algorithms.

Hybrid discrete PSO and OPF approach for optimization of biomass fueled micro-scale energy system

International Nuclear Information System (INIS)

Gómez-González, M.; López, A.; Jurado, F.

2013-01-01

Highlights: ► Method to determine the optimal location and size of biomass power plants. ► The proposed approach is a hybrid of PSO algorithm and optimal power flow. ► Comparison among the proposed algorithm and other methods. ► Computational costs are enough lower than that required for exhaustive search. - Abstract: This paper addresses generation of electricity in the specific aspect of finding the best location and sizing of biomass fueled gas micro-turbine power plants, taking into account the variables involved in the problem, such as the local distribution of biomass resources, biomass transportation and extraction costs, operation and maintenance costs, power losses costs, network operation costs, and technical constraints. In this paper a hybrid method is introduced employing discrete particle swarm optimization and optimal power flow. The approach can be applied to search the best sites and capacities to connect biomass fueled gas micro-turbine power systems in a distribution network among a large number of potential combinations and considering the technical constraints of the network. A fair comparison among the proposed algorithm and other methods is performed.

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

Directory of Open Access Journals (Sweden)

Hyunjoo Kim

2011-01-01

Full Text Available In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures. The framework is designed to address system and application heterogeneity and dynamics to ensure that application objectives and constraints are satisfied. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. For example, the framework presented in this paper can be used to provision the appropriate mix of resources based on application requirements and constraints. The framework also monitors the system/application state and adapts the application and/or resources to respond to changing requirements or environment. To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and Amazon EC2 instances of various types. Specifically, we show how different applications objectives such as acceleration, conservation and resilience can be effectively achieved while satisfying deadline and budget constraints, using an appropriate mix of dynamically provisioned resources. Our evaluations also demonstrate that public clouds can be used to complement and reinforce the scheduling and usage of traditional high performance computing infrastructure.

A Two-Step Hybrid Approach for Modeling the Nonlinear Dynamic Response of Piezoelectric Energy Harvesters

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Claudio Maruccio

2018-01-01

Full Text Available An effective hybrid computational framework is described here in order to assess the nonlinear dynamic response of piezoelectric energy harvesting devices. The proposed strategy basically consists of two steps. First, fully coupled multiphysics finite element (FE analyses are performed to evaluate the nonlinear static response of the device. An enhanced reduced-order model is then derived, where the global dynamic response is formulated in the state-space using lumped coefficients enriched with the information derived from the FE simulations. The electromechanical response of piezoelectric beams under forced vibrations is studied by means of the proposed approach, which is also validated by comparing numerical predictions with some experimental results. Such numerical and experimental investigations have been carried out with the main aim of studying the influence of material and geometrical parameters on the global nonlinear response. The advantage of the presented approach is that the overall computational and experimental efforts are significantly reduced while preserving a satisfactory accuracy in the assessment of the global behavior.

A simplified approach to characterizing a kilovoltage source spectrum for accurate dose computation

Energy Technology Data Exchange (ETDEWEB)

Poirier, Yannick; Kouznetsov, Alexei; Tambasco, Mauro [Department of Physics and Astronomy, University of Calgary, Calgary, Alberta T2N 4N2 (Canada); Department of Physics and Astronomy and Department of Oncology, University of Calgary and Tom Baker Cancer Centre, Calgary, Alberta T2N 4N2 (Canada)

2012-06-15

Purpose: To investigate and validate the clinical feasibility of using half-value layer (HVL) and peak tube potential (kVp) for characterizing a kilovoltage (kV) source spectrum for the purpose of computing kV x-ray dose accrued from imaging procedures. To use this approach to characterize a Varian Registered-Sign On-Board Imager Registered-Sign (OBI) source and perform experimental validation of a novel in-house hybrid dose computation algorithm for kV x-rays. Methods: We characterized the spectrum of an imaging kV x-ray source using the HVL and the kVp as the sole beam quality identifiers using third-party freeware Spektr to generate the spectra. We studied the sensitivity of our dose computation algorithm to uncertainties in the beam's HVL and kVp by systematically varying these spectral parameters. To validate our approach experimentally, we characterized the spectrum of a Varian Registered-Sign OBI system by measuring the HVL using a Farmer-type Capintec ion chamber (0.06 cc) in air and compared dose calculations using our computationally validated in-house kV dose calculation code to measured percent depth-dose and transverse dose profiles for 80, 100, and 125 kVp open beams in a homogeneous phantom and a heterogeneous phantom comprising tissue, lung, and bone equivalent materials. Results: The sensitivity analysis of the beam quality parameters (i.e., HVL, kVp, and field size) on dose computation accuracy shows that typical measurement uncertainties in the HVL and kVp ({+-}0.2 mm Al and {+-}2 kVp, respectively) source characterization parameters lead to dose computation errors of less than 2%. Furthermore, for an open beam with no added filtration, HVL variations affect dose computation accuracy by less than 1% for a 125 kVp beam when field size is varied from 5 Multiplication-Sign 5 cm{sup 2} to 40 Multiplication-Sign 40 cm{sup 2}. The central axis depth dose calculations and experimental measurements for the 80, 100, and 125 kVp energies agreed within

An Approach to Management of Health Care and Medical Diagnosis Using of a Hybrid Disease Diagnosis System

Directory of Open Access Journals (Sweden)

Hodjat Hamidi

2017-02-01

Full Text Available Introduction: In order to simplify the information exchange within the medical diagnosis process, a collaborative software agent’s framework is presented. The purpose of the framework is to allow the automated information exchange between different medicine specialists. Methods: This study presented architecture of a hybrid disease diagnosis system. The architecture employed a learning algorithm and used soft computing to build a medical knowledge base. These machine intelligences are combined in a complementary approach to overcome the weakness of each other. To evaluate the hybrid learning algorithm and compare it with other methods, 699 samples were used in each experiment, where 60% was for training, 20% was for cross validation, and 20% for testing. Results: The results were obtained from the experiments on the breast cancer dataset. Different methods of soft computing system were merged to create diagnostic software functionality. As it is shown in the structure, the system has the ability to learn and collect knowledge that can be used in the detection of new images. Currently, the system is at the design stage. The system is to evaluate the performance of hybrid learning algorithm. The preliminary results showed a better performance of this system than other methods. However, the results can be tested with hybrid system on larger data sets to improve hybrid learning algorithm. Conclusion: The purpose of this paper was to simplify the diagnosis process of a patient by splitting the medical domain concepts (e.g., causes, effects, symptoms, tests in human body systems (e.g., respiratory, cardiovascular, though maintaining the holistic perspective through the links between common concepts.

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

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Sonia Yassa

2013-01-01

Full Text Available We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

Science.gov (United States)

Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

Soft computing approach for reliability optimization: State-of-the-art survey

International Nuclear Information System (INIS)

Gen, Mitsuo; Yun, Young Su

2006-01-01

In the broadest sense, reliability is a measure of performance of systems. As systems have grown more complex, the consequences of their unreliable behavior have become severe in terms of cost, effort, lives, etc., and the interest in assessing system reliability and the need for improving the reliability of products and systems have become very important. Most solution methods for reliability optimization assume that systems have redundancy components in series and/or parallel systems and alternative designs are available. Reliability optimization problems concentrate on optimal allocation of redundancy components and optimal selection of alternative designs to meet system requirement. In the past two decades, numerous reliability optimization techniques have been proposed. Generally, these techniques can be classified as linear programming, dynamic programming, integer programming, geometric programming, heuristic method, Lagrangean multiplier method and so on. A Genetic Algorithm (GA), as a soft computing approach, is a powerful tool for solving various reliability optimization problems. In this paper, we briefly survey GA-based approach for various reliability optimization problems, such as reliability optimization of redundant system, reliability optimization with alternative design, reliability optimization with time-dependent reliability, reliability optimization with interval coefficients, bicriteria reliability optimization, and reliability optimization with fuzzy goals. We also introduce the hybrid approaches for combining GA with fuzzy logic, neural network and other conventional search techniques. Finally, we have some experiments with an example of various reliability optimization problems using hybrid GA approach

Conceptual design of distillation-based hybrid separation processes.

Science.gov (United States)

Skiborowski, Mirko; Harwardt, Andreas; Marquardt, Wolfgang

2013-01-01

Hybrid separation processes combine different separation principles and constitute a promising design option for the separation of complex mixtures. Particularly, the integration of distillation with other unit operations can significantly improve the separation of close-boiling or azeotropic mixtures. Although the design of single-unit operations is well understood and supported by computational methods, the optimal design of flowsheets of hybrid separation processes is still a challenging task. The large number of operational and design degrees of freedom requires a systematic and optimization-based design approach. To this end, a structured approach, the so-called process synthesis framework, is proposed. This article reviews available computational methods for the conceptual design of distillation-based hybrid processes for the separation of liquid mixtures. Open problems are identified that must be addressed to finally establish a structured process synthesis framework for such processes.

Study of an analog/logic processor for the design of an auto patch hybrid computer

International Nuclear Information System (INIS)

Koched, Hassen

1976-01-01

This paper presents the experimental study of an analog multiprocessor designed at SES/CEN-Saclay. An application of such a device as a basic component of an auto-patch hybrid computer is presented. First, the description of the processor, and a presentation of the theoretical concepts which governed the design of the processor are given. Experiments on an hybrid computer are then presented. Finally, different systems of automatic patching are presented, and conveniently modified, for the use of such a processor. (author) [fr

A Hybrid Approach on Tourism Demand Forecasting

Science.gov (United States)

Nor, M. E.; Nurul, A. I. M.; Rusiman, M. S.

2018-04-01

Tourism has become one of the important industries that contributes to the country’s economy. Tourism demand forecasting gives valuable information to policy makers, decision makers and organizations related to tourism industry in order to make crucial decision and planning. However, it is challenging to produce an accurate forecast since economic data such as the tourism data is affected by social, economic and environmental factors. In this study, an equally-weighted hybrid method, which is a combination of Box-Jenkins and Artificial Neural Networks, was applied to forecast Malaysia’s tourism demand. The forecasting performance was assessed by taking the each individual method as a benchmark. The results showed that this hybrid approach outperformed the other two models

Heart dosimetry in radiotherapy with hybrid computational phantoms

International Nuclear Information System (INIS)

Moignier, Cyril

2014-01-01

Cardiovascular diseases following radiotherapy are major secondary late effects raising questions among the scientific community, especially regarding the dose-effect relationship and confounding risk factors (chemotherapy, cholesterolemia, age at treatment, blood pressure,..). Post-radiation coronary diseases are one of the main causes of cardiac morbidity. Some approximations are made when coronary doses due to radiotherapy are estimated, especially regarding the morphology. For retrospective studies with old medical records, only radiographs are usually available with sometimes some contours made with a simulator. For recent medical records, CT scans displaying the anatomy in 3D are used for radiotherapy simulation but do not allow the coronary artery visualization due to low resolution and contrast. Currently, coronary doses are barely assessed in clinical practice, and when it is done, anatomical prior knowledge is generally used. This thesis proposes an original approach based on hybrid computational phantoms to study coronary artery doses following radiotherapy for left-side breast cancer and Hodgkin lymphoma. During the thesis, a method inserting hybrid computational phantoms in a DICOM format into the treatment planning system has been developed and validated. It has been adapted and tested in conditions where only radiographs provide anatomical information, as with old medical records for left side breast radiotherapy. The method has also been adapted to perform precise dose reconstructions to the coronary artery for patients treated for a mediastinal Hodgkin lymphoma and diagnosed with coronary stenosis through a coroscanner. A case-control study was carried out and the risk of coronary stenosis on a coronary artery segment was assessed to be multiplied by 1.049 at each additional gray on the median dose to the coronary artery segment. For recent medical records, coronary doses uncertainties related to an approach by anatomical prior knowledge

A hybrid modelling approach to simulating foot-and-mouth disease outbreaks in Australian livestock

Directory of Open Access Journals (Sweden)

Richard A Bradhurst

2015-03-01

Full Text Available Foot-and-mouth disease (FMD is a highly contagious and economically important viral disease of cloven-hoofed animals. Australia's freedom from FMD underpins a valuable trade in live animals and animal products. An outbreak of FMD would result in the loss of export markets and cause severe disruption to domestic markets. The prevention of, and contingency planning for, FMD are of key importance to government, industry, producers and the community. The spread and control of FMD is complex and dynamic due to a highly contagious multi-host pathogen operating in a heterogeneous environment across multiple jurisdictions. Epidemiological modelling is increasingly being recognized as a valuable tool for investigating the spread of disease under different conditions and the effectiveness of control strategies. Models of infectious disease can be broadly classified as: population-based models that are formulated from the top-down and employ population-level relationships to describe individual-level behaviour, individual-based models that are formulated from the bottom-up and aggregate individual-level behaviour to reveal population-level relationships, or hybrid models which combine the two approaches into a single model.The Australian Animal Disease Spread (AADIS hybrid model employs a deterministic equation-based model (EBM to model within-herd spread of FMD, and a stochastic, spatially-explicit agent-based model (ABM to model between-herd spread and control. The EBM provides concise and computationally efficient predictions of herd prevalence and clinical signs over time. The ABM captures the complex, stochastic and heterogeneous environment in which an FMD epidemic operates. The AADIS event-driven hybrid EBM/ABM architecture is a flexible, efficient and extensible framework for modelling the spread and control of disease in livestock on a national scale. We present an overview of the AADIS hybrid approach and a description of the model

Solving Problem of Graph Isomorphism by Membrane-Quantum Hybrid Model

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Artiom Alhazov

2015-10-01

Full Text Available This work presents the application of new parallelization methods based on membrane-quantum hybrid computing to graph isomorphism problem solving. Applied membrane-quantum hybrid computational model was developed by authors. Massive parallelism of unconventional computing is used to implement classic brute force algorithm efficiently. This approach does not suppose any restrictions of considered graphs types. The estimated performance of the model is less then quadratic that makes a very good result for the problem of \\textbf{NP} complexity.

Detection of cardiovascular anomalies: Hybrid systems approach

KAUST Repository

Ledezma, Fernando

2012-06-06

In this paper, we propose a hybrid interpretation of the cardiovascular system. Based on a model proposed by Simaan et al. (2009), we study the problem of detecting cardiovascular anomalies that can be caused by variations in some physiological parameters, using an observerbased approach. We present the first numerical results obtained. © 2012 IFAC.

Cardiac hybrid imaging

Energy Technology Data Exchange (ETDEWEB)

Gaemperli, Oliver [University Hospital Zurich, Cardiac Imaging, Zurich (Switzerland); University Hospital Zurich, Nuclear Cardiology, Cardiovascular Center, Zurich (Switzerland); Kaufmann, Philipp A. [University Hospital Zurich, Cardiac Imaging, Zurich (Switzerland); Alkadhi, Hatem [University Hospital Zurich, Institute of Diagnostic and Interventional Radiology, Zurich (Switzerland)

2014-05-15

Hybrid cardiac single photon emission computed tomography (SPECT)/CT imaging allows combined assessment of anatomical and functional aspects of cardiac disease. In coronary artery disease (CAD), hybrid SPECT/CT imaging allows detection of coronary artery stenosis and myocardial perfusion abnormalities. The clinical value of hybrid imaging has been documented in several subsets of patients. In selected groups of patients, hybrid imaging improves the diagnostic accuracy to detect CAD compared to the single imaging techniques. Additionally, this approach facilitates functional interrogation of coronary stenoses and guidance with regard to revascularization procedures. Moreover, the anatomical information obtained from CT coronary angiography or coronary artery calcium scores (CACS) adds prognostic information over perfusion data from SPECT. The use of cardiac hybrid imaging has been favoured by the dissemination of dedicated hybrid systems and the release of dedicated image fusion software, which allow simple patient throughput for hybrid SPECT/CT studies. Further technological improvements such as more efficient detector technology to allow for low-radiation protocols, ultra-fast image acquisition and improved low-noise image reconstruction algorithms will be instrumental to further promote hybrid SPECT/CT in research and clinical practice. (orig.)

Hybrid soft computing systems for electromyographic signals analysis: a review.

Science.gov (United States)

Xie, Hong-Bo; Guo, Tianruo; Bai, Siwei; Dokos, Socrates

2014-02-03

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Science.gov (United States)

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided. PMID:28790910

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Science.gov (United States)

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP) and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Directory of Open Access Journals (Sweden)

Keum-Shik Hong

2017-07-01

Full Text Available In this article, non-invasive hybrid brain–computer interface (hBCI technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG, due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spectroscopy (fNIRS, electromyography (EMG, electrooculography (EOG, and eye tracker. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy and reduce the signal detection time. Currently, such combinations of EEG + fNIRS and EEG + EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, and features relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain–computer interface (BCI accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP and motor imagination with P300. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

Hybrid Type II fuzzy system & data mining approach for surface finish

Directory of Open Access Journals (Sweden)

Tzu-Liang (Bill Tseng

2015-07-01

Full Text Available In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

A HYBRID APPROACH FOR RURAL FEEDER DESIGN

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DAMANJEET KAUR

2012-08-01

Full Text Available In this paper, a population based approach for conductor size selection in rural radial distribution system is presented. The proposed hybrid approach implies a particle swarm optimization (PSO approach in combination with mutant property of differential evolution (DE for conductor size selection in radial distribution system. The conductor size for each feeder segment is selected such that the total cost of capital investment and capitalized cost of energy losses is minimized while constraints of voltage at each node and current carrying capacity of conductor is within the limits. The applicability and effectiveness of the proposed method is demonstrated with the help of 32-node test system.

Hybrid rocket engine, theoretical model and experiment

Science.gov (United States)

Chelaru, Teodor-Viorel; Mingireanu, Florin

2011-06-01

The purpose of this paper is to build a theoretical model for the hybrid rocket engine/motor and to validate it using experimental results. The work approaches the main problems of the hybrid motor: the scalability, the stability/controllability of the operating parameters and the increasing of the solid fuel regression rate. At first, we focus on theoretical models for hybrid rocket motor and compare the results with already available experimental data from various research groups. A primary computation model is presented together with results from a numerical algorithm based on a computational model. We present theoretical predictions for several commercial hybrid rocket motors, having different scales and compare them with experimental measurements of those hybrid rocket motors. Next the paper focuses on tribrid rocket motor concept, which by supplementary liquid fuel injection can improve the thrust controllability. A complementary computation model is also presented to estimate regression rate increase of solid fuel doped with oxidizer. Finally, the stability of the hybrid rocket motor is investigated using Liapunov theory. Stability coefficients obtained are dependent on burning parameters while the stability and command matrixes are identified. The paper presents thoroughly the input data of the model, which ensures the reproducibility of the numerical results by independent researchers.

Improved Hybrid Opponent System for Professional Military Training

Directory of Open Access Journals (Sweden)

Michael Pelosi

2017-10-01

Full Text Available Described herein is a general-purpose software engineering architecture for autonomous, computer controlled opponent implementation in modern maneuver warfare simulation and training. The implementation has been developed, refined, and tested in the user crucible for several years. The approach represents a hybrid application of various well-known AI techniques, including domain modeling, agent modeling, and object-oriented programming. Inspired by computer chess approaches, the methodology combines this theoretical foundation with a hybrid and scalable portfolio of additional techniques. The result remains simple enough to be maintainable, comprehensible for the code writers as well as the end-users, and robust enough to handle a wide spectrum of possible mission scenarios and circumstances without modification.

A hybrid particle–field molecular dynamics approach: a route toward efficient coarse-grained models for biomembranes

International Nuclear Information System (INIS)

Milano, Giuseppe; De Nicola, Antonio; Kawakatsu, Toshihiro

2013-01-01

This paper gives an overview of the coarse-grained models of phospholipids recently developed by the authors in the frame of a hybrid particle–field molecular dynamics technique. This technique employs a special class of coarse-grained models that are gaining popularity because they allow simulations of large scale systems and, at the same time, they provide sufficiently detailed chemistry for the mapping scheme adopted. The comparison of the computational costs of our approach with standard molecular dynamics simulations is a function of the system size and the number of processors employed in the parallel calculations. Due to the low amount of data exchange, the larger the number of processors, the better are the performances of the hybrid particle–field models. This feature makes these models very promising ones in the exploration of several problems in biophysics. (paper)

Modelling the WWER-type reactor dynamics using a hybrid computer. Part 1

International Nuclear Information System (INIS)

Karpeta, C.

Results of simulation studies into reactor and steam generator dynamics of a WWER type power plant are presented. Spatial kinetics of the reactor core is described by a nodal approximation to diffusion equations, xenon poisoning equations and heat transfer equations. The simulation of the reactor model dynamics was performed on a hybrid computer. Models of both a horizontal and a vertical steam generator were developed. The dynamics was investigated over a large range of power by computing the transients on a digital computer. (author)

HYBRID EDUCATION: THE ESTIMATION IN THE CATEGORIES OF INFORMATION-AXIOLOGICAL APPROACH

Directory of Open Access Journals (Sweden)

A. S. Kizilova

2018-01-01

Full Text Available Introduction: a hybrid assessment of reality is a new information-axiological level of communication between people. The term "hybrid (hybrid training" has been used as a result of the integration of digital and communication technologies in the form of online courses.Materials and methods: the main Russian forms of education are analyzed. The evaluation of the forms of education in the categories of the information-axiological approach is made on the basis of the following idea: everything is interchangeable, since everything has value. The mixing principles and models used in the process of hybrid formation are considered. Due to the fact that any mixed training requires planning, the analysis of the project and the target group, content analysis and financial analysis in this process is carried out.Results: specific educational methods are studied at the Bauman MSTU, subject to a hybrid assessment in the categories of the information-axiological approach. The analysis showed that the above explanation of the term "hybrid formation" is extremely narrow and one-sided. In particular, the search for information on volunteer education and the search for a socially-based education was conducted not only in the Bauman MSTU, but in Russia as a whole. However, the result was the portals of international student organizations with their own projects. Another example of a different interpretation of the "hybrid education" may be the assumption of quite axiologically new duties.Discussion and Conclusions: hybrid education is not limited to any temporal and territorial framework. It can manifest itself not only in the Internet-sphere, but also in the most diverse spheres of everyday life, with the interaction of various people and entire societies.

An investigation into the reduction of log-layer mismatch in wall-modeled LES with a hybrid RANS/LES approach

Science.gov (United States)

Balin, Riccardo; Spalart, Philippe R.; Jansen, Kenneth E.

2017-11-01

Hybrid RANS/LES modeling approaches used in the context of wall-modeled LES (WMLES) of channel flows and boundary layers often suffer from a mismatch in the RANS and LES log-layer intercepts of the mean velocity profile. In the vicinity of the interface between the RANS and LES regions, the mean velocity gradient is too steep causing a departure from the log-law, an over-prediction of the velocity in the outer layer and an under-prediction of the skin-friction. This steep gradient is attributed to inadequate modeled Reynolds stresses in the upper portion of the RANS layer and at the interface. Channel flow computations were carried out with the IDDES approach of Shur et al. in WMLES mode based on the Spalart-Allmaras RANS model. This talk investigates the robustness of this approach for unstructured grids and explores changes required for grids where insufficient elevation of the Reynolds stresses is observed. Awards of computer time were provided by Innovative and Novel Computational Impact on Theory and Experiment (INCITE) and Early Science programs. Resources of the Argonne Leadership Computing Facility, a DOE Office of Science User Facility, were used.

Carbon nanotube reinforced hybrid composites: Computational modeling of environmental fatigue and usability for wind blades

DEFF Research Database (Denmark)

Dai, Gaoming; Mishnaevsky, Leon

2015-01-01

The potential of advanced carbon/glass hybrid reinforced composites with secondary carbon nanotube reinforcement for wind energy applications is investigated here with the use of computational experiments. Fatigue behavior of hybrid as well as glass and carbon fiber reinforced composites...... with the secondary CNT reinforcements (especially, aligned tubes) present superior fatigue performances than those without reinforcements, also under combined environmental and cyclic mechanical loading. This effect is stronger for carbon composites, than for hybrid and glass composites....

Disease processes as hybrid dynamical systems

Directory of Open Access Journals (Sweden)

Pietro Liò

2012-08-01

Full Text Available We investigate the use of hybrid techniques in complex processes of infectious diseases. Since predictive disease models in biomedicine require a multiscale approach for understanding the molecule-cell-tissue-organ-body interactions, heterogeneous methodologies are often employed for describing the different biological scales. Hybrid models provide effective means for complex disease modelling where the action and dosage of a drug or a therapy could be meaningfully investigated: the infection dynamics can be classically described in a continuous fashion, while the scheduling of multiple treatment discretely. We define an algebraic language for specifying general disease processes and multiple treatments, from which a semantics in terms of hybrid dynamical system can be derived. Then, the application of control-theoretic tools is proposed in order to compute the optimal scheduling of multiple therapies. The potentialities of our approach are shown in the case study of the SIR epidemic model and we discuss its applicability on osteomyelitis, a bacterial infection affecting the bone remodelling system in a specific and multiscale manner. We report that formal languages are helpful in giving a general homogeneous formulation for the different scales involved in a multiscale disease process; and that the combination of hybrid modelling and control theory provides solid grounds for computational medicine.

Hybrid soft computing systems for electromyographic signals analysis: a review

Science.gov (United States)

2014-01-01

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis. PMID:24490979

A hybrid approach to designing inbound-resupply strategies

NARCIS (Netherlands)

Dullaert, Wout; Vernimmen, Bert; Raa, Birger; Witlox, Frank

A new hybrid approach was developed to determine the optimal inbound-resupply strategy when suppliers ship goods to receivers. The optimal reorder level was calculated on the basis of a simulation of the distribution of demand and the lead time of the various sourcing alternatives. An evolutionary

OPTHYLIC: An Optimised Tool for Hybrid Limits Computation

Science.gov (United States)

Busato, Emmanuel; Calvet, David; Theveneaux-Pelzer, Timothée

2018-05-01

A software tool, computing observed and expected upper limits on Poissonian process rates using a hybrid frequentist-Bayesian CLs method, is presented. This tool can be used for simple counting experiments where only signal, background and observed yields are provided or for multi-bin experiments where binned distributions of discriminating variables are provided. It allows the combination of several channels and takes into account statistical and systematic uncertainties, as well as correlations of systematic uncertainties between channels. It has been validated against other software tools and analytical calculations, for several realistic cases.

A hybrid source-driven method to compute fast neutron fluence in reactor pressure vessel - 017

International Nuclear Information System (INIS)

Ren-Tai, Chiang

2010-01-01

A hybrid source-driven method is developed to compute fast neutron fluence with neutron energy greater than 1 MeV in nuclear reactor pressure vessel (RPV). The method determines neutron flux by solving a steady-state neutron transport equation with hybrid neutron sources composed of peripheral fixed fission neutron sources and interior chain-reacted fission neutron sources. The relative rod-by-rod power distribution of the peripheral assemblies in a nuclear reactor obtained from reactor core depletion calculations and subsequent rod-by-rod power reconstruction is employed as the relative rod-by-rod fixed fission neutron source distribution. All fissionable nuclides other than U-238 (such as U-234, U-235, U-236, Pu-239 etc) are replaced with U-238 to avoid counting the fission contribution twice and to preserve fast neutron attenuation for heavy nuclides in the peripheral assemblies. An example is provided to show the feasibility of the method. Since the interior fuels only have a marginal impact on RPV fluence results due to rapid attenuation of interior fast fission neutrons, a generic set or one of several generic sets of interior fuels can be used as the driver and only the neutron sources in the peripheral assemblies will be changed in subsequent hybrid source-driven fluence calculations. Consequently, this hybrid source-driven method can simplify and reduce cost for fast neutron fluence computations. This newly developed hybrid source-driven method should be a useful and simplified tool for computing fast neutron fluence at selected locations of interest in RPV of contemporary nuclear power reactors. (authors)

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

Science.gov (United States)

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin; Choo, Kim-Kwang Raymond

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO).

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware.

Directory of Open Access Journals (Sweden)

Firdaus Afifi

Full Text Available To deal with the large number of malicious mobile applications (e.g. mobile malware, a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS and particle swarm optimization (PSO. Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE and ant colony optimization (ANFIS-ACO.

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

Science.gov (United States)

Afifi, Firdaus; Anuar, Nor Badrul; Shamshirband, Shahaboddin

2016-01-01

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extraction and selection agent) to capture and manage the pcap file for data preparation phase. In our hybrid approach, we combine an adaptive neuro fuzzy inference system (ANFIS) and particle swarm optimization (PSO). Evaluations using data captured on a real-world Android device and the MalGenome dataset demonstrate the effectiveness of our approach, in comparison to two hybrid optimization methods which are differential evolution (ANFIS-DE) and ant colony optimization (ANFIS-ACO). PMID:27611312

A hybrid generative-discriminative approach to speaker diarization

NARCIS (Netherlands)

Noulas, A.K.; van Kasteren, T.; Kröse, B.J.A.

2008-01-01

In this paper we present a sound probabilistic approach to speaker diarization. We use a hybrid framework where a distribution over the number of speakers at each point of a multimodal stream is estimated with a discriminative model. The output of this process is used as input in a generative model

Course on hybrid calculation

International Nuclear Information System (INIS)

Weill, J.; Tellier; Bonnemay; Craigne; Chareton; Di Falco

1969-02-01

After a definition of hybrid calculation (combination of analogue and digital calculation) with a distinction between series and parallel hybrid computing, and a description of a hybrid computer structure and of task sharing between computers, this course proposes a description of hybrid hardware used in Saclay and Cadarache computing centres, and of operations performed by these systems. The next part addresses issues related to programming languages and software. The fourth part describes how a problem is organised for its processing on these computers. Methods of hybrid analysis are then addressed: resolution of optimisation problems, of partial differential equations, and of integral equations by means of different methods (gradient, maximum principle, characteristics, functional approximation, time slicing, Monte Carlo, Neumann iteration, Fischer iteration)

16th International Conference on Hybrid Intelligent Systems and the 8th World Congress on Nature and Biologically Inspired Computing

CERN Document Server

Haqiq, Abdelkrim; Alimi, Adel; Mezzour, Ghita; Rokbani, Nizar; Muda, Azah

2017-01-01

This book presents the latest research in hybrid intelligent systems. It includes 57 carefully selected papers from the 16th International Conference on Hybrid Intelligent Systems (HIS 2016) and the 8th World Congress on Nature and Biologically Inspired Computing (NaBIC 2016), held on November 21–23, 2016 in Marrakech, Morocco. HIS - NaBIC 2016 was jointly organized by the Machine Intelligence Research Labs (MIR Labs), USA; Hassan 1st University, Settat, Morocco and University of Sfax, Tunisia. Hybridization of intelligent systems is a promising research field in modern artificial/computational intelligence and is concerned with the development of the next generation of intelligent systems. The conference’s main aim is to inspire further exploration of the intriguing potential of hybrid intelligent systems and bio-inspired computing. As such, the book is a valuable resource for practicing engineers /scientists and researchers working in the field of computational intelligence and artificial intelligence.

Restraining approach for the spurious kinematic modes in hybrid equilibrium element

Science.gov (United States)

Parrinello, F.

2013-10-01

The present paper proposes a rigorous approach for the elimination of spurious kinematic modes in hybrid equilibrium elements, for three well known mesh patches. The approach is based on the identification of the dependent equations in the set of inter-element and boundary equilibrium equations of the sides involved in the spurious kinematic mode. Then the kinematic variables related to the dependent equations are reciprocally constrained and, by application of master slave elimination method, the set of inter-element equilibrium equations is reduced to full rank. The elastic solutions produced by means of the proposed approach verify the homogeneous, the inter-element and the boundary equilibrium equations. Hybrid stress formulation is developed in a rigorous mathematical setting. The results of linear elastic analysis obtained by the proposed approach and by classical displacement based method are compared for some structural examples.

Does the acceptance of hybrid learning affect learning approaches in France?

Science.gov (United States)

Marco, Lionel Di; Venot, Alain; Gillois, Pierre

2017-01-01

Acceptance of a learning technology affects students' intention to use that technology, but the influence of the acceptance of a learning technology on learning approaches has not been investigated in the literature. A deep learning approach is important in the field of health, where links must be created between skills, knowledge, and habits. Our hypothesis was that acceptance of a hybrid learning model would affect students' way of learning. We analysed these concepts, and their correlations, in the context of a flipped classroom method using a local learning management system. In a sample of all students within a single year of study in the midwifery program (n= 38), we used 3 validated scales to evaluate these concepts (the Study Process Questionnaire, My Intellectual Work Tools, and the Hybrid E-Learning Acceptance Model: Learner Perceptions). Our sample had a positive acceptance of the learning model, but a neutral intention to use it. Students reported that they were distractible during distance learning. They presented a better mean score for the deep approach than for the superficial approach (Paffected by acceptance of a hybrid learning model, due to the flexibility of the tool. However, we identified problems in the students' time utilization, which explains their neutral intention to use the system.

Hybrid automata models of cardiac ventricular electrophysiology for real-time computational applications.

Science.gov (United States)

Andalam, Sidharta; Ramanna, Harshavardhan; Malik, Avinash; Roop, Parthasarathi; Patel, Nitish; Trew, Mark L

2016-08-01

Virtual heart models have been proposed for closed loop validation of safety-critical embedded medical devices, such as pacemakers. These models must react in real-time to off-the-shelf medical devices. Real-time performance can be obtained by implementing models in computer hardware, and methods of compiling classes of Hybrid Automata (HA) onto FPGA have been developed. Models of ventricular cardiac cell electrophysiology have been described using HA which capture the complex nonlinear behavior of biological systems. However, many models that have been used for closed-loop validation of pacemakers are highly abstract and do not capture important characteristics of the dynamic rate response. We developed a new HA model of cardiac cells which captures dynamic behavior and we implemented the model in hardware. This potentially enables modeling the heart with over 1 million dynamic cells, making the approach ideal for closed loop testing of medical devices.

Cardiovascular dosimetry using hybrid computational phantoms after external radiotherapy

International Nuclear Information System (INIS)

Moignier, Alexandra

2014-01-01

Cardiovascular diseases following radiotherapy are major secondary late effects raising questions among the scientific community, especially regarding the dose-effect relationship and confounding risk factors (chemotherapy, cholesterolemia, age at treatment, blood pressure,..). Post-radiation coronary diseases are one of the main causes of cardiac morbidity. Some approximations are made when coronary doses due to radiotherapy are estimated, especially regarding the morphology. For retrospective studies with old medical records, only radiographs are usually available with sometimes some contours made with a simulator. For recent medical records, CT scans displaying the anatomy in 3D are used for radiotherapy simulation but do not allow the coronary artery visualization due to low resolution and contrast. Currently, coronary doses are barely assessed in clinical practice, and when it is done, anatomical prior knowledge is generally used. This thesis proposes an original approach based on hybrid computational phantoms to study coronary artery doses following radiotherapy for left-side breast cancer and Hodgkin lymphoma. During the thesis, a method inserting hybrid computational phantoms in a DICOM format into the treatment planning system has been developed and validated. It has been adapted and tested in conditions where only radiographs provide anatomical information, as with old medical records for left side breast radiotherapy. The method has also been adapted to perform precise dose reconstructions to the coronary artery for patients treated for a mediastinal Hodgkin lymphoma and diagnosed with coronary stenosis through a coro-scanner. A case-control study was carried out and the risk of coronary stenosis on a coronary artery segment was assessed to be multiplied by 1.049 at each additional gray on the median dose to the coronary artery segment. For recent medical records, coronary doses uncertainties related to an approach by anatomical prior knowledge

All-optical quantum computing with a hybrid solid-state processing unit

International Nuclear Information System (INIS)

Pei Pei; Zhang Fengyang; Li Chong; Song Heshan

2011-01-01

We develop an architecture of a hybrid quantum solid-state processing unit for universal quantum computing. The architecture allows distant and nonidentical solid-state qubits in distinct physical systems to interact and work collaboratively. All the quantum computing procedures are controlled by optical methods using classical fields and cavity QED. Our methods have a prominent advantage of the insensitivity to dissipation process benefiting from the virtual excitation of subsystems. Moreover, the quantum nondemolition measurements and state transfer for the solid-state qubits are proposed. The architecture opens promising perspectives for implementing scalable quantum computation in a broader sense that different solid-state systems can merge and be integrated into one quantum processor afterward.

A "Hybrid" Approach for Synthesizing Optimal Controllers of Hybrid Systems

DEFF Research Database (Denmark)

Zhao, Hengjun; Zhan, Naijun; Kapur, Deepak

2012-01-01

to discretization manageable and within bounds. A major advantage of our approach is not only that it avoids errors due to numerical computation, but it also gives a better optimal controller. In order to illustrate our approach, we use the real industrial example of an oil pump provided by the German company HYDAC...

Near-term hybrid vehicle program, phase 1. Appendix B: Design trade-off studies report. Volume 3: Computer program listings

Science.gov (United States)

1979-01-01

A description and listing is presented of two computer programs: Hybrid Vehicle Design Program (HYVELD) and Hybrid Vehicle Simulation Program (HYVEC). Both of the programs are modifications and extensions of similar programs developed as part of the Electric and Hybrid Vehicle System Research and Development Project.

A Hybrid Node Scheduling Approach Based on Energy Efficient Chain Routing for WSN

Directory of Open Access Journals (Sweden)

Yimei Kang

2014-04-01

Full Text Available Energy efficiency is usually a significant goal in wireless sensor networks (WSNs. In this work, an energy efficient chain (EEC data routing approach is first presented. The coverage and connectivity of WSNs are discussed based on EEC. A hybrid node scheduling approach is then proposed. It includes sleep scheduling for cyclically monitoring regions of interest in time-driven modes and wakeup scheduling for tracking emergency events in event-driven modes. A failure rate is introduced to the sleep scheduling to improve the reliability of the system. A wakeup sensor threshold and a sleep time threshold are introduced in the wakeup scheduling to reduce the consumption of energy to the possible extent. The results of the simulation show that the proposed algorithm can extend the effective lifetime of the network to twice that of PEAS. In addition, the proposed methods are computing efficient because they are very simple to implement.

Hybrid epidemics--a case study on computer worm conficker.

Directory of Open Access Journals (Sweden)

Changwang Zhang

Full Text Available Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm's spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm's effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols.

Hybrid epidemics--a case study on computer worm conficker.

Science.gov (United States)

Zhang, Changwang; Zhou, Shi; Chain, Benjamin M

2015-01-01

Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm's spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm's effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols.

Hybrid Epidemics—A Case Study on Computer Worm Conficker

Science.gov (United States)

Zhang, Changwang; Zhou, Shi; Chain, Benjamin M.

2015-01-01

Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm’s spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm’s effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols. PMID:25978309

Facile approach to prepare Pt decorated SWNT/graphene hybrid catalytic ink

Energy Technology Data Exchange (ETDEWEB)

Mayavan, Sundar, E-mail: sundarmayavan@cecri.res.in [Centre for Innovation in Energy Research, CSIR–Central Electrochemical Research Institute, Karaikudi 630006, Tamil Nadu (India); Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-701 (Korea, Republic of); Mandalam, Aditya; Balasubramanian, M. [Centre for Innovation in Energy Research, CSIR–Central Electrochemical Research Institute, Karaikudi 630006, Tamil Nadu (India); Sim, Jun-Bo [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-701 (Korea, Republic of); Choi, Sung-Min, E-mail: sungmin@kaist.ac.kr [Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology, Daejeon, 305-701 (Korea, Republic of)

2015-07-15

Highlights: • Pt NPs were in situ synthesized onto CNT–graphene support in aqueous solution. • The as-prepared material was used directly as a catalyst ink without further treatment. • Catalyst ink is active toward methanol oxidation. • This approach realizes both scalable and greener production of hybrid catalysts. - Abstract: Platinum nanoparticles were in situ synthesized onto hybrid support involving graphene and single walled carbon nanotube in aqueous solution. We investigate the reduction of graphene oxide, and platinum nanoparticle functionalization on hybrid support by X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray diffraction, scanning electron microscopy and transmission electron microscopy. The as-prepared platinum on hybrid support was used directly as a catalyst ink without further treatment and is active toward methanol oxidation. This work realizes both scalable and greener production of highly efficient hybrid catalysts, and would be valuable for practical applications of graphene based fuel cell catalysts.

Spatially hybrid computations for streamer discharges with generic features of pulled fronts: I. Planar fronts

International Nuclear Information System (INIS)

Li Chao; Ebert, Ute; Hundsdorfer, Willem

2010-01-01

Streamers are the first stage of sparks and lightning; they grow due to a strongly enhanced electric field at their tips; this field is created by a thin curved space charge layer. These multiple scales are already challenging when the electrons are approximated by densities. However, electron density fluctuations in the leading edge of the front and non-thermal stretched tails of the electron energy distribution (as a cause of X-ray emissions) require a particle model to follow the electron motion. But present computers cannot deal with all electrons in a fully developed streamer. Therefore, super-particle have to be introduced, which leads to wrong statistics and numerical artifacts. The method of choice is a hybrid computation in space where individual electrons are followed in the region of high electric field and low density while the bulk of the electrons is approximated by densities (or fluids). We here develop the hybrid coupling for planar fronts. First, to obtain a consistent flux at the interface between particle and fluid model in the hybrid computation, the widely used classical fluid model is replaced by an extended fluid model. Then the coupling algorithm and the numerical implementation of the spatially hybrid model are presented in detail, in particular, the position of the model interface and the construction of the buffer region. The method carries generic features of pulled fronts that can be applied to similar problems like large deviations in the leading edge of population fronts, etc.

Hybrid and Blended Learning: Modifying Pedagogy across Path, Pace, Time, and Place

Science.gov (United States)

O'Byrne, W. Ian; Pytash, Kristine E.

2015-01-01

Hybrid or blended learning is defined as a pedagogical approach that includes a combination of face-to-face instruction with computer-mediated instruction. The terms "blended learning", "hybrid learning", and "mixed-mode learning" are used interchangeably in current research; however, in the United States,…

Autumn Algorithm-Computation of Hybridization Networks for Realistic Phylogenetic Trees.

Science.gov (United States)

Huson, Daniel H; Linz, Simone

2018-01-01

A minimum hybridization network is a rooted phylogenetic network that displays two given rooted phylogenetic trees using a minimum number of reticulations. Previous mathematical work on their calculation has usually assumed the input trees to be bifurcating, correctly rooted, or that they both contain the same taxa. These assumptions do not hold in biological studies and "realistic" trees have multifurcations, are difficult to root, and rarely contain the same taxa. We present a new algorithm for computing minimum hybridization networks for a given pair of "realistic" rooted phylogenetic trees. We also describe how the algorithm might be used to improve the rooting of the input trees. We introduce the concept of "autumn trees", a nice framework for the formulation of algorithms based on the mathematics of "maximum acyclic agreement forests". While the main computational problem is hard, the run-time depends mainly on how different the given input trees are. In biological studies, where the trees are reasonably similar, our parallel implementation performs well in practice. The algorithm is available in our open source program Dendroscope 3, providing a platform for biologists to explore rooted phylogenetic networks. We demonstrate the utility of the algorithm using several previously studied data sets.

Maze learning by a hybrid brain-computer system.

Science.gov (United States)

Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

2016-09-13

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Maze learning by a hybrid brain-computer system

Science.gov (United States)

Wu, Zhaohui; Zheng, Nenggan; Zhang, Shaowu; Zheng, Xiaoxiang; Gao, Liqiang; Su, Lijuan

2016-09-01

The combination of biological and artificial intelligence is particularly driven by two major strands of research: one involves the control of mechanical, usually prosthetic, devices by conscious biological subjects, whereas the other involves the control of animal behaviour by stimulating nervous systems electrically or optically. However, to our knowledge, no study has demonstrated that spatial learning in a computer-based system can affect the learning and decision making behaviour of the biological component, namely a rat, when these two types of intelligence are wired together to form a new intelligent entity. Here, we show how rule operations conducted by computing components contribute to a novel hybrid brain-computer system, i.e., ratbots, exhibit superior learning abilities in a maze learning task, even when their vision and whisker sensation were blocked. We anticipate that our study will encourage other researchers to investigate combinations of various rule operations and other artificial intelligence algorithms with the learning and memory processes of organic brains to develop more powerful cyborg intelligence systems. Our results potentially have profound implications for a variety of applications in intelligent systems and neural rehabilitation.

Hybrid x-space: a new approach for MPI reconstruction.

Science.gov (United States)

Tateo, A; Iurino, A; Settanni, G; Andrisani, A; Stifanelli, P F; Larizza, P; Mazzia, F; Mininni, R M; Tangaro, S; Bellotti, R

2016-06-07

Magnetic particle imaging (MPI) is a new medical imaging technique capable of recovering the distribution of superparamagnetic particles from their measured induced signals. In literature there are two main MPI reconstruction techniques: measurement-based (MB) and x-space (XS). The MB method is expensive because it requires a long calibration procedure as well as a reconstruction phase that can be numerically costly. On the other side, the XS method is simpler than MB but the exact knowledge of the field free point (FFP) motion is essential for its implementation. Our simulation work focuses on the implementation of a new approach for MPI reconstruction: it is called hybrid x-space (HXS), representing a combination of the previous methods. Specifically, our approach is based on XS reconstruction because it requires the knowledge of the FFP position and velocity at each time instant. The difference with respect to the original XS formulation is how the FFP velocity is computed: we estimate it from the experimental measurements of the calibration scans, typical of the MB approach. Moreover, a compressive sensing technique is applied in order to reduce the calibration time, setting a fewer number of sampling positions. Simulations highlight that HXS and XS methods give similar results. Furthermore, an appropriate use of compressive sensing is crucial for obtaining a good balance between time reduction and reconstructed image quality. Our proposal is suitable for open geometry configurations of human size devices, where incidental factors could make the currents, the fields and the FFP trajectory irregular.

COED Transactions, Vol. X, No. 9, September 1978. Use of the Analog/Hybrid Computer in Boundary Layer and Convection Studies.

Science.gov (United States)

Mitchell, Eugene E., Ed.

In certain boundary layer or natural convection work, where a similarity transformation is valid, the equations can be reduced to a set of nonlinear ordinary differential equations. They are therefore well-suited to a fast solution on an analog/hybrid computer. This paper illustrates such usage of the analog/hybrid computer by a set of…

Fuzzy multiple linear regression: A computational approach

Science.gov (United States)

Juang, C. H.; Huang, X. H.; Fleming, J. W.

1992-01-01

This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.

Computation of hybrid static potentials in SU(3 lattice gauge theory

Directory of Open Access Journals (Sweden)

Reisinger Christian

2018-01-01

Full Text Available We compute hybrid static potentials in SU(3 lattice gauge theory. We present a method to automatically generate a large set of suitable creation operators with defined quantum numbers from elementary building blocks. We show preliminary results for several channels and discuss, which structures of the gluonic flux tube seem to be realized by the ground states in these channels.

New MPPT algorithm for PV applications based on hybrid dynamical approach

KAUST Repository

Elmetennani, Shahrazed

2016-10-24

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.

New MPPT algorithm for PV applications based on hybrid dynamical approach

KAUST Repository

Elmetennani, Shahrazed; Laleg-Kirati, Taous-Meriem; Djemai, M.; Tadjine, M.

2016-01-01

This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.

The UF family of hybrid phantoms of the developing human fetus for computational radiation dosimetry

International Nuclear Information System (INIS)

Maynard, Matthew R; Geyer, John W; Bolch, Wesley; Aris, John P; Shifrin, Roger Y

2011-01-01

Historically, the development of computational phantoms for radiation dosimetry has primarily been directed at capturing and representing adult and pediatric anatomy, with less emphasis devoted to models of the human fetus. As concern grows over possible radiation-induced cancers from medical and non-medical exposures of the pregnant female, the need to better quantify fetal radiation doses, particularly at the organ-level, also increases. Studies such as the European Union's SOLO (Epidemiological Studies of Exposed Southern Urals Populations) hope to improve our understanding of cancer risks following chronic in utero radiation exposure. For projects such as SOLO, currently available fetal anatomic models do not provide sufficient anatomical detail for organ-level dose assessment. To address this need, two fetal hybrid computational phantoms were constructed using high-quality magnetic resonance imaging and computed tomography image sets obtained for two well-preserved fetal specimens aged 11.5 and 21 weeks post-conception. Individual soft tissue organs, bone sites and outer body contours were segmented from these images using 3D-DOCTOR(TM) and then imported to the 3D modeling software package Rhinoceros(TM) for further modeling and conversion of soft tissue organs, certain bone sites and outer body contours to deformable non-uniform rational B-spline surfaces. The two specimen-specific phantoms, along with a modified version of the 38 week UF hybrid newborn phantom, comprised a set of base phantoms from which a series of hybrid computational phantoms was derived for fetal ages 8, 10, 15, 20, 25, 30, 35 and 38 weeks post-conception. The methodology used to construct the series of phantoms accounted for the following age-dependent parameters: (1) variations in skeletal size and proportion, (2) bone-dependent variations in relative levels of bone growth, (3) variations in individual organ masses and total fetal masses and (4) statistical percentile variations in

Applications integration in a hybrid cloud computing environment: modelling and platform

Science.gov (United States)

Li, Qing; Wang, Ze-yuan; Li, Wei-hua; Li, Jun; Wang, Cheng; Du, Rui-yang

2013-08-01

With the development of application services providers and cloud computing, more and more small- and medium-sized business enterprises use software services and even infrastructure services provided by professional information service companies to replace all or part of their information systems (ISs). These information service companies provide applications, such as data storage, computing processes, document sharing and even management information system services as public resources to support the business process management of their customers. However, no cloud computing service vendor can satisfy the full functional IS requirements of an enterprise. As a result, enterprises often have to simultaneously use systems distributed in different clouds and their intra enterprise ISs. Thus, this article presents a framework to integrate applications deployed in public clouds and intra ISs. A run-time platform is developed and a cross-computing environment process modelling technique is also developed to improve the feasibility of ISs under hybrid cloud computing environments.

Cloud computing-based energy optimization control framework for plug-in hybrid electric bus

International Nuclear Information System (INIS)

Yang, Chao; Li, Liang; You, Sixiong; Yan, Bingjie; Du, Xian

2017-01-01

Considering the complicated characteristics of traffic flow in city bus route and the nonlinear vehicle dynamics, optimal energy management integrated with clustering and recognition of driving conditions in plug-in hybrid electric bus is still a challenging problem. Motivated by this issue, this paper presents an innovative energy optimization control framework based on the cloud computing for plug-in hybrid electric bus. This framework, which includes offline part and online part, can realize the driving conditions clustering in offline part, and the energy management in online part. In offline part, utilizing the operating data transferred from a bus to the remote monitoring center, K-means algorithm is adopted to cluster the driving conditions, and then Markov probability transfer matrixes are generated to predict the possible operating demand of the bus driver. Next in online part, the current driving condition is real-time identified by a well-trained support vector machine, and Markov chains-based driving behaviors are accordingly selected. With the stochastic inputs, stochastic receding horizon control method is adopted to obtain the optimized energy management of hybrid powertrain. Simulations and hardware-in-loop test are carried out with the real-world city bus route, and the results show that the presented strategy could greatly improve the vehicle fuel economy, and as the traffic flow data feedback increases, the fuel consumption of every plug-in hybrid electric bus running in a specific bus route tends to be a stable minimum. - Highlights: • Cloud computing-based energy optimization control framework is proposed. • Driving cycles are clustered into 6 types by K-means algorithm. • Support vector machine is employed to realize the online recognition of driving condition. • Stochastic receding horizon control-based energy management strategy is designed for plug-in hybrid electric bus. • The proposed framework is verified by simulation and hard

Numerical schemes for the hybrid modeling approach of gas-particle turbulent flows

International Nuclear Information System (INIS)

Dorogan, K.

2012-01-01

Hybrid Moments/PDF methods have shown to be well suitable for the description of poly-dispersed turbulent two-phase flows in non-equilibrium which are encountered in some industrial situations involving chemical reactions, combustion or sprays. They allow to obtain a fine enough physical description of the poly-dispersity, non-linear source terms and convection phenomena. However, their approximations are noised with the statistical error, which in several situations may be a source of a bias. An alternative hybrid Moments-Moments/PDF approach examined in this work consists in coupling the Moments and the PDF descriptions, within the description of the dispersed phase itself. This hybrid method could reduce the statistical error and remove the bias. However, such a coupling is not straightforward in practice and requires the development of accurate and stable numerical schemes. The approaches introduced in this work rely on the combined use of the up-winding and relaxation-type techniques. They allow to obtain stable unsteady approximations for a system of partial differential equations containing non-smooth external data which are provided by the PDF part of the model. A comparison of the results obtained using the present method with those of the 'classical' hybrid approach is presented in terms of the numerical errors for a case of a co-current gas-particle wall jet. (author)

A novel hybridization approach for detection of citrus viroids.

Science.gov (United States)

Murcia, N; Serra, P; Olmos, A; Duran-Vila, N

2009-04-01

Citrus plants are natural hosts of several viroid species all belonging to the family Pospiviroidae. Previous attempts to detect viroids from field-grown species and cultivars yielded erratic results unless analyses were performed using Etrog citron a secondary bio-amplification host. To overcome the use of Etrog citron a number of RT-PCR approaches have been proposed with different degrees of success. Here we report the suitability of an easy to handle northern hybridization protocol for viroid detection of samples collected from field-grown citrus species and cultivars. The protocol involves: (i) Nucleic acid preparations from bark tissue samples collected from field-grown trees regardless of the growing season and storage conditions; (ii) Separation in 5% PAGE or 1% agarose, blotting to membrane and fixing; (iii) Hybridization with viroid-specific DIG-labelled probes and detection with anti-DIG-alkaline phosphatase conjugate and autoradiography with the CSPD substrate. The method has been tested with viroid-infected trees of sweet orange, lemon, mandarin, grapefruit, sour orange, Swingle citrumello, Tahiti lime and Mexican lime. This novel hybridization approach is extremely sensitive, easy to handle and shortens the time needed for reliable viroid indexing tests. The suitability of PCR generated DIG-labelled probes and the sensitivity achieved when the samples are separated and blotted from non-denaturing gels are discussed.

Hybrid approaches to nanometer-scale patterning: Exploiting tailored intermolecular interactions

International Nuclear Information System (INIS)

Mullen, Thomas J.; Srinivasan, Charan; Shuster, Mitchell J.; Horn, Mark W.; Andrews, Anne M.; Weiss, Paul S.

2008-01-01

In this perspective, we explore hybrid approaches to nanometer-scale patterning, where the precision of molecular self-assembly is combined with the sophistication and fidelity of lithography. Two areas - improving existing lithographic techniques through self-assembly and fabricating chemically patterned surfaces - will be discussed in terms of their advantages, limitations, applications, and future outlook. The creation of such chemical patterns enables new capabilities, including the assembly of biospecific surfaces to be recognized by, and to capture analytes from, complex mixtures. Finally, we speculate on the potential impact and upcoming challenges of these hybrid strategies.

Iterative Multiuser Equalization for Subconnected Hybrid mmWave Massive MIMO Architecture

Directory of Open Access Journals (Sweden)

R. Magueta

2017-01-01

Full Text Available Millimeter waves and massive MIMO are a promising combination to achieve the multi-Gb/s required by future 5G wireless systems. However, fully digital architectures are not feasible due to hardware limitations, which means that there is a need to design signal processing techniques for hybrid analog-digital architectures. In this manuscript, we propose a hybrid iterative block multiuser equalizer for subconnected millimeter wave massive MIMO systems. The low complexity user-terminals employ pure-analog random precoders, each with a single RF chain. For the base station, a subconnected hybrid analog-digital equalizer is designed to remove multiuser interference. The hybrid equalizer is optimized using the average bit-error-rate as a metric. Due to the coupling between the RF chains in the optimization problem, the computation of the optimal solutions is too complex. To address this problem, we compute the analog part of the equalizer sequentially over the RF chains using a dictionary built from the array response vectors. The proposed subconnected hybrid iterative multiuser equalizer is compared with a recently proposed fully connected approach. The results show that the performance of the proposed scheme is close to the fully connected hybrid approach counterpart after just a few iterations.

MOBILE CLOUD COMPUTING APPLIED TO HEALTHCARE APPROACH

OpenAIRE

Omar AlSheikSalem

2016-01-01

In the past few years it was clear that mobile cloud computing was established via integrating both mobile computing and cloud computing to be add in both storage space and processing speed. Integrating healthcare applications and services is one of the vast data approaches that can be adapted to mobile cloud computing. This work proposes a framework of a global healthcare computing based combining both mobile computing and cloud computing. This approach leads to integrate all of ...

Modelling of data uncertainties on hybrid computers

Energy Technology Data Exchange (ETDEWEB)

Schneider, Anke (ed.)

2016-06-15

The codes d{sup 3}f and r{sup 3}t are well established for modelling density-driven flow and nuclide transport in the far field of repositories for hazardous material in deep geological formations. They are applicable in porous media as well as in fractured rock or mudstone, for modelling salt- and heat transport as well as a free groundwater surface. Development of the basic framework of d{sup 3}f and r{sup 3}t had begun more than 20 years ago. Since that time significant advancements took place in the requirements for safety assessment as well as for computer hardware development. The period of safety assessment for a repository of high-level radioactive waste was extended to 1 million years, and the complexity of the models is steadily growing. Concurrently, the demands on accuracy increase. Additionally, model and parameter uncertainties become more and more important for an increased understanding of prediction reliability. All this leads to a growing demand for computational power that requires a considerable software speed-up. An effective way to achieve this is the use of modern, hybrid computer architectures which requires basically the set-up of new data structures and a corresponding code revision but offers a potential speed-up by several orders of magnitude. The original codes d{sup 3}f and r{sup 3}t were applications of the software platform UG /BAS 94/ whose development had begun in the early nineteennineties. However, UG had recently been advanced to the C++ based, substantially revised version UG4 /VOG 13/. To benefit also in the future from state-of-the-art numerical algorithms and to use hybrid computer architectures, the codes d{sup 3}f and r{sup 3}t were transferred to this new code platform. Making use of the fact that coupling between different sets of equations is natively supported in UG4, d{sup 3}f and r{sup 3}t were combined to one conjoint code d{sup 3}f++. A direct estimation of uncertainties for complex groundwater flow models with the

Computing the binding affinity of a ligand buried deep inside a protein with the hybrid steered molecular dynamics

International Nuclear Information System (INIS)

Villarreal, Oscar D.; Yu, Lili; Rodriguez, Roberto A.; Chen, Liao Y.

2017-01-01

Computing the ligand-protein binding affinity (or the Gibbs free energy) with chemical accuracy has long been a challenge for which many methods/approaches have been developed and refined with various successful applications. False positives and, even more harmful, false negatives have been and still are a common occurrence in practical applications. Inevitable in all approaches are the errors in the force field parameters we obtain from quantum mechanical computation and/or empirical fittings for the intra- and inter-molecular interactions. These errors propagate to the final results of the computed binding affinities even if we were able to perfectly implement the statistical mechanics of all the processes relevant to a given problem. And they are actually amplified to various degrees even in the mature, sophisticated computational approaches. In particular, the free energy perturbation (alchemical) approaches amplify the errors in the force field parameters because they rely on extracting the small differences between similarly large numbers. In this paper, we develop a hybrid steered molecular dynamics (hSMD) approach to the difficult binding problems of a ligand buried deep inside a protein. Sampling the transition along a physical (not alchemical) dissociation path of opening up the binding cavity- -pulling out the ligand- -closing back the cavity, we can avoid the problem of error amplifications by not relying on small differences between similar numbers. We tested this new form of hSMD on retinol inside cellular retinol-binding protein 1 and three cases of a ligand (a benzylacetate, a 2-nitrothiophene, and a benzene) inside a T4 lysozyme L99A/M102Q(H) double mutant. In all cases, we obtained binding free energies in close agreement with the experimentally measured values. This indicates that the force field parameters we employed are accurate and that hSMD (a brute force, unsophisticated approach) is free from the problem of error amplification suffered by

Hybrid magic state distillation for universal fault-tolerant quantum computation

OpenAIRE

Zheng, Wenqiang; Yu, Yafei; Pan, Jian; Zhang, Jingfu; Li, Jun; Li, Zhaokai; Suter, Dieter; Zhou, Xianyi; Peng, Xinhua; Du, Jiangfeng

2014-01-01

A set of stabilizer operations augmented by some special initial states known as 'magic states', gives the possibility of universal fault-tolerant quantum computation. However, magic state preparation inevitably involves nonideal operations that introduce noise. The most common method to eliminate the noise is magic state distillation (MSD) by stabilizer operations. Here we propose a hybrid MSD protocol by connecting a four-qubit H-type MSD with a five-qubit T-type MSD, in order to overcome s...

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

Energy Technology Data Exchange (ETDEWEB)

Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

2011-02-15

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

International Nuclear Information System (INIS)

Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.

2011-01-01

In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

Advanced control approach for hybrid systems based on solid oxide fuel cells

International Nuclear Information System (INIS)

Ferrari, Mario L.

2015-01-01

Highlights: • Advanced new control system for SOFC based hybrid plants. • Proportional–Integral approach with feed-forward technology. • Good control of fuel cell temperature. • All critical properties maintained inside safe conditions. - Abstract: This paper shows a new advanced control approach for operations in hybrid systems equipped with solid oxide fuel cell technology. This new tool, which combines feed-forward and standard proportional–integral techniques, controls the system during load changes avoiding failures and stress conditions detrimental to component life. This approach was selected to combine simplicity and good control performance. Moreover, the new approach presented in this paper eliminates the need for mass flow rate meters and other expensive probes, as usually required for a commercial plant. Compared to previous works, better performance is achieved in controlling fuel cell temperature (maximum gradient significantly lower than 3 K/min), reducing the pressure gap between cathode and anode sides (at least a 30% decrease during transient operations), and generating a higher safe margin (at least a 10% increase) for the Steam-to-Carbon Ratio. This new control system was developed and optimized using a hybrid system transient model implemented, validated and tested within previous works. The plant, comprising the coupling of a tubular solid oxide fuel cell stack with a microturbine, is equipped with a bypass valve able to connect the compressor outlet with the turbine inlet duct for rotational speed control. Following model development and tuning activities, several operative conditions were considered to show the new control system increased performance compared to previous tools (the same hybrid system model was used with the new control approach). Special attention was devoted to electrical load steps and ramps considering significant changes in ambient conditions

Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Jui-Yu Wu

2013-01-01

Full Text Available Stochastic global optimization (SGO algorithms such as the particle swarm optimization (PSO approach have become popular for solving unconstrained global optimization (UGO problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO method and an artificial immune algorithm-based PSO (AIA-PSO method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review

Science.gov (United States)

Fahimi, Farzad; Yaseen, Zaher Mundher; El-shafie, Ahmed

2017-05-01

Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.

Hybrid approach to structure modeling of the histamine H3 receptor: Multi-level assessment as a tool for model verification.

Directory of Open Access Journals (Sweden)

Jakub Jończyk

Full Text Available The crucial role of G-protein coupled receptors and the significant achievements associated with a better understanding of the spatial structure of known receptors in this family encouraged us to undertake a study on the histamine H3 receptor, whose crystal structure is still unresolved. The latest literature data and availability of different software enabled us to build homology models of higher accuracy than previously published ones. The new models are expected to be closer to crystal structures; and therefore, they are much more helpful in the design of potential ligands. In this article, we describe the generation of homology models with the use of diverse tools and a hybrid assessment. Our study incorporates a hybrid assessment connecting knowledge-based scoring algorithms with a two-step ligand-based docking procedure. Knowledge-based scoring employs probability theory for global energy minimum determination based on information about native amino acid conformation from a dataset of experimentally determined protein structures. For a two-step docking procedure two programs were applied: GOLD was used in the first step and Glide in the second. Hybrid approaches offer advantages by combining various theoretical methods in one modeling algorithm. The biggest advantage of hybrid methods is their intrinsic ability to self-update and self-refine when additional structural data are acquired. Moreover, the diversity of computational methods and structural data used in hybrid approaches for structure prediction limit inaccuracies resulting from theoretical approximations or fuzziness of experimental data. The results of docking to the new H3 receptor model allowed us to analyze ligand-receptor interactions for reference compounds.

Speed-up of ab initio hybrid Monte Carlo and ab initio path integral hybrid Monte Carlo simulations by using an auxiliary potential energy surface

International Nuclear Information System (INIS)

Nakayama, Akira; Taketsugu, Tetsuya; Shiga, Motoyuki

2009-01-01

Efficiency of the ab initio hybrid Monte Carlo and ab initio path integral hybrid Monte Carlo methods is enhanced by employing an auxiliary potential energy surface that is used to update the system configuration via molecular dynamics scheme. As a simple illustration of this method, a dual-level approach is introduced where potential energy gradients are evaluated by computationally less expensive ab initio electronic structure methods. (author)

A Hybrid Heuristic Optimization Approach for Leak Detection in Pipe Networks Using Ordinal Optimization Approach and the Symbiotic Organism Search

Directory of Open Access Journals (Sweden)

Chao-Chih Lin

2017-10-01

Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.

The UF family of hybrid phantoms of the developing human fetus for computational radiation dosimetry

Energy Technology Data Exchange (ETDEWEB)

Maynard, Matthew R; Geyer, John W; Bolch, Wesley [Department of Nuclear and Radiological Engineering, University of Florida, Gainesville, FL (United States); Aris, John P [Department of Anatomy and Cell Biology, University of Florida, Gainesville, FL (United States); Shifrin, Roger Y, E-mail: wbolch@ufl.edu [Department of Radiology, University of Florida, Gainesville, FL (United States)

2011-08-07

Historically, the development of computational phantoms for radiation dosimetry has primarily been directed at capturing and representing adult and pediatric anatomy, with less emphasis devoted to models of the human fetus. As concern grows over possible radiation-induced cancers from medical and non-medical exposures of the pregnant female, the need to better quantify fetal radiation doses, particularly at the organ-level, also increases. Studies such as the European Union's SOLO (Epidemiological Studies of Exposed Southern Urals Populations) hope to improve our understanding of cancer risks following chronic in utero radiation exposure. For projects such as SOLO, currently available fetal anatomic models do not provide sufficient anatomical detail for organ-level dose assessment. To address this need, two fetal hybrid computational phantoms were constructed using high-quality magnetic resonance imaging and computed tomography image sets obtained for two well-preserved fetal specimens aged 11.5 and 21 weeks post-conception. Individual soft tissue organs, bone sites and outer body contours were segmented from these images using 3D-DOCTOR(TM) and then imported to the 3D modeling software package Rhinoceros(TM) for further modeling and conversion of soft tissue organs, certain bone sites and outer body contours to deformable non-uniform rational B-spline surfaces. The two specimen-specific phantoms, along with a modified version of the 38 week UF hybrid newborn phantom, comprised a set of base phantoms from which a series of hybrid computational phantoms was derived for fetal ages 8, 10, 15, 20, 25, 30, 35 and 38 weeks post-conception. The methodology used to construct the series of phantoms accounted for the following age-dependent parameters: (1) variations in skeletal size and proportion, (2) bone-dependent variations in relative levels of bone growth, (3) variations in individual organ masses and total fetal masses and (4) statistical percentile variations

Mass Optimization of Battery/Supercapacitors Hybrid Systems Based on a Linear Programming Approach

Science.gov (United States)

Fleury, Benoit; Labbe, Julien

2014-08-01

The objective of this paper is to show that, on a specific launcher-type mission profile, a 40% gain of mass is expected using a battery/supercapacitors active hybridization instead of a single battery solution. This result is based on the use of a linear programming optimization approach to perform the mass optimization of the hybrid power supply solution.

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

OpenAIRE

Hong, Keum-Shik; Khan, Muhammad Jawad

2017-01-01

In this article, non-invasive hybrid brain–computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near infrared spec...

The UF family of reference hybrid phantoms for computational radiation dosimetry

International Nuclear Information System (INIS)

Lee, Choonsik; Lodwick, Daniel; Hurtado, Jorge; Pafundi, Deanna; Williams, Jonathan L; Bolch, Wesley E

2010-01-01

Computational human phantoms are computer models used to obtain dose distributions within the human body exposed to internal or external radiation sources. In addition, they are increasingly used to develop detector efficiencies for in vivo whole-body counters. Two classes of computational human phantoms have been widely utilized for dosimetry calculation: stylized and voxel phantoms that describe human anatomy through mathematical surface equations and 3D voxel matrices, respectively. Stylized phantoms are flexible in that changes to organ position and shape are possible given avoidance of region overlap, while voxel phantoms are typically fixed to a given patient anatomy, yet can be proportionally scaled to match individuals of larger or smaller stature, but of equivalent organ anatomy. Voxel phantoms provide much better anatomical realism as compared to stylized phantoms which are intrinsically limited by mathematical surface equations. To address the drawbacks of these phantoms, hybrid phantoms based on non-uniform rational B-spline (NURBS) surfaces have been introduced wherein anthropomorphic flexibility and anatomic realism are both preserved. Researchers at the University of Florida have introduced a series of hybrid phantoms representing the ICRP Publication 89 reference newborn, 15 year, and adult male and female. In this study, six additional phantoms are added to the UF family of hybrid phantoms-those of the reference 1 year, 5 year and 10 year child. Head and torso CT images of patients whose ages were close to the targeted ages were obtained under approved protocols. Major organs and tissues were segmented from these images using an image processing software, 3D-DOCTOR(TM). NURBS and polygon mesh surfaces were then used to model individual organs and tissues after importing the segmented organ models to the 3D NURBS modeling software, Rhinoceros(TM). The phantoms were matched to four reference datasets: (1) standard anthropometric data, (2) reference

Hybrid Lanczos-type product methods

Energy Technology Data Exchange (ETDEWEB)

Ressel, K.J. [Swiss Center for Scientific Computing, Zuerich (Switzerland)

1996-12-31

A general framework is proposed to construct hybrid iterative methods for the solution of large nonsymmetric systems of linear equations. This framework is based on Lanczos-type product methods, whose iteration polynomial consists of the Lanczos polynomial multiplied by some other arbitrary, {open_quotes}shadow{close_quotes} polynomial. By using for the shadow polynomial Chebyshev (more general Faber) polynomials or L{sup 2}-optimal polynomials, hybrid (Chebyshev-like) methods are incorporated into Lanczos-type product methods. In addition, to acquire spectral information on the system matrix, which is required for such a choice of shadow polynomials, the Lanczos-process can be employed either directly or in an QMR-like approach. The QMR like approach allows the cheap computation of the roots of the B-orthogonal polynomials and the residual polynomials associated with the QMR iteration. These roots can be used as a good approximation for the spectrum of the system matrix. Different choices for the shadow polynomials and their construction are analyzed. The resulting hybrid methods are compared with standard Lanczos-type product methods, like BiOStab, BiOStab({ell}) and BiOS.

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Directory of Open Access Journals (Sweden)

Laszlo Papp

2018-06-01

Full Text Available Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual- or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic, also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid

All-Particle Multiscale Computation of Hypersonic Rarefied Flow

Science.gov (United States)

Jun, E.; Burt, J. M.; Boyd, I. D.

2011-05-01

This study examines a new hybrid particle scheme used as an alternative means of multiscale flow simulation. The hybrid particle scheme employs the direct simulation Monte Carlo (DSMC) method in rarefied flow regions and the low diffusion (LD) particle method in continuum flow regions. The numerical procedures of the low diffusion particle method are implemented within an existing DSMC algorithm. The performance of the LD-DSMC approach is assessed by studying Mach 10 nitrogen flow over a sphere with a global Knudsen number of 0.002. The hybrid scheme results show good overall agreement with results from standard DSMC and CFD computation. Subcell procedures are utilized to improve computational efficiency and reduce sensitivity to DSMC cell size in the hybrid scheme. This makes it possible to perform the LD-DSMC simulation on a much coarser mesh that leads to a significant reduction in computation time.

Multilevel hybrid Chernoff tau-leap

KAUST Repository

Moraes, Alvaro

2015-04-08

In this work, we extend the hybrid Chernoff tau-leap method to the multilevel Monte Carlo (MLMC) setting. Inspired by the work of Anderson and Higham on the tau-leap MLMC method with uniform time steps, we develop a novel algorithm that is able to couple two hybrid Chernoff tau-leap paths at different levels. Using dual-weighted residual expansion techniques, we also develop a new way to estimate the variance of the difference of two consecutive levels and the bias. This is crucial because the computational work required to stabilize the coefficient of variation of the sample estimators of both quantities is often unaffordable for the deepest levels of the MLMC hierarchy. Our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This is achieved with nearly optimal computational work. Indeed, the computational complexity of our method is of order O(TOL−2), the same as with an exact method, but with a smaller constant. Our numerical examples show substantial gains with respect to the previous single-level approach and the Stochastic Simulation Algorithm.

Multilevel hybrid Chernoff tau-leap

KAUST Repository

Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2015-01-01

In this work, we extend the hybrid Chernoff tau-leap method to the multilevel Monte Carlo (MLMC) setting. Inspired by the work of Anderson and Higham on the tau-leap MLMC method with uniform time steps, we develop a novel algorithm that is able to couple two hybrid Chernoff tau-leap paths at different levels. Using dual-weighted residual expansion techniques, we also develop a new way to estimate the variance of the difference of two consecutive levels and the bias. This is crucial because the computational work required to stabilize the coefficient of variation of the sample estimators of both quantities is often unaffordable for the deepest levels of the MLMC hierarchy. Our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This is achieved with nearly optimal computational work. Indeed, the computational complexity of our method is of order O(TOL−2), the same as with an exact method, but with a smaller constant. Our numerical examples show substantial gains with respect to the previous single-level approach and the Stochastic Simulation Algorithm.

Hybrid generative-discriminative approach to age-invariant face recognition

Science.gov (United States)

Sajid, Muhammad; Shafique, Tamoor

2018-03-01

Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

What is computation : An epistemic approach

NARCIS (Netherlands)

Wiedermann, Jiří; van Leeuwen, Jan

2015-01-01

Traditionally, computations are seen as processes that transform information. Definitions of computation subsequently concentrate on a description of the mechanisms that lead to such processes. The bottleneck of this approach is twofold. First, it leads to a definition of computation that is too

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

Energy Technology Data Exchange (ETDEWEB)

Sreepathi, Sarat [ORNL; Kumar, Jitendra [ORNL; Mills, Richard T. [Argonne National Laboratory; Hoffman, Forrest M. [ORNL; Sripathi, Vamsi [Intel Corporation; Hargrove, William Walter [United States Department of Agriculture (USDA), United States Forest Service (USFS)

2017-09-01

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like the Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.

A hybrid computer simulation of reactor spatial dynamics

International Nuclear Information System (INIS)

Hinds, H.W.

1977-08-01

The partial differential equations describing the one-speed spatial dynamics of thermal neutron reactors were converted to a set of ordinary differential equations, using finite-difference approximations for the spatial derivatives. The variables were then normalized to a steady-state reference condition in a novel manner, to yield an equation set particularly suitable for implementation on a hybrid computer. One Applied Dynamics AD/FIVE analog-computer console is capable of solving, all in parallel, up to 30 simultaneous differential equations. This corresponds roughly to eight reactor nodes, each with two active delayed-neutron groups. To improve accuracy, an increase in the number of nodes is usually required. Using the Hsu-Howe multiplexing technique, an 8-node, one-dimensional module was switched back and forth between the left and right halves of the reactor, to simulate a 16-node model, also in one dimension. These two versions (8 or 16 nodes) of the model were tested on benchmark problems of the loss-of-coolant type, which were also solved using the digital code FORSIM, with two energy groups and 26 nodes. Good agreement was obtained between the two solution techniques. (author)

A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles

DEFF Research Database (Denmark)

Sousa, Tiago; Vale, Zita; Carvalho, Joao Paulo

2014-01-01

The massification of electric vehicles (EVs) can have a significant impact on the power system, requiring a new approach for the energy resource management. The energy resource management has the objective to obtain the optimal scheduling of the available resources considering distributed...... to determine the best solution in a reasonable amount of time. This paper presents a hybrid artificial intelligence technique to solve a complex energy resource management problem with a large number of resources, including EVs, connected to the electric network. The hybrid approach combines simulated...... annealing (SA) and ant colony optimization (ACO) techniques. The case study concerns different EVs penetration levels. Comparisons with a previous SA approach and a deterministic technique are also presented. For 2000 EVs scenario, the proposed hybrid approach found a solution better than the previous SA...

A novel Monte Carlo approach to hybrid local volatility models

NARCIS (Netherlands)

A.W. van der Stoep (Anton); L.A. Grzelak (Lech Aleksander); C.W. Oosterlee (Cornelis)

2017-01-01

textabstractWe present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant.

Cognitive Approaches for Medicine in Cloud Computing.

Science.gov (United States)

Ogiela, Urszula; Takizawa, Makoto; Ogiela, Lidia

2018-03-03

This paper will present the application potential of the cognitive approach to data interpretation, with special reference to medical areas. The possibilities of using the meaning approach to data description and analysis will be proposed for data analysis tasks in Cloud Computing. The methods of cognitive data management in Cloud Computing are aimed to support the processes of protecting data against unauthorised takeover and they serve to enhance the data management processes. The accomplishment of the proposed tasks will be the definition of algorithms for the execution of meaning data interpretation processes in safe Cloud Computing. • We proposed a cognitive methods for data description. • Proposed a techniques for secure data in Cloud Computing. • Application of cognitive approaches for medicine was described.

Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem.

Science.gov (United States)

Hyde, Damon; Schulz, Ralf; Brooks, Dana; Miller, Eric; Ntziachristos, Vasilis

2009-04-01

Hybrid imaging systems combining x-ray computed tomography (CT) and fluorescence tomography can improve fluorescence imaging performance by incorporating anatomical x-ray CT information into the optical inversion problem. While the use of image priors has been investigated in the past, little is known about the optimal use of forward photon propagation models in hybrid optical systems. In this paper, we explore the impact on reconstruction accuracy of the use of propagation models of varying complexity, specifically in the context of these hybrid imaging systems where significant structural information is known a priori. Our results demonstrate that the use of generically known parameters provides near optimal performance, even when parameter mismatch remains.

A hybrid computational strategy to address WGS variant analysis in >5000 samples.

Science.gov (United States)

Huang, Zhuoyi; Rustagi, Navin; Veeraraghavan, Narayanan; Carroll, Andrew; Gibbs, Richard; Boerwinkle, Eric; Venkata, Manjunath Gorentla; Yu, Fuli

2016-09-10

The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies. We present a high throughput framework including multiple variant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastructure consisting of cloud AWS, supercomputers and local high performance computing infrastructures. We present a novel binning approach for large scale joint variant calling and imputation which can scale up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity. As a proof of principle, we present results of analysis on Cohorts for Heart And Aging Research in Genomic Epidemiology (CHARGE) WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples was produced in under 6 weeks using four state-of-the-art callers. The callers used were SNPTools, GATK-HaplotypeCaller, GATK-UnifiedGenotyper and GotCloud. We used Amazon AWS, a 4000-core in-house cluster at Baylor College of Medicine, IBM power PC Blue BioU at Rice and Rhea at Oak Ridge National Laboratory (ORNL) for the computation. AWS was used for joint calling of 180 TB of BAM files, and ORNL and Rice supercomputers were used for the imputation and phasing step. All other steps were carried out on the local compute cluster. The entire operation used 5.2 million core hours and only transferred a total of 6 TB of data across the platforms. Even with increasing sizes of whole genome datasets, ensemble joint calling of SNVs for low

A diagnostic expert system for NPP based on hybrid knowledge approach

International Nuclear Information System (INIS)

Yang, Joon On; Chang, Soon Heung

1989-01-01

This paper describes a diagnostic expert system, HYPOSS (Hybrid Knowledge Based Plant Operation Supporting System), which has been developed to support operators' decision making during the transients of nuclear power plant. HYPOSS adopts the hybrid knowledge approach which combines shallow and deep knowledge to couple the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure: structural, functional, behavioral and heuristic knowledge. The structural and functional knowledge is represented by three fundamental primitives and five types of functions respectively. The behavioral knowledge is represented using constraints. The inference procedure is based on the human problem solving behavior modeled in HYPOSS. For the validation of HYPOSS, several tests have been performed based on the data produced by a plant simulator. The results of validation studies showed a good applicability of HYPOSS to the anomaly diagnosis of nuclear power plant

Computing platforms for software-defined radio

CERN Document Server

Nurmi, Jari; Isoaho, Jouni; Garzia, Fabio

2017-01-01

This book addresses Software-Defined Radio (SDR) baseband processing from the computer architecture point of view, providing a detailed exploration of different computing platforms by classifying different approaches, highlighting the common features related to SDR requirements and by showing pros and cons of the proposed solutions. Coverage includes architectures exploiting parallelism by extending single-processor environment (such as VLIW, SIMD, TTA approaches), multi-core platforms distributing the computation to either a homogeneous array or a set of specialized heterogeneous processors, and architectures exploiting fine-grained, coarse-grained, or hybrid reconfigurability. Describes a computer engineering approach to SDR baseband processing hardware; Discusses implementation of numerous compute-intensive signal processing algorithms on single and multicore platforms; Enables deep understanding of optimization techniques related to power and energy consumption of multicore platforms using several basic a...

A new hybrid BCI paradigm based on P300 and SSVEP.

Science.gov (United States)

Wang, Minjue; Daly, Ian; Allison, Brendan Z; Jin, Jing; Zhang, Yu; Chen, Lanlan; Wang, Xingyu

2015-04-15

P300 and steady-state visual evoked potential (SSVEP) approaches have been widely used for brain-computer interface (BCI) systems. However, neither of these approaches can work for all subjects. Some groups have reported that a hybrid BCI that combines two or more approaches might provide BCI functionality to more users. Hybrid P300/SSVEP BCIs have only recently been developed and validated, and very few avenues to improve performance have been explored. The present study compares an established hybrid P300/SSVEP BCIs paradigm to a new paradigm in which shape changing, instead of color changing, is adopted for P300 evocation to decrease the degradation on SSVEP strength. The result shows that the new hybrid paradigm presented in this paper yields much better performance than the normal hybrid paradigm. A performance increase of nearly 20% in SSVEP classification is achieved using the new hybrid paradigm in comparison with the normal hybrid paradigm. All the paradigms except the normal hybrid paradigm used in this paper obtain 100% accuracy in P300 classification. The new hybrid P300/SSVEP BCIs paradigm in which shape changing, instead of color changing, could obtain as high classification accuracy of SSVEP as the traditional SSVEP paradigm and could obtain as high classification accuracy of P300 as the traditional P300 paradigm. P300 did not interfere with the SSVEP response using the new hybrid paradigm presented in this paper, which was superior to the normal hybrid P300/SSVEP paradigm. Copyright © 2014 Elsevier B.V. All rights reserved.

Parallel Computing Characteristics of CUPID code under MPI and Hybrid environment

Energy Technology Data Exchange (ETDEWEB)

Lee, Jae Ryong; Yoon, Han Young [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Jeon, Byoung Jin; Choi, Hyoung Gwon [Seoul National Univ. of Science and Technology, Seoul (Korea, Republic of)

2014-05-15

In this paper, a characteristic of parallel algorithm is presented for solving an elliptic type equation of CUPID via domain decomposition method using the MPI and the parallel performance is estimated in terms of a scalability which shows the speedup ratio. In addition, the time-consuming pattern of major subroutines is studied. Two different grid systems are taken into account: 40,000 meshes for coarse system and 320,000 meshes for fine system. Since the matrix of the CUPID code differs according to whether the flow is single-phase or two-phase, the effect of matrix shape is evaluated. Finally, the effect of the preconditioner for matrix solver is also investigated. Finally, the hybrid (OpenMP+MPI) parallel algorithm is introduced and discussed in detail for solving pressure solver. Component-scale thermal-hydraulics code, CUPID has been developed for two-phase flow analysis, which adopts a three-dimensional, transient, three-field model, and parallelized to fulfill a recent demand for long-transient and highly resolved multi-phase flow behavior. In this study, the parallel performance of the CUPID code was investigated in terms of scalability. The CUPID code was parallelized with domain decomposition method. The MPI library was adopted to communicate the information at the neighboring domain. For managing the sparse matrix effectively, the CSR storage format is used. To take into account the characteristics of the pressure matrix which turns to be asymmetric for two-phase flow, both single-phase and two-phase calculations were run. In addition, the effect of the matrix size and preconditioning was also investigated. The fine mesh calculation shows better scalability than the coarse mesh because the number of coarse mesh does not need to decompose the computational domain excessively. The fine mesh can be present good scalability when dividing geometry with considering the ratio between computation and communication time. For a given mesh, single-phase flow

Hybrid Arrays for Chemical Sensing

Science.gov (United States)

Kramer, Kirsten E.; Rose-Pehrsson, Susan L.; Johnson, Kevin J.; Minor, Christian P.

In recent years, multisensory approaches to environment monitoring for chemical detection as well as other forms of situational awareness have become increasingly popular. A hybrid sensor is a multimodal system that incorporates several sensing elements and thus produces data that are multivariate in nature and may be significantly increased in complexity compared to data provided by single-sensor systems. Though a hybrid sensor is itself an array, hybrid sensors are often organized into more complex sensing systems through an assortment of network topologies. Part of the reason for the shift to hybrid sensors is due to advancements in sensor technology and computational power available for processing larger amounts of data. There is also ample evidence to support the claim that a multivariate analytical approach is generally superior to univariate measurements because it provides additional redundant and complementary information (Hall, D. L.; Linas, J., Eds., Handbook of Multisensor Data Fusion, CRC, Boca Raton, FL, 2001). However, the benefits of a multisensory approach are not automatically achieved. Interpretation of data from hybrid arrays of sensors requires the analyst to develop an application-specific methodology to optimally fuse the disparate sources of data generated by the hybrid array into useful information characterizing the sample or environment being observed. Consequently, multivariate data analysis techniques such as those employed in the field of chemometrics have become more important in analyzing sensor array data. Depending on the nature of the acquired data, a number of chemometric algorithms may prove useful in the analysis and interpretation of data from hybrid sensor arrays. It is important to note, however, that the challenges posed by the analysis of hybrid sensor array data are not unique to the field of chemical sensing. Applications in electrical and process engineering, remote sensing, medicine, and of course, artificial

A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part I: application in the Lorenz system

Directory of Open Access Journals (Sweden)

Lili Lei

2012-05-01

Full Text Available A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU. When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS. The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU time and data storage requirements.

A hybrid stochastic hierarchy equations of motion approach to treat the low temperature dynamics of non-Markovian open quantum systems

Science.gov (United States)

Moix, Jeremy M.; Cao, Jianshu

2013-10-01

The hierarchical equations of motion technique has found widespread success as a tool to generate the numerically exact dynamics of non-Markovian open quantum systems. However, its application to low temperature environments remains a serious challenge due to the need for a deep hierarchy that arises from the Matsubara expansion of the bath correlation function. Here we present a hybrid stochastic hierarchical equation of motion (sHEOM) approach that alleviates this bottleneck and leads to a numerical cost that is nearly independent of temperature. Additionally, the sHEOM method generally converges with fewer hierarchy tiers allowing for the treatment of larger systems. Benchmark calculations are presented on the dynamics of two level systems at both high and low temperatures to demonstrate the efficacy of the approach. Then the hybrid method is used to generate the exact dynamics of systems that are nearly impossible to treat by the standard hierarchy. First, exact energy transfer rates are calculated across a broad range of temperatures revealing the deviations from the Förster rates. This is followed by computations of the entanglement dynamics in a system of two qubits at low temperature spanning the weak to strong system-bath coupling regimes.

A Hybrid Analysis Approach to Improve Financial Distress Forecasting: Empirical Evidence from Iran

Directory of Open Access Journals (Sweden)

Shakiba Khademolqorani

2015-01-01

Full Text Available Bankruptcy prediction is an important problem facing financial decision support for stakeholders of firms, including auditors, managers, shareholders, debt-holders, and potential investors, as well as academic researchers. Popular discourse on financial distress forecasting focuses on developing the discrete models to improve the prediction. The aim of this paper is to develop a novel hybrid financial distress model based on combining various statistical and machine learning methods. Then multiple attribute decision making method is exploited to choose the optimized model from the implemented ones. Proposed approaches have also been applied in Iranian companies that performed previous models and it can be consolidated with the help of the hybrid approach.

A hybrid bio-jetting approach for directly engineering living cells

International Nuclear Information System (INIS)

Kwok, Albert; Irvine, Scott; Arumuganathar, Sumathy; Jayasinghe, Suwan N; McEwan, Jean R

2008-01-01

This paper reports developments on a hybrid cell-engineering protocol coupling both bio-electrosprays and aerodynamically assisted bio-jets for process-handling living cells. The current work demonstrates the ability to couple these two cell-jetting protocols for handling a wide range of cells for deposition. The post-treated cells are assessed for their viability by way of flow cytometry, which illustrates a significant population of viable cells post-treatment in comparison to those controls. This work is the first example of coupling these two protocols for the process handling of living cells. The hybrid protocol demonstrates the achievement of stable cone jetting of a cellular suspension in the single-needle configuration which was previously unachieved with single-needle bio-electrosprays. Furthermore the living cells explored in these investigations expressed GFP, thus demonstrating the ability to couple gene therapy with this hybrid protocol. Hence, this approach could one day be explored for building biologically viable tissues incorporating a therapeutic payload for combating a range of cellular/tissue-based pathologies

Computational approaches to energy materials

CERN Document Server

Catlow, Richard; Walsh, Aron

2013-01-01

The development of materials for clean and efficient energy generation and storage is one of the most rapidly developing, multi-disciplinary areas of contemporary science, driven primarily by concerns over global warming, diminishing fossil-fuel reserves, the need for energy security, and increasing consumer demand for portable electronics. Computational methods are now an integral and indispensable part of the materials characterisation and development process.   Computational Approaches to Energy Materials presents a detailed survey of current computational techniques for the

Stock selection using a hybrid MCDM approach

Directory of Open Access Journals (Sweden)

Tea Poklepović

2014-12-01

Full Text Available The problem of selecting the right stocks to invest in is of immense interest for investors on both emerging and developed capital markets. Moreover, an investor should take into account all available data regarding stocks on the particular market. This includes fundamental and stock market indicators. The decision making process includes several stocks to invest in and more than one criterion. Therefore, the task of selecting the stocks to invest in can be viewed as a multiple criteria decision making (MCDM problem. Using several MCDM methods often leads to divergent rankings. The goal of this paper is to resolve these possible divergent results obtained from different MCDM methods using a hybrid MCDM approach based on Spearman’s rank correlation coefficient. Five MCDM methods are selected: COPRAS, linear assignment, PROMETHEE, SAW and TOPSIS. The weights for all criteria are obtained by using the AHP method. Data for this study includes information on stock returns and traded volumes from March 2012 to March 2014 for 19 stocks on the Croatian capital market. It also includes the most important fundamental and stock market indicators for selected stocks. Rankings using five selected MCDM methods in the stock selection problem yield divergent results. However, after applying the proposed approach the final hybrid rankings are obtained. The results show that the worse stocks to invest in happen to be the same when the industry is taken into consideration or when not. However, when the industry is taken into account, the best stocks to invest in are slightly different, because some industries are more profitable than the others.

A hybrid approach to device integration on a genetic analysis platform

International Nuclear Information System (INIS)

Brennan, Des; Justice, John; Aherne, Margaret; Galvin, Paul; Jary, Dorothee; Kurg, Ants; Berik, Evgeny; Macek, Milan

2012-01-01

Point-of-care (POC) systems require significant component integration to implement biochemical protocols associated with molecular diagnostic assays. Hybrid platforms where discrete components are combined in a single platform are a suitable approach to integration, where combining multiple device fabrication steps on a single substrate is not possible due to incompatible or costly fabrication steps. We integrate three devices each with a specific system functionality: (i) a silicon electro-wetting-on-dielectric (EWOD) device to move and mix sample and reagent droplets in an oil phase, (ii) a polymer microfluidic chip containing channels and reservoirs and (iii) an aqueous phase glass microarray for fluorescence microarray hybridization detection. The EWOD device offers the possibility of fully integrating on-chip sample preparation using nanolitre sample and reagent volumes. A key challenge is sample transfer from the oil phase EWOD device to the aqueous phase microarray for hybridization detection. The EWOD device, waveguide performance and functionality are maintained during the integration process. An on-chip biochemical protocol for arrayed primer extension (APEX) was implemented for single nucleotide polymorphism (SNiP) analysis. The prepared sample is aspirated from the EWOD oil phase to the aqueous phase microarray for hybridization. A bench-top instrumentation system was also developed around the integrated platform to drive the EWOD electrodes, implement APEX sample heating and image the microarray after hybridization. (paper)

Dynamical coupling of plasmons and molecular excitations by hybrid quantum/classical calculations: time-domain approach

International Nuclear Information System (INIS)

Sakko, Arto; Rossi, Tuomas P; Nieminen, Risto M

2014-01-01

The presence of plasmonic material influences the optical properties of nearby molecules in untrivial ways due to the dynamical plasmon-molecule coupling. We combine quantum and classical calculation schemes to study this phenomenon in a hybrid system that consists of a Na 2 molecule located in the gap between two Au/Ag nanoparticles. The molecule is treated quantum-mechanically with time-dependent density-functional theory, and the nanoparticles with quasistatic classical electrodynamics. The nanoparticle dimer has a plasmon resonance in the visible part of the electromagnetic spectrum, and the Na 2 molecule has an electron-hole excitation in the same energy range. Due to the dynamical interaction of the two subsystems the plasmon and the molecular excitations couple, creating a hybridized molecular-plasmon excited state. This state has unique properties that yield e.g. enhanced photoabsorption compared to the freestanding Na 2 molecule. The computational approach used enables decoupling of the mutual plasmon-molecule interaction, and our analysis verifies that it is not legitimate to neglect the backcoupling effect when describing the dynamical interaction between plasmonic material and nearby molecules. Time-resolved analysis shows nearly instantaneous formation of the coupled state, and provides an intuitive picture of the underlying physics. (paper)

Imaging performance of a hybrid x-ray computed tomography-fluorescence molecular tomography system using priors.

Science.gov (United States)

Ale, Angelique; Schulz, Ralf B; Sarantopoulos, Athanasios; Ntziachristos, Vasilis

2010-05-01

The performance is studied of two newly introduced and previously suggested methods that incorporate priors into inversion schemes associated with data from a recently developed hybrid x-ray computed tomography and fluorescence molecular tomography system, the latter based on CCD camera photon detection. The unique data set studied attains accurately registered data of high spatially sampled photon fields propagating through tissue along 360 degrees projections. Approaches that incorporate structural prior information were included in the inverse problem by adding a penalty term to the minimization function utilized for image reconstructions. Results were compared as to their performance with simulated and experimental data from a lung inflammation animal model and against the inversions achieved when not using priors. The importance of using priors over stand-alone inversions is also showcased with high spatial sampling simulated and experimental data. The approach of optimal performance in resolving fluorescent biodistribution in small animals is also discussed. Inclusion of prior information from x-ray CT data in the reconstruction of the fluorescence biodistribution leads to improved agreement between the reconstruction and validation images for both simulated and experimental data.

A Hybrid ACO Approach to the Matrix Bandwidth Minimization Problem

Science.gov (United States)

Pintea, Camelia-M.; Crişan, Gloria-Cerasela; Chira, Camelia

The evolution of the human society raises more and more difficult endeavors. For some of the real-life problems, the computing time-restriction enhances their complexity. The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous permutation of the rows and the columns of a square matrix in order to keep its nonzero entries close to the main diagonal. The MBMP is a highly investigated {NP}-complete problem, as it has broad applications in industry, logistics, artificial intelligence or information recovery. This paper describes a new attempt to use the Ant Colony Optimization framework in tackling MBMP. The introduced model is based on the hybridization of the Ant Colony System technique with new local search mechanisms. Computational experiments confirm a good performance of the proposed algorithm for the considered set of MBMP instances.

An unified approach for the design of focal-plane arrays in satellite communication

NARCIS (Netherlands)

Zamanifekri, A.; Smolders, A.B.

2012-01-01

The aim of this paper is to present a hybrid approach for designing focal plane arrays using commercially available software, considering the fact that the main trade-off in the EM simulation is the accuracy versus computational power. The presented approach is hybrid method based on FDTD/PO. The

Proposing Hybrid Architecture to Implement Cloud Computing in Higher Education Institutions Using a Meta-synthesis Appro

Directory of Open Access Journals (Sweden)

hamid reza bazi

2017-12-01

Full Text Available Cloud computing is a new technology that considerably helps Higher Education Institutions (HEIs to develop and create competitive advantage with inherent characteristics such as flexibility, scalability, accessibility, reliability, fault tolerant and economic efficiency. Due to the numerous advantages of cloud computing, and in order to take advantage of cloud computing infrastructure, services of universities and HEIs need to migrate to the cloud. However, this transition involves many challenges, one of which is lack or shortage of appropriate architecture for migration to the technology. Using a reliable architecture for migration ensures managers to mitigate risks in the cloud computing technology. Therefore, organizations always search for suitable cloud computing architecture. In previous studies, these important features have received less attention and have not been achieved in a comprehensive way. The aim of this study is to use a meta-synthesis method for the first time to analyze the previously published studies and to suggest appropriate hybrid cloud migration architecture (IUHEC. We reviewed many papers from relevant journals and conference proceedings. The concepts extracted from these papers are classified to related categories and sub-categories. Then, we developed our proposed hybrid architecture based on these concepts and categories. The proposed architecture was validated by a panel of experts and Lawshe’s model was used to determine the content validity. Due to its innovative yet user-friendly nature, comprehensiveness, and high security, this architecture can help HEIs have an effective migration to cloud computing environment.

Hybrid and hierarchical nanoreinforced polymer composites: Computational modelling of structure–properties relationships

DEFF Research Database (Denmark)

Mishnaevsky, Leon; Dai, Gaoming

2014-01-01

by using computational micromechanical models. It is shown that while glass/carbon fibers hybrid composites clearly demonstrate higher stiffness and lower weight with increasing the carbon content, they can have lower strength as compared with usual glass fiber polymer composites. Secondary...... nanoreinforcement can drastically increase the fatigue lifetime of composites. Especially, composites with the nanoplatelets localized in the fiber/matrix interface layer (fiber sizing) ensure much higher fatigue lifetime than those with the nanoplatelets in the matrix....

Computer networking a top-down approach

CERN Document Server

Kurose, James

2017-01-01

Unique among computer networking texts, the Seventh Edition of the popular Computer Networking: A Top Down Approach builds on the author’s long tradition of teaching this complex subject through a layered approach in a “top-down manner.” The text works its way from the application layer down toward the physical layer, motivating readers by exposing them to important concepts early in their study of networking. Focusing on the Internet and the fundamentally important issues of networking, this text provides an excellent foundation for readers interested in computer science and electrical engineering, without requiring extensive knowledge of programming or mathematics. The Seventh Edition has been updated to reflect the most important and exciting recent advances in networking.

Cloud computing methods and practical approaches

CERN Document Server

Mahmood, Zaigham

2013-01-01

This book presents both state-of-the-art research developments and practical guidance on approaches, technologies and frameworks for the emerging cloud paradigm. Topics and features: presents the state of the art in cloud technologies, infrastructures, and service delivery and deployment models; discusses relevant theoretical frameworks, practical approaches and suggested methodologies; offers guidance and best practices for the development of cloud-based services and infrastructures, and examines management aspects of cloud computing; reviews consumer perspectives on mobile cloud computing an

Neuro-genetic hybrid approach for the solution of non-convex economic dispatch problem

International Nuclear Information System (INIS)

Malik, T.N.; Asar, A.U.

2009-01-01

ED (Economic Dispatch) is non-convex constrained optimization problem, and is used for both on line and offline studies in power system operation. Conventionally, it is solved as convex problem using optimization techniques by approximating generator input/output characteristic. Curves of monotonically increasing nature thus resulting in an inaccurate dispatch. The GA (Genetic Algorithm) has been used for the solution of this problem owing to its inherent ability to address the convex and non-convex problems equally. This approach brings the solution to the global minimum region of search space in a short time and then takes longer time to converge to near optimal results. GA based hybrid approaches are used to fine tune the near optimal results produced by GA. This paper proposes NGH (Neuro Genetic Hybrid) approach to solve the economic dispatch with valve point effect. The proposed approach combines the GA with the ANN (Artificial Neural Network) using SI (Swarm Intelligence) learning rule. The GA acts as a global optimizer and the neural network fine tunes the GA results to the desired targets. Three machines standard test system has been tested for validation of the approach. Comparing the results with GA and NGH model based on back-propagation learning, the proposed approach gives contrast improvements showing the promise of the approach. (author)

A distributed computing model for telemetry data processing

Science.gov (United States)

Barry, Matthew R.; Scott, Kevin L.; Weismuller, Steven P.

1994-05-01

We present a new approach to distributing processed telemetry data among spacecraft flight controllers within the control centers at NASA's Johnson Space Center. This approach facilitates the development of application programs which integrate spacecraft-telemetered data and ground-based synthesized data, then distributes this information to flight controllers for analysis and decision-making. The new approach combines various distributed computing models into one hybrid distributed computing model. The model employs both client-server and peer-to-peer distributed computing models cooperating to provide users with information throughout a diverse operations environment. Specifically, it provides an attractive foundation upon which we are building critical real-time monitoring and control applications, while simultaneously lending itself to peripheral applications in playback operations, mission preparations, flight controller training, and program development and verification. We have realized the hybrid distributed computing model through an information sharing protocol. We shall describe the motivations that inspired us to create this protocol, along with a brief conceptual description of the distributed computing models it employs. We describe the protocol design in more detail, discussing many of the program design considerations and techniques we have adopted. Finally, we describe how this model is especially suitable for supporting the implementation of distributed expert system applications.

A distributed computing model for telemetry data processing

Science.gov (United States)

Barry, Matthew R.; Scott, Kevin L.; Weismuller, Steven P.

1994-01-01

We present a new approach to distributing processed telemetry data among spacecraft flight controllers within the control centers at NASA's Johnson Space Center. This approach facilitates the development of application programs which integrate spacecraft-telemetered data and ground-based synthesized data, then distributes this information to flight controllers for analysis and decision-making. The new approach combines various distributed computing models into one hybrid distributed computing model. The model employs both client-server and peer-to-peer distributed computing models cooperating to provide users with information throughout a diverse operations environment. Specifically, it provides an attractive foundation upon which we are building critical real-time monitoring and control applications, while simultaneously lending itself to peripheral applications in playback operations, mission preparations, flight controller training, and program development and verification. We have realized the hybrid distributed computing model through an information sharing protocol. We shall describe the motivations that inspired us to create this protocol, along with a brief conceptual description of the distributed computing models it employs. We describe the protocol design in more detail, discussing many of the program design considerations and techniques we have adopted. Finally, we describe how this model is especially suitable for supporting the implementation of distributed expert system applications.

PWR hybrid computer model for assessing the safety implications of control systems

International Nuclear Information System (INIS)

Smith, O.L.; Renier, J.P.; Difilippo, F.C.; Clapp, N.E.; Sozer, A.; Booth, R.S.; Craddick, W.G.; Morris, D.G.

1986-03-01

The ORNL study of safety-related aspects of nuclear power plant control systems consists of two interrelated tasks: (1) failure mode and effects analysis (FMEA) that identified single and multiple component failures that might lead to significant plant upsets and (2) computer models that used these failures as initial conditions and traced the dynamic impact on the control system and remainder of the plant. This report describes the simulation of Oconee Unit 1, the first plant analyzed. A first-principles, best-estimate model was developed and implemented on a hybrid computer consisting of AD-4 analog and PDP-10 digital machines. Controls were placed primarily on the analog to use its interactive capability to simulate operator action. 48 refs., 138 figs., 15 tabs

A programming approach to computability

CERN Document Server

Kfoury, A J; Arbib, Michael A

1982-01-01

Computability theory is at the heart of theoretical computer science. Yet, ironically, many of its basic results were discovered by mathematical logicians prior to the development of the first stored-program computer. As a result, many texts on computability theory strike today's computer science students as far removed from their concerns. To remedy this, we base our approach to computability on the language of while-programs, a lean subset of PASCAL, and postpone consideration of such classic models as Turing machines, string-rewriting systems, and p. -recursive functions till the final chapter. Moreover, we balance the presentation of un solvability results such as the unsolvability of the Halting Problem with a presentation of the positive results of modern programming methodology, including the use of proof rules, and the denotational semantics of programs. Computer science seeks to provide a scientific basis for the study of information processing, the solution of problems by algorithms, and the design ...

A Hybrid approach for aeroacoustic analysis of the engine exhaust system

OpenAIRE

Sathyanarayana, Y; Munjal, ML

2000-01-01

This paper presents a new hybrid approach for prediction of noise radiation from engine exhaust systems. It couples the time domain analysis of the engine and the frequency domain analysis of the muffler, and has the advantages of both. In this approach, cylinder/cavity is analyzed in the time domain to calculate the exhaust mass flux history at the exhaust valve by means of the method of characteristics, avoiding the tedious procedure of interpolation at every mesh point and solving a number...

Simultaneous detection of P300 and steady-state visually evoked potentials for hybrid brain-computer interface.

Science.gov (United States)

Combaz, Adrien; Van Hulle, Marc M

2015-01-01

We study the feasibility of a hybrid Brain-Computer Interface (BCI) combining simultaneous visual oddball and Steady-State Visually Evoked Potential (SSVEP) paradigms, where both types of stimuli are superimposed on a computer screen. Potentially, such a combination could result in a system being able to operate faster than a purely P300-based BCI and encode more targets than a purely SSVEP-based BCI. We analyse the interactions between the brain responses of the two paradigms, and assess the possibility to detect simultaneously the brain activity evoked by both paradigms, in a series of 3 experiments where EEG data are analysed offline. Despite differences in the shape of the P300 response between pure oddball and hybrid condition, we observe that the classification accuracy of this P300 response is not affected by the SSVEP stimulation. We do not observe either any effect of the oddball stimulation on the power of the SSVEP response in the frequency of stimulation. Finally results from the last experiment show the possibility of detecting both types of brain responses simultaneously and suggest not only the feasibility of such hybrid BCI but also a gain over pure oddball- and pure SSVEP-based BCIs in terms of communication rate.

The business case for condition-based maintenance: a hybrid (non-) financial approach

NARCIS (Netherlands)

Tiddens, W.W.; Tinga, T.; Braaksma, A.J.J.; Brouwer, O.; Cepin, Marko; Bris, Radim

2017-01-01

Although developing business cases is key for evaluating project success, the costs and benefits of condition-based maintenance (CBM) implementations are often not explicitly defined and evaluated. Using the design science methodology, we developed a hybrid business case approach to help managers

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

Science.gov (United States)

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

2017-05-18

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.

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

Directory of Open Access Journals (Sweden)

Jie Liu

2017-05-01

Full Text Available 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.

Heterotic computing: exploiting hybrid computational devices.

Science.gov (United States)

Kendon, Viv; Sebald, Angelika; Stepney, Susan

2015-07-28

Current computational theory deals almost exclusively with single models: classical, neural, analogue, quantum, etc. In practice, researchers use ad hoc combinations, realizing only recently that they can be fundamentally more powerful than the individual parts. A Theo Murphy meeting brought together theorists and practitioners of various types of computing, to engage in combining the individual strengths to produce powerful new heterotic devices. 'Heterotic computing' is defined as a combination of two or more computational systems such that they provide an advantage over either substrate used separately. This post-meeting collection of articles provides a wide-ranging survey of the state of the art in diverse computational paradigms, together with reflections on their future combination into powerful and practical applications. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

Directory of Open Access Journals (Sweden)

Fernando Sánchez Lasheras

2015-03-01

Full Text Available Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS technique with the principal component analysis (PCA, dendrograms and classification and regression trees (CARTs. Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.. Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.

Fuzzy hybrid MCDM approach for selection of wind turbine service technicians

Directory of Open Access Journals (Sweden)

Goutam Kumar Bose

2016-01-01

Full Text Available This research paper is aimed to present a fuzzy Hybrid Multi-criteria decision making (MCDM methodology for selecting employees. The present study aspires to present the hybrid approach of Fuzzy multiple MCDM techniques with tactical viewpoint to support the recruitment process of wind turbine service technicians. The methodology is based on the application of Fuzzy ARAS (Additive Ratio Assessment and Fuzzy MOORA (Multi-Objective Optimization on basis of Ratio Analysis which are integrated through group decision making (GDM method in the model for selection of wind turbine service technicians’ ranking. Here a group of experts from different fields of expertise are engaged to finalize the decision. Series of tests are conducted regarding physical fitness, technical written test, practical test along with general interview and medical examination to facilitate the final selection using the above techniques. In contrast to single decision making approaches, the proposed group decision making model efficiently supports the wind turbine service technicians ranking process. The effectiveness of the proposed approach manifest from the case study of service technicians required for the maintenance department of wind power plant using Fuzzy ARAS and Fuzzy MOORA. This set of potential technicians is evaluated based on five main criteria.

Use of 99mTc-sestamibi Single-photon Emission Computed Tomography / X-ray Computed Tomography in the Diagnosis of Hybrid Oncocytic / Chromophobe Tumor in a Pediatric Patient.

Science.gov (United States)

Almassi, Nima; Gorin, Michael A; Purysko, Andrei S; Rowe, Steven P; Kaouk, Jihad; Allaf, Mohamad E; Campbell, Steven C; Rhee, Audrey

2018-03-01

The differential diagnosis of solid renal neoplasms in adolescence includes aggressive malignancy and indolent oncocytic tumors, which are typically indistinguishable using conventional imaging. We report the use of 99m Tc-sestamibi single-photon emission computed tomography / x-ray computed tomography (SPECT/CT) in characterizing enhancing renal neoplasms in a pediatric patient. Genetic testing suggested a hereditary syndrome associated with aggressive malignancy, whereas renal mass biopsy suggested an oncocytic tumor. 99m Tc-sestamibi SPECT/CT indicated probable oncocytomas or hybrid oncocytic / chomophobe tumors. Enucleative resection was performed with final pathology demonstrating hybrid oncocytic / chomophobe tumors. This case highlights the potential utility of 99m Tc-sestamibi SPECT/CT in characterizing indeterminate enhancing renal neoplasm in pediatric patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Hybrid RANS/LES applied to complex terrain

DEFF Research Database (Denmark)

Bechmann, Andreas; Sørensen, Niels N.

2011-01-01

Large Eddy Simulation (LES) of the wind in complex terrain is limited by computational cost. The number of computational grid points required to resolve the near-ground turbulent structures (eddies) are very high. The traditional solution to the problem has been to apply a wall function...... aspect ratio in the RANS layer and thereby resolve the mean near-wall velocity profile. The method is applicable to complex terrain and the benefits of traditional LES are kept intact. Using the hybrid method, simulations of the wind over a natural complex terrain near Wellington in New Zealand...... that accounts for the whole near-wall region. Recently, a hybrid method was proposed in which the eddies close to the ground were modelled in a Reynolds-averaged sense (RANS) and the eddies above this region were simulated using LES. The advantage of the approach is the ability to use shallow cells of high...

A decision support system based on hybrid knowledge approach for nuclear power plant operation

International Nuclear Information System (INIS)

Yang, J.O.; Chang, S.H.

1991-01-01

This paper describes a diagnostic expert system, HYPOSS (Hybrid Knowledge Based Plant Operation Supporting System), which has been developed to support operators' decision making during the transients of nuclear power plant. HYPOSS adopts the hybrid knowledge approach which combines shallow and deep knowledge to couple the merits of both approaches. In HYPOSS, four types of knowledge are used according to the steps of diagnosis procedure: structural, functional, behavioral and heuristic knowledge. Frames and rules are adopted to represent the various knowledge types. Rule-based deduction and abduction are used for shallow and deep knowledge based reasoning respectively. The event-based operational guidelines are provided to the operator according to the diagnosed results

Towards scalable quantum communication and computation: Novel approaches and realizations

Science.gov (United States)

Jiang, Liang

Quantum information science involves exploration of fundamental laws of quantum mechanics for information processing tasks. This thesis presents several new approaches towards scalable quantum information processing. First, we consider a hybrid approach to scalable quantum computation, based on an optically connected network of few-qubit quantum registers. Specifically, we develop a novel scheme for scalable quantum computation that is robust against various imperfections. To justify that nitrogen-vacancy (NV) color centers in diamond can be a promising realization of the few-qubit quantum register, we show how to isolate a few proximal nuclear spins from the rest of the environment and use them for the quantum register. We also demonstrate experimentally that the nuclear spin coherence is only weakly perturbed under optical illumination, which allows us to implement quantum logical operations that use the nuclear spins to assist the repetitive-readout of the electronic spin. Using this technique, we demonstrate more than two-fold improvement in signal-to-noise ratio. Apart from direct application to enhance the sensitivity of the NV-based nano-magnetometer, this experiment represents an important step towards the realization of robust quantum information processors using electronic and nuclear spin qubits. We then study realizations of quantum repeaters for long distance quantum communication. Specifically, we develop an efficient scheme for quantum repeaters based on atomic ensembles. We use dynamic programming to optimize various quantum repeater protocols. In addition, we propose a new protocol of quantum repeater with encoding, which efficiently uses local resources (about 100 qubits) to identify and correct errors, to achieve fast one-way quantum communication over long distances. Finally, we explore quantum systems with topological order. Such systems can exhibit remarkable phenomena such as quasiparticles with anyonic statistics and have been proposed as

A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems

KAUST Repository

Harshvardhan,; West, Brandon; Fidel, Adam; Amato, Nancy M.; Rauchwerger, Lawrence

2015-01-01

that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM

Computer architecture a quantitative approach

CERN Document Server

Hennessy, John L

2019-01-01

Computer Architecture: A Quantitative Approach, Sixth Edition has been considered essential reading by instructors, students and practitioners of computer design for over 20 years. The sixth edition of this classic textbook is fully revised with the latest developments in processor and system architecture. It now features examples from the RISC-V (RISC Five) instruction set architecture, a modern RISC instruction set developed and designed to be a free and openly adoptable standard. It also includes a new chapter on domain-specific architectures and an updated chapter on warehouse-scale computing that features the first public information on Google's newest WSC. True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.

Genome-Wide Prediction of the Performance of Three-Way Hybrids in Barley

Directory of Open Access Journals (Sweden)

Zuo Li

2017-03-01

Full Text Available Predicting the grain yield performance of three-way hybrids is challenging. Three-way crosses are relevant for hybrid breeding in barley ( L. and maize ( L. adapted to East Africa. The main goal of our study was to implement and evaluate genome-wide prediction approaches of the performance of three-way hybrids using data of single-cross hybrids for a scenario in which parental lines of the three-way hybrids originate from three genetically distinct subpopulations. We extended the ridge regression best linear unbiased prediction (RRBLUP and devised a genomic selection model allowing for subpopulation-specific marker effects (GSA-RRBLUP: general and subpopulation-specific additive RRBLUP. Using an empirical barley data set, we showed that applying GSA-RRBLUP tripled the prediction ability of three-way hybrids from 0.095 to 0.308 compared with RRBLUP, modeling one additive effect for all three subpopulations. The experimental findings were further substantiated with computer simulations. Our results emphasize the potential of GSA-RRBLUP to improve genome-wide hybrid prediction of three-way hybrids for scenarios of genetically diverse parental populations. Because of the advantages of the GSA-RRBLUP model in dealing with hybrids from different parental populations, it may also be a promising approach to boost the prediction ability for hybrid breeding programs based on genetically diverse heterotic groups.

Mixed H∞ and passive control for linear switched systems via hybrid control approach

Science.gov (United States)

Zheng, Qunxian; Ling, Youzhu; Wei, Lisheng; Zhang, Hongbin

2018-03-01

This paper investigates the mixed H∞ and passive control problem for linear switched systems based on a hybrid control strategy. To solve this problem, first, a new performance index is proposed. This performance index can be viewed as the mixed weighted H∞ and passivity performance. Then, the hybrid controllers are used to stabilise the switched systems. The hybrid controllers consist of dynamic output-feedback controllers for every subsystem and state updating controllers at the switching instant. The design of state updating controllers not only depends on the pre-switching subsystem and the post-switching subsystem, but also depends on the measurable output signal. The hybrid controllers proposed in this paper can include some existing ones as special cases. Combine the multiple Lyapunov functions approach with the average dwell time technique, new sufficient conditions are obtained. Under the new conditions, the closed-loop linear switched systems are globally uniformly asymptotically stable with a mixed H∞ and passivity performance index. Moreover, the desired hybrid controllers can be constructed by solving a set of linear matrix inequalities. Finally, a numerical example and a practical example are given.

5th Symposium on Hybrid RANS-LES Methods

CERN Document Server

Haase, Werner; Peng, Shia-Hui; Schwamborn, Dieter

2015-01-01

This book gathers the proceedings of the Fifth Symposium on Hybrid RANS-LES Methods, which was held on March 19-21 in College Station, Texas, USA. The different chapters, written by leading experts, reports on the most recent developments in flow physics modelling, and gives a special emphasis to industrially relevant applications of hybrid RANS-LES methods and other turbulence-resolving modelling approaches. The book addresses academic researchers, graduate students, industrial engineers, as well as industrial R&D managers and consultants dealing with turbulence modelling, simulation and measurement, and with multidisciplinary applications of computational fluid dynamics (CFD), such as flow control, aero-acoustics, aero-elasticity and CFD-based multidisciplinary optimization. It discusses in particular advanced hybrid RANS-LES methods. Further topics include wall-modelled Large Eddy Simulation (WMLES) methods, embedded LES, and a comparison of the LES methods with both hybrid RANS-LES and URANS methods. ...

Nonlinear optics quantum computing with circuit QED.

Science.gov (United States)

Adhikari, Prabin; Hafezi, Mohammad; Taylor, J M

2013-02-08

One approach to quantum information processing is to use photons as quantum bits and rely on linear optical elements for most operations. However, some optical nonlinearity is necessary to enable universal quantum computing. Here, we suggest a circuit-QED approach to nonlinear optics quantum computing in the microwave regime, including a deterministic two-photon phase gate. Our specific example uses a hybrid quantum system comprising a LC resonator coupled to a superconducting flux qubit to implement a nonlinear coupling. Compared to the self-Kerr nonlinearity, we find that our approach has improved tolerance to noise in the qubit while maintaining fast operation.

A hybrid NIRS-EEG system for self-paced brain computer interface with online motor imagery.

Science.gov (United States)

Koo, Bonkon; Lee, Hwan-Gon; Nam, Yunjun; Kang, Hyohyeong; Koh, Chin Su; Shin, Hyung-Cheul; Choi, Seungjin

2015-04-15

For a self-paced motor imagery based brain-computer interface (BCI), the system should be able to recognize the occurrence of a motor imagery, as well as the type of the motor imagery. However, because of the difficulty of detecting the occurrence of a motor imagery, general motor imagery based BCI studies have been focusing on the cued motor imagery paradigm. In this paper, we present a novel hybrid BCI system that uses near infrared spectroscopy (NIRS) and electroencephalography (EEG) systems together to achieve online self-paced motor imagery based BCI. We designed a unique sensor frame that records NIRS and EEG simultaneously for the realization of our system. Based on this hybrid system, we proposed a novel analysis method that detects the occurrence of a motor imagery with the NIRS system, and classifies its type with the EEG system. An online experiment demonstrated that our hybrid system had a true positive rate of about 88%, a false positive rate of 7% with an average response time of 10.36 s. As far as we know, there is no report that explored hemodynamic brain switch for self-paced motor imagery based BCI with hybrid EEG and NIRS system. From our experimental results, our hybrid system showed enough reliability for using in a practical self-paced motor imagery based BCI. Copyright © 2014 Elsevier B.V. All rights reserved.

FMT-XCT: in vivo animal studies with hybrid fluorescence molecular tomography-X-ray computed tomography.

Science.gov (United States)

Ale, Angelique; Ermolayev, Vladimir; Herzog, Eva; Cohrs, Christian; de Angelis, Martin Hrabé; Ntziachristos, Vasilis

2012-06-01

The development of hybrid optical tomography methods to improve imaging performance has been suggested over a decade ago and has been experimentally demonstrated in animals and humans. Here we examined in vivo performance of a camera-based hybrid fluorescence molecular tomography (FMT) system for 360° imaging combined with X-ray computed tomography (XCT). Offering an accurately co-registered, information-rich hybrid data set, FMT-XCT has new imaging possibilities compared to stand-alone FMT and XCT. We applied FMT-XCT to a subcutaneous 4T1 tumor mouse model, an Aga2 osteogenesis imperfecta model and a Kras lung cancer mouse model, using XCT information during FMT inversion. We validated in vivo imaging results against post-mortem planar fluorescence images of cryoslices and histology data. Besides offering concurrent anatomical and functional information, FMT-XCT resulted in the most accurate FMT performance to date. These findings indicate that addition of FMT optics into the XCT gantry may be a potent upgrade for small-animal XCT systems.

A Novel Combined Hybrid Approach to Enable Revascularisation of a Trauma-Induced Subclavian Artery Injury

Directory of Open Access Journals (Sweden)

C.N. Sabbagh

Full Text Available : Introduction: This case highlights the complexity of upper limb revascularization after a subclavian artery traumatic injury and strengthens the role of a hybrid/multi-disciplinary approach to such injuries. Report: A 45-year-old male patient presented with an acute right upper limb following a traumatic injury to the right subclavian artery due to a motor vehicle accident (MVA. Associated injuries included an unstable cervical spine injury, a large open right clavicular injury, and a brain injury, which limited the potential revascularisation options available. The arm was revascularised using a hybrid endovascular/open surgical approach, namely embolization of the proximal subclavian artery (just distal to vertebral artery and a right common femoral artery to distal axillary artery bypass using prosthetic material. Discussion: Blunt injuries to the subclavian artery are often high impact, complex and associated with multiple injuries to surrounding structures, which limit the role of standard procedures used in the elective setting. This case highlights the role of multidisciplinary team involvement, using a hybrid approach and a novel distal inflow site to restore upper limb perfusion. Keywords: Upper limb, Ischemia, Trauma, Revascularization

Hybrid modeling approach to improve the forecasting capability for the gaseous radionuclide in a nuclear site

International Nuclear Information System (INIS)

Jeong, Hyojoon; Hwang, Wontae; Kim, Eunhan; Han, Moonhee

2012-01-01

Highlights: ► This study is to improve the reliability of air dispersion modeling. ► Tracer experiments assumed gaseous radionuclides were conducted at a nuclear site. ► The performance of a hybrid modeling combined ISC with ANFIS was investigated.. ► Hybrid modeling approach shows better performance rather than a single ISC model. - Abstract: Predicted air concentrations of radioactive materials are important for an environmental impact assessment for the public health. In this study, the performance of a hybrid modeling combined with the industrial source complex (ISC) model and an adaptive neuro-fuzzy inference system (ANFIS) for predicting tracer concentrations was investigated. Tracer dispersion experiments were performed to produce the field data assuming the accidental release of radioactive material. ANFIS was trained in order that the outputs of the ISC model are similar to the measured data. Judging from the higher correlation coefficients between the measured and the calculated ones, the hybrid modeling approach could be an appropriate technique for an improvement of the modeling capability to predict the air concentrations for radioactive materials.

A bottom-up approach for the synthesis of highly ordered fullerene-intercalated graphene hybrids

Directory of Open Access Journals (Sweden)

Dimitrios eGournis

2015-02-01

Full Text Available Much of the research effort on graphene focuses on its use as a building block for the development of new hybrid nanostructures with well-defined dimensions and properties suitable for applications such as gas storage, heterogeneous catalysis, gas/liquid separations, nanosensing and biomedicine. Towards this aim, here we describe a new bottom-up approach, which combines self-assembly with the Langmuir Schaefer deposition technique to synthesize graphene-based layered hybrid materials hosting fullerene molecules within the interlayer space. Our film preparation consists in a bottom-up layer-by-layer process that proceeds via the formation of a hybrid organo-graphene oxide Langmuir film. The structure and composition of these hybrid fullerene-containing thin multilayers deposited on hydrophobic substrates were characterized by a combination of X-ray diffraction, Raman and X-ray photoelectron spectroscopies, atomic force microscopy and conductivity measurements. The latter revealed that the presence of C60 within the interlayer spacing leads to an increase in electrical conductivity of the hybrid material as compared to the organo-graphene matrix alone.

Hybrid Metaheuristic Approach for Nonlocal Optimization of Molecular Systems.

Science.gov (United States)

Dresselhaus, Thomas; Yang, Jack; Kumbhar, Sadhana; Waller, Mark P

2013-04-09

Accurate modeling of molecular systems requires a good knowledge of the structure; therefore, conformation searching/optimization is a routine necessity in computational chemistry. Here we present a hybrid metaheuristic optimization (HMO) algorithm, which combines ant colony optimization (ACO) and particle swarm optimization (PSO) for the optimization of molecular systems. The HMO implementation meta-optimizes the parameters of the ACO algorithm on-the-fly by the coupled PSO algorithm. The ACO parameters were optimized on a set of small difluorinated polyenes where the parameters exhibited small variance as the size of the molecule increased. The HMO algorithm was validated by searching for the closed form of around 100 molecular balances. Compared to the gradient-based optimized molecular balance structures, the HMO algorithm was able to find low-energy conformations with a 87% success rate. Finally, the computational effort for generating low-energy conformation(s) for the phenylalanyl-glycyl-glycine tripeptide was approximately 60 CPU hours with the ACO algorithm, in comparison to 4 CPU years required for an exhaustive brute-force calculation.

Approaches to Low Fuel Regression Rate in Hybrid Rocket Engines

Directory of Open Access Journals (Sweden)

Dario Pastrone

2012-01-01

Full Text Available Hybrid rocket engines are promising propulsion systems which present appealing features such as safety, low cost, and environmental friendliness. On the other hand, certain issues hamper the development hoped for. The present paper discusses approaches addressing improvements to one of the most important among these issues: low fuel regression rate. To highlight the consequence of such an issue and to better understand the concepts proposed, fundamentals are summarized. Two approaches are presented (multiport grain and high mixture ratio which aim at reducing negative effects without enhancing regression rate. Furthermore, fuel material changes and nonconventional geometries of grain and/or injector are presented as methods to increase fuel regression rate. Although most of these approaches are still at the laboratory or concept scale, many of them are promising.

A New Hybrid Approach for Wind Speed Prediction Using Fast Block Least Mean Square Algorithm and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Ummuhan Basaran Filik

2016-01-01

Full Text Available A new hybrid wind speed prediction approach, which uses fast block least mean square (FBLMS algorithm and artificial neural network (ANN method, is proposed. FBLMS is an adaptive algorithm which has reduced complexity with a very fast convergence rate. A hybrid approach is proposed which uses two powerful methods: FBLMS and ANN method. In order to show the efficiency and accuracy of the proposed approach, seven-year real hourly collected wind speed data sets belonging to Turkish State Meteorological Service of Bozcaada and Eskisehir regions are used. Two different ANN structures are used to compare with this approach. The first six-year data is handled as a train set; the remaining one-year hourly data is handled as test data. Mean absolute error (MAE and root mean square error (RMSE are used for performance evaluations. It is shown for various cases that the performance of the new hybrid approach gives better results than the different conventional ANN structure.

Optimization of a continuous hybrid impeller mixer via computational fluid dynamics.

Science.gov (United States)

Othman, N; Kamarudin, S K; Takriff, M S; Rosli, M I; Engku Chik, E M F; Meor Adnan, M A K

2014-01-01

This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-ε turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination.

A hybrid optical switch architecture to integrate IP into optical networks to provide flexible and intelligent bandwidth on demand for cloud computing

Science.gov (United States)

Yang, Wei; Hall, Trevor J.

2013-12-01

The Internet is entering an era of cloud computing to provide more cost effective, eco-friendly and reliable services to consumer and business users. As a consequence, the nature of the Internet traffic has been fundamentally transformed from a pure packet-based pattern to today's predominantly flow-based pattern. Cloud computing has also brought about an unprecedented growth in the Internet traffic. In this paper, a hybrid optical switch architecture is presented to deal with the flow-based Internet traffic, aiming to offer flexible and intelligent bandwidth on demand to improve fiber capacity utilization. The hybrid optical switch is capable of integrating IP into optical networks for cloud-based traffic with predictable performance, for which the delay performance of the electronic module in the hybrid optical switch architecture is evaluated through simulation.

Control and fault diagnosis based sliding mode observer of a multicellular converter: Hybrid approach

KAUST Repository

Benzineb, Omar

2013-01-01

In this article, the diagnosis of a three cell converter is developed. The hybrid nature of the system represented by the presence of continuous and discrete dynamics is taken into account in the control design. The idea is based on using a hybrid control and an observer-type sliding mode to generate residuals from the observation errors of the system. The simulation results are presented at the end to illustrate the performance of the proposed approach. © 2013 FEI STU.

Hybridization of powertrain and downsizing of IC engine - A way to reduce fuel consumption and pollutant emissions - Part 1

International Nuclear Information System (INIS)

Katrasnik, Tomaz

2007-01-01

The aim of this two part paper is to present the results of extensive simulation and analytical analysis of the energy conversion efficiency in parallel hybrid powertrains. The simulation approach is based on an accurate and fast forward facing simulation model of a parallel hybrid powertrain and a conventional internal combustion engine powertrain. The model of the ICE is based on a verified dynamic model that provides sufficiently small time steps to model adequately the dynamics of electric systems during transient test cycles. Models of the electrical devices enable computation of the instantaneous energy consumption, production and storage as well as computation of the instantaneous energy losses and component efficiencies. Moreover, the paper offers an analytical approach based on the energy balance in order to analyze and predict the energy conversion efficiency of hybrid powertrains. The analysis covers a broad range of parallel hybrid powertrain configurations from mild to full hybrids. Combined simulation and analytical analysis enables deep insight into the energy conversion phenomena in hybrid powertrains. The paper reveals the conditions and influences that lead to improved fuel economy of hybrid powertrains with the emphasis on determining the optimum hybridization ratio. The theoretical background, simulation program and brief analysis of one test cycle are presented in Part 1, whereas the extensive analysis and parametric study is presented in the companion paper, Part 2

Symmetric and asymmetric hybrid cryptosystem based on compressive sensing and computer generated holography

Science.gov (United States)

Ma, Lihong; Jin, Weimin

2018-01-01

A novel symmetric and asymmetric hybrid optical cryptosystem is proposed based on compressive sensing combined with computer generated holography. In this method there are six encryption keys, among which two decryption phase masks are different from the two random phase masks used in the encryption process. Therefore, the encryption system has the feature of both symmetric and asymmetric cryptography. On the other hand, because computer generated holography can flexibly digitalize the encrypted information and compressive sensing can significantly reduce data volume, what is more, the final encryption image is real function by phase truncation, the method favors the storage and transmission of the encryption data. The experimental results demonstrate that the proposed encryption scheme boosts the security and has high robustness against noise and occlusion attacks.

A hybrid approach to the computational aeroacoustics of human voice production

Czech Academy of Sciences Publication Activity Database

Šidlof, Petr; Zörner, S.; Huppe, A.

2015-01-01

Roč. 14, č. 3 (2015), s. 473-488 ISSN 1617-7959 R&D Projects: GA ČR(CZ) GAP101/11/0207 Institutional support: RVO:61388998 Keywords : computational aeroacoustics * parallel CFD * human voice * vocal folds * ventricular folds Subject RIV: BI - Acoustics Impact factor: 3.032, year: 2015

A hybrid data compression approach for online backup service

Science.gov (United States)

Wang, Hua; Zhou, Ke; Qin, MingKang

2009-08-01

With the popularity of Saas (Software as a service), backup service has becoming a hot topic of storage application. Due to the numerous backup users, how to reduce the massive data load is a key problem for system designer. Data compression provides a good solution. Traditional data compression application used to adopt a single method, which has limitations in some respects. For example data stream compression can only realize intra-file compression, de-duplication is used to eliminate inter-file redundant data, compression efficiency cannot meet the need of backup service software. This paper proposes a novel hybrid compression approach, which includes two levels: global compression and block compression. The former can eliminate redundant inter-file copies across different users, the latter adopts data stream compression technology to realize intra-file de-duplication. Several compressing algorithms were adopted to measure the compression ratio and CPU time. Adaptability using different algorithm in certain situation is also analyzed. The performance analysis shows that great improvement is made through the hybrid compression policy.

Evaluation of wind power generation potential using a three hybrid approach for households in Ardebil Province, Iran

International Nuclear Information System (INIS)

Qolipour, Mojtaba; Mostafaeipour, Ali; Shamshirband, Shahaboddin; Alavi, Omid; Goudarzi, Hossein; Petković, Dalibor

2016-01-01

Highlights: • Technical–economic feasibility of small wind turbines for six areas in Ardabil province of Iran was investigated. • Hybrid approach of Data Envelopment Analysis, Balanced Scorecard, and Game Theory was analyzed. • HOMER software was used for economic evaluation. • Technical–economic feasibility was studied using wind speed data during 2008–2014. • The areas of Airport, Nir, Namin, BilaSavar, Firozabad and Ardabil were ranked from first to last, respectively. - Abstract: The objective of the present paper is to conduct a thorough technical–economic evaluation for the construction of small wind turbines in six areas within Ardabil province of Iran using the Hybrid Optimization of Multiple Energy Resources software, and also to rank these areas by a hybrid approach composed of Data Envelopment Analysis, Balanced Scorecard, and Game Theory methodologies. Higher accuracy of the proposed hybrid approach and its ability to properly detect the relationships between the decision-making components make it preferable over the simple Data Envelopment Analysis method. Technical–economic feasibility study is conducted by analyzing wind speed data for period from 2008 to 2014 using Hybrid Optimization of Multiple Energy Resources software. In the next step, the type of equipment used in the design, benefit, costs, total net costs, depreciation and amount of generated electricity are obtained separately for each location. The results show that; Airport, Nir, Namin, Bilasavar, Firozabad and Ardabil were rank first to last respectively.

Transcatheter pulmonary valve replacement by hybrid approach using a novel polymeric prosthetic heart valve: proof of concept in sheep.

Directory of Open Access Journals (Sweden)

Ben Zhang

Full Text Available Since 2000, transcatheter pulmonary valve replacement has steadily advanced. However, the available prosthetic valves are restricted to bioprosthesis which have defects like poor durability. Polymeric heart valve is thought as a promising alternative to bioprosthesis. In this study, we introduced a novel polymeric transcatheter pulmonary valve and evaluated its feasibility and safety in sheep by a hybrid approach.We designed a novel polymeric trileaflet transcatheter pulmonary valve with a balloon-expandable stent, and the valve leaflets were made of 0.1-mm expanded polytetrafluoroethylene (ePTFE coated with phosphorylcholine. We chose glutaraldehyde-treated bovine pericardium valves as control. Pulmonary valve stents were implanted in situ by a hybrid transapical approach in 10 healthy sheep (8 for polymeric valve and 2 for bovine pericardium valve, weighing an average of 22.5±2.0 kg. Angiography and cardiac catheter examination were performed after implantation to assess immediate valvular functionality. After 4-week follow-up, angiography, echocardiography, computed tomography, and cardiac catheter examination were used to assess early valvular function. One randomly selected sheep with polymeric valve was euthanized and the explanted valved stent was analyzed macroscopically and microscopically.Implantation was successful in 9 sheep. Angiography at implantation showed all 9 prosthetic valves demonstrated orthotopic position and normal functionality. All 9 sheep survived at 4-week follow-up. Four-week follow-up revealed no evidence of valve stent dislocation or deformation and normal valvular and cardiac functionality. The cardiac catheter examination showed the peak-peak transvalvular pressure gradient of the polymeric valves was 11.9±5.0 mmHg, while that of two bovine pericardium valves were 11 and 17 mmHg. Gross morphology demonstrated good opening and closure characteristics. No thrombus or calcification was seen macroscopically

Noise Threshold and Resource Cost of Fault-Tolerant Quantum Computing with Majorana Fermions in Hybrid Systems.

Science.gov (United States)

Li, Ying

2016-09-16

Fault-tolerant quantum computing in systems composed of both Majorana fermions and topologically unprotected quantum systems, e.g., superconducting circuits or quantum dots, is studied in this Letter. Errors caused by topologically unprotected quantum systems need to be corrected with error-correction schemes, for instance, the surface code. We find that the error-correction performance of such a hybrid topological quantum computer is not superior to a normal quantum computer unless the topological charge of Majorana fermions is insusceptible to noise. If errors changing the topological charge are rare, the fault-tolerance threshold is much higher than the threshold of a normal quantum computer and a surface-code logical qubit could be encoded in only tens of topological qubits instead of about 1,000 normal qubits.

A hybrid life cycle and multi-criteria decision analysis approach for identifying sustainable development strategies of Beijing's taxi fleet

International Nuclear Information System (INIS)

Cai, Yanpeng; Applegate, Scott; Yue, Wencong; Cai, Jianying; Wang, Xuan; Liu, Gengyuan; Li, Chunhui

2017-01-01

To identify and evaluate sustainable strategies of taxi fleet in Beijing in terms of economic, policy, and environmental implications, a hybrid approach was developed through incorporating multi-criteria decision analysis (MCDA) methods within a general life-cycle analysis (LCA) framework. The approach can (a) help comprehensive evaluate environmental impacts of multiple types of vehicles, (b) facilitate analysis of environmental, economic and policy features of such vehicles, and (c) identify desirable taxi fleet development strategies for the city. The developed approach represented an improvement of the decision-making capability for taxi implementation based on multiple available technologies and their performance that can be specifically tailored to Beijing. The results demonstrated that the proposed approach could comprehensively reflect multiple implications of strategies for the taxi fleet in Beijing to reduce air pollution in the city. The results also indicated that the electric vehicle powered with the year 2020 electricity projections would be the ideal solution, outranking the other alternatives. The conventional vehicle ranked the lowest among the alternatives. The plug-in hybrid vehicle powered by 2020 electricity projects ranked the third, followed by the plug-in hybrid vehicle ranking the fourth, and the hybrid vehicle ranking the fifth. - Highlights: • An hybrid approach was proposed for evaluating sustainable strategies of Beijing's taxi fleet. • This approach was based on the combination of multi-criteria decision analysis methods and life-cycle assessment. • Environmental, economic and policy performances of multiple strategies were compared. • Detailed responses of taxi drivers and local residents were interviewed. • The electric vehicle would be the ideal solution for Beijing Taxi fleet.

A Gaussian process regression based hybrid approach for short-term wind speed prediction

International Nuclear Information System (INIS)

Zhang, Chi; Wei, Haikun; Zhao, Xin; Liu, Tianhong; Zhang, Kanjian

2016-01-01

Highlights: • A novel hybrid approach is proposed for short-term wind speed prediction. • This method combines the parametric AR model with the non-parametric GPR model. • The relative importance of different inputs is considered. • Different types of covariance functions are considered and combined. • It can provide both accurate point forecasts and satisfactory prediction intervals. - Abstract: This paper proposes a hybrid model based on autoregressive (AR) model and Gaussian process regression (GPR) for probabilistic wind speed forecasting. In the proposed approach, the AR model is employed to capture the overall structure from wind speed series, and the GPR is adopted to extract the local structure. Additionally, automatic relevance determination (ARD) is used to take into account the relative importance of different inputs, and different types of covariance functions are combined to capture the characteristics of the data. The proposed hybrid model is compared with the persistence model, artificial neural network (ANN), and support vector machine (SVM) for one-step ahead forecasting, using wind speed data collected from three wind farms in China. The forecasting results indicate that the proposed method can not only improve point forecasts compared with other methods, but also generate satisfactory prediction intervals.

Computational Thinking and Practice - A Generic Approach to Computing in Danish High Schools

DEFF Research Database (Denmark)

Caspersen, Michael E.; Nowack, Palle

2014-01-01

Internationally, there is a growing awareness on the necessity of providing relevant computing education in schools, particularly high schools. We present a new and generic approach to Computing in Danish High Schools based on a conceptual framework derived from ideas related to computational thi...

Heuristic hybrid game approach for fleet condition-based maintenance planning

International Nuclear Information System (INIS)

Feng, Qiang; Bi, Xiong; Zhao, Xiujie; Chen, Yiran; Sun, Bo

2017-01-01

The condition-based maintenance (CBM) method is commonly used to select appropriate maintenance opportunities according to equipment status over a period of time. The CBM of aircraft fleets is a fleet maintenance planning problem. In this problem, mission requirements, resource constraints, and aircraft statuses are considered to find an optimal strategy set. Given that the maintenance strategies for each aircraft are finite, fleet CBM can be treated as a combinatorial optimization problem. In this study, the process of making a decision on the CBM of military fleets is analyzed. The fleet CBM problem is treated as a two-stage dynamic decision-making problem. Aircraft are divided into dispatch and standby sets; thus, the problem scale is significantly reduced. A heuristic hybrid game (HHG) approach comprising a competition game and a cooperative game is proposed on the basis of heuristic rule. In the dispatch set, a competition game approach is proposed to search for a local optimal strategy matrix. A cooperative game method for the two sets is also proposed to ensure global optimization. Finally, a case study regarding a fleet comprising 20 aircraft is conducted, with the results proving that the approach efficiently generates outcomes that meet the mission risk-oriented schedule requirement. - Highlights: • A new heuristic hybrid game method for fleet condition-based maintenance is proposed. • The problem is simplified by hierarchical solving based on dispatch and standby set. • The local optimal solution is got by competition game algorithm for dispatch set. • The global optimal solution is got by cooperative game algorithm between two sets.

Super-resolution nanofabrication with metal-ion doped hybrid material through an optical dual-beam approach

International Nuclear Information System (INIS)

Cao, Yaoyu; Li, Xiangping; Gu, Min

2014-01-01

We apply an optical dual-beam approach to a metal-ion doped hybrid material to achieve nanofeatures beyond the optical diffraction limit. By spatially inhibiting the photoreduction and the photopolymerization, we realize a nano-line, consisting of polymer matrix and in-situ generated gold nanoparticles, with a lateral size of sub 100 nm, corresponding to a factor of 7 improvement compared to the diffraction limit. With the existence of gold nanoparticles, a plasmon enhanced super-resolution fabrication mechanism in the hybrid material is observed, which benefits in a further reduction in size of the fabricated feature. The demonstrated nanofeature in hybrid materials paves the way for realizing functional nanostructures

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

Directory of Open Access Journals (Sweden)

Oliveira Rui

2010-09-01

Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

Quantum Computing: a Quantum Group Approach

OpenAIRE

Wang, Zhenghan

2013-01-01

There is compelling theoretical evidence that quantum physics will change the face of information science. Exciting progress has been made during the last two decades towards the building of a large scale quantum computer. A quantum group approach stands out as a promising route to this holy grail, and provides hope that we may have quantum computers in our future.

Tuneable resolution as a systems biology approach for multi-scale, multi-compartment computational models.

Science.gov (United States)

Kirschner, Denise E; Hunt, C Anthony; Marino, Simeone; Fallahi-Sichani, Mohammad; Linderman, Jennifer J

2014-01-01

The use of multi-scale mathematical and computational models to study complex biological processes is becoming increasingly productive. Multi-scale models span a range of spatial and/or temporal scales and can encompass multi-compartment (e.g., multi-organ) models. Modeling advances are enabling virtual experiments to explore and answer questions that are problematic to address in the wet-lab. Wet-lab experimental technologies now allow scientists to observe, measure, record, and analyze experiments focusing on different system aspects at a variety of biological scales. We need the technical ability to mirror that same flexibility in virtual experiments using multi-scale models. Here we present a new approach, tuneable resolution, which can begin providing that flexibility. Tuneable resolution involves fine- or coarse-graining existing multi-scale models at the user's discretion, allowing adjustment of the level of resolution specific to a question, an experiment, or a scale of interest. Tuneable resolution expands options for revising and validating mechanistic multi-scale models, can extend the longevity of multi-scale models, and may increase computational efficiency. The tuneable resolution approach can be applied to many model types, including differential equation, agent-based, and hybrid models. We demonstrate our tuneable resolution ideas with examples relevant to infectious disease modeling, illustrating key principles at work. © 2014 The Authors. WIREs Systems Biology and Medicine published by Wiley Periodicals, Inc.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

International Nuclear Information System (INIS)

Pousinho, H.M.I.; Mendes, V.M.F.; Catalao, J.P.S.

2011-01-01

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Dual-acting of Hybrid Compounds - A New Dawn in the Discovery of Multi-target Drugs: Lead Generation Approaches.

Science.gov (United States)

Abdolmaleki, Azizeh; Ghasemi, Jahan B

2017-01-01

Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Machine learning and computer vision approaches for phenotypic profiling.

Science.gov (United States)

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

Molecular and morphological approaches for species delimitation and hybridization investigations of two Cichla species

Directory of Open Access Journals (Sweden)

Andrea A. F. Mourão

Full Text Available ABSTRACT The hybridization is a widely-discussed issue in several studies with fish species. For some authors, hybridization may be related with diversification and speciation of several groups, or also with the extinction of populations or species. Difficulties to differentiate species and hybrids may be a problem to correctly apply a management of wild species, because hybrid lineages, especially the advanced ones, may resemble the parental species. The genus Cichla Bloch & Schneider, 1801 constitutes an interesting experimental model, considering that hybridization and taxonomic uncertainties hinder a correct identification. Considering these problems, in this study, we developed genetic methodologies and applied meristic and morphometric approaches in wild samples in order to identify species and for test a possible hybridization between Cichla kelberi Kullander & Ferreira, 2006 and Cichla piquiti Kullander & Ferreira, 2006. For this, C. kelberi, C. piquiti and potential hybrid ( carijó individuals were collected in Paraná and Tietê rivers (SP, Brazil. For meristic and morphometric methods, the individuals were analyzed using the statistical software Pcord 5:31, while for molecular methods, primers for PCR-multiplex were designed and enzyme for PCR-RFLP were selected, under the species-specific nucleotide. All results indicated that the carijó is not an interspecific hybrid, because it presented identical genetic pattern and morphology closed to C. piquiti. Thus, we propose that carijó is a C. piquiti morphotype. In addition, this study promotes a new molecular tool that could be used in future research, monitoring and management programs of the genus Cichla.

Requirements for Control Room Computer-Based Procedures for use in Hybrid Control Rooms

Energy Technology Data Exchange (ETDEWEB)

Le Blanc, Katya Lee [Idaho National Lab. (INL), Idaho Falls, ID (United States); Oxstrand, Johanna Helene [Idaho National Lab. (INL), Idaho Falls, ID (United States); Joe, Jeffrey Clark [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2015-05-01

Many plants in the U.S. are currently undergoing control room modernization. The main drivers for modernization are the aging and obsolescence of existing equipment, which typically results in a like-for-like replacement of analogue equipment with digital systems. However, the modernization efforts present an opportunity to employ advanced technology that would not only extend the life, but enhance the efficiency and cost competitiveness of nuclear power. Computer-based procedures (CBPs) are one example of near-term advanced technology that may provide enhanced efficiencies above and beyond like for like replacements of analog systems. Researchers in the LWRS program are investigating the benefits of advanced technologies such as CBPs, with the goal of assisting utilities in decision making during modernization projects. This report will describe the existing research on CBPs, discuss the unique issues related to using CBPs in hybrid control rooms (i.e., partially modernized analog control rooms), and define the requirements of CBPs for hybrid control rooms.

A hybrid approach to automatic de-identification of psychiatric notes.

Science.gov (United States)

Lee, Hee-Jin; Wu, Yonghui; Zhang, Yaoyun; Xu, Jun; Xu, Hua; Roberts, Kirk

2017-11-01

De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing system for automatic de-identification of psychiatric notes, which was designed to participate in the 2016 CEGS N-GRID shared task Track 1. The system has a hybrid structure that combines machine leaning techniques and rule-based approaches. The rule-based components exploit the structure of the psychiatric notes as well as characteristic surface patterns of PHI mentions. The machine learning components utilize supervised learning with rich features. In addition, the system performance was boosted with integration of additional data to the training set through domain adaptation. The hybrid system showed overall micro-averaged F-score 90.74 on the test set, second-best among all the participants of the CEGS N-GRID task. Copyright © 2017. Published by Elsevier Inc.

A hybrid approach for efficient anomaly detection using metaheuristic methods

Directory of Open Access Journals (Sweden)

Tamer F. Ghanem

2015-07-01

Full Text Available Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms. The proposed approach has taken some inspiration of negative selection-based detector generation. The evaluation of this approach is performed using NSL-KDD dataset which is a modified version of the widely used KDD CUP 99 dataset. The results show its effectiveness in generating a suitable number of detectors with an accuracy of 96.1% compared to other competitors of machine learning algorithms.

Computational fluid dynamics a practical approach

CERN Document Server

Tu, Jiyuan; Liu, Chaoqun

2018-01-01

Computational Fluid Dynamics: A Practical Approach, Third Edition, is an introduction to CFD fundamentals and commercial CFD software to solve engineering problems. The book is designed for a wide variety of engineering students new to CFD, and for practicing engineers learning CFD for the first time. Combining an appropriate level of mathematical background, worked examples, computer screen shots, and step-by-step processes, this book walks the reader through modeling and computing, as well as interpreting CFD results. This new edition has been updated throughout, with new content and improved figures, examples and problems.

Hybrid stochastic simplifications for multiscale gene networks

Directory of Open Access Journals (Sweden)

Debussche Arnaud

2009-09-01

Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.

TwitterNEED: a hybrid approach for named entity extraction and disambiguation for tweets

NARCIS (Netherlands)

Habib, Mena Badieh; van Keulen, Maurice

Twitter is a rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing tasks. In this paper, we present TwitterNEED, a hybrid approach for Named Entity Extraction and Named Entity Disambiguation for tweets. We

Hybrid Methods in Designing Sierpinski Gasket Antennas

Directory of Open Access Journals (Sweden)

Mudrik Alaydrus

2010-12-01

Full Text Available Sierpinki gasket antennas as example of fractal antennas show multiband characteristics. The computer simulation of Sierpinksi gasket monopole with finite ground needs prohibitively large computer memory and more computational time. Hybrid methods consist of surface integral equation method and physical optics or uniform geometrical theory of diffraction should alleviate this computational burdens. The so-called full hybridization of the different methods with modifying the incoming electromagnetic waves in case of hybrid method surface integral equation method and physical optics and modification of the Greens function for hybrid method surface integral equation method and uniform geometrical theory of diffraction plays the central role in the observation. Comparison between results of different methods are given and also measurements of three Sierpinksi gasket antennas. The multiband characteristics of the antennas still can be seen with some reduction and enhancement of resonances.

Output Tracking Control of Switched Hybrid Systems: A Fliess Functional Expansion Approach

Directory of Open Access Journals (Sweden)

Fenghua He

2013-01-01

Full Text Available The output tracking problem is investigated for a nonlinear affine system with multiple modes of continuous control inputs. We convert the family of nonlinear affine systems under consideration into a switched hybrid system by introducing a multiple-valued logic variable. The Fliess functional expansion is adopted to express the input and output relationship of the switched hybrid system. The optimal switching control is determined for a multiple-step output tracking performance index. The proposed approach is applied to a multitarget tracking problem for a flight vehicle aiming for one real target with several decoys flying around it in the terminal guidance course. These decoys appear as apparent targets and have to be distinguished with the approaching of the flight vehicle. The guidance problem of one flight vehicle versus multiple apparent targets should be considered if no large miss distance might be caused due to the limitation of the flight vehicle maneuverability. The target orientation at each time interval is determined. Simulation results show the effectiveness of the proposed method.

An Odometry-free Approach for Simultaneous Localization and Online Hybrid Map Building

Directory of Open Access Journals (Sweden)

Wei Hong Chin

2016-11-01

Full Text Available In this paper, a new approach is proposed for mobile robot localization and hybrid map building simultaneously without using any odometry hardware system. The proposed method termed as Genetic Bayesian ARAM which comprises two main components: 1 Steady state genetic algorithm (SSGA for self-localization and occupancy grid map building; 2 Bayesian Adaptive Resonance Associative Memory (ARAM for online topological map building. The model of the explored environment is formed as a hybrid representation, both topological and grid-based, and it is incrementally constructed during the exploration process. During occupancy map building, robot estimated self-position is updated by SSGA. At the same time, robot estimated self position is transmit to Bayesian ARAM for topological map building and localization. The effectiveness of our proposed approach is validated by a number of standardized benchmark datasets and real experimental results carried on mobile robot. Benchmark datasets are used to verify the proposed method capable of generating topological map in different environment conditions. Real robot experiment is to verify the proposed method can be implemented in real world.

Fractal approach to computer-analytical modelling of tree crown

International Nuclear Information System (INIS)

Berezovskaya, F.S.; Karev, G.P.; Kisliuk, O.F.; Khlebopros, R.G.; Tcelniker, Yu.L.

1993-09-01

In this paper we discuss three approaches to the modeling of a tree crown development. These approaches are experimental (i.e. regressive), theoretical (i.e. analytical) and simulation (i.e. computer) modeling. The common assumption of these is that a tree can be regarded as one of the fractal objects which is the collection of semi-similar objects and combines the properties of two- and three-dimensional bodies. We show that a fractal measure of crown can be used as the link between the mathematical models of crown growth and light propagation through canopy. The computer approach gives the possibility to visualize a crown development and to calibrate the model on experimental data. In the paper different stages of the above-mentioned approaches are described. The experimental data for spruce, the description of computer system for modeling and the variant of computer model are presented. (author). 9 refs, 4 figs

A hybrid computational model to explore the topological characteristics of epithelial tissues.

Science.gov (United States)

González-Valverde, Ismael; García-Aznar, José Manuel

2017-11-01

Epithelial tissues show a particular topology where cells resemble a polygon-like shape, but some biological processes can alter this tissue topology. During cell proliferation, mitotic cell dilation deforms the tissue and modifies the tissue topology. Additionally, cells are reorganized in the epithelial layer and these rearrangements also alter the polygon distribution. We present here a computer-based hybrid framework focused on the simulation of epithelial layer dynamics that combines discrete and continuum numerical models. In this framework, we consider topological and mechanical aspects of the epithelial tissue. Individual cells in the tissue are simulated by an off-lattice agent-based model, which keeps the information of each cell. In addition, we model the cell-cell interaction forces and the cell cycle. Otherwise, we simulate the passive mechanical behaviour of the cell monolayer using a material that approximates the mechanical properties of the cell. This continuum approach is solved by the finite element method, which uses a dynamic mesh generated by the triangulation of cell polygons. Forces generated by cell-cell interaction in the agent-based model are also applied on the finite element mesh. Cell movement in the agent-based model is driven by the displacements obtained from the deformed finite element mesh of the continuum mechanical approach. We successfully compare the results of our simulations with some experiments about the topology of proliferating epithelial tissues in Drosophila. Our framework is able to model the emergent behaviour of the cell monolayer that is due to local cell-cell interactions, which have a direct influence on the dynamics of the epithelial tissue. Copyright © 2017 John Wiley & Sons, Ltd.

A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

Energy Technology Data Exchange (ETDEWEB)

Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal); Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal)

2011-01-15

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches. (author)

a Holistic Approach for Inspection of Civil Infrastructures Based on Computer Vision Techniques

Science.gov (United States)

Stentoumis, C.; Protopapadakis, E.; Doulamis, A.; Doulamis, N.

2016-06-01

In this work, it is examined the 2D recognition and 3D modelling of concrete tunnel cracks, through visual cues. At the time being, the structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, then, categorize their severity. The described approach targets at minimum human intervention, for autonomous inspection of civil infrastructures. The shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. Although efforts have been made in the field, synergies among proposed techniques are still missing. The holistic approach of this paper exploits the state of the art techniques of pattern recognition and stereo-matching, in order to build accurate 3D crack models. The innovation lies in the hybrid approach for the CNN detector initialization, and the use of the modified census transformation for stereo matching along with a binary fusion of two state-of-the-art optimization schemes. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair. The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. What is promising is that the computer vision workflow described in this work can be transferred, with adaptations of course, to other infrastructure such as pipelines, bridges and large industrial facilities that are in the need of continuous state assessment during their operational life cycle.

An Introduction to the Hybrid Approach of Neural Networks and the Linear Regression Model : An Illustration in the Hedonic Pricing Model of Building Costs

OpenAIRE

浅野, 美代子; マーコ, ユー K.W.

2007-01-01

This paper introduces the hybrid approach of neural networks and linear regression model proposed by Asano and Tsubaki (2003). Neural networks are often credited with its superiority in data consistency whereas the linear regression model provides simple interpretation of the data enabling researchers to verify their hypotheses. The hybrid approach aims at combing the strengths of these two well-established statistical methods. A step-by-step procedure for performing the hybrid approach is pr...

Hybrid CFD/CAA Modeling for Liftoff Acoustic Predictions

Science.gov (United States)

Strutzenberg, Louise L.; Liever, Peter A.

2011-01-01

This paper presents development efforts at the NASA Marshall Space flight Center to establish a hybrid Computational Fluid Dynamics and Computational Aero-Acoustics (CFD/CAA) simulation system for launch vehicle liftoff acoustics environment analysis. Acoustic prediction engineering tools based on empirical jet acoustic strength and directivity models or scaled historical measurements are of limited value in efforts to proactively design and optimize launch vehicles and launch facility configurations for liftoff acoustics. CFD based modeling approaches are now able to capture the important details of vehicle specific plume flow environment, identifY the noise generation sources, and allow assessment of the influence of launch pad geometric details and sound mitigation measures such as water injection. However, CFD methodologies are numerically too dissipative to accurately capture the propagation of the acoustic waves in the large CFD models. The hybrid CFD/CAA approach combines the high-fidelity CFD analysis capable of identifYing the acoustic sources with a fast and efficient Boundary Element Method (BEM) that accurately propagates the acoustic field from the source locations. The BEM approach was chosen for its ability to properly account for reflections and scattering of acoustic waves from launch pad structures. The paper will present an overview of the technology components of the CFD/CAA framework and discuss plans for demonstration and validation against test data.

When Differential Privacy Meets Randomized Perturbation: A Hybrid Approach for Privacy-Preserving Recommender System

KAUST Repository

Liu, Xiao; Liu, An; Zhang, Xiangliang; Li, Zhixu; Liu, Guanfeng; Zhao, Lei; Zhou, Xiaofang

2017-01-01

result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP

A hybrid approach for fusing 4D-MRI temporal information with 3D-CT for the study of lung and lung tumor motion

Energy Technology Data Exchange (ETDEWEB)

Yang, Y. X.; Van Reeth, E.; Poh, C. L., E-mail: clpoh@ntu.edu.sg [School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore 637459 (Singapore); Teo, S.-K. [Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 138632 (Singapore); Tan, C. H. [Department of Diagnostic Radiology, Tan Tock Seng Hospital, Singapore 308433 (Singapore); Tham, I. W. K. [Department of Radiation Oncology, National University Cancer Institute, Singapore 119082 (Singapore)

2015-08-15

Purpose: Accurate visualization of lung motion is important in many clinical applications, such as radiotherapy of lung cancer. Advancement in imaging modalities [e.g., computed tomography (CT) and MRI] has allowed dynamic imaging of lung and lung tumor motion. However, each imaging modality has its advantages and disadvantages. The study presented in this paper aims at generating synthetic 4D-CT dataset for lung cancer patients by combining both continuous three-dimensional (3D) motion captured by 4D-MRI and the high spatial resolution captured by CT using the authors’ proposed approach. Methods: A novel hybrid approach based on deformable image registration (DIR) and finite element method simulation was developed to fuse a static 3D-CT volume (acquired under breath-hold) and the 3D motion information extracted from 4D-MRI dataset, creating a synthetic 4D-CT dataset. Results: The study focuses on imaging of lung and lung tumor. Comparing the synthetic 4D-CT dataset with the acquired 4D-CT dataset of six lung cancer patients based on 420 landmarks, accurate results (average error <2 mm) were achieved using the authors’ proposed approach. Their hybrid approach achieved a 40% error reduction (based on landmarks assessment) over using only DIR techniques. Conclusions: The synthetic 4D-CT dataset generated has high spatial resolution, has excellent lung details, and is able to show movement of lung and lung tumor over multiple breathing cycles.

Quantum chemical approaches: semiempirical molecular orbital and hybrid quantum mechanical/molecular mechanical techniques.

Science.gov (United States)

Bryce, Richard A; Hillier, Ian H

2014-01-01

The use of computational quantum chemical methods to aid drug discovery is surveyed. An overview of the various computational models spanning ab initio, density function theory, semiempirical molecular orbital (MO), and hybrid quantum mechanical (QM)/molecular mechanical (MM) methods is given and their strengths and weaknesses are highlighted, focussing on the challenge of obtaining the accuracy essential for them to make a meaningful contribution to drug discovery. Particular attention is given to hybrid QM/MM and semiempirical MO methods which have the potential to yield the necessary accurate predictions of macromolecular structure and reactivity. These methods are shown to have advanced the study of many aspects of substrate-ligand interactions relevant to drug discovery. Thus, the successful parametrization of semiempirical MO methods and QM/MM methods can be used to model noncovalent substrate-protein interactions, and to lead to improved scoring functions. QM/MM methods can be used in crystal structure refinement and are particularly valuable for modelling covalent protein-ligand interactions and can thus aid the design of transition state analogues. An extensive collection of examples from the areas of metalloenzyme structure, enzyme inhibition, and ligand binding affinities and scoring functions are used to illustrate the power of these techniques.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems

Science.gov (United States)

Winkelmann, Stefanie; Schütte, Christof

2017-09-01

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems.

Science.gov (United States)

Winkelmann, Stefanie; Schütte, Christof

2017-09-21

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.

SCOTCH: Secure Counting Of encrypTed genomiC data using a Hybrid approach.

Science.gov (United States)

Chenghong, Wang; Jiang, Yichen; Mohammed, Noman; Chen, Feng; Jiang, Xiaoqian; Al Aziz, Md Momin; Sadat, Md Nazmus; Wang, Shuang

2017-01-01

As genomic data are usually at large scale and highly sensitive, it is essential to enable both efficient and secure analysis, by which the data owner can securely delegate both computation and storage on untrusted public cloud. Counting query of genotypes is a basic function for many downstream applications in biomedical research (e.g., computing allele frequency, calculating chi-squared statistics, etc.). Previous solutions show promise on secure counting of outsourced data but the efficiency is still a big limitation for real world applications. In this paper, we propose a novel hybrid solution to combine a rigorous theoretical model (homomorphic encryption) and the latest hardware-based infrastructure (i.e., Software Guard Extensions) to speed up the computation while preserving the privacy of both data owners and data users. Our results demonstrated efficiency by using the real data from the personal genome project.

Computing Optimal Stochastic Portfolio Execution Strategies: A Parametric Approach Using Simulations

Science.gov (United States)

Moazeni, Somayeh; Coleman, Thomas F.; Li, Yuying

2010-09-01

Computing optimal stochastic portfolio execution strategies under appropriate risk consideration presents great computational challenge. We investigate a parametric approach for computing optimal stochastic strategies using Monte Carlo simulations. This approach allows reduction in computational complexity by computing coefficients for a parametric representation of a stochastic dynamic strategy based on static optimization. Using this technique, constraints can be similarly handled using appropriate penalty functions. We illustrate the proposed approach to minimize the expected execution cost and Conditional Value-at-Risk (CVaR).

Towards an Ecological Inquiry in Child-Computer Interaction

DEFF Research Database (Denmark)

Smith, Rachel Charlotte; Iversen, Ole Sejer; Hjermitslev, Thomas

2013-01-01

The paper introduces an Ecological Inquiry as a methodological approach for designing technology with children. The inquiry is based on the ‘ecological turn’ in HCI, Ubiquitous Computing and Participatory Design that shift the emphasis of design from technological artifacts to entire use ecologies...... into which technologies are integrated. Our Ecological Inquiry extends Cooperative Inquiry in three directions: from understanding to emergence of social practices and meanings, from design of artifacts to hybrid environments, and from a focus on technology to appropriations through design and use. We...... exemplify our approach in a case study in which we designed social technologies for hybrid learning environments with children in two schools, and discuss how an Ecological Inquiry can inform existing approaches in CCI....

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

A hybrid MCDM approach for ranking suppliers by considering ethical factors

OpenAIRE

Azadfallah, Mohammad

2016-01-01

One of the negative effects of cooperating with un-ethically behaving suppliers is that it may devastate the companies' credibility among employees, customers and the public. In this paper, a hybrid Multiple Criteria Decision Making (MCDM) approach (Disjunctive-WPM method) is proposed to resolve this limitation. The proposed methods consist of the following steps: 1. drop unethical solutions and 2. rank the remaining solutions. Therefore, the aim of t...

Hybrid closure of atrial septal defect: A modified approach

Directory of Open Access Journals (Sweden)

Kshitij Sheth

2015-01-01

Full Text Available A 3.5-year-old girl underwent transcatheter closure of patent ductus arteriosus in early infancy during which time her secundum atrial septal defect (ASD was left alone. When she came for elective closure of ASD, she was found to have bilaterally blocked femoral veins. The defect was successfully closed with an Amplatzer septal occluder (ASO; St. Jude Medical, Plymouth, MN, USA using a hybrid approach via a sub-mammary mini-thoracotomy incision without using cardiopulmonary bypass. At the end of 1-year follow-up, the child is asymptomatic with device in a stable position without any residual shunt.

Mathematical Modeling of Hybrid Electrical Engineering Systems

Directory of Open Access Journals (Sweden)

A. A. Lobaty

2016-01-01

Full Text Available A large class of systems that have found application in various industries and households, electrified transportation facilities and energy sector has been classified as electrical engineering systems. Their characteristic feature is a combination of continuous and discontinuous modes of operation, which is reflected in the appearance of a relatively new term “hybrid systems”. A wide class of hybrid systems is pulsed DC converters operating in a pulse width modulation, which are non-linear systems with variable structure. Using various methods for linearization it is possible to obtain linear mathematical models that rather accurately simulate behavior of such systems. However, the presence in the mathematical models of exponential nonlinearities creates considerable difficulties in the implementation of digital hardware. The solution can be found while using an approximation of exponential functions by polynomials of the first order, that, however, violates the rigor accordance of the analytical model with characteristics of a real object. There are two practical approaches to synthesize algorithms for control of hybrid systems. The first approach is based on the representation of the whole system by a discrete model which is described by difference equations that makes it possible to synthesize discrete algorithms. The second approach is based on description of the system by differential equations. The equations describe synthesis of continuous algorithms and their further implementation in a digital computer included in the control loop system. The paper considers modeling of a hybrid electrical engineering system using differential equations. Neglecting the pulse duration, it has been proposed to describe behavior of vector components in phase coordinates of the hybrid system by stochastic differential equations containing generally non-linear differentiable random functions. A stochastic vector-matrix equation describing dynamics of the

Computation and brain processes, with special reference to neuroendocrine systems.

Science.gov (United States)

Toni, Roberto; Spaletta, Giulia; Casa, Claudia Della; Ravera, Simone; Sandri, Giorgio

2007-01-01

" provides a continuum essential to chip "function". We conclude that it is reasonable to predict the existence of hybrid computational processes in the course of many human, brain integrating activities, urging development of cybernetic approaches based on this modelling for adequate reproduction of a variety of cerebral performances.

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review.

Science.gov (United States)

Gambhir, Shalini; Malik, Sanjay Kumar; Kumar, Yugal

2016-12-01

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for

A hybrid approach to parameter identification of linear delay differential equations involving multiple delays

Science.gov (United States)

Marzban, Hamid Reza

2018-05-01

In this paper, we are concerned with the parameter identification of linear time-invariant systems containing multiple delays. The approach is based upon a hybrid of block-pulse functions and Legendre's polynomials. The convergence of the proposed procedure is established and an upper error bound with respect to the L2-norm associated with the hybrid functions is derived. The problem under consideration is first transformed into a system of algebraic equations. The least squares technique is then employed for identification of the desired parameters. Several multi-delay systems of varying complexity are investigated to evaluate the performance and capability of the proposed approximation method. It is shown that the proposed approach is also applicable to a class of nonlinear multi-delay systems. It is demonstrated that the suggested procedure provides accurate results for the desired parameters.

Computational analysis of electrical conduction in hybrid nanomaterials with embedded non-penetrating conductive particles

Science.gov (United States)

Cai, Jizhe; Naraghi, Mohammad

2016-08-01

In this work, a comprehensive multi-resolution two-dimensional (2D) resistor network model is proposed to analyze the electrical conductivity of hybrid nanomaterials made of insulating matrix with conductive particles such as CNT reinforced nanocomposites and thick film resistors. Unlike existing approaches, our model takes into account the impenetrability of the particles and their random placement within the matrix. Moreover, our model presents a detailed description of intra-particle conductivity via finite element analysis, which to the authors’ best knowledge has not been addressed before. The inter-particle conductivity is assumed to be primarily due to electron tunneling. The model is then used to predict the electrical conductivity of electrospun carbon nanofibers as a function of microstructural parameters such as turbostratic domain alignment and aspect ratio. To simulate the microstructure of single CNF, randomly positioned nucleation sites were seeded and grown as turbostratic particles with anisotropic growth rates. Particle growth was in steps and growth of each particle in each direction was stopped upon contact with other particles. The study points to the significant contribution of both intra-particle and inter-particle conductivity to the overall conductivity of hybrid composites. Influence of particle alignment and anisotropic growth rate ratio on electrical conductivity is also discussed. The results show that partial alignment in contrast to complete alignment can result in maximum electrical conductivity of whole CNF. High degrees of alignment can adversely affect conductivity by lowering the probability of the formation of a conductive path. The results demonstrate approaches to enhance electrical conductivity of hybrid materials through controlling their microstructure which is applicable not only to carbon nanofibers, but also many other types of hybrid composites such as thick film resistors.

Hybrid Fourier pseudospectral/discontinuous Galerkin time-domain method for wave propagation

Science.gov (United States)

Pagán Muñoz, Raúl; Hornikx, Maarten

2017-11-01

The Fourier Pseudospectral time-domain (Fourier PSTD) method was shown to be an efficient way of modelling acoustic propagation problems as described by the linearized Euler equations (LEE), but is limited to real-valued frequency independent boundary conditions and predominantly staircase-like boundary shapes. This paper presents a hybrid approach to solve the LEE, coupling Fourier PSTD with a nodal Discontinuous Galerkin (DG) method. DG exhibits almost no restrictions with respect to geometrical complexity or boundary conditions. The aim of this novel method is to allow the computation of complex geometries and to be a step towards the implementation of frequency dependent boundary conditions by using the benefits of DG at the boundaries, while keeping the efficient Fourier PSTD in the bulk of the domain. The hybridization approach is based on conformal meshes to avoid spatial interpolation of the DG solutions when transferring values from DG to Fourier PSTD, while the data transfer from Fourier PSTD to DG is done utilizing spectral interpolation of the Fourier PSTD solutions. The accuracy of the hybrid approach is presented for one- and two-dimensional acoustic problems and the main sources of error are investigated. It is concluded that the hybrid methodology does not introduce significant errors compared to the Fourier PSTD stand-alone solver. An example of a cylinder scattering problem is presented and accurate results have been obtained when using the proposed approach. Finally, no instabilities were found during long-time calculation using the current hybrid methodology on a two-dimensional domain.

Optimization of a Continuous Hybrid Impeller Mixer via Computational Fluid Dynamics

Directory of Open Access Journals (Sweden)

N. Othman

2014-01-01

Full Text Available This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD using computational fluid dynamics (CFD. In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT. Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-ε turbulence model was performed using CFD techniques. The multiple reference frame (MRF was implemented and a steady state was initially achieved followed by a transient condition for RTD determination.

Computing magnetic anisotropy constants of single molecule magnets

Indian Academy of Sciences (India)

We present here a theoretical approach to compute the molecular magnetic anisotropy parameters, and for single molecule magnets in any given spin eigenstate of exchange spin Hamiltonian. We first describe a hybrid constant -valence bond (VB) technique of solving spin Hamiltonians employing full spatial ...

A hybrid approach to decision making and information fusion: Combining humans and artificial agents

NARCIS (Netherlands)

Groen, Frans C.A.; Pavlin, Gregor; Winterboer, Andi; Evers, Vanessa

This paper argues that hybrid human–agent systems can support powerful solutions to relevant problems such as Environmental Crisis management. However, it shows that such solutions require comprehensive approaches covering different aspects of data processing, model construction and the usage. In

Toward exascale computing through neuromorphic approaches.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D.

2010-09-01

While individual neurons function at relatively low firing rates, naturally-occurring nervous systems not only surpass manmade systems in computing power, but accomplish this feat using relatively little energy. It is asserted that the next major breakthrough in computing power will be achieved through application of neuromorphic approaches that mimic the mechanisms by which neural systems integrate and store massive quantities of data for real-time decision making. The proposed LDRD provides a conceptual foundation for SNL to make unique advances toward exascale computing. First, a team consisting of experts from the HPC, MESA, cognitive and biological sciences and nanotechnology domains will be coordinated to conduct an exercise with the outcome being a concept for applying neuromorphic computing to achieve exascale computing. It is anticipated that this concept will involve innovative extension and integration of SNL capabilities in MicroFab, material sciences, high-performance computing, and modeling and simulation of neural processes/systems.

Data of NODDI diffusion metrics in the brain and computer simulation of hybrid diffusion imaging (HYDI acquisition scheme

Directory of Open Access Journals (Sweden)

Chandana Kodiweera

2016-06-01

Full Text Available This article provides NODDI diffusion metrics in the brains of 52 healthy participants and computer simulation data to support compatibility of hybrid diffusion imaging (HYDI, “Hybrid diffusion imaging” [1] acquisition scheme in fitting neurite orientation dispersion and density imaging (NODDI model, “NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain” [2]. HYDI is an extremely versatile diffusion magnetic resonance imaging (dMRI technique that enables various analyzes methods using a single diffusion dataset. One of the diffusion data analysis methods is the NODDI computation, which models the brain tissue with three compartments: fast isotropic diffusion (e.g., cerebrospinal fluid, anisotropic hindered diffusion (e.g., extracellular space, and anisotropic restricted diffusion (e.g., intracellular space. The NODDI model produces microstructural metrics in the developing brain, aging brain or human brain with neurologic disorders. The first dataset provided here are the means and standard deviations of NODDI metrics in 48 white matter region-of-interest (ROI averaging across 52 healthy participants. The second dataset provided here is the computer simulation with initial conditions guided by the first dataset as inputs and gold standard for model fitting. The computer simulation data provide a direct comparison of NODDI indices computed from the HYDI acquisition [1] to the NODDI indices computed from the originally proposed acquisition [2]. These data are related to the accompanying research article “Age Effects and Sex Differences in Human Brain White Matter of Young to Middle-Aged Adults: A DTI, NODDI, and q-Space Study” [3].

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Science.gov (United States)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Software approach to automatic patching of analog computer

Science.gov (United States)

1973-01-01

The Automatic Patching Verification program (APV) is described which provides the hybrid computer programmer with a convenient method of performing a static check of the analog portion of his study. The static check insures that the program is patched as specified, and that the computing components being used are operating correctly. The APV language the programmer uses to specify his conditions and interconnections is similar to the FORTRAN language in syntax. The APV control program reads APV source program statements from an assigned input device. Each source program statement is processed immediately after it is read. A statement may select an analog console, set an analog mode, set a potentiometer or DAC, or read from the analog console and perform a test. Statements are read and processed sequentially. If an error condition is detected, an output occurs on an assigned output device. When an end statement is read, the test is terminated.

CoFe2O4-TiO2 Hybrid Nanomaterials: Synthesis Approaches Based on the Oil-in-Water Microemulsion Reaction Method

Directory of Open Access Journals (Sweden)

Arturo Adrián Rodríguez-Rodríguez

2017-01-01

Full Text Available CoFe2O4 nanoparticles decorated and wrapped with TiO2 nanoparticles have been prepared by mixing well-dispersed CoFe2O4 with amorphous TiO2 (impregnation approach and growing amorphous TiO2 over the magnetic core (seed approach, respectively, followed by thermal treatment to achieve TiO2 crystallinity. Synthesis strategies were based on the oil-in-water microemulsion reaction method. Thermally treated nanomaterials were characterized in terms of structure, morphology, and composition, to confirm hybrid nanoparticles formation and relate with the synthesis approaches; textural, optical, and magnetic properties were evaluated. X-ray diffraction revealed coexistence of cubic spinel-type CoFe2O4 and tetragonal anatase TiO2. Electron microscopy images depicted crystalline nanoparticles (sizes below 25 nm, with homogeneous Ti distribution for the hybrid nanoparticles synthesized by seed approach. EDX microanalysis and ICP-AES corroborated established chemical composition. XPS evidenced chemical states, as well as TiO2 predominance over CoFe2O4 surface. According to BET measurements, the hybrid nanoparticles were mesoporous. UV-Vis spectroscopy showed optical response along the UV-visible light region. Magnetic properties suggested the breaking order of magnetic domains due to modification with TiO2, especially for mediated seed approach sample. The properties of the obtained hybrid nanoparticles were different in comparison with its individual components. The results highlight the usefulness of designed microemulsion approaches for the straightforward synthesis of CoFe2O4-TiO2 nanostructured hybrids.

Recent Advances on Hybrid Intelligent Systems

CERN Document Server

Melin, Patricia; Kacprzyk, Janusz

2013-01-01

This book presents recent advances on hybrid intelligent systems using soft computing techniques for intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain groups of papers around a similar subject. The first part consists of papers with the main theme of hybrid intelligent systems for control and robotics, which are basically state of the art papers that propose new models and concepts, which can be the basis for achieving intelligent control and mobile robotics. The second part contains papers with the main theme of hybrid intelligent systems for pattern recognition and time series prediction, which are basically papers using nature-inspired techniques, like evolutionary algo...

Semiempirical Quantum Chemical Calculations Accelerated on a Hybrid Multicore CPU-GPU Computing Platform.

Science.gov (United States)

Wu, Xin; Koslowski, Axel; Thiel, Walter

2012-07-10

In this work, we demonstrate that semiempirical quantum chemical calculations can be accelerated significantly by leveraging the graphics processing unit (GPU) as a coprocessor on a hybrid multicore CPU-GPU computing platform. Semiempirical calculations using the MNDO, AM1, PM3, OM1, OM2, and OM3 model Hamiltonians were systematically profiled for three types of test systems (fullerenes, water clusters, and solvated crambin) to identify the most time-consuming sections of the code. The corresponding routines were ported to the GPU and optimized employing both existing library functions and a GPU kernel that carries out a sequence of noniterative Jacobi transformations during pseudodiagonalization. The overall computation times for single-point energy calculations and geometry optimizations of large molecules were reduced by one order of magnitude for all methods, as compared to runs on a single CPU core.

Time series analysis of infrared satellite data for detecting thermal anomalies: a hybrid approach

Science.gov (United States)

Koeppen, W. C.; Pilger, E.; Wright, R.

2011-07-01

We developed and tested an automated algorithm that analyzes thermal infrared satellite time series data to detect and quantify the excess energy radiated from thermal anomalies such as active volcanoes. Our algorithm enhances the previously developed MODVOLC approach, a simple point operation, by adding a more complex time series component based on the methods of the Robust Satellite Techniques (RST) algorithm. Using test sites at Anatahan and Kīlauea volcanoes, the hybrid time series approach detected ~15% more thermal anomalies than MODVOLC with very few, if any, known false detections. We also tested gas flares in the Cantarell oil field in the Gulf of Mexico as an end-member scenario representing very persistent thermal anomalies. At Cantarell, the hybrid algorithm showed only a slight improvement, but it did identify flares that were undetected by MODVOLC. We estimate that at least 80 MODIS images for each calendar month are required to create good reference images necessary for the time series analysis of the hybrid algorithm. The improved performance of the new algorithm over MODVOLC will result in the detection of low temperature thermal anomalies that will be useful in improving our ability to document Earth's volcanic eruptions, as well as detecting low temperature thermal precursors to larger eruptions.

PWR hybrid computer model for assessing the safety implications of control systems

International Nuclear Information System (INIS)

Smith, O.L.; Booth, R.S.; Clapp, N.E.; DiFilippo, F.C.; Renier, J.P.; Sozer, A.

1985-01-01

The ORNL study of safety-related aspects of control systems consists of two interrelated tasks, (1) a failure mode and effects analysis that, in part, identifies single and multiple component failures that may lead to significant plant upsets, and (2) a hybrid computer model that uses these failures as initial conditions and traces the dynamic impact on the control system and remainder of the plant. The second task is reported here. The initial step in model development was to define a suitable interface between the FMEA and computer simulation tasks. This involved identifying primary plant components that must be simulated in dynamic detail and secondary components that can be treated adequately by the FMEA alone. The FMEA in general explores broader spectra of initiating events that may collapse into a reduced number of computer runs. A portion of the FMEA includes consideration of power supply failures. Consequences of the transients may feedback on the initiating causes, and there may be an interactive relationship between the FMEA and the computer simulation. Since the thrust of this program is to investigate control system behavior, the controls are modeled in detail to accurately reproduce characteristic response under normal and off-normal transients. The balance of the model, including neutronics, thermohydraulics and component submodels, is developed in sufficient detail to provide a suitable support for the control system

Nanotubule and Tour Molecule Based Molecular Electronics: Suggestion for a Hybrid Approach

Science.gov (United States)

Srivastava, Deepak; Saini, Subhash (Technical Monitor)

1998-01-01

Recent experimental and theoretical attempts and results indicate two distinct broad pathways towards future molecular electronic devices and architectures. The first is the approach via Tour type ladder molecules and their junctions which can be fabricated with solution phase chemical approaches. Second are fullerenes or nanotubules and their junctions which may have better conductance, switching and amplifying characteristics but can not be made through well controlled and defined chemical means. A hybrid approach combining the two pathways to take advantage of the characteristics of both is suggested. Dimension and scale of such devices would be somewhere in between isolated molecule and nanotubule based devices but it maybe possible to use self-assembly towards larger functional and logicalunits.

A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

Science.gov (United States)

Oh, Yoo Rhee; Kim, Hong Kook

In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

Science.gov (United States)

Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

2016-08-06

In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

An efficient hybrid technique in RCS predictions of complex targets at high frequencies

Science.gov (United States)

Algar, María-Jesús; Lozano, Lorena; Moreno, Javier; González, Iván; Cátedra, Felipe

2017-09-01

Most computer codes in Radar Cross Section (RCS) prediction use Physical Optics (PO) and Physical theory of Diffraction (PTD) combined with Geometrical Optics (GO) and Geometrical Theory of Diffraction (GTD). The latter approaches are computationally cheaper and much more accurate for curved surfaces, but not applicable for the computation of the RCS of all surfaces of a complex object due to the presence of caustic problems in the analysis of concave surfaces or flat surfaces in the far field. The main contribution of this paper is the development of a hybrid method based on a new combination of two asymptotic techniques: GTD and PO, considering the advantages and avoiding the disadvantages of each of them. A very efficient and accurate method to analyze the RCS of complex structures at high frequencies is obtained with the new combination. The proposed new method has been validated comparing RCS results obtained for some simple cases using the proposed approach and RCS using the rigorous technique of Method of Moments (MoM). Some complex cases have been examined at high frequencies contrasting the results with PO. This study shows the accuracy and the efficiency of the hybrid method and its suitability for the computation of the RCS at really large and complex targets at high frequencies.

Computational experiment approach to advanced secondary mathematics curriculum

CERN Document Server

Abramovich, Sergei

2014-01-01

This book promotes the experimental mathematics approach in the context of secondary mathematics curriculum by exploring mathematical models depending on parameters that were typically considered advanced in the pre-digital education era. This approach, by drawing on the power of computers to perform numerical computations and graphical constructions, stimulates formal learning of mathematics through making sense of a computational experiment. It allows one (in the spirit of Freudenthal) to bridge serious mathematical content and contemporary teaching practice. In other words, the notion of teaching experiment can be extended to include a true mathematical experiment. When used appropriately, the approach creates conditions for collateral learning (in the spirit of Dewey) to occur including the development of skills important for engineering applications of mathematics. In the context of a mathematics teacher education program, this book addresses a call for the preparation of teachers capable of utilizing mo...

An approach for identification of unknown viruses using sequencing-by-hybridization.

Science.gov (United States)

Katoski, Sarah E; Meyer, Hermann; Ibrahim, Sofi

2015-09-01

Accurate identification of biological threat agents, especially RNA viruses, in clinical or environmental samples can be challenging because the concentration of viral genomic material in a given sample is usually low, viral genomic RNA is liable to degradation, and RNA viruses are extremely diverse. A two-tiered approach was used for initial identification, then full genomic characterization of 199 RNA viruses belonging to virus families Arenaviridae, Bunyaviridae, Filoviridae, Flaviviridae, and Togaviridae. A Sequencing-by-hybridization (SBH) microarray was used to tentatively identify a viral pathogen then, the identity is confirmed by guided next-generation sequencing (NGS). After optimization and evaluation of the SBH and NGS methodologies with various virus species and strains, the approach was used to test the ability to identify viruses in blinded samples. The SBH correctly identified two Ebola viruses in the blinded samples within 24 hr, and by using guided amplicon sequencing with 454 GS FLX, the identities of the viruses in both samples were confirmed. SBH provides at relatively low-cost screening of biological samples against a panel of viral pathogens that can be custom-designed on a microarray. Once the identity of virus is deduced from the highest hybridization signal on the SBH microarray, guided (amplicon) NGS sequencing can be used not only to confirm the identity of the virus but also to provide further information about the strain or isolate, including a potential genetic manipulation. This approach can be useful in situations where natural or deliberate biological threat incidents might occur and a rapid response is required. © 2015 Wiley Periodicals, Inc.

A hybrid approach for minimizing makespan in permutation flowshop scheduling

DEFF Research Database (Denmark)

Govindan, Kannan; Balasundaram, R.; Baskar, N.

2017-01-01

This work proposes a hybrid approach for solving traditional flowshop scheduling problems to reduce the makespan (total completion time). To solve scheduling problems, a combination of Decision Tree (DT) and Scatter Search (SS) algorithms are used. Initially, the DT is used to generate a seed...... solution which is then given input to the SS to obtain optimal / near optimal solutions of makespan. The DT used the entropy function to convert the given problem into a tree structured format / set of rules. The SS provides an extensive investigation of the search space through diversification...

What will be eventually true of polymomial hybrid automata

DEFF Research Database (Denmark)

Fränzle, Martin

2001-01-01

Hybrid automata have been introduced in both control engineering and computer science as a formal model for the dynamics of hybrid discrete-continuous systems. While computability issues concerning safety properties have been extensively studied, liveness properties have remained largely uninvest......Hybrid automata have been introduced in both control engineering and computer science as a formal model for the dynamics of hybrid discrete-continuous systems. While computability issues concerning safety properties have been extensively studied, liveness properties have remained largely...... uninvestigated. In this article, we investigate decidability of state recurrence and of progress properties. First, we show that state recurrence and progress are in general undecidable for polynomial hybrid automata. Then, we demonstrate that they are closely related for hybrid automata subject to a simple...... model of noise, even though these automata are infinite-state systems. Based on this, we augment a semi-decision procedure for recurrence with a semi-decision method for length-boundedness of paths in such a way that we obtain an automatic verification method for progress properties of linear...

Evaluation and Validation of Assembling Corrected PacBio Long Reads for Microbial Genome Completion via Hybrid Approaches.

Science.gov (United States)

Lin, Hsin-Hung; Liao, Yu-Chieh

2015-01-01

Despite the ever-increasing output of next-generation sequencing data along with developing assemblers, dozens to hundreds of gaps still exist in de novo microbial assemblies due to uneven coverage and large genomic repeats. Third-generation single-molecule, real-time (SMRT) sequencing technology avoids amplification artifacts and generates kilobase-long reads with the potential to complete microbial genome assembly. However, due to the low accuracy (~85%) of third-generation sequences, a considerable amount of long reads (>50X) are required for self-correction and for subsequent de novo assembly. Recently-developed hybrid approaches, using next-generation sequencing data and as few as 5X long reads, have been proposed to improve the completeness of microbial assembly. In this study we have evaluated the contemporary hybrid approaches and demonstrated that assembling corrected long reads (by runCA) produced the best assembly compared to long-read scaffolding (e.g., AHA, Cerulean and SSPACE-LongRead) and gap-filling (SPAdes). For generating corrected long reads, we further examined long-read correction tools, such as ECTools, LSC, LoRDEC, PBcR pipeline and proovread. We have demonstrated that three microbial genomes including Escherichia coli K12 MG1655, Meiothermus ruber DSM1279 and Pdeobacter heparinus DSM2366 were successfully hybrid assembled by runCA into near-perfect assemblies using ECTools-corrected long reads. In addition, we developed a tool, Patch, which implements corrected long reads and pre-assembled contigs as inputs, to enhance microbial genome assemblies. With the additional 20X long reads, short reads of S. cerevisiae W303 were hybrid assembled into 115 contigs using the verified strategy, ECTools + runCA. Patch was subsequently applied to upgrade the assembly to a 35-contig draft genome. Our evaluation of the hybrid approaches shows that assembling the ECTools-corrected long reads via runCA generates near complete microbial genomes, suggesting

Design-order, non-conformal low-Mach fluid algorithms using a hybrid CVFEM/DG approach

Science.gov (United States)

Domino, Stefan P.

2018-04-01

A hybrid, design-order sliding mesh algorithm, which uses a control volume finite element method (CVFEM), in conjunction with a discontinuous Galerkin (DG) approach at non-conformal interfaces, is outlined in the context of a low-Mach fluid dynamics equation set. This novel hybrid DG approach is also demonstrated to be compatible with a classic edge-based vertex centered (EBVC) scheme. For the CVFEM, element polynomial, P, promotion is used to extend the low-order P = 1 CVFEM method to higher-order, i.e., P = 2. An equal-order low-Mach pressure-stabilized methodology, with emphasis on the non-conformal interface boundary condition, is presented. A fully implicit matrix solver approach that accounts for the full stencil connectivity across the non-conformal interface is employed. A complete suite of formal verification studies using the method of manufactured solutions (MMS) is performed to verify the order of accuracy of the underlying methodology. The chosen suite of analytical verification cases range from a simple steady diffusion system to a traveling viscous vortex across mixed-order non-conformal interfaces. Results from all verification studies demonstrate either second- or third-order spatial accuracy and, for transient solutions, second-order temporal accuracy. Significant accuracy gains in manufactured solution error norms are noted even with modest promotion of the underlying polynomial order. The paper also demonstrates the CVFEM/DG methodology on two production-like simulation cases that include an inner block subjected to solid rotation, i.e., each of the simulations include a sliding mesh, non-conformal interface. The first production case presented is a turbulent flow past a high-rate-of-rotation cube (Re, 4000; RPM, 3600) on like and mixed-order polynomial interfaces. The final simulation case is a full-scale Vestas V27 225 kW wind turbine (tower and nacelle omitted) in which a hybrid topology, low-order mesh is used. Both production simulations

A HOLISTIC APPROACH FOR INSPECTION OF CIVIL INFRASTRUCTURES BASED ON COMPUTER VISION TECHNIQUES

Directory of Open Access Journals (Sweden)

C. Stentoumis

2016-06-01

Full Text Available In this work, it is examined the 2D recognition and 3D modelling of concrete tunnel cracks, through visual cues. At the time being, the structural integrity inspection of large-scale infrastructures is mainly performed through visual observations by human inspectors, who identify structural defects, rate them and, then, categorize their severity. The described approach targets at minimum human intervention, for autonomous inspection of civil infrastructures. The shortfalls of existing approaches in crack assessment are being addressed by proposing a novel detection scheme. Although efforts have been made in the field, synergies among proposed techniques are still missing. The holistic approach of this paper exploits the state of the art techniques of pattern recognition and stereo-matching, in order to build accurate 3D crack models. The innovation lies in the hybrid approach for the CNN detector initialization, and the use of the modified census transformation for stereo matching along with a binary fusion of two state-of-the-art optimization schemes. The described approach manages to deal with images of harsh radiometry, along with severe radiometric differences in the stereo pair. The effectiveness of this workflow is evaluated on a real dataset gathered in highway and railway tunnels. What is promising is that the computer vision workflow described in this work can be transferred, with adaptations of course, to other infrastructure such as pipelines, bridges and large industrial facilities that are in the need of continuous state assessment during their operational life cycle.

Computing sextic centrifugal distortion constants by DFT: A benchmark analysis on halogenated compounds

Science.gov (United States)

Pietropolli Charmet, Andrea; Stoppa, Paolo; Tasinato, Nicola; Giorgianni, Santi

2017-05-01

This work presents a benchmark study on the calculation of the sextic centrifugal distortion constants employing cubic force fields computed by means of density functional theory (DFT). For a set of semi-rigid halogenated organic compounds several functionals (B2PLYP, B3LYP, B3PW91, M06, M06-2X, O3LYP, X3LYP, ωB97XD, CAM-B3LYP, LC-ωPBE, PBE0, B97-1 and B97-D) were used for computing the sextic centrifugal distortion constants. The effects related to the size of basis sets and the performances of hybrid approaches, where the harmonic data obtained at higher level of electronic correlation are coupled with cubic force constants yielded by DFT functionals, are presented and discussed. The predicted values were compared to both the available data published in the literature and those obtained by calculations carried out at increasing level of electronic correlation: Hartree-Fock Self Consistent Field (HF-SCF), second order Møller-Plesset perturbation theory (MP2), and coupled-cluster single and double (CCSD) level of theory. Different hybrid approaches, having the cubic force field computed at DFT level of theory coupled to harmonic data computed at increasing level of electronic correlation (up to CCSD level of theory augmented by a perturbational estimate of the effects of connected triple excitations, CCSD(T)) were considered. The obtained results demonstrate that they can represent reliable and computationally affordable methods to predict sextic centrifugal terms with an accuracy almost comparable to that yielded by the more expensive anharmonic force fields fully computed at MP2 and CCSD levels of theory. In view of their reduced computational cost, these hybrid approaches pave the route to the study of more complex systems.

Hybrid Information Flow Analysis for Programs with Arrays

Directory of Open Access Journals (Sweden)

Gergö Barany

2016-07-01

Full Text Available Information flow analysis checks whether certain pieces of (confidential data may affect the results of computations in unwanted ways and thus leak information. Dynamic information flow analysis adds instrumentation code to the target software to track flows at run time and raise alarms if a flow policy is violated; hybrid analyses combine this with preliminary static analysis. Using a subset of C as the target language, we extend previous work on hybrid information flow analysis that handled pointers to scalars. Our extended formulation handles arrays, pointers to array elements, and pointer arithmetic. Information flow through arrays of pointers is tracked precisely while arrays of non-pointer types are summarized efficiently. A prototype of our approach is implemented using the Frama-C program analysis and transformation framework. Work on a full machine-checked proof of the correctness of our approach using Isabelle/HOL is well underway; we present the existing parts and sketch the rest of the correctness argument.

Software Systems for High-performance Quantum Computing

Energy Technology Data Exchange (ETDEWEB)

Humble, Travis S [ORNL; Britt, Keith A [ORNL

2016-01-01

Quantum computing promises new opportunities for solving hard computational problems, but harnessing this novelty requires breakthrough concepts in the design, operation, and application of computing systems. We define some of the challenges facing the development of quantum computing systems as well as software-based approaches that can be used to overcome these challenges. Following a brief overview of the state of the art, we present models for the quantum programming and execution models, the development of architectures for hybrid high-performance computing systems, and the realization of software stacks for quantum networking. This leads to a discussion of the role that conventional computing plays in the quantum paradigm and how some of the current challenges for exascale computing overlap with those facing quantum computing.

Communication: Hole localization in Al-doped quartz SiO{sub 2} within ab initio hybrid-functional DFT

Energy Technology Data Exchange (ETDEWEB)

Gerosa, Matteo [Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Di Valentin, Cristiana; Pacchioni, Gianfranco [Dipartimento di Scienza dei Materiali, Università di Milano-Bicocca, via R. Cozzi 55, 20125 Milan (Italy); Bottani, Carlo Enrico, E-mail: carlo.bottani@polimi.it [Department of Energy, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Center for Nano Science and Technology @Polimi, Istituto Italiano di Tecnologia, via Pascoli 70/3, 20133 Milano (Italy); Onida, Giovanni [Dipartimento di Fisica dell’ Universita’ degli Studi di Milano and European Theoretical Spectroscopy Facility (ETSF), Via Celoria 16, 20133 Milan (Italy)

2015-09-21

We investigate the long-standing problem of hole localization at the Al impurity in quartz SiO{sub 2}, using a relatively recent DFT hybrid-functional method in which the exchange fraction is obtained ab initio, based on an analogy with the static many-body COHSEX approximation to the electron self-energy. As the amount of the admixed exact exchange in hybrid functionals has been shown to be determinant for properly capturing the hole localization, this problem constitutes a prototypical benchmark for the accuracy of the method, allowing one to assess to what extent self-interaction effects are avoided. We obtain good results in terms of description of the charge localization and structural distortion around the Al center, improving with respect to the more popular B3LYP hybrid-functional approach. We also discuss the accuracy of computed hyperfine parameters, by comparison with previous calculations based on other self-interaction-free methods, as well as experimental values. We discuss and rationalize the limitations of our approach in computing defect-related excitation energies in low-dielectric-constant insulators.

Numerical Solution of Piecewise Constant Delay Systems Based on a Hybrid Framework

Directory of Open Access Journals (Sweden)

H. R. Marzban

2016-01-01

Full Text Available An efficient numerical scheme for solving delay differential equations with a piecewise constant delay function is developed in this paper. The proposed approach is based on a hybrid of block-pulse functions and Taylor’s polynomials. The operational matrix of delay corresponding to the proposed hybrid functions is introduced. The sparsity of this matrix significantly reduces the computation time and memory requirement. The operational matrices of integration, delay, and product are employed to transform the problem under consideration into a system of algebraic equations. It is shown that the developed approach is also applicable to a special class of nonlinear piecewise constant delay differential equations. Several numerical experiments are examined to verify the validity and applicability of the presented technique.

Electric/Hybrid Vehicle Simulation

Science.gov (United States)

Slusser, R. A.; Chapman, C. P.; Brennand, J. P.

1985-01-01

ELVEC computer program provides vehicle designer with simulation tool for detailed studies of electric and hybrid vehicle performance and cost. ELVEC simulates performance of user-specified electric or hybrid vehicle under user specified driving schedule profile or operating schedule. ELVEC performs vehicle design and life cycle cost analysis.

A gas kinetic scheme for hybrid simulation of partially rarefied flows

Science.gov (United States)

Colonia, S.; Steijl, R.; Barakos, G.

2017-06-01

Approaches to predict flow fields that display rarefaction effects incur a cost in computational time and memory considerably higher than methods commonly employed for continuum flows. For this reason, to simulate flow fields where continuum and rarefied regimes coexist, hybrid techniques have been introduced. In the present work, analytically defined gas-kinetic schemes based on the Shakhov and Rykov models for monoatomic and diatomic gas flows, respectively, are proposed and evaluated with the aim to be used in the context of hybrid simulations. This should reduce the region where more expensive methods are needed by extending the validity of the continuum formulation. Moreover, since for high-speed rare¦ed gas flows it is necessary to take into account the nonequilibrium among the internal degrees of freedom, the extension of the approach to employ diatomic gas models including rotational relaxation process is a mandatory first step towards realistic simulations. Compared to previous works of Xu and coworkers, the presented scheme is de¦ned directly on the basis of kinetic models which involve a Prandtl number correction. Moreover, the methods are defined fully analytically instead of making use of Taylor expansion for the evaluation of the required derivatives. The scheme has been tested for various test cases and Mach numbers proving to produce reliable predictions in agreement with other approaches for near-continuum flows. Finally, the performance of the scheme, in terms of memory and computational time, compared to discrete velocity methods makes it a compelling alternative in place of more complex methods for hybrid simulations of weakly rarefied flows.

Multilayer Approach for Advanced Hybrid Lithium Battery

KAUST Repository

Ming, Jun

2016-06-06

Conventional intercalated rechargeable batteries have shown their capacity limit, and the development of an alternative battery system with higher capacity is strongly needed for sustainable electrical vehicles and hand-held devices. Herein, we introduce a feasible and scalable multilayer approach to fabricate a promising hybrid lithium battery with superior capacity and multivoltage plateaus. A sulfur-rich electrode (90 wt % S) is covered by a dual layer of graphite/Li4Ti5O12, where the active materials S and Li4Ti5O12 can both take part in redox reactions and thus deliver a high capacity of 572 mAh gcathode -1 (vs the total mass of electrode) or 1866 mAh gs -1 (vs the mass of sulfur) at 0.1C (with the definition of 1C = 1675 mA gs -1). The battery shows unique voltage platforms at 2.35 and 2.1 V, contributed from S, and 1.55 V from Li4Ti5O12. A high rate capability of 566 mAh gcathode -1 at 0.25C and 376 mAh gcathode -1 at 1C with durable cycle ability over 100 cycles can be achieved. Operando Raman and electron microscope analysis confirm that the graphite/Li4Ti5O12 layer slows the dissolution/migration of polysulfides, thereby giving rise to a higher sulfur utilization and a slower capacity decay. This advanced hybrid battery with a multilayer concept for marrying different voltage plateaus from various electrode materials opens a way of providing tunable capacity and multiple voltage platforms for energy device applications. © 2016 American Chemical Society.

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

Directory of Open Access Journals (Sweden)

Christoff Fourie

2014-11-01

Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

Hybrid-augmented intelligence:collaboration and cognition

Institute of Scientific and Technical Information of China (English)

Nan-ning ZHENG; Zi-yi LIU; Peng-ju REN; Yong-qiang MA; Shi-tao CHEN; Si-yu YU; Jian-ru XUE

2017-01-01

The long-term goal of artificial intelligence (AI) is to make machines learn and think like human beings. Due to the high levels of uncertainty and vulnerability in human life and the open-ended nature of problems that humans are facing, no matter how intelligent machines are, they are unable to completely replace humans. Therefore, it is necessary to introduce human cognitive capabilities or human-like cognitive models into AI systems to develop a new form of AI, that is, hybrid-augmented intelligence. This form of AI or machine intelligence is a feasible and important developing model. Hybrid-augmented intelligence can be divided into two basic models:one is human-in-the-loop augmented intelligence with human-computer collaboration, and the other is cognitive computing based augmented intelligence, in which a cognitive model is embedded in the machine learning system. This survey describes a basic framework for human-computer collaborative hybrid-augmented intelligence, and the basic elements of hybrid-augmented intelligence based on cognitive computing. These elements include intuitive reasoning, causal models, evolution of memory and knowledge, especially the role and basic principles of intuitive reasoning for complex problem solving, and the cognitive learning framework for visual scene understanding based on memory and reasoning. Several typical applications of hybrid-augmented intelligence in related fields are given.

A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

Science.gov (United States)

Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.

2000-01-01

Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

COED Transactions, Vol. X, No. 10, October 1978. Simulation of a Sampled-Data System on a Hybrid Computer.

Science.gov (United States)

Mitchell, Eugene E., Ed.

The simulation of a sampled-data system is described that uses a full parallel hybrid computer. The sampled data system simulated illustrates the proportional-integral-derivative (PID) discrete control of a continuous second-order process representing a stirred-tank. The stirred-tank is simulated using continuous analog components, while PID…

ANIBAL - a Hybrid Computer Language for EAI 680-PDP 8/I, FPP 12

DEFF Research Database (Denmark)

Højberg, Kristian Søe

1974-01-01

and special hybrid computer commands. ANIBAL consists of a general-purpose analog interface subroutine ANI and the macro processor 8BAL (DECUS NO 8-497A.1). When a source program with FORTRAN and 8BAL statements is processed, the FORTRAN statements are transferred unchanged, while the 8BAL code is translated...... essentially to ANI sub-routine calls, which are defined in a macro library. The resulting code is translated by the standard FORTRAN compiler. The language is very flexible as the instructions can be changed and commands can be added to or excluded from the library ....

An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand.

Science.gov (United States)

Soekadar, Surjo R; Witkowski, Matthias; Vitiello, Nicola; Birbaumer, Niels

2015-06-01

The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5 ± 3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24 ± 16.71%, BPI: 65.93 ± 24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65 ± 11.28, BPI: 76.03 ± 18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments.

Hybrid method based on embedded coupled simulation of vortex particles in grid based solution

Science.gov (United States)

Kornev, Nikolai

2017-09-01

The paper presents a novel hybrid approach developed to improve the resolution of concentrated vortices in computational fluid mechanics. The method is based on combination of a grid based and the grid free computational vortex (CVM) methods. The large scale flow structures are simulated on the grid whereas the concentrated structures are modeled using CVM. Due to this combination the advantages of both methods are strengthened whereas the disadvantages are diminished. The procedure of the separation of small concentrated vortices from the large scale ones is based on LES filtering idea. The flow dynamics is governed by two coupled transport equations taking two-way interaction between large and fine structures into account. The fine structures are mapped back to the grid if their size grows due to diffusion. Algorithmic aspects of the hybrid method are discussed. Advantages of the new approach are illustrated on some simple two dimensional canonical flows containing concentrated vortices.

Convergence Analysis of a Class of Computational Intelligence Approaches

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Junfeng Chen

2013-01-01

Full Text Available Computational intelligence approaches is a relatively new interdisciplinary field of research with many promising application areas. Although the computational intelligence approaches have gained huge popularity, it is difficult to analyze the convergence. In this paper, a computational model is built up for a class of computational intelligence approaches represented by the canonical forms of generic algorithms, ant colony optimization, and particle swarm optimization in order to describe the common features of these algorithms. And then, two quantification indices, that is, the variation rate and the progress rate, are defined, respectively, to indicate the variety and the optimality of the solution sets generated in the search process of the model. Moreover, we give four types of probabilistic convergence for the solution set updating sequences, and their relations are discussed. Finally, the sufficient conditions are derived for the almost sure weak convergence and the almost sure strong convergence of the model by introducing the martingale theory into the Markov chain analysis.

A Hybrid Approach to Processing Big Data Graphs on Memory-Restricted Systems

KAUST Repository

Harshvardhan,

2015-05-01

With the advent of big-data, processing large graphs quickly has become increasingly important. Most existing approaches either utilize in-memory processing techniques that can only process graphs that fit completely in RAM, or disk-based techniques that sacrifice performance. In this work, we propose a novel RAM-Disk hybrid approach to graph processing that can scale well from a single shared-memory node to large distributed-memory systems. It works by partitioning the graph into sub graphs that fit in RAM and uses a paging-like technique to load sub graphs. We show that without modifying the algorithms, this approach can scale from small memory-constrained systems (such as tablets) to large-scale distributed machines with 16, 000+ cores.

Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

Directory of Open Access Journals (Sweden)

Yan Li

2018-01-01

Full Text Available In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

Developing traction control strategy for a plug-in hybrid electric vehicle using innovative optimization based approaches

Energy Technology Data Exchange (ETDEWEB)

Zhou, L.; Gu, J.; Dong, Z. [Victoria Univ., BC (Canada). Dept. of Mechanical Engineering

2010-07-01

This paper described a traction control system designed for hybrid vehicles with multiple power plants and drive axles. Model-based design tools were used to develop the traction control system and plug-in hybrid vehicle models. Optimization studies were conducted in a finite number of operating states in order to maximize the electrical and mechanical energy conversion efficiency of an extended range electric vehicle. Four global optimization algorithms were then evaluated in relation to their CPU times. The studied algorithms included a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm, a hybrid adaptive metamodel optimization (HAM) and space elimination and unimodal region reduction (SEUMRE) algorithm. A comparative evaluation of the algorithms demonstrated that the PSO algorithm obtained optimal results, while the HAM algorithm used significantly less computational time. Results of the optimization studies were then implemented in a controller model. Results of the study showed that the energy efficiency of the vehicle improved using the developed controller model. 4 refs., 2 tabs., 8 figs.

An optimal control strategy for hybrid actuator systems: Application to an artificial muscle with electric motor assist.

Science.gov (United States)

Ishihara, Koji; Morimoto, Jun

2018-03-01

Humans use multiple muscles to generate such joint movements as an elbow motion. With multiple lightweight and compliant actuators, joint movements can also be efficiently generated. Similarly, robots can use multiple actuators to efficiently generate a one degree of freedom movement. For this movement, the desired joint torque must be properly distributed to each actuator. One approach to cope with this torque distribution problem is an optimal control method. However, solving the optimal control problem at each control time step has not been deemed a practical approach due to its large computational burden. In this paper, we propose a computationally efficient method to derive an optimal control strategy for a hybrid actuation system composed of multiple actuators, where each actuator has different dynamical properties. We investigated a singularly perturbed system of the hybrid actuator model that subdivided the original large-scale control problem into smaller subproblems so that the optimal control outputs for each actuator can be derived at each control time step and applied our proposed method to our pneumatic-electric hybrid actuator system. Our method derived a torque distribution strategy for the hybrid actuator by dealing with the difficulty of solving real-time optimal control problems. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Hybrid treatment of penetrating aortic ulcer

International Nuclear Information System (INIS)

Lara, Juan Antonio Herrero; Martins-Romeo, Daniela de Araujo; Escudero, Carlos Caparros; Falcon, Maria del Carmen Prieto; Batista, Vinicius Bianchi; Vazquez, Rosa Maria Lepe

2015-01-01

Penetrating atherosclerotic aortic ulcer is a rare entity with poor prognosis in the setting of acute aortic syndrome. In the literature, cases like the present one, located in the aortic arch, starting with chest pain and evolving with dysphonia, are even rarer. The present report emphasizes the role played by computed tomography in the diagnosis of penetrating atherosclerotic ulcer as well as in the differentiation of this condition from other acute aortic syndromes. Additionally, the authors describe a new therapeutic approach represented by a hybrid endovascular surgical procedure for treatment of the disease. (author)

Hybrid treatment of penetrating aortic ulcer

Energy Technology Data Exchange (ETDEWEB)

Lara, Juan Antonio Herrero; Martins-Romeo, Daniela de Araujo; Escudero, Carlos Caparros; Falcon, Maria del Carmen Prieto; Batista, Vinicius Bianchi, E-mail: jaherrero5@hotmail.com [Unidade de Gestao Clinica (UGC) de Diagnostico por Imagem - Hosppital Universitario Virgen Macarena, Sevilha (Spain); Vazquez, Rosa Maria Lepe [Unit of Radiodiagnosis - Hospital Nuestra Senora de la Merced, Osuna, Sevilha (Spain)

2015-05-15

Penetrating atherosclerotic aortic ulcer is a rare entity with poor prognosis in the setting of acute aortic syndrome. In the literature, cases like the present one, located in the aortic arch, starting with chest pain and evolving with dysphonia, are even rarer. The present report emphasizes the role played by computed tomography in the diagnosis of penetrating atherosclerotic ulcer as well as in the differentiation of this condition from other acute aortic syndromes. Additionally, the authors describe a new therapeutic approach represented by a hybrid endovascular surgical procedure for treatment of the disease. (author)

A comparative approach to closed-loop computation.

Science.gov (United States)

Roth, E; Sponberg, S; Cowan, N J

2014-04-01

Neural computation is inescapably closed-loop: the nervous system processes sensory signals to shape motor output, and motor output consequently shapes sensory input. Technological advances have enabled neuroscientists to close, open, and alter feedback loops in a wide range of experimental preparations. The experimental capability of manipulating the topology-that is, how information can flow between subsystems-provides new opportunities to understand the mechanisms and computations underlying behavior. These experiments encompass a spectrum of approaches from fully open-loop, restrained preparations to the fully closed-loop character of free behavior. Control theory and system identification provide a clear computational framework for relating these experimental approaches. We describe recent progress and new directions for translating experiments at one level in this spectrum to predictions at another level. Operating across this spectrum can reveal new understanding of how low-level neural mechanisms relate to high-level function during closed-loop behavior. Copyright © 2013 Elsevier Ltd. All rights reserved.

Simulation of a Wall-Bounded Flow using a Hybrid LES/RAS Approach with Turbulence Recycling

Science.gov (United States)

Quinlan, Jesse R.; Mcdaniel, James; Baurle, Robert A.

2012-01-01

Simulations of a supersonic recessed-cavity flow are performed using a hybrid large-eddy/ Reynolds-averaged simulation approach utilizing an inflow turbulence recycling procedure and hybridized inviscid flux scheme. Calorically perfect air enters the three-dimensional domain at a free stream Mach number of 2.92. Simulations are performed to assess grid sensitivity of the solution, efficacy of the turbulence recycling, and effect of the shock sensor used with the hybridized inviscid flux scheme. Analysis of the turbulent boundary layer upstream of the rearward-facing step for each case indicates excellent agreement with theoretical predictions. Mean velocity and pressure results are compared to Reynolds-averaged simulations and experimental data for each case, and these comparisons indicate good agreement on the finest grid. Simulations are repeated on a coarsened grid, and results indicate strong grid density sensitivity. The effect of turbulence recycling on the solution is illustrated by performing coarse grid simulations with and without inflow turbulence recycling. Two shock sensors, one of Ducros and one of Larsson, are assessed for use with the hybridized inviscid flux reconstruction scheme.

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Directory of Open Access Journals (Sweden)

Narinder Singh

2018-03-01

Full Text Available The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO and Whale Optimizer Algorithm (WOA. We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO algorithm: (i firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

Directory of Open Access Journals (Sweden)

Jinyi Long

2017-01-01

Full Text Available The hybrid brain computer interface (BCI based on motor imagery (MI and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Directory of Open Access Journals (Sweden)

Ahmed Hussein

2018-01-01

Full Text Available Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury.

Science.gov (United States)

Rohm, Martin; Schneiders, Matthias; Müller, Constantin; Kreilinger, Alex; Kaiser, Vera; Müller-Putz, Gernot R; Rupp, Rüdiger

2013-10-01

The bilateral loss of the grasp function associated with a lesion of the cervical spinal cord severely limits the affected individuals' ability to live independently and return to gainful employment after sustaining a spinal cord injury (SCI). Any improvement in lost or limited grasp function is highly desirable. With current neuroprostheses, relevant improvements can be achieved in end users with preserved shoulder and elbow, but missing hand function. The aim of this single case study is to show that (1) with the support of hybrid neuroprostheses combining functional electrical stimulation (FES) with orthoses, restoration of hand, finger and elbow function is possible in users with high-level SCI and (2) shared control principles can be effectively used to allow for a brain-computer interface (BCI) control, even if only moderate BCI performance is achieved after extensive training. The individual in this study is a right-handed 41-year-old man who sustained a traumatic SCI in 2009 and has a complete motor and sensory lesion at the level of C4. He is unable to generate functionally relevant movements of the elbow, hand and fingers on either side. He underwent extensive FES training (30-45min, 2-3 times per week for 6 months) and motor imagery (MI) BCI training (415 runs in 43 sessions over 12 months). To meet individual needs, the system was designed in a modular fashion including an intelligent control approach encompassing two input modalities, namely an MI-BCI and shoulder movements. After one year of training, the end user's MI-BCI performance ranged from 50% to 93% (average: 70.5%). The performance of the hybrid system was evaluated with different functional assessments. The user was able to transfer objects of the grasp-and-release-test and he succeeded in eating a pretzel stick, signing a document and eating an ice cream cone, which he was unable to do without the system. This proof-of-concept study has demonstrated that with the support of hybrid FES

The hybrid thermography approach applied to architectural structures

Science.gov (United States)

Sfarra, S.; Ambrosini, D.; Paoletti, D.; Nardi, I.; Pasqualoni, G.

2017-07-01

This work contains an overview of infrared thermography (IRT) method and its applications relating to the investigation of architectural structures. In this method, the passive approach is usually used in civil engineering, since it provides a panoramic view of the thermal anomalies to be interpreted also thanks to the use of photographs focused on the region of interest (ROI). The active approach, is more suitable for laboratory or indoor inspections, as well as for objects having a small size. The external stress to be applied is thermal, coming from non-natural apparatus such as lamps or hot / cold air jets. In addition, the latter permits to obtain quantitative information related to defects not detectable to the naked eyes. Very recently, the hybrid thermography (HIRT) approach has been introduced to the attention of the scientific panorama. It can be applied when the radiation coming from the sun, directly arrives (i.e., possibly without the shadow cast effect) on a surface exposed to the air. A large number of thermograms must be collected and a post-processing analysis is subsequently applied via advanced algorithms. Therefore, an appraisal of the defect depth can be obtained passing through the calculation of the combined thermal diffusivity of the materials above the defect. The approach is validated herein by working, in a first stage, on a mosaic sample having known defects while, in a second stage, on a Church built in L'Aquila (Italy) and covered with a particular masonry structure called apparecchio aquilano. The results obtained appear promising.

EEG Classification for Hybrid Brain-Computer Interface Using a Tensor Based Multiclass Multimodal Analysis Scheme.

Science.gov (United States)

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.

Integration of Computed Tomography and Three-Dimensional Echocardiography for Hybrid Three-Dimensional Printing in Congenital Heart Disease.

Science.gov (United States)

Gosnell, Jordan; Pietila, Todd; Samuel, Bennett P; Kurup, Harikrishnan K N; Haw, Marcus P; Vettukattil, Joseph J

2016-12-01

Three-dimensional (3D) printing is an emerging technology aiding diagnostics, education, and interventional, and surgical planning in congenital heart disease (CHD). Three-dimensional printing has been derived from computed tomography, cardiac magnetic resonance, and 3D echocardiography. However, individually the imaging modalities may not provide adequate visualization of complex CHD. The integration of the strengths of two or more imaging modalities has the potential to enhance visualization of cardiac pathomorphology. We describe the feasibility of hybrid 3D printing from two imaging modalities in a patient with congenitally corrected transposition of the great arteries (L-TGA). Hybrid 3D printing may be useful as an additional tool for cardiologists and cardiothoracic surgeons in planning interventions in children and adults with CHD.

Early experience with the Occlutech PLD occluder for mitral paravalvar leak closure through a hybrid transapical approach.

Science.gov (United States)

Bedair, Radwa; Morgan, Gareth J; Bapat, Vinayak; Kapetanakis, Stamatis; Goreczny, Sebastian; Simpson, John; Qureshi, Shakeel A

2016-12-10

We sought to evaluate the feasibility and efficacy of hybrid transapical closure of paravalvar mitral leaks using a new Occlutech PLD occluder in patients with heart failure and/or haemolytic anaemia. Retrospective analysis of clinical and procedural data was undertaken for patients who had attempted closure of paravalvar mitral leaks via a hybrid transapical approach with the Occlutech PLD occluder. Eight patients (four males, median age 69 years) underwent closure of 10 mitral paravalvar leaks using eight Occlutech PLD occluders and two AMPLATZER Vascular Plugs (AVP II). Successful deployment, with significant reduction of the paravalvar leak was achieved in seven patients with short procedure (median 131 min) and fluoroscopy times (median 22 min). One patient had mechanical interference with prosthetic valve function, requiring surgery. Another patient with a high EuroSCORE (48.8%) died of multi-organ failure two days after the procedure. Clinical improvement in either heart failure or haemolysis was seen in all discharged patients. In our series of patients with challenging anatomy, the Occlutech PLD occluders performed well when implanted via a hybrid transapical approach. Further work is needed to assess this methodology fully for a wider population and to assess other deployment approaches for this promising new occluder.

Automated DNA electrophoresis, hybridization and detection

International Nuclear Information System (INIS)

Zapolski, E.J.; Gersten, D.M.; Golab, T.J.; Ledley, R.S.

1986-01-01

A fully automated, computer controlled system for nucleic acid hybridization analysis has been devised and constructed. In practice, DNA is digested with restriction endonuclease enzyme(s) and loaded into the system by pipette; 32 P-labelled nucleic acid probe(s) is loaded into the nine hybridization chambers. Instructions for all the steps in the automated process are specified by answering questions that appear on the computer screen at the start of the experiment. Subsequent steps are performed automatically. The system performs horizontal electrophoresis in agarose gel, fixed the fragments to a solid phase matrix, denatures, neutralizes, prehybridizes, hybridizes, washes, dries and detects the radioactivity according to the specifications given by the operator. The results, printed out at the end, give the positions on the matrix to which radioactivity remains hybridized following stringent washing

Computational Approaches to Nucleic Acid Origami.

Science.gov (United States)

Jabbari, Hosna; Aminpour, Maral; Montemagno, Carlo

2015-10-12

Recent advances in experimental DNA origami have dramatically expanded the horizon of DNA nanotechnology. Complex 3D suprastructures have been designed and developed using DNA origami with applications in biomaterial science, nanomedicine, nanorobotics, and molecular computation. Ribonucleic acid (RNA) origami has recently been realized as a new approach. Similar to DNA, RNA molecules can be designed to form complex 3D structures through complementary base pairings. RNA origami structures are, however, more compact and more thermodynamically stable due to RNA's non-canonical base pairing and tertiary interactions. With all these advantages, the development of RNA origami lags behind DNA origami by a large gap. Furthermore, although computational methods have proven to be effective in designing DNA and RNA origami structures and in their evaluation, advances in computational nucleic acid origami is even more limited. In this paper, we review major milestones in experimental and computational DNA and RNA origami and present current challenges in these fields. We believe collaboration between experimental nanotechnologists and computer scientists are critical for advancing these new research paradigms.

Synthesis, spectroscopic investigations, DFT studies, molecular docking and antimicrobial potential of certain new indole-isatin molecular hybrids: Experimental and theoretical approaches

Science.gov (United States)

Almutairi, Maha S.; Zakaria, Azza S.; Ignasius, P. Primsa; Al-Wabli, Reem I.; Joe, Isaac Hubert; Attia, Mohamed I.

2018-02-01

Indole-isatin molecular hybrids 5a-i have been synthesized and characterized by different spectroscopic methods to be evaluated as new antimicrobial agents against a panel of Gram positive bacteria, Gram negative bacteria, and moulds. Compound 5h was selected as a representative example of the prepared compounds 5a-i to perform computational investigations. Its vibrational properties have been studied using FT-IR and FT-Raman with the aid of density functional theory approach. The natural bond orbital analysis as well as HOMO and LUMO molecular orbitals investigations of compound 5h were carried out to explore its possible intermolecular delocalization or hyperconjugation and its possible interactions with the target protein. Molecular docking of compound 5h predicted its binding mode with the fungal target protein.

A hybrid approach to quantify software reliability in nuclear safety systems

International Nuclear Information System (INIS)

Arun Babu, P.; Senthil Kumar, C.; Murali, N.

2012-01-01

Highlights: ► A novel method to quantify software reliability using software verification and mutation testing in nuclear safety systems. ► Contributing factors that influence software reliability estimate. ► Approach to help regulators verify the reliability of safety critical software system during software licensing process. -- Abstract: Technological advancements have led to the use of computer based systems in safety critical applications. As computer based systems are being introduced in nuclear power plants, effective and efficient methods are needed to ensure dependability and compliance to high reliability requirements of systems important to safety. Even after several years of research, quantification of software reliability remains controversial and unresolved issue. Also, existing approaches have assumptions and limitations, which are not acceptable for safety applications. This paper proposes a theoretical approach combining software verification and mutation testing to quantify the software reliability in nuclear safety systems. The theoretical results obtained suggest that the software reliability depends on three factors: the test adequacy, the amount of software verification carried out and the reusability of verified code in the software. The proposed approach may help regulators in licensing computer based safety systems in nuclear reactors.

Hybrid Modeling KMeans – Genetic Algorithms in the Health Care Data

Directory of Open Access Journals (Sweden)

Tessy Badriyah

2013-06-01

Full Text Available K-Means is one of the major algorithms widely used in clustering due to its good computational performance. However, K-Means is very sensitive to the initially selected points which randomly selected, and therefore it does not always generate optimum solutions. Genetic algorithm approach can be applied to solve this problem. In this research we examine the potential of applying hybrid GA- KMeans with focus on the area of health care data. We proposed a new technique using hybrid method combining KMeans Clustering and Genetic Algorithms, called the “Hybrid K-Means Genetic Algorithms” (HKGA. HKGA combines the power of Genetic Algorithms and the efficiency of K-Means Clustering. We compare our results with other conventional algorithms and also with other published research as well. Our results demonstrate that the HKGA achieves very good results and in some cases superior to other methods. Keywords: Machine Learning, K-Means, Genetic Algorithms, Hybrid KMeans Genetic Algorithm (HGKA.

Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach

International Nuclear Information System (INIS)

Monjoly, Stéphanie; André, Maïna; Calif, Rudy; Soubdhan, Ted

2017-01-01

This paper introduces a new approach for the forecasting of solar radiation series at 1 h ahead. We investigated on several techniques of multiscale decomposition of clear sky index K_c data such as Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Decomposition. From these differents methods, we built 11 decomposition components and 1 residu signal presenting different time scales. We performed classic forecasting models based on linear method (Autoregressive process AR) and a non linear method (Neural Network model). The choice of forecasting method is adaptative on the characteristic of each component. Hence, we proposed a modeling process which is built from a hybrid structure according to the defined flowchart. An analysis of predictive performances for solar forecasting from the different multiscale decompositions and forecast models is presented. From multiscale decomposition, the solar forecast accuracy is significantly improved, particularly using the wavelet decomposition method. Moreover, multistep forecasting with the proposed hybrid method resulted in additional improvement. For example, in terms of RMSE error, the obtained forecasting with the classical NN model is about 25.86%, this error decrease to 16.91% with the EMD-Hybrid Model, 14.06% with the EEMD-Hybid model and to 7.86% with the WD-Hybrid Model. - Highlights: • Hourly forecasting of GHI in tropical climate with many cloud formation processes. • Clear sky Index decomposition using three multiscale decomposition methods. • Combination of multiscale decomposition methods with AR-NN models to predict GHI. • Comparison of the proposed hybrid model with the classical models (AR, NN). • Best results using Wavelet-Hybrid model in comparison with classical models.

Swarm intelligence-based approach for optimal design of CMOS differential amplifier and comparator circuit using a hybrid salp swarm algorithm

Science.gov (United States)

Asaithambi, Sasikumar; Rajappa, Muthaiah

2018-05-01

In this paper, an automatic design method based on a swarm intelligence approach for CMOS analog integrated circuit (IC) design is presented. The hybrid meta-heuristics optimization technique, namely, the salp swarm algorithm (SSA), is applied to the optimal sizing of a CMOS differential amplifier and the comparator circuit. SSA is a nature-inspired optimization algorithm which mimics the navigating and hunting behavior of salp. The hybrid SSA is applied to optimize the circuit design parameters and to minimize the MOS transistor sizes. The proposed swarm intelligence approach was successfully implemented for an automatic design and optimization of CMOS analog ICs using Generic Process Design Kit (GPDK) 180 nm technology. The circuit design parameters and design specifications are validated through a simulation program for integrated circuit emphasis simulator. To investigate the efficiency of the proposed approach, comparisons have been carried out with other simulation-based circuit design methods. The performances of hybrid SSA based CMOS analog IC designs are better than the previously reported studies.

A Hybrid Column Generation approach for an Industrial Waste Collection Routing Problem

DEFF Research Database (Denmark)

Hauge, Kristian; Larsen, Jesper; Lusby, Richard Martin

2014-01-01

, while empty containers must be returned to the depot to await further assignments. Unlike, the traditional ROROR problem, where vehicles may transport one skip container at a time regardless of whether it is full or not, we consider cases in which a vehicle can transport up to eight containers, at most...... two of which can be full. We propose a Generalized Set Partitioning formulation of the problem and describe a hybrid column generation procedure to solve it. A fast Tabu Search heuristic is used to generate new columns. The proposed methodology is tested on nine data sets, four of which are actual......, real-world problem instances. Results indicate that the hybrid column generation outperforms a purely heuristic approach in terms of both running time and solution quality. High quality solutions to problems containing up to 100 orders can be solved in approximately 15 minutes....

Virtual machine consolidation enhancement using hybrid regression algorithms

Directory of Open Access Journals (Sweden)

Amany Abdelsamea

2017-11-01

Full Text Available Cloud computing data centers are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. The reason for this extremely high energy consumption is not just the quantity of computing resources and the power inefficiency of hardware, but rather lies in the inefficient usage of these resources. VM consolidation involves live migration of VMs hence the capability of transferring a VM between physical servers with a close to zero down time. It is an effective way to improve the utilization of resources and increase energy efficiency in cloud data centers. VM consolidation consists of host overload/underload detection, VM selection and VM placement. Most of the current VM consolidation approaches apply either heuristic-based techniques, such as static utilization thresholds, decision-making based on statistical analysis of historical data; or simply periodic adaptation of the VM allocation. Most of those algorithms rely on CPU utilization only for host overload detection. In this paper we propose using hybrid factors to enhance VM consolidation. Specifically we developed a multiple regression algorithm that uses CPU utilization, memory utilization and bandwidth utilization for host overload detection. The proposed algorithm, Multiple Regression Host Overload Detection (MRHOD, significantly reduces energy consumption while ensuring a high level of adherence to Service Level Agreements (SLA since it gives a real indication of host utilization based on three parameters (CPU, Memory, Bandwidth utilizations instead of one parameter only (CPU utilization. Through simulations we show that our approach reduces power consumption by 6 times compared to single factor algorithms using random workload. Also using PlanetLab workload traces we show that MRHOD improves

Specification and Verification of Hybrid System

International Nuclear Information System (INIS)

Widjaja, Belawati H.

1997-01-01

Hybrid systems are reactive systems which intermix between two components, discrete components and continuous components. The continuous components are usually called plants, subject to disturbances which cause the state variables of the systems changing continuously by physical laws and/or by the control laws. The discrete components can be digital computers, sensor and actuators controlled by programs. These programs are designed to select, control and supervise the behavior of the continuous components. Specification and verification of hybrid systems has recently become an active area of research in both computer science and control engineering, many papers concerning hybrid system have been published. This paper gives a design methodology for hybrid systems as an example to the specification and verification of hybrid systems. The design methodology is based on the cooperation between two disciplines, control engineering and computer science. The methodology brings into the design of control loops and decision loops. The external behavior of control loops are specified in a notation which is understandable by the two disciplines. The design of control loops which employed theory of differential equation is done by control engineers, and its correctness is also guaranteed analytically or experimentally by control engineers. The decision loops are designed in computing science based on the specifications of control loops. The verification of systems requirements can be done by computing scientists using a formal reasoning mechanism. For illustrating the proposed design, a problem of balancing an inverted pendulum which is a popular experiment device in control theory is considered, and the Mean Value Calculus is chosen as a formal notation for specifying the control loops and designing the decision loops

Computational neuropharmacology: dynamical approaches in drug discovery.

Science.gov (United States)

Aradi, Ildiko; Erdi, Péter

2006-05-01

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.

An event driven hybrid identity management approach to privacy enhanced e-health.

Science.gov (United States)

Sánchez-Guerrero, Rosa; Almenárez, Florina; Díaz-Sánchez, Daniel; Marín, Andrés; Arias, Patricia; Sanvido, Fabio

2012-01-01

Credential-based authorization offers interesting advantages for ubiquitous scenarios involving limited devices such as sensors and personal mobile equipment: the verification can be done locally; it offers a more reduced computational cost than its competitors for issuing, storing, and verification; and it naturally supports rights delegation. The main drawback is the revocation of rights. Revocation requires handling potentially large revocation lists, or using protocols to check the revocation status, bringing extra communication costs not acceptable for sensors and other limited devices. Moreover, the effective revocation consent--considered as a privacy rule in sensitive scenarios--has not been fully addressed. This paper proposes an event-based mechanism empowering a new concept, the sleepyhead credentials, which allows to substitute time constraints and explicit revocation by activating and deactivating authorization rights according to events. Our approach is to integrate this concept in IdM systems in a hybrid model supporting delegation, which can be an interesting alternative for scenarios where revocation of consent and user privacy are critical. The delegation includes a SAML compliant protocol, which we have validated through a proof-of-concept implementation. This article also explains the mathematical model describing the event-based model and offers estimations of the overhead introduced by the system. The paper focus on health care scenarios, where we show the flexibility of the proposed event-based user consent revocation mechanism.

Hybrid direct and iterative solvers for h refined grids with singularities

KAUST Repository

Paszyński, Maciej R.

2015-04-27

This paper describes a hybrid direct and iterative solver for two and three dimensional h adaptive grids with point singularities. The point singularities are eliminated by using a sequential linear computational cost solver O(N) on CPU [1]. The remaining Schur complements are submitted to incomplete LU preconditioned conjugated gradient (ILUPCG) iterative solver. The approach is compared to the standard algorithm performing static condensation over the entire mesh and executing the ILUPCG algorithm on top of it. The hybrid solver is applied for two or three dimensional grids automatically h refined towards point or edge singularities. The automatic refinement is based on the relative error estimations between the coarse and fine mesh solutions [2], and the optimal refinements are selected using the projection based interpolation. The computational mesh is partitioned into sub-meshes with local point and edge singularities separated. This is done by using the following greedy algorithm.

A Hybrid Approach to Cognitive Engineering: Supporting Development of a Revolutionary Warfighter-Centered Command and Control System

National Research Council Canada - National Science Library

Ockerman, Jennifer; McKneely, Jennifer A; Koterba, Nathan

2005-01-01

...) for the requirements analysis and design of revolutionary command and control systems and domains. This hybrid approach uses knowledge elicitation and representation techniques from several current cognitive engineering methodologies...

Insight and Evidence Motivating the Simplification of Dual-Analysis Hybrid Systems into Single-Analysis Hybrid Systems

Science.gov (United States)

Todling, Ricardo; Diniz, F. L. R.; Takacs, L. L.; Suarez, M. J.

2018-01-01

Many hybrid data assimilation systems currently used for NWP employ some form of dual-analysis system approach. Typically a hybrid variational analysis is responsible for creating initial conditions for high-resolution forecasts, and an ensemble analysis system is responsible for creating sample perturbations used to form the flow-dependent part of the background error covariance required in the hybrid analysis component. In many of these, the two analysis components employ different methodologies, e.g., variational and ensemble Kalman filter. In such cases, it is not uncommon to have observations treated rather differently between the two analyses components; recentering of the ensemble analysis around the hybrid analysis is used to compensated for such differences. Furthermore, in many cases, the hybrid variational high-resolution system implements some type of four-dimensional approach, whereas the underlying ensemble system relies on a three-dimensional approach, which again introduces discrepancies in the overall system. Connected to these is the expectation that one can reliably estimate observation impact on forecasts issued from hybrid analyses by using an ensemble approach based on the underlying ensemble strategy of dual-analysis systems. Just the realization that the ensemble analysis makes substantially different use of observations as compared to their hybrid counterpart should serve as enough evidence of the implausibility of such expectation. This presentation assembles numerous anecdotal evidence to illustrate the fact that hybrid dual-analysis systems must, at the very minimum, strive for consistent use of the observations in both analysis sub-components. Simpler than that, this work suggests that hybrid systems can reliably be constructed without the need to employ a dual-analysis approach. In practice, the idea of relying on a single analysis system is appealing from a cost-maintenance perspective. More generally, single-analysis systems avoid

Hybrid-PIC Computer Simulation of the Plasma and Erosion Processes in Hall Thrusters

Science.gov (United States)

Hofer, Richard R.; Katz, Ira; Mikellides, Ioannis G.; Gamero-Castano, Manuel

2010-01-01

HPHall software simulates and tracks the time-dependent evolution of the plasma and erosion processes in the discharge chamber and near-field plume of Hall thrusters. HPHall is an axisymmetric solver that employs a hybrid fluid/particle-in-cell (Hybrid-PIC) numerical approach. HPHall, originally developed by MIT in 1998, was upgraded to HPHall-2 by the Polytechnic University of Madrid in 2006. The Jet Propulsion Laboratory has continued the development of HPHall-2 through upgrades to the physical models employed in the code, and the addition of entirely new ones. Primary among these are the inclusion of a three-region electron mobility model that more accurately depicts the cross-field electron transport, and the development of an erosion sub-model that allows for the tracking of the erosion of the discharge chamber wall. The code is being developed to provide NASA science missions with a predictive tool of Hall thruster performance and lifetime that can be used to validate Hall thrusters for missions.

A hybrid system approach to airspeed, angle of attack and sideslip estimation in Unmanned Aerial Vehicles

KAUST Repository

Shaqura, Mohammad

2015-06-01

Fixed wing Unmanned Aerial Vehicles (UAVs) are an increasingly common sensing platform, owing to their key advantages: speed, endurance and ability to explore remote areas. While these platforms are highly efficient, they cannot easily be equipped with air data sensors commonly found on their larger scale manned counterparts. Indeed, such sensors are bulky, expensive and severely reduce the payload capability of the UAVs. In consequence, UAV controllers (humans or autopilots) have little information on the actual mode of operation of the wing (normal, stalled, spin) which can cause catastrophic losses of control when flying in turbulent weather conditions. In this article, we propose a real-time air parameter estimation scheme that can run on commercial, low power autopilots in real-time. The computational method is based on a hybrid decomposition of the modes of operation of the UAV. A Bayesian approach is considered for estimation, in which the estimated airspeed, angle of attack and sideslip are described statistically. An implementation on a UAV is presented, and the performance and computational efficiency of this method are validated using hardware in the loop (HIL) simulation and experimental flight data and compared with classical Extended Kalman Filter estimation. Our benchmark tests shows that this method is faster than EKF by up to two orders of magnitude. © 2015 IEEE.

The theory of hybrid stochastic algorithms

International Nuclear Information System (INIS)

Duane, S.; Kogut, J.B.

1986-01-01

The theory of hybrid stochastic algorithms is developed. A generalized Fokker-Planck equation is derived and is used to prove that the correct equilibrium distribution is generated by the algorithm. Systematic errors following from the discrete time-step used in the numerical implementation of the scheme are computed. Hybrid algorithms which simulate lattice gauge theory with dynamical fermions are presented. They are optimized in computer simulations and their systematic errors and efficiencies are studied. (orig.)

Computational and Game-Theoretic Approaches for Modeling Bounded Rationality

NARCIS (Netherlands)

L. Waltman (Ludo)

2011-01-01

textabstractThis thesis studies various computational and game-theoretic approaches to economic modeling. Unlike traditional approaches to economic modeling, the approaches studied in this thesis do not rely on the assumption that economic agents behave in a fully rational way. Instead, economic

Possibilities of the new hybrid technology single photon emission computer technology/computer tomography (SPECT/CT) and the first impressions of its application

International Nuclear Information System (INIS)

Kostadinova, I.

2010-01-01

With the help of the new hybrid technique SPECT/ CT it is possible, using the only investigation, to acquire a combine image of the investigated organ, visualizing its function and structure. Combining the possibilities of the new multimodality method, which combines the possibilities of the Single Photon Emission Computer Tomography - SPECT and Computer Tomography - CT, it is possible to precisely localize the pathologically changed organs function. With the further combination of the tomographic gamma camera with diagnostic CT, a detailed morphological evaluation of the finding was possible. The main clinical application of the new hybrid diagnostic is in the fields of cardiology, oncology, orthopedics with more and more extension of those, not connected with oncology, such as - thyroid, parathyroid, brain (especially localization of the epileptic foci), visualization of local infection and recently for the purposes of the radiotherapy planning. According to the literature data, around 35% of SPECT-investigations have to be combined with CT in order to increase the specificity of the diagnosis, which changes the interpretation of the result in 56% of the cases. After installation of the SPECT/CT camera in the University hospital 'Alexandrovska' in January 2009, the following changes have been done: the number of the investigated patients have increased, including number of heart, thyroid (especially scintigraphy with 131I), bones and parathyroid glands. As a result of the application of the hybrid technique, a shortage of the investigated time was realized and a decrease prize in comparison with the individual application of the investigations. Summarizing the literature data and the preliminary impression of the first multimodality scanner in our country in the University hospital 'Alexandrovska' it could be said, that there is continuously increasing information for the new clinical applications of SPECT/CT. It is now accepted, that its usage will increase in

An Unscented Kalman-Particle Hybrid Filter for Space Object Tracking

Science.gov (United States)

Raihan A. V, Dilshad; Chakravorty, Suman

2018-03-01

Optimal and consistent estimation of the state of space objects is pivotal to surveillance and tracking applications. However, probabilistic estimation of space objects is made difficult by the non-Gaussianity and nonlinearity associated with orbital mechanics. In this paper, we present an unscented Kalman-particle hybrid filtering framework for recursive Bayesian estimation of space objects. The hybrid filtering scheme is designed to provide accurate and consistent estimates when measurements are sparse without incurring a large computational cost. It employs an unscented Kalman filter (UKF) for estimation when measurements are available. When the target is outside the field of view (FOV) of the sensor, it updates the state probability density function (PDF) via a sequential Monte Carlo method. The hybrid filter addresses the problem of particle depletion through a suitably designed filter transition scheme. To assess the performance of the hybrid filtering approach, we consider two test cases of space objects that are assumed to undergo full three dimensional orbital motion under the effects of J 2 and atmospheric drag perturbations. It is demonstrated that the hybrid filters can furnish fast, accurate and consistent estimates outperforming standard UKF and particle filter (PF) implementations.

Energy level alignment at hybridized organic-metal interfaces from a GW projection approach

Science.gov (United States)

Chen, Yifeng; Tamblyn, Isaac; Quek, Su Ying

Energy level alignments at organic-metal interfaces are of profound importance in numerous (opto)electronic applications. Standard density functional theory (DFT) calculations generally give incorrect energy level alignments and missing long-range polarization effects. Previous efforts to address this problem using the many-electron GW method have focused on physisorbed systems where hybridization effects are insignificant. Here, we use state-of-the-art GW methods to predict the level alignment at the amine-Au interface, where molecular levels do hybridize with metallic states. This non-trivial hybridization implies that DFT result is a poor approximation to the quasiparticle states. However, we find that the self-energy operator is approximately diagonal in the molecular basis, allowing us to use a projection approach to predict the level alignments. Our results indicate that the metallic substrate reduces the HOMO-LUMO gap by 3.5 4.0 eV, depending on the molecular coverage/presence of Au adatoms. Our GW results are further compared with those of a simple image charge model that describes the level alignment in physisorbed systems. Syq and YC acknowledge Grant NRF-NRFF2013-07 and the medium-sized centre program from the National Research Foundation, Singapore.

Hybrid Wavelet De-noising and Rank-Set Pair Analysis approach for forecasting hydro-meteorological time series

Science.gov (United States)

WANG, D.; Wang, Y.; Zeng, X.

2017-12-01

Accurate, fast forecasting of hydro-meteorological time series is presently a major challenge in drought and flood mitigation. This paper proposes a hybrid approach, Wavelet De-noising (WD) and Rank-Set Pair Analysis (RSPA), that takes full advantage of a combination of the two approaches to improve forecasts of hydro-meteorological time series. WD allows decomposition and reconstruction of a time series by the wavelet transform, and hence separation of the noise from the original series. RSPA, a more reliable and efficient version of Set Pair Analysis, is integrated with WD to form the hybrid WD-RSPA approach. Two types of hydro-meteorological data sets with different characteristics and different levels of human influences at some representative stations are used to illustrate the WD-RSPA approach. The approach is also compared to three other generic methods: the conventional Auto Regressive Integrated Moving Average (ARIMA) method, Artificial Neural Networks (ANNs) (BP-error Back Propagation, MLP-Multilayer Perceptron and RBF-Radial Basis Function), and RSPA alone. Nine error metrics are used to evaluate the model performance. The results show that WD-RSPA is accurate, feasible, and effective. In particular, WD-RSPA is found to be the best among the various generic methods compared in this paper, even when the extreme events are included within a time series.

Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains

Directory of Open Access Journals (Sweden)

Weichao Zhuang

2016-05-01

Full Text Available Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sensitivity to parameters, such as varying vehicle weight, which is a concern especially for the design of hybrid buses. Therefore, the iterative optimization process is applied to design a robust multi-mode hybrid electric bus under different loading scenarios as the final design challenge of this paper.

A hybrid computation method for determining fluctuations of temperature in branched structures

International Nuclear Information System (INIS)

Czomber, L.

1982-01-01

A hybrid computation method for determining temperature fluctuations at discrete points of slab like geometries is developed on the basis of a new formulation of the finite difference method. For this purpose, a new finite difference method is combined with an exact solution of the heat equation within the range of values of the Laplace transformation. Whereas the exact solution can be applied to arbitraryly large ranges, the finite difference formulation is given for structural ranges which need finer discretization. The boundary conditions of the exact solution are substituted by finite difference terms for the boundary residual flow or an internal heat source, depending on the problem. The resulting system of conditional equations contains only the node parameters of the finite difference method. (orig.) [de

Hybrid MPI/OpenMP parallelization of the explicit Volterra integral equation solver for multi-core computer architectures

KAUST Repository

Al Jarro, Ahmed

2011-08-01

A hybrid MPI/OpenMP scheme for efficiently parallelizing the explicit marching-on-in-time (MOT)-based solution of the time-domain volume (Volterra) integral equation (TD-VIE) is presented. The proposed scheme equally distributes tested field values and operations pertinent to the computation of tested fields among the nodes using the MPI standard; while the source field values are stored in all nodes. Within each node, OpenMP standard is used to further accelerate the computation of the tested fields. Numerical results demonstrate that the proposed parallelization scheme scales well for problems involving three million or more spatial discretization elements. © 2011 IEEE.

Identifying reports of randomized controlled trials (RCTs) via a hybrid machine learning and crowdsourcing approach.

Science.gov (United States)

Wallace, Byron C; Noel-Storr, Anna; Marshall, Iain J; Cohen, Aaron M; Smalheiser, Neil R; Thomas, James

2017-11-01

Identifying all published reports of randomized controlled trials (RCTs) is an important aim, but it requires extensive manual effort to separate RCTs from non-RCTs, even using current machine learning (ML) approaches. We aimed to make this process more efficient via a hybrid approach using both crowdsourcing and ML. We trained a classifier to discriminate between citations that describe RCTs and those that do not. We then adopted a simple strategy of automatically excluding citations deemed very unlikely to be RCTs by the classifier and deferring to crowdworkers otherwise. Combining ML and crowdsourcing provides a highly sensitive RCT identification strategy (our estimates suggest 95%-99% recall) with substantially less effort (we observed a reduction of around 60%-80%) than relying on manual screening alone. Hybrid crowd-ML strategies warrant further exploration for biomedical curation/annotation tasks. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Communication: Evaluating non-empirical double hybrid functionals for spin-state energetics in transition-metal complexes

Science.gov (United States)

Wilbraham, Liam; Adamo, Carlo; Ciofini, Ilaria

2018-01-01

The computationally assisted, accelerated design of inorganic functional materials often relies on the ability of a given electronic structure method to return the correct electronic ground state of the material in question. Outlining difficulties with current density functionals and wave function-based approaches, we highlight why double hybrid density functionals represent promising candidates for this purpose. In turn, we show that PBE0-DH (and PBE-QIDH) offers a significant improvement over its hybrid parent functional PBE0 [as well as B3LYP* and coupled cluster singles and doubles with perturbative triples (CCSD(T))] when computing spin-state splitting energies, using high-level diffusion Monte Carlo calculations as a reference. We refer to the opposing influence of Hartree-Fock (HF) exchange and MP2, which permits higher levels of HF exchange and a concomitant reduction in electronic density error, as the reason for the improved performance of double-hybrid functionals relative to hybrid functionals. Additionally, using 16 transition metal (Fe and Co) complexes, we show that low-spin states are stabilised by increasing contributions from MP2 within the double hybrid formulation. Furthermore, this stabilisation effect is more prominent for high field strength ligands than low field strength ligands.

An integrated optimization approach for a hybrid energy system in electric vehicles

International Nuclear Information System (INIS)

Hung, Yi-Hsuan; Wu, Chien-Hsun

2012-01-01

Highlights: ► Second-order control-oriented dynamics for a battery/supercapacitor EV is modeled. ► Multiple for-loop programming and global searchwith constraints are main design principles of integrated optimization algorithm (IOA). ► Optimal hybridization is derived based on maximizing energy storage capacity. ► Optimal energy management in three EV operation modes is searched based on minimizing total consumed power. ► Simulation results prove that 6+% of total energy is saved by the IOA method. -- Abstract: This paper develops a simple but innovative integrated optimization approach (IOA) for deriving the best solutions of component sizing and control strategies of a hybrid energy system (HES) which consists of a lithium battery and a supercapacitor module. To implement IOA, a multiple for-loop structure with a preset cost function is needed to globally calculate the best hybridization and energy management of the HES. For system hybridization, the optimal size ratio is evaluated by maximizing the HES energy stored capacity at various costs. For energy management, the optimal power distribution combined with a three-mode rule-based strategy is searched to minimize the total consumed energy. Combining above two for-loop structures and giving a time-dependent test scenario, the IOA is derived by minimizing the accumulated HES power. Simulation results show that 6% of the total HES energy can be saved in the IOA case compared with the original system in two driving cycles: ECE and UDDS, and two vehicle weights, respectively. It proves that the IOA effectively derives the maximum energy storage capacity and the minimum energy consumption of the HES at the same time. Experimental verification will be carried out in the near future.

Treatment of early and late reflections in a hybrid computer model for room acoustics

DEFF Research Database (Denmark)

Naylor, Graham

1992-01-01

The ODEON computer model for acoustics in large rooms is intended for use both in design (by predicting room acoustical indices quickly and easily) and in research (by forming the basis of an auralization system and allowing study of various room acoustical phenomena). These conflicting demands...... preclude the use of both ``pure'' image source and ``pure'' particle tracing methods. A hybrid model has been developed, in which rays discover potential image sources up to a specified order. Thereafter, the same ray tracing process is used in a different way to rapidly generate a dense reverberant decay...

Students' Game Performance Improvements during a Hybrid Sport Education-Step-Game-Approach Volleyball Unit

Science.gov (United States)

Araújo, Rui; Mesquita, Isabel; Hastie, Peter; Pereira, Cristiana

2016-01-01

The purpose of this study was to examine a hybrid combination of sport education and the step-game-approach (SGA) on students' gameplay performance in volleyball, taking into account their sex and skill-level. Seventeen seventh-grade students (seven girls, 10 boys, average age 11.8) participated in a 25-lesson volleyball season, in which the…

Efficient Adjoint Computation of Hybrid Systems of Differential Algebraic Equations with Applications in Power Systems

Energy Technology Data Exchange (ETDEWEB)

Abhyankar, Shrirang [Argonne National Lab. (ANL), Argonne, IL (United States); Anitescu, Mihai [Argonne National Lab. (ANL), Argonne, IL (United States); Constantinescu, Emil [Argonne National Lab. (ANL), Argonne, IL (United States); Zhang, Hong [Argonne National Lab. (ANL), Argonne, IL (United States)

2016-03-31

Sensitivity analysis is an important tool to describe power system dynamic behavior in response to parameter variations. It is a central component in preventive and corrective control applications. The existing approaches for sensitivity calculations, namely, finite-difference and forward sensitivity analysis, require a computational effort that increases linearly with the number of sensitivity parameters. In this work, we investigate, implement, and test a discrete adjoint sensitivity approach whose computational effort is effectively independent of the number of sensitivity parameters. The proposed approach is highly efficient for calculating trajectory sensitivities of larger systems and is consistent, within machine precision, with the function whose sensitivity we are seeking. This is an essential feature for use in optimization applications. Moreover, our approach includes a consistent treatment of systems with switching, such as DC exciters, by deriving and implementing the adjoint jump conditions that arise from state and time-dependent discontinuities. The accuracy and the computational efficiency of the proposed approach are demonstrated in comparison with the forward sensitivity analysis approach.

Implementation of a cell-wise block-Gauss-Seidel iterative method for SN transport on a hybrid parallel computer architecture

International Nuclear Information System (INIS)

Rosa, Massimiliano; Warsa, James S.; Perks, Michael

2011-01-01

We have implemented a cell-wise, block-Gauss-Seidel (bGS) iterative algorithm, for the solution of the S_n transport equations on the Roadrunner hybrid, parallel computer architecture. A compute node of this massively parallel machine comprises AMD Opteron cores that are linked to a Cell Broadband Engine™ (Cell/B.E.)"1. LAPACK routines have been ported to the Cell/B.E. in order to make use of its parallel Synergistic Processing Elements (SPEs). The bGS algorithm is based on the LU factorization and solution of a linear system that couples the fluxes for all S_n angles and energy groups on a mesh cell. For every cell of a mesh that has been parallel decomposed on the higher-level Opteron processors, a linear system is transferred to the Cell/B.E. and the parallel LAPACK routines are used to compute a solution, which is then transferred back to the Opteron, where the rest of the computations for the S_n transport problem take place. Compared to standard parallel machines, a hundred-fold speedup of the bGS was observed on the hybrid Roadrunner architecture. Numerical experiments with strong and weak parallel scaling demonstrate the bGS method is viable and compares favorably to full parallel sweeps (FPS) on two-dimensional, unstructured meshes when it is applied to optically thick, multi-material problems. As expected, however, it is not as efficient as FPS in optically thin problems. (author)

An Approach to Evaluate Stability for Cable-Based Parallel Camera Robots with Hybrid Tension-Stiffness Properties

Directory of Open Access Journals (Sweden)

Huiling Wei

2015-12-01

Full Text Available This paper focuses on studying the effect of cable tensions and stiffness on the stability of cable-based parallel camera robots. For this purpose, the tension factor and the stiffness factor are defined, and the expression of stability is deduced. A new approach is proposed to calculate the hybrid-stability index with the minimum cable tension and the minimum singular value. Firstly, the kinematic model of a cable-based parallel camera robot is established. Based on the model, the tensions are solved and a tension factor is defined. In order to obtain the tension factor, an optimization of the cable tensions is carried out. Then, an expression of the system's stiffness is deduced and a stiffness factor is defined. Furthermore, an approach to evaluate the stability of the cable-based camera robots with hybrid tension-stiffness properties is presented. Finally, a typical three-degree-of-freedom cable-based parallel camera robot with four cables is studied as a numerical example. The simulation results show that the approach is both reasonable and effective.

A hybrid modeling approach for option pricing

Science.gov (United States)

Hajizadeh, Ehsan; Seifi, Abbas

2011-11-01

The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.

Verification of a hybrid adjoint methodology in Titan for single photon emission computed tomography - 316

International Nuclear Information System (INIS)

Royston, K.; Haghighat, A.; Yi, C.

2010-01-01

The hybrid deterministic transport code TITAN is being applied to a Single Photon Emission Computed Tomography (SPECT) simulation of a myocardial perfusion study. The TITAN code's hybrid methodology allows the use of a discrete ordinates solver in the phantom region and a characteristics method solver in the collimator region. Currently we seek to validate the adjoint methodology in TITAN for this application using a SPECT model that has been created in the MCNP5 Monte Carlo code. The TITAN methodology was examined based on the response of a single voxel detector placed in front of the heart with and without collimation. For the case without collimation, the TITAN response for single voxel-sized detector had a -9.96% difference relative to the MCNP5 response. To simulate collimation, the adjoint source was specified in directions located within the collimator acceptance angle. For a single collimator hole with a diameter matching the voxel dimension, a difference of -0.22% was observed. Comparisons to groupings of smaller collimator holes of two different sizes resulted in relative differences of 0.60% and 0.12%. The number of adjoint source directions within an acceptance angle was increased and showed no significant change in accuracy. Our results indicate that the hybrid adjoint methodology of TITAN yields accurate solutions greater than a factor of two faster than MCNP5. (authors)

A hybrid method for the parallel computation of Green's functions

DEFF Research Database (Denmark)

Petersen, Dan Erik; Li, Song; Stokbro, Kurt

2009-01-01

of the large number of times this calculation needs to be performed, this is computationally very expensive even on supercomputers. The classical approach is based on recurrence formulas which cannot be efficiently parallelized. This practically prevents the solution of large problems with hundreds...... of thousands of atoms. We propose new recurrences for a general class of sparse matrices to calculate Green's and lesser Green's function matrices which extend formulas derived by Takahashi and others. We show that these recurrences may lead to a dramatically reduced computational cost because they only...... require computing a small number of entries of the inverse matrix. Then. we propose a parallelization strategy for block tridiagonal matrices which involves a combination of Schur complement calculations and cyclic reduction. It achieves good scalability even on problems of modest size....

Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2013-01-01

Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

A Computer-Aided FPS-Oriented Approach for Construction Briefing

Institute of Scientific and Technical Information of China (English)

Xiaochun Luo; Qiping Shen

2008-01-01

Function performance specification (FPS) is one of the value management (VM) techniques de- veloped for the explicit statement of optimum product definition. This technique is widely used in software engineering and manufacturing industry, and proved to be successful to perform product defining tasks. This paper describes an FPS-odented approach for construction briefing, which is critical to the successful deliv- ery of construction projects. Three techniques, i.e., function analysis system technique, shared space, and computer-aided toolkit, are incorporated into the proposed approach. A computer-aided toolkit is developed to facilitate the implementation of FPS in the briefing processes. This approach can facilitate systematic, ef- ficient identification, clarification, and representation of client requirements in trail running. The limitations of the approach and future research work are also discussed at the end of the paper.

Introducing Computational Approaches in Intermediate Mechanics

Science.gov (United States)

Cook, David M.

2006-12-01

In the winter of 2003, we at Lawrence University moved Lagrangian mechanics and rigid body dynamics from a required sophomore course to an elective junior/senior course, freeing 40% of the time for computational approaches to ordinary differential equations (trajectory problems, the large amplitude pendulum, non-linear dynamics); evaluation of integrals (finding centers of mass and moment of inertia tensors, calculating gravitational potentials for various sources); and finding eigenvalues and eigenvectors of matrices (diagonalizing the moment of inertia tensor, finding principal axes), and to generating graphical displays of computed results. Further, students begin to use LaTeX to prepare some of their submitted problem solutions. Placed in the middle of the sophomore year, this course provides the background that permits faculty members as appropriate to assign computer-based exercises in subsequent courses. Further, students are encouraged to use our Computational Physics Laboratory on their own initiative whenever that use seems appropriate. (Curricular development supported in part by the W. M. Keck Foundation, the National Science Foundation, and Lawrence University.)

A case of dual ectopy thyroid along the thyroglossal tract demonstrated on 99mTc-Pertechnatate hybrid single photon emission computed tomography/computed tomography

International Nuclear Information System (INIS)

Agarwal, Krishan Kant; Karunanithi, Sellam; Jain, Sachin; Tripathi, Madhavi

2014-01-01

Ectopic thyroid tissue (ETT) refers to the presence of thyroid tissue in locations other than the normal anterior neck region between the second and fourth tracheal cartilages. Multiple ectopia of the thyroid is extremely rare. Here we report a case of 10-year-old girl with anterior midline neck swelling and hypothyroidism with dual ectopia of thyroid gland without orthotopic thyroid gland. Planar 99 m-technetium pertechnatate scan identified ETT corresponding to the palpable neck swelling. Single photon emission computed tomography/computed tomography (SPECT/CT) demonstrated ETT in two locations, one corresponding to the palpable mass and another in the in the sublingual location. This case thus demonstrates the important role of hybrid SPECT/CT in the identification of dual ectopia along the thyroglossal tract

An Integrated Computer-Aided Approach for Environmental Studies

DEFF Research Database (Denmark)

Gani, Rafiqul; Chen, Fei; Jaksland, Cecilia

1997-01-01

A general framework for an integrated computer-aided approach to solve process design, control, and environmental problems simultaneously is presented. Physicochemical properties and their relationships to the molecular structure play an important role in the proposed integrated approach. The sco...... and applicability of the integrated approach is highlighted through examples involving estimation of properties and environmental pollution prevention. The importance of mixture effects on some environmentally important properties is also demonstrated....

Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine

Science.gov (United States)

Yang, Zhutian; Wu, Zhilu; Yin, Zhendong; Quan, Taifan; Sun, Hongjian

2013-01-01

Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches. PMID:23344380

Hybrid Correlation Energy (HyCE): An Approach Based on Separate Evaluations of Internal and External Components.

Science.gov (United States)

Ivanic, Joseph; Schmidt, Michael W

2018-06-04

A novel hybrid correlation energy (HyCE) approach is proposed that determines the total correlation energy via distinct computation of its internal and external components. This approach evolved from two related studies. First, rigorous assessment of the accuracies and size extensivities of a number of electron correlation methods, that include perturbation theory (PT2), coupled-cluster (CC), configuration interaction (CI), and coupled electron pair approximation (CEPA), shows that the CEPA(0) variant of the latter and triples-corrected CC methods consistently perform very similarly. These findings were obtained by comparison to near full CI results for four small molecules and by charting recovered correlation energies for six steadily growing chain systems. Second, by generating valence virtual orbitals (VVOs) and utilizing the CEPA(0) method, we were able to partition total correlation energies into internal (or nondynamic) and external (or dynamic) parts for the aforementioned six chain systems and a benchmark test bed of 36 molecules. When using triple-ζ basis sets it was found that per orbital internal correlation energies were appreciably larger than per orbital external energies and that the former showed far more chemical variation than the latter. Additionally, accumulations of external correlation energies were seen to proceed smoothly, and somewhat linearly, as the virtual space is gradually increased. Combination of these two studies led to development of the HyCE approach, whereby the internal and external correlation energies are determined separately by CEPA(0)/VVO and PT2/external calculations, respectively. When applied to the six chain systems and the 36-molecule benchmark test set it was found that HyCE energies followed closely those of triples-corrected CC and CEPA(0) while easily outperforming MP2 and CCSD. The success of the HyCE approach is more notable when considering that its cost is only slightly more than MP2 and significantly cheaper

Computational analysis on plug-in hybrid electric motorcycle chassis

Science.gov (United States)

Teoh, S. J.; Bakar, R. A.; Gan, L. M.

2013-12-01

Plug-in hybrid electric motorcycle (PHEM) is an alternative to promote sustainability lower emissions. However, the PHEM overall system packaging is constrained by limited space in a motorcycle chassis. In this paper, a chassis applying the concept of a Chopper is analysed to apply in PHEM. The chassis 3dimensional (3D) modelling is built with CAD software. The PHEM power-train components and drive-train mechanisms are intergraded into the 3D modelling to ensure the chassis provides sufficient space. Besides that, a human dummy model is built into the 3D modelling to ensure the rider?s ergonomics and comfort. The chassis 3D model then undergoes stress-strain simulation. The simulation predicts the stress distribution, displacement and factor of safety (FOS). The data are used to identify the critical point, thus suggesting the chassis design is applicable or need to redesign/ modify to meet the require strength. Critical points mean highest stress which might cause the chassis to fail. This point occurs at the joints at triple tree and bracket rear absorber for a motorcycle chassis. As a conclusion, computational analysis predicts the stress distribution and guideline to develop a safe prototype chassis.

Hybrid Donor-Dot Devices made using Top-down Ion Implantation for Quantum Computing

Science.gov (United States)

Bielejec, Edward; Bishop, Nathan; Carroll, Malcolm

2012-02-01

We present progress towards fabricating hybrid donor -- quantum dots (QD) for quantum computing. These devices will exploit the long coherence time of the donor system and the surface state manipulation associated with a QD. Fabrication requires detection of single ions implanted with 10's of nanometer precision. We show in this talk, 100% detection efficiency for single ions using a single ion Geiger mode avalanche (SIGMA) detector integrated into a Si MOS QD process flow. The NanoImplanter (nI) a focused ion beam system is used for precision top-down placement of the implanted ion. This machine has a 10 nm resolution combined with a mass velocity filter, allowing for the use of multi-species liquid metal ion sources (LMIS) to implant P and Sb ions, and a fast blanking and chopping system for single ion implants. The combination of the nI and integration of the SIGMA with the MOS QD process flow establishes a path to fabricate hybrid single donor-dot devices. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

Science.gov (United States)

Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

Directory of Open Access Journals (Sweden)

Miraemiliana Murat

2017-09-01

Full Text Available Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM, Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD, Histogram of Oriented Gradients (HOG, Hu invariant moments (Hu and Zernike moments (ZM. Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN, random forest (RF, support vector machine (SVM, k-nearest neighbour (k-NN, linear discriminant analysis (LDA and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM. In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS and Pearson’s coefficient correlation (PCC. The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia

Computer-oriented approach to fault-tree construction

International Nuclear Information System (INIS)

Salem, S.L.; Apostolakis, G.E.; Okrent, D.

1976-11-01

A methodology for systematically constructing fault trees for general complex systems is developed and applied, via the Computer Automated Tree (CAT) program, to several systems. A means of representing component behavior by decision tables is presented. The method developed allows the modeling of components with various combinations of electrical, fluid and mechanical inputs and outputs. Each component can have multiple internal failure mechanisms which combine with the states of the inputs to produce the appropriate output states. The generality of this approach allows not only the modeling of hardware, but human actions and interactions as well. A procedure for constructing and editing fault trees, either manually or by computer, is described. The techniques employed result in a complete fault tree, in standard form, suitable for analysis by current computer codes. Methods of describing the system, defining boundary conditions and specifying complex TOP events are developed in order to set up the initial configuration for which the fault tree is to be constructed. The approach used allows rapid modifications of the decision tables and systems to facilitate the analysis and comparison of various refinements and changes in the system configuration and component modeling

A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

Jian Xiong

2012-01-01

Full Text Available This paper addresses multiobjective flexible job-shop scheduling problem (FJSP with three simultaneously considered objectives: minimizing makespan, minimizing total workload, and minimizing maximal workload. A hybrid multiobjective evolutionary approach (H-MOEA is developed to solve the problem. According to the characteristic of FJSP, a modified crowding distance measure is introduced to maintain the diversity of individuals. In the proposed H-MOEA, well-designed chromosome representation and genetic operators are developed for FJSP. Moreover, a local search procedure based on critical path theory is incorporated in H-MOEA to improve the convergence ability of the algorithm. Experiment results on several well-known benchmark instances demonstrate the efficiency and stability of the proposed algorithm. The comparison with other recently published approaches validates that H-MOEA can obtain Pareto-optimal solutions with better quality and/or diversity.

Probabilistic modelling and analysis of stand-alone hybrid power systems

International Nuclear Information System (INIS)

Lujano-Rojas, Juan M.; Dufo-López, Rodolfo; Bernal-Agustín, José L.

2013-01-01

As a part of the Hybrid Intelligent Algorithm, a model based on an ANN (artificial neural network) has been proposed in this paper to represent hybrid system behaviour considering the uncertainty related to wind speed and solar radiation, battery bank lifetime, and fuel prices. The Hybrid Intelligent Algorithm suggests a combination of probabilistic analysis based on a Monte Carlo simulation approach and artificial neural network training embedded in a genetic algorithm optimisation model. The installation of a typical hybrid system was analysed. Probabilistic analysis was used to generate an input–output dataset of 519 samples that was later used to train the ANNs to reduce the computational effort required. The generalisation ability of the ANNs was measured in terms of RMSE (Root Mean Square Error), MBE (Mean Bias Error), MAE (Mean Absolute Error), and R-squared estimators using another data group of 200 samples. The results obtained from the estimation of the expected energy not supplied, the probability of a determined reliability level, and the estimation of expected value of net present cost show that the presented model is able to represent the main characteristics of a typical hybrid power system under uncertain operating conditions. - Highlights: • This paper presents a probabilistic model for stand-alone hybrid power system. • The model considers the main sources of uncertainty related to renewable resources. • The Hybrid Intelligent Algorithm has been applied to represent hybrid system behaviour. • The installation of a typical hybrid system was analysed. • The results obtained from the study case validate the presented model

A Hybrid Soft-computing Method for Image Analysis of Digital Plantar Scanners.