Prediction Study on Anti-Slide Control of Railway Vehicle Based on RBF Neural Networks

Science.gov (United States)

Yang, Lijun; Zhang, Jimin

While railway vehicle braking, Anti-slide control system will detect operating status of each wheel-sets e.g. speed difference and deceleration etc. Once the detected value on some wheel-set is over pre-defined threshold, brake effort on such wheel-set will be adjusted automatically to avoid blocking. Such method takes effect on guarantee safety operation of vehicle and avoid wheel-set flatness, however it cannot adapt itself to the rail adhesion variation. While wheel-sets slide, the operating status is chaotic time series with certain law, and can be predicted with the law and experiment data in certain time. The predicted values can be used as the input reference signals of vehicle anti-slide control system, to judge and control the slide status of wheel-sets. In this article, the RBF neural networks is taken to predict wheel-set slide status in multi-step with weight vector adjusted based on online self-adaptive algorithm, and the center & normalizing parameters of active function of the hidden unit of RBF neural networks' hidden layer computed with K-means clustering algorithm. With multi-step prediction simulation, the predicted signal with appropriate precision can be used by anti-slide system to trace actively and adjust wheel-set slide tendency, so as to adapt to wheel-rail adhesion variation and reduce the risk of wheel-set blocking.

FRAN and RBF-PSO as two components of a hyper framework to recognize protein folds.

Science.gov (United States)

Abbasi, Elham; Ghatee, Mehdi; Shiri, M E

2013-09-01

In this paper, an intelligent hyper framework is proposed to recognize protein folds from its amino acid sequence which is a fundamental problem in bioinformatics. This framework includes some statistical and intelligent algorithms for proteins classification. The main components of the proposed framework are the Fuzzy Resource-Allocating Network (FRAN) and the Radial Bases Function based on Particle Swarm Optimization (RBF-PSO). FRAN applies a dynamic method to tune up the RBF network parameters. Due to the patterns complexity captured in protein dataset, FRAN classifies the proteins under fuzzy conditions. Also, RBF-PSO applies PSO to tune up the RBF classifier. Experimental results demonstrate that FRAN improves prediction accuracy up to 51% and achieves acceptable multi-class results for protein fold prediction. Although RBF-PSO provides reasonable results for protein fold recognition up to 48%, it is weaker than FRAN in some cases. However the proposed hyper framework provides an opportunity to use a great range of intelligent methods and can learn from previous experiences. Thus it can avoid the weakness of some intelligent methods in terms of memory, computational time and static structure. Furthermore, the performance of this system can be enhanced throughout the system life-cycle. Copyright © 2013 Elsevier Ltd. All rights reserved.

Magnaporthe oryzae Glycine-Rich Secretion Protein, Rbf1 Critically Participates in Pathogenicity through the Focal Formation of the Biotrophic Interfacial Complex.

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Takeshi Nishimura

2016-10-01

Full Text Available Magnaporthe oryzae, the fungus causing rice blast disease, should contend with host innate immunity to develop invasive hyphae (IH within living host cells. However, molecular strategies to establish the biotrophic interactions are largely unknown. Here, we report the biological function of a M. oryzae-specific gene, Required-for-Focal-BIC-Formation 1 (RBF1. RBF1 expression was induced in appressoria and IH only when the fungus was inoculated to living plant tissues. Long-term successive imaging of live cell fluorescence revealed that the expression of RBF1 was upregulated each time the fungus crossed a host cell wall. Like other symplastic effector proteins of the rice blast fungus, Rbf1 accumulated in the biotrophic interfacial complex (BIC and was translocated into the rice cytoplasm. RBF1-knockout mutants (Δrbf1 were severely deficient in their virulence to rice leaves, but were capable of proliferating in abscisic acid-treated or salicylic acid-deficient rice plants. In rice leaves, Δrbf1 inoculation caused necrosis and induced defense-related gene expression, which led to a higher level of diterpenoid phytoalexin accumulation than the wild-type fungus did. Δrbf1 showed unusual differentiation of IH and dispersal of the normally BIC-focused effectors around the short primary hypha and the first bulbous cell. In the Δrbf1-invaded cells, symplastic effectors were still translocated into rice cells but with a lower efficiency. These data indicate that RBF1 is a virulence gene essential for the focal BIC formation, which is critical for the rice blast fungus to suppress host immune responses.

PERAMALAN DERET WAKTU MENGGUNAKAN MODEL FUNGSI BASIS RADIAL (RBF DAN AUTO REGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

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DT Wiyanti

2013-07-01

Full Text Available Salah satu metode peramalan yang paling dikembangkan saat ini adalah time series, yakni menggunakan pendekatan kuantitatif dengan data masa lampau yang dijadikan acuan untuk peramalan masa depan. Berbagai penelitian telah mengusulkan metode-metode untuk menyelesaikan time series, di antaranya statistik, jaringan syaraf, wavelet, dan sistem fuzzy. Metode-metode tersebut memiliki kekurangan dan keunggulan yang berbeda. Namun permasalahan yang ada dalam dunia nyata merupakan masalah yang kompleks. Satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam artikel ini dibahas penggabungan dua buah metode yaitu Auto Regressive Integrated Moving Average (ARIMA dan Radial Basis Function (RBF. Alasan penggabungan kedua metode ini adalah karena adanya asumsi bahwa metode tunggal tidak dapat secara total mengidentifikasi semua karakteristik time series. Pada artikel ini dibahas peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB dan data inflasi komoditi Indonesia; kedua data berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012. Kedua data tersebut masing-masing memiliki enam variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih akurat dibandingkan dengan penggunaan salah satu metode saja. Hal ini terlihat dalam visual plot, MAPE, dan RMSE dari semua variabel pada dua data uji coba. The accuracy of time series forecasting is the subject of many decision-making processes. Time series use a quantitative approach to employ data from the past to make forecast for the future. Many researches have proposed several methods to solve time series, such as using statistics, neural networks, wavelets, and fuzzy systems. These methods have different advantages and disadvantages. But often the problem in the real world is just too complex that a

A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

OpenAIRE

Tao, Yong; Zheng, Jiaqi; Lin, Yuanchang

2016-01-01

A sliding mode control method based on radial basis function (RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network par...

Numerical Solution of Stokes Flow in a Circular Cavity Using Mesh-free Local RBF-DQ

DEFF Research Database (Denmark)

Kutanaai, S Soleimani; Roshan, Naeem; Vosoughi, A

2012-01-01

This work reports the results of a numerical investigation of Stokes flow problem in a circular cavity as an irregular geometry using mesh-free local radial basis function-based differential quadrature (RBF-DQ) method. This method is the combination of differential quadrature approximation of der...... in solution of partial differential equations (PDEs).......This work reports the results of a numerical investigation of Stokes flow problem in a circular cavity as an irregular geometry using mesh-free local radial basis function-based differential quadrature (RBF-DQ) method. This method is the combination of differential quadrature approximation...... is applied on a two-dimensional geometry. The obtained results from the numerical simulations are compared with those gained by previous works. Outcomes prove that the current technique is in very good agreement with previous investigations and this fact that RBF-DQ method is an accurate and flexible method...

On-line identification of hybrid systems using an adaptive growing and pruning RBF neural network

DEFF Research Database (Denmark)

Alizadeh, Tohid

2008-01-01

This paper introduces an adaptive growing and pruning radial basis function (GAP-RBF) neural network for on-line identification of hybrid systems. The main idea is to identify a global nonlinear model that can predict the continuous outputs of hybrid systems. In the proposed approach, GAP......-RBF neural network uses a modified unscented kalman filter (UKF) with forgetting factor scheme as the required on-line learning algorithm. The effectiveness of the resulting identification approach is tested and evaluated on a simulated benchmark hybrid system....

Modeling of mass transfer of Phospholipids in separation process with supercritical CO2 fluid by RBF artificial neural networks

Science.gov (United States)

An artificial Radial Basis Function (RBF) neural network model was developed for the prediction of mass transfer of the phospholipids from canola meal in supercritical CO2 fluid. The RBF kind of artificial neural networks (ANN) with orthogonal least squares (OLS) learning algorithm were used for mod...

Nonlinear adaptive PID control for greenhouse environment based on RBF network.

Science.gov (United States)

Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui

2012-01-01

This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.

Image compression using moving average histogram and RBF network

International Nuclear Information System (INIS)

Khowaja, S.; Ismaili, I.A.

2015-01-01

Modernization and Globalization have made the multimedia technology as one of the fastest growing field in recent times but optimal use of bandwidth and storage has been one of the topics which attract the research community to work on. Considering that images have a lion share in multimedia communication, efficient image compression technique has become the basic need for optimal use of bandwidth and space. This paper proposes a novel method for image compression based on fusion of moving average histogram and RBF (Radial Basis Function). Proposed technique employs the concept of reducing color intensity levels using moving average histogram technique followed by the correction of color intensity levels using RBF networks at reconstruction phase. Existing methods have used low resolution images for the testing purpose but the proposed method has been tested on various image resolutions to have a clear assessment of the said technique. The proposed method have been tested on 35 images with varying resolution and have been compared with the existing algorithms in terms of CR (Compression Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), computational complexity. The outcome shows that the proposed methodology is a better trade off technique in terms of compression ratio, PSNR which determines the quality of the image and computational complexity. (author)

Fault Diagnosis of Hydraulic Servo Valve Based on Genetic Optimization RBF-BP Neural Network

Directory of Open Access Journals (Sweden)

Li-Ping FAN

2014-04-01

Full Text Available Electro-hydraulic servo valves are core components of the hydraulic servo system of rolling mills. It is necessary to adopt an effective fault diagnosis method to keep the hydraulic servo valve in a good work state. In this paper, RBF and BP neural network are integrated effectively to build a double hidden layers RBF-BP neural network for fault diagnosis. In the process of training the neural network, genetic algorithm (GA is used to initialize and optimize the connection weights and thresholds of the network. Several typical fault states are detected by the constructed GA-optimized fault diagnosis scheme. Simulation results shown that the proposed fault diagnosis scheme can give satisfactory effect.

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

Science.gov (United States)

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

China’s primary energy demands in 2020: Predictions from an MPSO–RBF estimation model

International Nuclear Information System (INIS)

Yu Shiwei; Wei Yiming; Wang Ke

2012-01-01

Highlights: ► A Mix-encoding PSO and RBF network-based energy demand forecasting model is proposed. ► The proposed model has simpler structure and smaller estimated errors than other ANN models. ► China’s energy demand could reach 6.25 billion, 4.16 billion, and 5.29 billion tons tce. ► China’s energy efficiency in 2020 will increase by more than 30% compared with 2009. - Abstract: In the present study, a Mix-encoding Particle Swarm Optimization and Radial Basis Function (MPSO–RBF) network-based energy demand forecasting model is proposed and applied to forecast China’s energy consumption until 2020. The energy demand is analyzed for the period from 1980 to 2009 based on GDP, population, proportion of industry in GDP, urbanization rate, and share of coal energy. The results reveal that the proposed MPSO–RBF based model has fewer hidden nodes and smaller estimated errors compared with other ANN-based estimation models. The average annual growth of China’s energy demand will be 6.70%, 2.81%, and 5.08% for the period between 2010 and 2020 in three scenarios and could reach 6.25 billion, 4.16 billion, and 5.29 billion tons coal equivalent in 2020. Regardless of future scenarios, China’s energy efficiency in 2020 will increase by more than 30% compared with 2009.

On a possible mechanism of the brain for responding to dynamical features extracted from input signals

International Nuclear Information System (INIS)

Liu Zengrong; Chen Guanrong

2003-01-01

Based on the general theory of nonlinear dynamical systems, a possible mechanism for responding to some dynamical features extracted from input signals in brain activities is described and discussed. This mechanism is first converted to a nonlinear dynamical configuration--a generalized synchronization of complex dynamical systems. Then, some general conditions for achieving such synchronizations are derived. It is shown that dynamical systems have potentials of producing different responses for different features extracted from various input signals, which may be used to describe brain activities. For illustration, some numerical examples are given with simulation figures

Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach

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Weiwei Liu

2017-01-01

Full Text Available University spin-outs (USOs, creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According to this distribution, a radial basis function (RBF neural network (NN model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university’s equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs.

[Identification of spill oil species based on low concentration synchronous fluorescence spectra and RBF neural network].

Science.gov (United States)

Liu, Qian-qian; Wang, Chun-yan; Shi, Xiao-feng; Li, Wen-dong; Luan, Xiao-ning; Hou, Shi-lin; Zhang, Jin-liang; Zheng, Rong-er

2012-04-01

In this paper, a new method was developed to differentiate the spill oil samples. The synchronous fluorescence spectra in the lower nonlinear concentration range of 10(-2) - 10(-1) g x L(-1) were collected to get training data base. Radial basis function artificial neural network (RBF-ANN) was used to identify the samples sets, along with principal component analysis (PCA) as the feature extraction method. The recognition rate of the closely-related oil source samples is 92%. All the results demonstrated that the proposed method could identify the crude oil samples effectively by just one synchronous spectrum of the spill oil sample. The method was supposed to be very suitable to the real-time spill oil identification, and can also be easily applied to the oil logging and the analysis of other multi-PAHs or multi-fluorescent mixtures.

Aerodynamic Shape Optimization Design of Wing-Body Configuration Using a Hybrid FFD-RBF Parameterization Approach

Science.gov (United States)

Liu, Yuefeng; Duan, Zhuoyi; Chen, Song

2017-10-01

Aerodynamic shape optimization design aiming at improving the efficiency of an aircraft has always been a challenging task, especially when the configuration is complex. In this paper, a hybrid FFD-RBF surface parameterization approach has been proposed for designing a civil transport wing-body configuration. This approach is simple and efficient, with the FFD technique used for parameterizing the wing shape and the RBF interpolation approach used for handling the wing body junction part updating. Furthermore, combined with Cuckoo Search algorithm and Kriging surrogate model with expected improvement adaptive sampling criterion, an aerodynamic shape optimization design system has been established. Finally, the aerodynamic shape optimization design on DLR F4 wing-body configuration has been carried out as a study case, and the result has shown that the approach proposed in this paper is of good effectiveness.

A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems

Directory of Open Access Journals (Sweden)

Yong Tao

2016-01-01

Full Text Available A sliding mode control method based on radial basis function (RBF neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

Modeling of Tsunami Equations and Atmospheric Swirling Flows with a Graphics Processing Unit (GPU) and Radial Basis Functions (RBF)

Science.gov (United States)

Schmidt, J.; Piret, C.; Zhang, N.; Kadlec, B. J.; Liu, Y.; Yuen, D. A.; Wright, G. B.; Sevre, E. O.

2008-12-01

The faster growth curves in the speed of GPUs relative to CPUs in recent years and its rapidly gained popularity has spawned a new area of development in computational technology. There is much potential in utilizing GPUs for solving evolutionary partial differential equations and producing the attendant visualization. We are concerned with modeling tsunami waves, where computational time is of extreme essence, for broadcasting warnings. In order to test the efficacy of the GPU on the set of shallow-water equations, we employed the NVIDIA board 8600M GT on a MacBook Pro. We have compared the relative speeds between the CPU and the GPU on a single processor for two types of spatial discretization based on second-order finite-differences and radial basis functions. RBFs are a more novel method based on a gridless and a multi- scale, adaptive framework. Using the NVIDIA 8600M GT, we received a speed up factor of 8 in favor of GPU for the finite-difference method and a factor of 7 for the RBF scheme. We have also studied the atmospheric dynamics problem of swirling flows over a spherical surface and found a speed-up of 5.3 using the GPU. The time steps employed for the RBF method are larger than those used in finite-differences, because of the much fewer number of nodal points needed by RBF. Thus, in modeling the same physical time, RBF acting in concert with GPU would be the fastest way to go.

Radial basis function (RBF) neural network control for mechanical systems design, analysis and Matlab simulation

CERN Document Server

Liu, Jinkun

2013-01-01

Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design.   This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...

Two fast and accurate heuristic RBF learning rules for data classification.

Science.gov (United States)

Rouhani, Modjtaba; Javan, Dawood S

2016-03-01

This paper presents new Radial Basis Function (RBF) learning methods for classification problems. The proposed methods use some heuristics to determine the spreads, the centers and the number of hidden neurons of network in such a way that the higher efficiency is achieved by fewer numbers of neurons, while the learning algorithm remains fast and simple. To retain network size limited, neurons are added to network recursively until termination condition is met. Each neuron covers some of train data. The termination condition is to cover all training data or to reach the maximum number of neurons. In each step, the center and spread of the new neuron are selected based on maximization of its coverage. Maximization of coverage of the neurons leads to a network with fewer neurons and indeed lower VC dimension and better generalization property. Using power exponential distribution function as the activation function of hidden neurons, and in the light of new learning approaches, it is proved that all data became linearly separable in the space of hidden layer outputs which implies that there exist linear output layer weights with zero training error. The proposed methods are applied to some well-known datasets and the simulation results, compared with SVM and some other leading RBF learning methods, show their satisfactory and comparable performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Entropy Generation Due to Natural Convection in a Partially Heated Cavity by Local RBF-DQ Method

DEFF Research Database (Denmark)

Soleimani, S.; Qajarjazi, A.; Bararnia, H.

2011-01-01

The Local Radial Basis Function-Differential Quadrature (RBF-DQ) method is applied to twodimensional incompressible Navier-Stokes equations in primitive form. Numerical results of heatlines and entropy generation due to heat transfer and fluid friction have been obtained for laminar natural...

Proposal of Non-Contact Type Interface of Command Input Using Lip Motion Features

Science.gov (United States)

Sato, Yoshiyuki; Kageyama, Yoichi; Nishida, Makoto

Lip motion features are of practical use in identifying individuals. It is therefore important to develop non-contact type interface. For the interface using lip motion features, individual differences such as accents and dialects in commands should be accepted. In this paper, we propose a method to identify commands by analyzing three kinds of lip motion features. They are lip width, lip length, and ratio of width and length. The analysis is made on the basis of these features' relative values obtained from the primary and object frame. The proposed method has three steps. First, we extracted the lip motion features on the basis of both positions and shapes of lip in each frame of facial images. Second, standard patterns were created from features of six utterances per command. The standard pattern is able to reduce the relative difference in the lip motion features. Third, similarities among commands were computed by Dynamic-Programming (DP) matching. And then, the command with the largest similarity was selected as the target one. Our experimental results suggest that proposed method is useful to construct the non-contact type interface of command input using lip motion features.

Reinforcement learning on slow features of high-dimensional input streams.

Directory of Open Access Journals (Sweden)

Robert Legenstein

Full Text Available Humans and animals are able to learn complex behaviors based on a massive stream of sensory information from different modalities. Early animal studies have identified learning mechanisms that are based on reward and punishment such that animals tend to avoid actions that lead to punishment whereas rewarded actions are reinforced. However, most algorithms for reward-based learning are only applicable if the dimensionality of the state-space is sufficiently small or its structure is sufficiently simple. Therefore, the question arises how the problem of learning on high-dimensional data is solved in the brain. In this article, we propose a biologically plausible generic two-stage learning system that can directly be applied to raw high-dimensional input streams. The system is composed of a hierarchical slow feature analysis (SFA network for preprocessing and a simple neural network on top that is trained based on rewards. We demonstrate by computer simulations that this generic architecture is able to learn quite demanding reinforcement learning tasks on high-dimensional visual input streams in a time that is comparable to the time needed when an explicit highly informative low-dimensional state-space representation is given instead of the high-dimensional visual input. The learning speed of the proposed architecture in a task similar to the Morris water maze task is comparable to that found in experimental studies with rats. This study thus supports the hypothesis that slowness learning is one important unsupervised learning principle utilized in the brain to form efficient state representations for behavioral learning.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization.

Science.gov (United States)

Wang, Zhongqi; Yang, Bo; Kang, Yonggang; Yang, Yuan

2016-01-01

Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Science.gov (United States)

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

Position Control of a Pneumatic Muscle Actuator Using RBF Neural Network Tuned PID Controller

Directory of Open Access Journals (Sweden)

Jie Zhao

2015-01-01

Full Text Available Pneumatic Muscle Actuator (PMA has a broad application prospect in soft robotics. However, PMA has highly nonlinear and hysteretic properties among force, displacement, and pressure, which lead to difficulty in accurate position control. A phenomenological model is developed to portray the hysteretic behavior of PMA. This phenomenological model consists of linear component and hysteretic component force. The latter component is described by Duhem model. An experimental apparatus is built up and sets of experimental data are acquired. Based on the experimental data, parameters of the model are identified. Validation of the model is performed. Then a novel cascade position PID controller is devised for a 1-DOF manipulator actuated by PMA. The outer loop of the controller is to cope with position control whilst the inner loop deals with pressure dynamics within PMA. To enhance the adaptability of the PID algorithm to the high nonlinearities of the manipulator, PID parameters are tuned online using RBF Neural Network. Experiments are performed and comparison between position response of RBF Neural Network based PID controller and that of classic PID controller demonstrates the effectiveness of the novel adaptive controller on the manipulator.

Development of a Prediction Model Based on RBF Neural Network for Sheet Metal Fixture Locating Layout Design and Optimization

Directory of Open Access Journals (Sweden)

Zhongqi Wang

2016-01-01

Full Text Available Fixture plays an important part in constraining excessive sheet metal part deformation at machining, assembly, and measuring stages during the whole manufacturing process. However, it is still a difficult and nontrivial task to design and optimize sheet metal fixture locating layout at present because there is always no direct and explicit expression describing sheet metal fixture locating layout and responding deformation. To that end, an RBF neural network prediction model is proposed in this paper to assist design and optimization of sheet metal fixture locating layout. The RBF neural network model is constructed by training data set selected by uniform sampling and finite element simulation analysis. Finally, a case study is conducted to verify the proposed method.

Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network

Directory of Open Access Journals (Sweden)

Shaohua Luo

2014-01-01

Full Text Available This paper is concerned with the problem of the nonlinear dynamic surface control (DSC of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.

Identification and functional analysis of a second RBF-2 binding site within the HIV-1 promoter

International Nuclear Information System (INIS)

Dahabieh, Matthew S.; Ooms, Marcel; Malcolm, Tom; Simon, Viviana; Sadowski, Ivan

2011-01-01

Transcription from the HIV-1 long terminal repeat (LTR) is mediated by numerous host transcription factors. In this study we characterized an E-box motif (RBE1) within the core promoter that was previously implicated in both transcriptional activation and repression. We show that RBE1 is a binding site for the RBF-2 transcription factor complex (USF1, USF2, and TFII-I), previously shown to bind an upstream viral element, RBE3. The RBE1 and RBE3 elements formed complexes of identical mobility and protein constituents in gel shift assays, both with Jurkat T-cell nuclear extracts and recombinant USF/TFII-I. Furthermore, both elements are regulators of HIV-1 expression; mutations in LTR-luciferase reporters and in HIV-1 molecular clones resulted in decreased transcription, virion production, and proviral expression in infected cells. Collectively, our data indicate that RBE1 is a bona fide RBF-2 binding site and that the RBE1 and RBE3 elements are necessary for mediating proper transcription from the HIV-1 LTR.

PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

Directory of Open Access Journals (Sweden)

Yuanchang Zhong

2014-01-01

Full Text Available The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

RBF neural network based PI pitch controller for a class of 5-MW wind turbines using particle swarm optimization algorithm.

Science.gov (United States)

Poultangari, Iman; Shahnazi, Reza; Sheikhan, Mansour

2012-09-01

In order to control the pitch angle of blades in wind turbines, commonly the proportional and integral (PI) controller due to its simplicity and industrial usability is employed. The neural networks and evolutionary algorithms are tools that provide a suitable ground to determine the optimal PI gains. In this paper, a radial basis function (RBF) neural network based PI controller is proposed for collective pitch control (CPC) of a 5-MW wind turbine. In order to provide an optimal dataset to train the RBF neural network, particle swarm optimization (PSO) evolutionary algorithm is used. The proposed method does not need the complexities, nonlinearities and uncertainties of the system under control. The simulation results show that the proposed controller has satisfactory performance. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Multi-Input Converter with MPPT Feature for Wind-PV Power Generation System

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Chih-Lung Shen

2013-01-01

Full Text Available A multi-input converter (MIC to process wind-PV power is proposed, designed, analyzed, simulated, and implemented. The MIC cannot only process solar energy but deal with wind power, of which structure is derived from forward-type DC/DC converter to step-down/up voltage for charger systems, DC distribution applications, or grid connection. The MIC comprises an upper modified double-ended forward, a lower modified double-ended forward, a common output inductor, and a DSP-based system controller. The two modified double-ended forwards can operate individually or simultaneously so as to accommodate the variation of the hybrid renewable energy under different atmospheric conditions. While the MIC operates at interleaving mode, better performance can be achieved and volume also is reduced. The proposed MIC is capable of recycling the energy stored in the leakage inductance and obtaining high step-up output voltage. In order to draw maximum power from wind turbine and PV panel, perturb-and-observe method is adopted to achieve maximum power point tracking (MPPT feature. The MIC is constructed, analyzed, simulated, and tested. Simulations and hardware measurements have demonstrated the feasibility and functionality of the proposed multi-input converter.

The transcription factor Rbf1 is the master regulator for b-mating type controlled pathogenic development in Ustilago maydis.

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Kai Heimel

Full Text Available In the phytopathogenic basidiomycete Ustilago maydis, sexual and pathogenic development are tightly connected and controlled by the heterodimeric bE/bW transcription factor complex encoded by the b-mating type locus. The formation of the active bE/bW heterodimer leads to the formation of filaments, induces a G2 cell cycle arrest, and triggers pathogenicity. Here, we identify a set of 345 bE/bW responsive genes which show altered expression during these developmental changes; several of these genes are associated with cell cycle coordination, morphogenesis and pathogenicity. 90% of the genes that show altered expression upon bE/bW-activation require the zinc finger transcription factor Rbf1, one of the few factors directly regulated by the bE/bW heterodimer. Rbf1 is a novel master regulator in a multilayered network of transcription factors that facilitates the complex regulatory traits of sexual and pathogenic development.

GNSS Positioning Performance Analysis Using PSO-RBF Estimation Model

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Jgouta Meriem

2017-06-01

Full Text Available Positioning solutions need to be more precise and available. The most frequent method used nowadays includes a GPS receiver, sometimes supported by other sensors. Generally, GPS and GNSS suffer from spreading perturbations that produce biases on pseudo-range measurements. With a view to optimize the use of the satellites received, we offer a positioning algorithm with pseudo range error modelling with the contribution of an appropriate filtering process. Extended Kalman Filter, The Rao- Blackwellized filter are among the most widely used algorithms to predict errors and to filter the high frequency noise. This paper describes a new method of estimating the pseudo-range errors based on the PSO-RBF model which achieves an optimal training criterion. This model is appropriate of its method to predict the GPS corrections for accurate positioning, it reduce the positioning errors at high velocities by more than 50% compared to the RLS or EKF methods.

Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

International Nuclear Information System (INIS)

Anbazhagan, S.; Kumarappan, N.

2014-01-01

Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

Prediction of water formation temperature in natural gas dehydrators using radial basis function (RBF neural networks

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Tatar Afshin

2016-03-01

Full Text Available Raw natural gases usually contain water. It is very important to remove the water from these gases through dehydration processes due to economic reasons and safety considerations. One of the most important methods for water removal from these gases is using dehydration units which use Triethylene glycol (TEG. The TEG concentration at which all water is removed and dew point characteristics of mixture are two important parameters, which should be taken into account in TEG dehydration system. Hence, developing a reliable and accurate model to predict the performance of such a system seems to be very important in gas engineering operations. This study highlights the use of intelligent modeling techniques such as Multilayer perceptron (MLP and Radial Basis Function Neural Network (RBF-ANN to predict the equilibrium water dew point in a stream of natural gas based on the TEG concentration of stream and contractor temperature. Literature data set used in this study covers temperatures from 10 °C to 80 °C and TEG concentrations from 90.000% to 99.999%. Results showed that both models are accurate in prediction of experimental data and the MLP model gives more accurate predictions compared to RBF model.

Eye movement identification based on accumulated time feature

Science.gov (United States)

Guo, Baobao; Wu, Qiang; Sun, Jiande; Yan, Hua

2017-06-01

Eye movement is a new kind of feature for biometrical recognition, it has many advantages compared with other features such as fingerprint, face, and iris. It is not only a sort of static characteristics, but also a combination of brain activity and muscle behavior, which makes it effective to prevent spoofing attack. In addition, eye movements can be incorporated with faces, iris and other features recorded from the face region into multimode systems. In this paper, we do an exploring study on eye movement identification based on the eye movement datasets provided by Komogortsev et al. in 2011 with different classification methods. The time of saccade and fixation are extracted from the eye movement data as the eye movement features. Furthermore, the performance analysis was conducted on different classification methods such as the BP, RBF, ELMAN and SVM in order to provide a reference to the future research in this field.

[GSH fermentation process modeling using entropy-criterion based RBF neural network model].

Science.gov (United States)

Tan, Zuoping; Wang, Shitong; Deng, Zhaohong; Du, Guocheng

2008-05-01

The prediction accuracy and generalization of GSH fermentation process modeling are often deteriorated by noise existing in the corresponding experimental data. In order to avoid this problem, we present a novel RBF neural network modeling approach based on entropy criterion. It considers the whole distribution structure of the training data set in the parameter learning process compared with the traditional MSE-criterion based parameter learning, and thus effectively avoids the weak generalization and over-learning. Then the proposed approach is applied to the GSH fermentation process modeling. Our results demonstrate that this proposed method has better prediction accuracy, generalization and robustness such that it offers a potential application merit for the GSH fermentation process modeling.

A novel optimization algorithm based on epsilon constraint-RBF neural network for tuning PID controller in decoupled HVAC system

International Nuclear Information System (INIS)

Attaran, Seyed Mohammad; Yusof, Rubiyah; Selamat, Hazlina

2016-01-01

Highlights: • Decoupling of a heating, ventilation, and air conditioning system is presented. • RBF models were identified by Epsilon constraint method for temperature and humidity. • Control settings derived from optimization of the decoupled model. • Epsilon constraint-RBF based on PID controller was implemented to keep thermal comfort and minimize energy. • Enhancements of controller parameters of the HVAC system are desired. - Abstract: The energy efficiency of a heating, ventilating and air conditioning (HVAC) system optimized using a radial basis function neural network (RBFNN) combined with the epsilon constraint (EC) method is reported. The new method adopts the advanced algorithm of RBFNN for the HVAC system to estimate the residual errors, increase the control signal and reduce the error results. The objective of this study is to develop and simulate the EC-RBFNN for a self tuning PID controller for a decoupled bilinear HVAC system to control the temperature and relative humidity (RH) produced by the system. A case study indicates that the EC-RBFNN algorithm has a much better accuracy than optimization PID itself and PID-RBFNN, respectively.

Lattice Dynamics of NaCI, KCI, RbCl and RbF

Energy Technology Data Exchange (ETDEWEB)

Raunio, G; Rolandson, S [Physics Dept., Chalmers Univ. of Technology, Goet eborg (Sweden)

1970-07-01

In a series of earlier papers experimental results on phonon dispersion relations at 80 K in NaCl, KCl, RbCl and RbF have been reported. We now present calculations on these halides using the extended shell model approach with both ions polarizable and including next-nearest neighbour interactions. The parameters obtained in a least squares fit to the experimental points in the symmetry directions have been used to calculate the phonon frequencies in 512,000 equally spaced points in an octant of the Brillouin zone, -whereby, after sorting these into intervals of width {delta}{omega} = 2 x 10{sup 11} rad/sec , the frequency spectrum was obtained. From these spectra the variation of the Debye temperature with temperature was also calculated. The agreement with results from specific heat measurements for NaCl and KCl is quite satisfactory at low temperatures.

Toward optimal feature selection using ranking methods and classification algorithms

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Novaković Jasmina

2011-01-01

Full Text Available We presented a comparison between several feature ranking methods used on two real datasets. We considered six ranking methods that can be divided into two broad categories: statistical and entropy-based. Four supervised learning algorithms are adopted to build models, namely, IB1, Naive Bayes, C4.5 decision tree and the RBF network. We showed that the selection of ranking methods could be important for classification accuracy. In our experiments, ranking methods with different supervised learning algorithms give quite different results for balanced accuracy. Our cases confirm that, in order to be sure that a subset of features giving the highest accuracy has been selected, the use of many different indices is recommended.

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

Science.gov (United States)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

Structural health monitoring and damage assessment using measured FRFs from multiple sensors. Part II. Decision making with RBF networks

Energy Technology Data Exchange (ETDEWEB)

Zang, C.; Friswell, M.I. [Dept. of Aerospace Engineering, Univ. of Bristol, Bristol (United Kingdom); Imregun, M. [Dept. of Mechanical Engineering, Imperial Coll., London (United Kingdom)

2003-07-01

This paper is the second of two papers concerned with structural health monitoring and damage assessment using measured FRFs from multiple sensors, and discusses the decision making technique with radial basis function (RBF) neural networks. In PART 1 of the paper, the correlation criteria showed their capability to indicate various changes to the structure's state. PART 2, presented here, develops the methodology of decision theory to identify precisely all of the structure states. Although, the statistical approach can be used for classification, interpreting the information is difficult. Neural network techniques have been proven to possess many advantages for classification due to their learning ability and good generalization. In this paper, the radial basis function neural network is applied for function approximation and recognition. The key idea is to partition the input space (the indicators of the correlation criteria) into a number of subspaces that are in the form of hyper spheres. Then, the widely used k-mean clustering algorithm was selected as a logical approach to detecting the structure states. A bookshelf structure with measured frequency responses from 24 accelerometers was used to demonstrate the effectiveness of the method. The results show the successful classification of all structure states, for instance, the undamaged and damage states, damage locations and damage levels, and the environmental variability. (orig.)

Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

OpenAIRE

Ali Reza Zirak; Sobhan Roshani

2016-01-01

A radial basis function (RBF) artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA) is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE) for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power a...

Thermodynamic studies of (RbF + RbCl + H2O) and (CsF + CsCl + H2O) ternary systems from potentiometric measurements at T = 298.2 K

International Nuclear Information System (INIS)

Huang, Xiaoting; Li, Shu’ni; Zhai, Quanguo; Jiang, Yucheng; Hu, Mancheng

2016-01-01

Graphical abstract: Thermodynamic properties, such as mean activity coefficients, osmotic coefficients and excess Gibbs free energies, of the RbF + RbCl + H 2 O and CsF + CsCl + H 2 O ternary systems were determined from potentiometric measurement at 298.2 K. The Pitzer model and the Harned rule were used to fit the experimental data. - Highlights: • Thermodynamic properties of RbF + RbCl + H 2 O and CsF + CsCl + H 2 O ternary systems were determined. • The Pitzer model and the Harned rule were used to correlate the experimental data. • The mean activity coefficients, osmotic coefficients, and the excess Gibbs free energy were also obtained. - Abstract: Thermodynamic properties of (RbF + RbCl + H 2 O) and (CsF + CsCl + H 2 O) systems were determined by the potentiometric method for different ionic strength fractions y B of RbCl/CsCl at 298.2 K. The Pitzer model and the Harned rule were used to fit the experimental values. The Pitzer mixing parameters and the Harned coefficients were evaluated. In addition, the mean ionic activity coefficients of RbF/CsF and RbCl/CsCl, the osmotic coefficients, and the excess Gibbs energies of the systems studied were calculated.

Pilot study of a novel tool for input-free automated identification of transition zone prostate tumors using T2- and diffusion-weighted signal and textural features.

Science.gov (United States)

Stember, Joseph N; Deng, Fang-Ming; Taneja, Samir S; Rosenkrantz, Andrew B

2014-08-01

To present results of a pilot study to develop software that identifies regions suspicious for prostate transition zone (TZ) tumor, free of user input. Eight patients with TZ tumors were used to develop the model by training a Naïve Bayes classifier to detect tumors based on selection of most accurate predictors among various signal and textural features on T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) maps. Features tested as inputs were: average signal, signal standard deviation, energy, contrast, correlation, homogeneity and entropy (all defined on T2WI); and average ADC. A forward selection scheme was used on the remaining 20% of training set supervoxels to identify important inputs. The trained model was tested on a different set of ten patients, half with TZ tumors. In training cases, the software tiled the TZ with 4 × 4-voxel "supervoxels," 80% of which were used to train the classifier. Each of 100 iterations selected T2WI energy and average ADC, which therefore were deemed the optimal model input. The two-feature model was applied blindly to the separate set of test patients, again without operator input of suspicious foci. The software correctly predicted presence or absence of TZ tumor in all test patients. Furthermore, locations of predicted tumors corresponded spatially with locations of biopsies that had confirmed their presence. Preliminary findings suggest that this tool has potential to accurately predict TZ tumor presence and location, without operator input. © 2013 Wiley Periodicals, Inc.

Feature determination from powered wheelchair user joystick input characteristics for adapting driving assistance [version 3; referees: 2 approved

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Michael Gillham

2018-05-01

Full Text Available Background: Many powered wheelchair users find their medical condition and their ability to drive the wheelchair will change over time. In order to maintain their independent mobility, the powered chair will require adjustment over time to suit the user's needs, thus regular input from healthcare professionals is required. These limited resources can result in the user having to wait weeks for appointments, resulting in the user losing independent mobility, consequently affecting their quality of life and that of their family and carers. In order to provide an adaptive assistive driving system, a range of features need to be identified which are suitable for initial system setup and can automatically provide data for re-calibration over the long term. Methods: A questionnaire was designed to collect information from powered wheelchair users with regard to their symptoms and how they changed over time. Another group of volunteer participants were asked to drive a test platform and complete a course which represented manoeuvring in a very confined space as quickly as possible. Two of those participants were also monitored over a longer period in their normal home daily environment. Features, thought to be suitable, were examined using pattern recognition classifiers to determine their suitability for identifying the changing user input over time. Results: The results are not designed to provide absolute insight into the individual user behaviour, as no ground truth of their ability has been determined, they do nevertheless demonstrate the utility of the measured features to provide evidence of the users’ changing ability over time whilst driving a powered wheelchair. Conclusions: Determining the driving features and adjustable elements provides the initial step towards developing an adaptable assistive technology for the user when the ground truths of the individual and their machine have been learned by a smart pattern recognition system.

Individualized drug dosing using RBF-Galerkin method: Case of anemia management in chronic kidney disease.

Science.gov (United States)

Mirinejad, Hossein; Gaweda, Adam E; Brier, Michael E; Zurada, Jacek M; Inanc, Tamer

2017-09-01

Anemia is a common comorbidity in patients with chronic kidney disease (CKD) and is frequently associated with decreased physical component of quality of life, as well as adverse cardiovascular events. Current treatment methods for renal anemia are mostly population-based approaches treating individual patients with a one-size-fits-all model. However, FDA recommendations stipulate individualized anemia treatment with precise control of the hemoglobin concentration and minimal drug utilization. In accordance with these recommendations, this work presents an individualized drug dosing approach to anemia management by leveraging the theory of optimal control. A Multiple Receding Horizon Control (MRHC) approach based on the RBF-Galerkin optimization method is proposed for individualized anemia management in CKD patients. Recently developed by the authors, the RBF-Galerkin method uses the radial basis function approximation along with the Galerkin error projection to solve constrained optimal control problems numerically. The proposed approach is applied to generate optimal dosing recommendations for individual patients. Performance of the proposed approach (MRHC) is compared in silico to that of a population-based anemia management protocol and an individualized multiple model predictive control method for two case scenarios: hemoglobin measurement with and without observational errors. In silico comparison indicates that hemoglobin concentration with MRHC method has less variation among the methods, especially in presence of measurement errors. In addition, the average achieved hemoglobin level from the MRHC is significantly closer to the target hemoglobin than that of the other two methods, according to the analysis of variance (ANOVA) statistical test. Furthermore, drug dosages recommended by the MRHC are more stable and accurate and reach the steady-state value notably faster than those generated by the other two methods. The proposed method is highly efficient for

Adaptive Global Sliding Mode Control for MEMS Gyroscope Using RBF Neural Network

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Yundi Chu

2015-01-01

Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.

Temperature-based estimation of global solar radiation using soft computing methodologies

Science.gov (United States)

Mohammadi, Kasra; Shamshirband, Shahaboddin; Danesh, Amir Seyed; Abdullah, Mohd Shahidan; Zamani, Mazdak

2016-07-01

Precise knowledge of solar radiation is indeed essential in different technological and scientific applications of solar energy. Temperature-based estimation of global solar radiation would be appealing owing to broad availability of measured air temperatures. In this study, the potentials of soft computing techniques are evaluated to estimate daily horizontal global solar radiation (DHGSR) from measured maximum, minimum, and average air temperatures ( T max, T min, and T avg) in an Iranian city. For this purpose, a comparative evaluation between three methodologies of adaptive neuro-fuzzy inference system (ANFIS), radial basis function support vector regression (SVR-rbf), and polynomial basis function support vector regression (SVR-poly) is performed. Five combinations of T max, T min, and T avg are served as inputs to develop ANFIS, SVR-rbf, and SVR-poly models. The attained results show that all ANFIS, SVR-rbf, and SVR-poly models provide favorable accuracy. Based upon all techniques, the higher accuracies are achieved by models (5) using T max- T min and T max as inputs. According to the statistical results, SVR-rbf outperforms SVR-poly and ANFIS. For SVR-rbf (5), the mean absolute bias error, root mean square error, and correlation coefficient are 1.1931 MJ/m2, 2.0716 MJ/m2, and 0.9380, respectively. The survey results approve that SVR-rbf can be used efficiently to estimate DHGSR from air temperatures.

System identification of an unmanned quadcopter system using MRAN neural

Science.gov (United States)

Pairan, M. F.; Shamsudin, S. S.

2017-12-01

This project presents the performance analysis of the radial basis function neural network (RBF) trained with Minimal Resource Allocating Network (MRAN) algorithm for real-time identification of quadcopter. MRAN’s performance is compared with the RBF with Constant Trace algorithm for 2500 input-output pair data sampling. MRAN utilizes adding and pruning hidden neuron strategy to obtain optimum RBF structure, increase prediction accuracy and reduce training time. The results indicate that MRAN algorithm produces fast training time and more accurate prediction compared with standard RBF. The model proposed in this paper is capable of identifying and modelling a nonlinear representation of the quadcopter flight dynamics.

Chemical sensors are hybrid-input memristors

Science.gov (United States)

Sysoev, V. I.; Arkhipov, V. E.; Okotrub, A. V.; Pershin, Y. V.

2018-04-01

Memristors are two-terminal electronic devices whose resistance depends on the history of input signal (voltage or current). Here we demonstrate that the chemical gas sensors can be considered as memristors with a generalized (hybrid) input, namely, with the input consisting of the voltage, analyte concentrations and applied temperature. The concept of hybrid-input memristors is demonstrated experimentally using a single-walled carbon nanotubes chemical sensor. It is shown that with respect to the hybrid input, the sensor exhibits some features common with memristors such as the hysteretic input-output characteristics. This different perspective on chemical gas sensors may open new possibilities for smart sensor applications.

Classification of Broken Rice Kernels using 12D Features

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SUNDER ALI KHOWAJA

2016-07-01

Full Text Available Integrating the technological aspect for assessment of rice quality is very much needed for the Asian markets where rice is one of the major exports. Methods based on image analysis has been proposed for automated quality assessment by taking into account some of the textural features. These features are good at classifying when rice grains are scanned in controlled environment but it is not suitable for practical implementation. Rice grains are placed randomly on the scanner which neither maintains the uniformity in intensity regions nor the placement strategy is kept ideal thus resulting in false classification of grains. The aim of this research is to propose a method for extracting set of features which can overcome the said issues. This paper uses morphological features along-with gray level and Hough transform based features to overcome the false classification in the existing methods. RBF (Radial Basis function is used as a classification mechanism to classify between complete grains and broken grains. Furthermore the broken grains are classified into two classes? i.e. acceptable grains and non-acceptable grains. This research also uses image enhancement technique prior to the feature extraction and classification process based on top-hat transformation. The proposed method has been simulated in MATLAB to visually analyze and validate the results.

Neural computing thermal comfort index PMV for the indoor environment intelligent control system

Science.gov (United States)

Liu, Chang; Chen, Yifei

2013-03-01

Providing indoor thermal comfort and saving energy are two main goals of indoor environmental control system. An intelligent comfort control system by combining the intelligent control and minimum power control strategies for the indoor environment is presented in this paper. In the system, for realizing the comfort control, the predicted mean vote (PMV) is designed as the control goal, and with chastening formulas of PMV, it is controlled to optimize for improving indoor comfort lever by considering six comfort related variables. On the other hand, a RBF neural network based on genetic algorithm is designed to calculate PMV for better performance and overcoming the nonlinear feature of the PMV calculation better. The formulas given in the paper are presented for calculating the expected output values basing on the input samples, and the RBF network model is trained depending on input samples and the expected output values. The simulation result is proved that the design of the intelligent calculation method is valid. Moreover, this method has a lot of advancements such as high precision, fast dynamic response and good system performance are reached, it can be used in practice with requested calculating error.

Research on 3D power distribution of PWR reactor core based on RBF neural network

International Nuclear Information System (INIS)

Xia Hong; Li Bin; Liu Jianxin

2014-01-01

Real-time monitor for 3D power distribution is critical to nuclear safety and high efficiency of NPP's operation as well as the control system optimization. A method was proposed to set up a real-time monitor system for 3D power distribution by using of ex-core neutron detecting system and RBF neural network for improving the instantaneity of the monitoring results and reducing the fitting error of the 3D power distribution. A series of experiments were operated on a 300 MW PWR simulation system. The results demonstrate that the new monitor system works very well under condition of certain burnup range during the fuel cycle and reconstructs the real-time 3D distribution of reactor core power. The accuracy of the model is improved effectively with the help of several methods. (authors)

Robotics control using isolated word recognition of voice input

Science.gov (United States)

Weiner, J. M.

1977-01-01

A speech input/output system is presented that can be used to communicate with a task oriented system. Human speech commands and synthesized voice output extend conventional information exchange capabilities between man and machine by utilizing audio input and output channels. The speech input facility is comprised of a hardware feature extractor and a microprocessor implemented isolated word or phrase recognition system. The recognizer offers a medium sized (100 commands), syntactically constrained vocabulary, and exhibits close to real time performance. The major portion of the recognition processing required is accomplished through software, minimizing the complexity of the hardware feature extractor.

Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data.

Science.gov (United States)

Song, Sutao; Zhan, Zhichao; Long, Zhiying; Zhang, Jiacai; Yao, Li

2011-02-16

Support vector machine (SVM) has been widely used as accurate and reliable method to decipher brain patterns from functional MRI (fMRI) data. Previous studies have not found a clear benefit for non-linear (polynomial kernel) SVM versus linear one. Here, a more effective non-linear SVM using radial basis function (RBF) kernel is compared with linear SVM. Different from traditional studies which focused either merely on the evaluation of different types of SVM or the voxel selection methods, we aimed to investigate the overall performance of linear and RBF SVM for fMRI classification together with voxel selection schemes on classification accuracy and time-consuming. Six different voxel selection methods were employed to decide which voxels of fMRI data would be included in SVM classifiers with linear and RBF kernels in classifying 4-category objects. Then the overall performances of voxel selection and classification methods were compared. Results showed that: (1) Voxel selection had an important impact on the classification accuracy of the classifiers: in a relative low dimensional feature space, RBF SVM outperformed linear SVM significantly; in a relative high dimensional space, linear SVM performed better than its counterpart; (2) Considering the classification accuracy and time-consuming holistically, linear SVM with relative more voxels as features and RBF SVM with small set of voxels (after PCA) could achieve the better accuracy and cost shorter time. The present work provides the first empirical result of linear and RBF SVM in classification of fMRI data, combined with voxel selection methods. Based on the findings, if only classification accuracy was concerned, RBF SVM with appropriate small voxels and linear SVM with relative more voxels were two suggested solutions; if users concerned more about the computational time, RBF SVM with relative small set of voxels when part of the principal components were kept as features was a better choice.

Short-Term Power Load Point Prediction Based on the Sharp Degree and Chaotic RBF Neural Network

Directory of Open Access Journals (Sweden)

Dongxiao Niu

2015-01-01

Full Text Available In order to realize the predicting and positioning of short-term load inflection point, this paper made reference to related research in the field of computer image recognition. It got a load sharp degree sequence by the transformation of the original load sequence based on the algorithm of sharp degree. Then this paper designed a forecasting model based on the chaos theory and RBF neural network. It predicted the load sharp degree sequence based on the forecasting model to realize the positioning of short-term load inflection point. Finally, in the empirical example analysis, this paper predicted the daily load point of a region using the actual load data of the certain region to verify the effectiveness and applicability of this method. Prediction results showed that most of the test sample load points could be accurately predicted.

Hybrid feedback feedforward: An efficient design of adaptive neural network control.

Science.gov (United States)

Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

2016-04-01

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.

READDATA: a FORTRAN 77 codeword input package

International Nuclear Information System (INIS)

Lander, P.A.

1983-07-01

A new codeword input package has been produced as a result of the incompatibility between different dialects of FORTRAN, especially when character variables are passed as parameters. This report is for those who wish to use a codeword input package with FORTRAN 77. The package, called ''Readdata'', attempts to combine the best features of its predecessors such as BINPUT and pseudo-BINPUT. (author)

Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network

Directory of Open Access Journals (Sweden)

ZHANG Yongzhi

2016-10-01

Full Text Available A dynamic fuzzy RBF neural network model was built to predict the mechanical properties of welded joints, and the purpose of the model was to overcome the shortcomings of static neural networks including structural identification, dynamic sample training and learning algorithm. The structure and parameters of the model are no longer head of default, dynamic adaptive adjustment in the training, suitable for dynamic sample data for learning, learning algorithm introduces hierarchical learning and fuzzy rule pruning strategy, to accelerate the training speed of model and make the model more compact. Simulation of the model was carried out by using three kinds of thickness and different process TC4 titanium alloy TIG welding test data. The results show that the model has higher prediction accuracy, which is suitable for predicting the mechanical properties of welded joints, and has opened up a new way for the on-line control of the welding process.

Identification of four class emotion from Indonesian spoken language using acoustic and lexical features

Science.gov (United States)

Kasyidi, Fatan; Puji Lestari, Dessi

2018-03-01

One of the important aspects in human to human communication is to understand emotion of each party. Recently, interactions between human and computer continues to develop, especially affective interaction where emotion recognition is one of its important components. This paper presents our extended works on emotion recognition of Indonesian spoken language to identify four main class of emotions: Happy, Sad, Angry, and Contentment using combination of acoustic/prosodic features and lexical features. We construct emotion speech corpus from Indonesia television talk show where the situations are as close as possible to the natural situation. After constructing the emotion speech corpus, the acoustic/prosodic and lexical features are extracted to train the emotion model. We employ some machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes, and Random Forest to get the best model. The experiment result of testing data shows that the best model has an F-measure score of 0.447 by using only the acoustic/prosodic feature and F-measure score of 0.488 by using both acoustic/prosodic and lexical features to recognize four class emotion using the SVM RBF Kernel.

Using Random Forests to Select Optimal Input Variables for Short-Term Wind Speed Forecasting Models

Directory of Open Access Journals (Sweden)

Hui Wang

2017-10-01

Full Text Available Achieving relatively high-accuracy short-term wind speed forecasting estimates is a precondition for the construction and grid-connected operation of wind power forecasting systems for wind farms. Currently, most research is focused on the structure of forecasting models and does not consider the selection of input variables, which can have significant impacts on forecasting performance. This paper presents an input variable selection method for wind speed forecasting models. The candidate input variables for various leading periods are selected and random forests (RF is employed to evaluate the importance of all variable as features. The feature subset with the best evaluation performance is selected as the optimal feature set. Then, kernel-based extreme learning machine is constructed to evaluate the performance of input variables selection based on RF. The results of the case study show that by removing the uncorrelated and redundant features, RF effectively extracts the most strongly correlated set of features from the candidate input variables. By finding the optimal feature combination to represent the original information, RF simplifies the structure of the wind speed forecasting model, shortens the training time required, and substantially improves the model’s accuracy and generalization ability, demonstrating that the input variables selected by RF are effective.

ORIGNATE: PC input processor for ORIGEN-S

International Nuclear Information System (INIS)

Bowman, S.M.

1992-01-01

ORIGNATE is a personal computer program that serves as a user- friendly interface for the ORIGEN-S isotopic generation and depletion code. It is designed to assist an ORIGEN-S user in preparing an input file for execution of light-water-reactor fuel depletion and decay cases. Output from ORIGNATE is a card-image input file that may be uploaded to a mainframe computer to execute ORIGEN-S in SCALE-4. ORIGNATE features a pulldown menu system that accesses sophisticated data entry screens. The program allows the user to quickly set up an ORIGEN-S input file and perform error checking

A novel method for flow pattern identification in unstable operational conditions using gamma ray and radial basis function

International Nuclear Information System (INIS)

Roshani, G.H.; Nazemi, E.; Roshani, M.M.

2017-01-01

Changes of fluid properties (especially density) strongly affect the performance of radiation-based multiphase flow meter and could cause error in recognizing the flow pattern and determining void fraction. In this work, we proposed a methodology based on combination of multi-beam gamma ray attenuation and dual modality densitometry techniques using RBF neural network in order to recognize the flow regime and determine the void fraction in gas-liquid two phase flows independent of the liquid phase changes. The proposed system is consisted of one 137 Cs source, two transmission detectors and one scattering detector. The registered counts in two transmission detectors were used as the inputs of one primary Radial Basis Function (RBF) neural network for recognizing the flow regime independent of liquid phase density. Then, after flow regime identification, three RBF neural networks were utilized for determining the void fraction independent of liquid phase density. Registered count in scattering detector and first transmission detector were used as the inputs of these three RBF neural networks. Using this simple methodology, all the flow patterns were correctly recognized and the void fraction was predicted independent of liquid phase density with mean relative error (MRE) of less than 3.28%. - Highlights: • Flow regime and void fraction were determined in two phase flows independent of the liquid phase density changes. • An experimental structure was set up and the required data was obtained. • 3 detectors and one gamma source were used in detection geometry. • RBF networks were utilized for flow regime and void fraction determination.

Fast radial basis functions for engineering applications

CERN Document Server

Biancolini, Marco Evangelos

2017-01-01

This book presents the first “How To” guide to the use of radial basis functions (RBF). It provides a clear vision of their potential, an overview of ready-for-use computational tools and precise guidelines to implement new engineering applications of RBF. Radial basis functions (RBF) are a mathematical tool mature enough for useful engineering applications. Their mathematical foundation is well established and the tool has proven to be effective in many fields, as the mathematical framework can be adapted in several ways. A candidate application can be faced considering the features of RBF:  multidimensional space (including 2D and 3D), numerous radial functions available, global and compact support, interpolation/regression. This great flexibility makes RBF attractive – and their great potential has only been partially discovered. This is because of the difficulty in taking a first step toward RBF as they are not commonly part of engineers’ cultural background, but also due to the numerical complex...

EEG signal classification using PSO trained RBF neural network for epilepsy identification

Directory of Open Access Journals (Sweden)

Sandeep Kumar Satapathy

Full Text Available The electroencephalogram (EEG is a low amplitude signal generated in the brain, as a result of information flow during the communication of several neurons. Hence, careful analysis of these signals could be useful in understanding many human brain disorder diseases. One such disease topic is epileptic seizure identification, which can be identified via a classification process of the EEG signal after preprocessing with the discrete wavelet transform (DWT. To classify the EEG signal, we used a radial basis function neural network (RBFNN. As shown herein, the network can be trained to optimize the mean square error (MSE by using a modified particle swarm optimization (PSO algorithm. The key idea behind the modification of PSO is to introduce a method to overcome the problem of slow searching in and around the global optimum solution. The effectiveness of this procedure was verified by an experimental analysis on a benchmark dataset which is publicly available. The result of our experimental analysis revealed that the improvement in the algorithm is significant with respect to RBF trained by gradient descent and canonical PSO. Here, two classes of EEG signals were considered: the first being an epileptic and the other being non-epileptic. The proposed method produced a maximum accuracy of 99% as compared to the other techniques. Keywords: Electroencephalography, Radial basis function neural network, Particle swarm optimization, Discrete wavelet transform, Machine learning

A Practical pedestrian approach to parsimonious regression with inaccurate inputs

Directory of Open Access Journals (Sweden)

Seppo Karrila

2014-04-01

Full Text Available A measurement result often dictates an interval containing the correct value. Interval data is also created by roundoff, truncation, and binning. We focus on such common interval uncertainty in data. Inaccuracy in model inputs is typically ignored on model fitting. We provide a practical approach for regression with inaccurate data: the mathematics is easy, and the linear programming formulations simple to use even in a spreadsheet. This self-contained elementary presentation introduces interval linear systems and requires only basic knowledge of algebra. Feature selection is automatic; but can be controlled to find only a few most relevant inputs; and joint feature selection is enabled for multiple modeled outputs. With more features than cases, a novel connection to compressed sensing emerges: robustness against interval errors-in-variables implies model parsimony, and the input inaccuracies determine the regularization term. A small numerical example highlights counterintuitive results and a dramatic difference to total least squares.

Modeling recognition memory using the similarity structure of natural input

NARCIS (Netherlands)

Lacroix, J.P.W.; Murre, J.M.J.; Postma, E.O.; van den Herik, H.J.

2006-01-01

The natural input memory (NIM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During

Automatic face morphing for transferring facial animation

NARCIS (Netherlands)

Bui Huu Trung, B.H.T.; Bui, T.D.; Poel, Mannes; Heylen, Dirk K.J.; Nijholt, Antinus; Hamza, H.M.

2003-01-01

In this paper, we introduce a novel method of automatically finding the training set of RBF networks for morphing a prototype face to represent a new face. This is done by automatically specifying and adjusting corresponding feature points on a target face. The RBF networks are then used to transfer

Decoupled ARX and RBF Neural Network Modeling Using PCA and GA Optimization for Nonlinear Distributed Parameter Systems.

Science.gov (United States)

Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing

2018-02-01

Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.

[Prosody, speech input and language acquisition].

Science.gov (United States)

Jungheim, M; Miller, S; Kühn, D; Ptok, M

2014-04-01

In order to acquire language, children require speech input. The prosody of the speech input plays an important role. In most cultures adults modify their code when communicating with children. Compared to normal speech this code differs especially with regard to prosody. For this review a selective literature search in PubMed and Scopus was performed. Prosodic characteristics are a key feature of spoken language. By analysing prosodic features, children gain knowledge about underlying grammatical structures. Child-directed speech (CDS) is modified in a way that meaningful sequences are highlighted acoustically so that important information can be extracted from the continuous speech flow more easily. CDS is said to enhance the representation of linguistic signs. Taking into consideration what has previously been described in the literature regarding the perception of suprasegmentals, CDS seems to be able to support language acquisition due to the correspondence of prosodic and syntactic units. However, no findings have been reported, stating that the linguistically reduced CDS could hinder first language acquisition.

FLUTAN input specifications

International Nuclear Information System (INIS)

Borgwaldt, H.; Baumann, W.; Willerding, G.

1991-05-01

FLUTAN is a highly vectorized computer code for 3-D fluiddynamic and thermal-hydraulic analyses in cartesian and cylinder coordinates. It is related to the family of COMMIX codes originally developed at Argonne National Laboratory, USA. To a large extent, FLUTAN relies on basic concepts and structures imported from COMMIX-1B and COMMIX-2 which were made available to KfK in the frame of cooperation contracts in the fast reactor safety field. While on the one hand not all features of the original COMMIX versions have been implemented in FLUTAN, the code on the other hand includes some essential innovative options like CRESOR solution algorithm, general 3-dimensional rebalacing scheme for solving the pressure equation, and LECUSSO-QUICK-FRAM techniques suitable for reducing 'numerical diffusion' in both the enthalphy and momentum equations. This report provides users with detailed input instructions, presents formulations of the various model options, and explains by means of comprehensive sample input, how to use the code. (orig.) [de

Modeling Recognition Memory Using the Similarity Structure of Natural Input

Science.gov (United States)

Lacroix, Joyca P. W.; Murre, Jaap M. J.; Postma, Eric O.; van den Herik, H. Jaap

2006-01-01

The natural input memory (NAM) model is a new model for recognition memory that operates on natural visual input. A biologically informed perceptual preprocessing method takes local samples (eye fixations) from a natural image and translates these into a feature-vector representation. During recognition, the model compares incoming preprocessed…

Slow feature analysis: unsupervised learning of invariances.

Science.gov (United States)

Wiskott, Laurenz; Sejnowski, Terrence J

2002-04-01

Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.

Recurrent network models for perfect temporal integration of fluctuating correlated inputs.

Directory of Open Access Journals (Sweden)

Hiroshi Okamoto

2009-06-01

Full Text Available Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.

Organic carbon recovery modeling for a rotating belt filter and its impact assessment on a plant-wide scale

DEFF Research Database (Denmark)

Behera, Chitta Ranjan; Santoro, Domenico; Gernaey, Krist V.

2018-01-01

In this study, we perform a systematic plant-wide assessment of the organic carbon recovery concept on wastewater treatment plants by an advanced cellulose recovery enabling technology called rotating belt filter (RBF). To this end, first, an empirical model is developed to describe organic carbon...... recovery by the RBF, which is then used for the plant-wide performance evaluation to further understand the impact of organic carbon recovery by framing four different scenarios. The key features of the scenario analysis are: (i) an RBF operating with thick mat increases methane production (around 10...... %) and brings down aeration energy demand (by 8 %) compared to the primary clarifier (PC) and, (ii) the sludge retention time (SRT) of the activated sludge (AS) tank increases by 55 % when an RBF runs with thick mat and therefore promotes higher nitrification rate, (iii) organic carbon recovery by the RBF does...

Seismic modeling with radial basis function-generated finite differences (RBF-FD) – a simplified treatment of interfaces

Energy Technology Data Exchange (ETDEWEB)

Martin, Bradley, E-mail: brma7253@colorado.edu; Fornberg, Bengt, E-mail: Fornberg@colorado.edu

2017-04-15

In a previous study of seismic modeling with radial basis function-generated finite differences (RBF-FD), we outlined a numerical method for solving 2-D wave equations in domains with material interfaces between different regions. The method was applicable on a mesh-free set of data nodes. It included all information about interfaces within the weights of the stencils (allowing the use of traditional time integrators), and was shown to solve problems of the 2-D elastic wave equation to 3rd-order accuracy. In the present paper, we discuss a refinement of that method that makes it simpler to implement. It can also improve accuracy for the case of smoothly-variable model parameter values near interfaces. We give several test cases that demonstrate the method solving 2-D elastic wave equation problems to 4th-order accuracy, even in the presence of smoothly-curved interfaces with jump discontinuities in the model parameters.

Blood pressure-renal blood flow relationships in conscious angiotensin II- and phenylephrine-infused rats.

Science.gov (United States)

Polichnowski, Aaron J; Griffin, Karen A; Long, Jianrui; Williamson, Geoffrey A; Bidani, Anil K

2013-10-01

Chronic ANG II infusion in rodents is widely used as an experimental model of hypertension, yet very limited data are available describing the resulting blood pressure-renal blood flow (BP-RBF) relationships in conscious rats. Accordingly, male Sprague-Dawley rats (n = 19) were instrumented for chronic measurements of BP (radiotelemetry) and RBF (Transonic Systems, Ithaca, NY). One week later, two or three separate 2-h recordings of BP and RBF were obtained in conscious rats at 24-h intervals, in addition to separate 24-h BP recordings. Rats were then administered either ANG II (n = 11, 125 ng·kg(-1)·min(-1)) or phenylephrine (PE; n = 8, 50 mg·kg(-1)·day(-1)) as a control, ANG II-independent, pressor agent. Three days later the BP-RBF and 24-h BP recordings were repeated over several days. Despite similar increases in BP, PE led to significantly greater BP lability at the heart beat and very low frequency bandwidths. Conversely, ANG II, but not PE, caused significant renal vasoconstriction (a 62% increase in renal vascular resistance and a 21% decrease in RBF) and increased variability in BP-RBF relationships. Transfer function analysis of BP (input) and RBF (output) were consistent with a significant potentiation of the renal myogenic mechanism during ANG II administration, likely contributing, in part, to the exaggerated reductions in RBF during periods of BP elevations. We conclude that relatively equipressor doses of ANG II and PE lead to greatly different ambient BP profiles and effects on the renal vasculature when assessed in conscious rats. These data may have important implications regarding the pathogenesis of hypertension-induced injury in these models of hypertension.

Originate: PC input processor for origen-S

International Nuclear Information System (INIS)

Bowman, S.M.

1994-01-01

ORIGINATE is a personal computer program developed at Oak Ridge National Laboratory to serve as a user-friendly interface for the ORIGEN-S isotopic generation and depletion code. It is designed to assist an ORIGEN-S user in preparing an input file for execution of light-water-reactor fuel depletion and decay cases. Output from ORIGINATE is a card-image input file that may be uploaded to a mainframe computer to execute ORIGEN-S in SCALE-4. ORIGINATE features a pull down menu system that accesses sophisticated data entry screens. The program allows the user to quickly set up an ORIGEN-S input file and perform error checking. This capability increases productivity and decreases chance of user error. (authors). 6 refs., 3 tabs

Implementation of an RBF neural network on embedded systems: real-time face tracking and identity verification.

Science.gov (United States)

Yang, Fan; Paindavoine, M

2003-01-01

This paper describes a real time vision system that allows us to localize faces in video sequences and verify their identity. These processes are image processing techniques based on the radial basis function (RBF) neural network approach. The robustness of this system has been evaluated quantitatively on eight video sequences. We have adapted our model for an application of face recognition using the Olivetti Research Laboratory (ORL), Cambridge, UK, database so as to compare the performance against other systems. We also describe three hardware implementations of our model on embedded systems based on the field programmable gate array (FPGA), zero instruction set computer (ZISC) chips, and digital signal processor (DSP) TMS320C62, respectively. We analyze the algorithm complexity and present results of hardware implementations in terms of the resources used and processing speed. The success rates of face tracking and identity verification are 92% (FPGA), 85% (ZISC), and 98.2% (DSP), respectively. For the three embedded systems, the processing speeds for images size of 288 /spl times/ 352 are 14 images/s, 25 images/s, and 4.8 images/s, respectively.

Spatiotemporal Features for Asynchronous Event-based Data

Directory of Open Access Journals (Sweden)

Xavier eLagorce

2015-02-01

Full Text Available Bio-inspired asynchronous event-based vision sensors are currently introducing a paradigm shift in visual information processing. These new sensors rely on a stimulus-driven principle of light acquisition similar to biological retinas. They are event-driven and fully asynchronous, thereby reducing redundancy and encoding exact times of input signal changes, leading to a very precise temporal resolution. Approaches for higher-level computer vision often rely on the realiable detection of features in visual frames, but similar definitions of features for the novel dynamic and event-based visual input representation of silicon retinas have so far been lacking. This article addresses the problem of learning and recognizing features for event-based vision sensors, which capture properties of truly spatiotemporal volumes of sparse visual event information. A novel computational architecture for learning and encoding spatiotemporal features is introduced based on a set of predictive recurrent reservoir networks, competing via winner-take-all selection. Features are learned in an unsupervised manner from real-world input recorded with event-based vision sensors. It is shown that the networks in the architecture learn distinct and task-specific dynamic visual features, and can predict their trajectories over time.

Robustness of Input features from Noisy Silhouettes in Human Pose Estimation

DEFF Research Database (Denmark)

Gong, Wenjuan; Fihl, Preben; Gonzàlez, Jordi

2014-01-01

. In this paper, we explore this problem. First, We compare performances of several image features widely used for human pose estimation and explore their performances against each other and select one with best performance. Second, iterative closest point algorithm is introduced for a new quantitative...... of silhouette samples of different noise levels and compare with the selected feature on a public dataset: Human Eva dataset....

Multimodal Feature Learning for Video Captioning

Directory of Open Access Journals (Sweden)

Sujin Lee

2018-01-01

Full Text Available Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD and Microsoft Research Video-to-Text (MSR-VTT, demonstrate the performance of the proposed model.

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

International Nuclear Information System (INIS)

Tsai, Tai Ming; Wang, Wei Hui

2009-01-01

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

Diagnosis of mechanical pumping system using neural networks and system parameters analysis

Energy Technology Data Exchange (ETDEWEB)

Tsai, Tai Ming; Wang, Wei Hui [National Taiwan Ocean University, Keelung (China)

2009-01-15

Normally, a mechanical pumping system is equipped to monitor some of the important input and output signals which are set to the prescribed values. This paper addressed dealing with these signals to establish the database of input- output relation by using a number of neural network models through learning algorithms. These signals encompass normal and abnormal running conditions. The abnormal running conditions were artificially generated. Meanwhile, for the purpose of setting up an on-line diagnosis network, the learning speed and accuracy of three kinds of networks, viz., the backpropagation (BPN), radial basis function (RBF) and adaptive linear (ADALINE) neural networks have been compared and assessed. The assessment criteria of the networks are compared with the correlation result matrix in terms of the neuron vectors. Both BPN and RBF are judged by the maximum vector based on the post-regression analysis, and the ADALINE is judged by the minimum vector based on the least mean square error analysis. By ignoring the neural network training time, it has been shown that if the mechanical diagnosis system is tackled off-line, the RBF method is suggested. However, for on-line diagnosis, the BPN method is recommended

OFFSCALE: PC input processor for SCALE-4 criticality sequences

International Nuclear Information System (INIS)

Bowman, S.M.

1991-01-01

OFFSCALE is a personal computer program that serves as a user-friendly interface for the Criticality Safety Analysis Sequences (CSAS) available in SCALE-4. It is designed to assist a SCALE-4 user in preparing an input file for execution of criticality safety problems. Output from OFFSCALE is a card-image input file that may be uploaded to a mainframe computer to execute the CSAS4 control module in SCALE-4. OFFSCALE features a pulldown menu system that accesses sophisticated data entry screens. The program allows the user to quickly set up a CSAS4 input file and perform data checking

Effects of Textual Enhancement and Input Enrichment on L2 Development

Science.gov (United States)

Rassaei, Ehsan

2015-01-01

Research on second language (L2) acquisition has recently sought to include formal instruction into second and foreign language classrooms in a more unobtrusive and implicit manner. Textual enhancement and input enrichment are two techniques which are aimed at drawing learners' attention to specific linguistic features in input and at the same…

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

A study on a new algorithm to optimize ball mill system based on modeling and GA

International Nuclear Information System (INIS)

Wang Heng; Jia Minping; Huang Peng; Chen Zuoliang

2010-01-01

Aiming at the disadvantage of conventional optimization method for ball mill pulverizing system, a novel approach based on RBF neural network and genetic algorithm was proposed in the present paper. Firstly, the experiments and measurement for fill level based on vibration signals of mill shell was introduced. Then, main factors which affected the power consumption of ball mill pulverizing system were analyzed, and the input variables of RBF neural network were determined. RBF neural network was used to map the complex non-linear relationship between the electric consumption and process parameters and the non-linear model of power consumption was built. Finally, the model was optimized by genetic algorithm and the optimal work conditions of ball mill pulverizing system were determined. The results demonstrate that the method is reliable and practical, and can reduce the electric consumption obviously and effectively.

Opening the "Black Box" of efficiency measurement : Input allocation in multi-output settings

NARCIS (Netherlands)

Dierynck, B.; Cherchye, L.J.H.; Sabbe, J.; Roodhooft, F.; de Rock, B.

2013-01-01

We develop a new data envelopment analysis (DEA)-based methodology for measuring the efficiency of decision-making units (DMUs) characterized by multiple inputs and multiple outputs. The distinguishing feature of our method is that it explicitly includes information about output-specific inputs and

CBM First-level Event Selector Input Interface Demonstrator

Science.gov (United States)

Hutter, Dirk; de Cuveland, Jan; Lindenstruth, Volker

2017-10-01

CBM is a heavy-ion experiment at the future FAIR facility in Darmstadt, Germany. Featuring self-triggered front-end electronics and free-streaming read-out, event selection will exclusively be done by the First Level Event Selector (FLES). Designed as an HPC cluster with several hundred nodes its task is an online analysis and selection of the physics data at a total input data rate exceeding 1 TByte/s. To allow efficient event selection, the FLES performs timeslice building, which combines the data from all given input links to self-contained, potentially overlapping processing intervals and distributes them to compute nodes. Partitioning the input data streams into specialized containers allows performing this task very efficiently. The FLES Input Interface defines the linkage between the FEE and the FLES data transport framework. A custom FPGA PCIe board, the FLES Interface Board (FLIB), is used to receive data via optical links and transfer them via DMA to the host’s memory. The current prototype of the FLIB features a Kintex-7 FPGA and provides up to eight 10 GBit/s optical links. A custom FPGA design has been developed for this board. DMA transfers and data structures are optimized for subsequent timeslice building. Index tables generated by the FPGA enable fast random access to the written data containers. In addition the DMA target buffers can directly serve as InfiniBand RDMA source buffers without copying the data. The usage of POSIX shared memory for these buffers allows data access from multiple processes. An accompanying HDL module has been developed to integrate the FLES link into the front-end FPGA designs. It implements the front-end logic interface as well as the link protocol. Prototypes of all Input Interface components have been implemented and integrated into the FLES test framework. This allows the implementation and evaluation of the foreseen CBM read-out chain.

Coal demand prediction based on a support vector machine model

Energy Technology Data Exchange (ETDEWEB)

Jia, Cun-liang; Wu, Hai-shan; Gong, Dun-wei [China University of Mining & Technology, Xuzhou (China). School of Information and Electronic Engineering

2007-01-15

A forecasting model for coal demand of China using a support vector regression was constructed. With the selected embedding dimension, the output vectors and input vectors were constructed based on the coal demand of China from 1980 to 2002. After compared with lineal kernel and Sigmoid kernel, a radial basis function(RBF) was adopted as the kernel function. By analyzing the relationship between the error margin of prediction and the model parameters, the proper parameters were chosen. The support vector machines (SVM) model with multi-input and single output was proposed. Compared the predictor based on RBF neural networks with test datasets, the results show that the SVM predictor has higher precision and greater generalization ability. In the end, the coal demand from 2003 to 2006 is accurately forecasted. l0 refs., 2 figs., 4 tabs.

Going beyond Input Quantity: "Wh"-Questions Matter for Toddlers' Language and Cognitive Development

Science.gov (United States)

Rowe, Meredith L.; Leech, Kathryn A.; Cabrera, Natasha

2017-01-01

There are clear associations between the overall quantity of input children are exposed to and their vocabulary acquisition. However, by uncovering specific features of the input that matter, we can better understand the mechanisms involved in vocabulary learning. We examine whether exposure to "wh"-questions, a challenging quality of…

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

Science.gov (United States)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

On multilevel RBF collocation to solve nonlinear PDEs arising from endogenous stochastic volatility models

Science.gov (United States)

Bastani, Ali Foroush; Dastgerdi, Maryam Vahid; Mighani, Abolfazl

2018-06-01

The main aim of this paper is the analytical and numerical study of a time-dependent second-order nonlinear partial differential equation (PDE) arising from the endogenous stochastic volatility model, introduced in [Bensoussan, A., Crouhy, M. and Galai, D., Stochastic equity volatility related to the leverage effect (I): equity volatility behavior. Applied Mathematical Finance, 1, 63-85, 1994]. As the first step, we derive a consistent set of initial and boundary conditions to complement the PDE, when the firm is financed by equity and debt. In the sequel, we propose a Newton-based iteration scheme for nonlinear parabolic PDEs which is an extension of a method for solving elliptic partial differential equations introduced in [Fasshauer, G. E., Newton iteration with multiquadrics for the solution of nonlinear PDEs. Computers and Mathematics with Applications, 43, 423-438, 2002]. The scheme is based on multilevel collocation using radial basis functions (RBFs) to solve the resulting locally linearized elliptic PDEs obtained at each level of the Newton iteration. We show the effectiveness of the resulting framework by solving a prototypical example from the field and compare the results with those obtained from three different techniques: (1) a finite difference discretization; (2) a naive RBF collocation and (3) a benchmark approximation, introduced for the first time in this paper. The numerical results confirm the robustness, higher convergence rate and good stability properties of the proposed scheme compared to other alternatives. We also comment on some possible research directions in this field.

Optimizing Support Vector Machine Parameters with Genetic Algorithm for Credit Risk Assessment

Science.gov (United States)

Manurung, Jonson; Mawengkang, Herman; Zamzami, Elviawaty

2017-12-01

Support vector machine (SVM) is a popular classification method known to have strong generalization capabilities. SVM can solve the problem of classification and linear regression or nonlinear kernel which can be a learning algorithm for the ability of classification and regression. However, SVM also has a weakness that is difficult to determine the optimal parameter value. SVM calculates the best linear separator on the input feature space according to the training data. To classify data which are non-linearly separable, SVM uses kernel tricks to transform the data into a linearly separable data on a higher dimension feature space. The kernel trick using various kinds of kernel functions, such as : linear kernel, polynomial, radial base function (RBF) and sigmoid. Each function has parameters which affect the accuracy of SVM classification. To solve the problem genetic algorithms are proposed to be applied as the optimal parameter value search algorithm thus increasing the best classification accuracy on SVM. Data taken from UCI repository of machine learning database: Australian Credit Approval. The results show that the combination of SVM and genetic algorithms is effective in improving classification accuracy. Genetic algorithms has been shown to be effective in systematically finding optimal kernel parameters for SVM, instead of randomly selected kernel parameters. The best accuracy for data has been upgraded from kernel Linear: 85.12%, polynomial: 81.76%, RBF: 77.22% Sigmoid: 78.70%. However, for bigger data sizes, this method is not practical because it takes a lot of time.

Research on oral test modeling based on multi-feature fusion

Science.gov (United States)

Shi, Yuliang; Tao, Yiyue; Lei, Jun

2018-04-01

In this paper, the spectrum of speech signal is taken as an input of feature extraction. The advantage of PCNN in image segmentation and other processing is used to process the speech spectrum and extract features. And a new method combining speech signal processing and image processing is explored. At the same time of using the features of the speech map, adding the MFCC to establish the spectral features and integrating them with the features of the spectrogram to further improve the accuracy of the spoken language recognition. Considering that the input features are more complicated and distinguishable, we use Support Vector Machine (SVM) to construct the classifier, and then compare the extracted test voice features with the standard voice features to achieve the spoken standard detection. Experiments show that the method of extracting features from spectrograms using PCNN is feasible, and the fusion of image features and spectral features can improve the detection accuracy.

The comparison of visual working memory representations with perceptual inputs.

Science.gov (United States)

Hyun, Joo-seok; Woodman, Geoffrey F; Vogel, Edward K; Hollingworth, Andrew; Luck, Steven J

2009-08-01

The human visual system can notice differences between memories of previous visual inputs and perceptions of new visual inputs, but the comparison process that detects these differences has not been well characterized. In this study, the authors tested the hypothesis that differences between the memory of a stimulus array and the perception of a new array are detected in a manner that is analogous to the detection of simple features in visual search tasks. That is, just as the presence of a task-relevant feature in visual search can be detected in parallel, triggering a rapid shift of attention to the object containing the feature, the presence of a memory-percept difference along a task-relevant dimension can be detected in parallel, triggering a rapid shift of attention to the changed object. Supporting evidence was obtained in a series of experiments in which manual reaction times, saccadic reaction times, and event-related potential latencies were examined. However, these experiments also showed that a slow, limited-capacity process must occur before the observer can make a manual change detection response.

On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices

Directory of Open Access Journals (Sweden)

Babita Majhi

2014-09-01

Full Text Available This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer and the output mean square error (MSE of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500 and Dow Jones Industrial Average (DJIA stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF-based forecasting model in terms of the mean average percentage error (MAPE, directional accuracy (DA, Thelis’ U and average relative variance (ARV values.

Deep Feature Consistent Variational Autoencoder

OpenAIRE

Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

2016-01-01

We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

Fast metabolite identification with Input Output Kernel Regression

Science.gov (United States)

Brouard, Céline; Shen, Huibin; Dührkop, Kai; d'Alché-Buc, Florence; Böcker, Sebastian; Rousu, Juho

2016-01-01

Motivation: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching these fingerprints against existing molecular structure databases. In this work we propose to address the metabolite identification problem using a structured output prediction approach. This type of approach is not limited to vector output space and can handle structured output space such as the molecule space. Results: We use the Input Output Kernel Regression method to learn the mapping between tandem mass spectra and molecular structures. The principle of this method is to encode the similarities in the input (spectra) space and the similarities in the output (molecule) space using two kernel functions. This method approximates the spectra-molecule mapping in two phases. The first phase corresponds to a regression problem from the input space to the feature space associated to the output kernel. The second phase is a preimage problem, consisting in mapping back the predicted output feature vectors to the molecule space. We show that our approach achieves state-of-the-art accuracy in metabolite identification. Moreover, our method has the advantage of decreasing the running times for the training step and the test step by several orders of magnitude over the preceding methods. Availability and implementation: Contact: celine.brouard@aalto.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307628

Characteristic features of determining the labor input and estimated cost of the development and manufacture of equipment

Science.gov (United States)

Kurmanaliyev, T. I.; Breslavets, A. V.

1974-01-01

The difficulties in obtaining exact calculation data for the labor input and estimated cost are noted. The method of calculating the labor cost of the design work using the provisional normative indexes with respect to individual forms of operations is proposed. Values of certain coefficients recommended for use in the practical calculations of the labor input for the development of new scientific equipment for space research are presented.

Renal nerves dynamically regulate renal blood flow in conscious, healthy rabbits.

Science.gov (United States)

Schiller, Alicia M; Pellegrino, Peter R; Zucker, Irving H

2016-01-15

Despite significant clinical interest in renal denervation as a therapy, the role of the renal nerves in the physiological regulation of renal blood flow (RBF) remains debated. We hypothesized that the renal nerves physiologically regulate beat-to-beat RBF variability (RBFV). This was tested in chronically instrumented, healthy rabbits that underwent either bilateral surgical renal denervation (DDNx) or a sham denervation procedure (INV). Artifact-free segments of RBF and arterial pressure (AP) from calmly resting, conscious rabbits were used to extract RBFV and AP variability for time-domain, frequency-domain, and nonlinear analysis. Whereas steady-state measures of RBF, AP, and heart rate did not statistically differ between groups, DDNx rabbits had greater RBFV than INV rabbits. AP-RBF transfer function analysis showed greater admittance gain in DDNx rabbits than in INV rabbits, particularly in the low-frequency (LF) range where systemic sympathetic vasomotion gives rise to AP oscillations. In the LF range, INV rabbits exhibited a negative AP-RBF phase shift and low coherence, consistent with the presence of an active control system. Neither of these features were present in the LF range of DDNx rabbits, which showed no phase shift and high coherence, consistent with a passive, Ohm's law pressure-flow relationship. Renal denervation did not significantly affect nonlinear RBFV measures of chaos, self-affinity, or complexity, nor did it significantly affect glomerular filtration rate or extracellular fluid volume. Cumulatively, these data suggest that the renal nerves mediate LF renal sympathetic vasomotion, which buffers RBF from LF AP oscillations in conscious, healthy rabbits. Copyright © 2016 the American Physiological Society.

Blast noise classification with common sound level meter metrics.

Science.gov (United States)

Cvengros, Robert M; Valente, Dan; Nykaza, Edward T; Vipperman, Jeffrey S

2012-08-01

A common set of signal features measurable by a basic sound level meter are analyzed, and the quality of information carried in subsets of these features are examined for their ability to discriminate military blast and non-blast sounds. The analysis is based on over 120 000 human classified signals compiled from seven different datasets. The study implements linear and Gaussian radial basis function (RBF) support vector machines (SVM) to classify blast sounds. Using the orthogonal centroid dimension reduction technique, intuition is developed about the distribution of blast and non-blast feature vectors in high dimensional space. Recursive feature elimination (SVM-RFE) is then used to eliminate features containing redundant information and rank features according to their ability to separate blasts from non-blasts. Finally, the accuracy of the linear and RBF SVM classifiers is listed for each of the experiments in the dataset, and the weights are given for the linear SVM classifier.

A new approach for detecting local features

DEFF Research Database (Denmark)

Nguyen, Phuong Giang; Andersen, Hans Jørgen

2010-01-01

Local features up to now are often mentioned in the meaning of interest points. A patch around each point is formed to compute descriptors or feature vectors. Therefore, in order to satisfy different invariant imaging conditions such as scales and viewpoints, an input image is often represented i...

Optocoupled line receiver input discriminates against narrow noise pulses

CERN Document Server

Napier, T M

1977-01-01

Describes a simple optocoupled interface which connects a data line to the receiving end of a data link that features pulse length discrimination to enhance noise pulse rejection. A rugged red LED, D /sub 1/, can bypass any reasonable fault currents to protect the relatively fragile optocoupler input diode. (0 refs).

The Ineffectiveness of the Provision of Input on the Problematic Grammatical Feature of Articles

Science.gov (United States)

Morgan, Gareth

2017-01-01

This study examined the value of giving specific input on the use of articles on an undergraduate English for Academic Purposes (EAP) course. This topic was chosen as previous cohorts had generated a noticeable amount of errors in their use of articles, and developing written grammatical accuracy was one of the course's aims. Participants were…

How input fluctuations reshape the dynamics of a biological switching system

Science.gov (United States)

Hu, Bo; Kessler, David A.; Rappel, Wouter-Jan; Levine, Herbert

2012-12-01

An important task in quantitative biology is to understand the role of stochasticity in biochemical regulation. Here, as an extension of our recent work [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.107.148101 107, 148101 (2011)], we study how input fluctuations affect the stochastic dynamics of a simple biological switch. In our model, the on transition rate of the switch is directly regulated by a noisy input signal, which is described as a non-negative mean-reverting diffusion process. This continuous process can be a good approximation of the discrete birth-death process and is much more analytically tractable. Within this setup, we apply the Feynman-Kac theorem to investigate the statistical features of the output switching dynamics. Consistent with our previous findings, the input noise is found to effectively suppress the input-dependent transitions. We show analytically that this effect becomes significant when the input signal fluctuates greatly in amplitude and reverts slowly to its mean.

Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference

OpenAIRE

Bao, Ruying; Liang, Sihang; Wang, Qingcan

2018-01-01

Deep neural networks have been demonstrated to be vulnerable to adversarial attacks, where small perturbations are intentionally added to the original inputs to fool the classifier. In this paper, we propose a defense method, Featurized Bidirectional Generative Adversarial Networks (FBGAN), to capture the semantic features of the input and filter the non-semantic perturbation. FBGAN is pre-trained on the clean dataset in an unsupervised manner, adversarially learning a bidirectional mapping b...

Soft computing based feature selection for environmental sound classification

NARCIS (Netherlands)

Shakoor, A.; May, T.M.; Van Schijndel, N.H.

2010-01-01

Environmental sound classification has a wide range of applications,like hearing aids, mobile communication devices, portable media players, and auditory protection devices. Sound classification systemstypically extract features from the input sound. Using too many features increases complexity

Input research and testing of code TOODY. Quarterly report, July--September 1971

International Nuclear Information System (INIS)

Haynie, G.A.

1997-01-01

The purpose of this report is to simplify and further explain input instructions for Code TOODY and to demonstrate the ability of the code to reproduce cylinder test results. This input is intended to be a supplement to, and not a replacement for, the existing TOODY manual. The TOODY manual should be read and understood before attempting to read this report. Problems arise in the preparation of the input data in four areas: material definition, initial shape definition, the restart feature, and the limiting of output. Aside from these areas, the code is adequately discussed in the manual, 'TOODY, A Computer Program For Calculating Problems Of Motion In Two Dimensions'

Genetic search feature selection for affective modeling

DEFF Research Database (Denmark)

Martínez, Héctor P.; Yannakakis, Georgios N.

2010-01-01

Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

Enhancing MINIX 3.X input/output performance

OpenAIRE

Pessolani, Pablo Andrés; Weisz, Gustavo; Bardus, Marisa; Hein, César

2008-01-01

MINIX 3.X is an open-source operating system designed to be highly reliable, flexible, and secure. The kernel is extremely small and user processes, specialized servers and device driver runs as user-mode insulated processes. These features, the tiny amount of kernel code, and other aspects greatly enhance system reliability. The drawbacks of running device drivers in user-mode are the performance penalties on input/output ports access, kernel data structures access, interrupt indirect man...

Effect of Feature Dimensionality on Object-based Land Cover ...

African Journals Online (AJOL)

Geographic object-based image analysis (GEOBIA) allows the easy integration of such additional features into the classification process. This paper compares the performance of three supervised classifiers in a GEOBIA environment as an increasing number of object features are included as classification input.

Enhanced feature integration in musicians

DEFF Research Database (Denmark)

Hansen, Niels Christian; Højlund, Andreas; Møller, Cecilie

the classical oddball control paradigm which used identical sounds. This novel finding supports the dependent processing hypothesis suggesting that musicians recruit overlapping neural resources facilitating more holistic representations of domain-relevant stimuli. These specialised refinements in predictive......Distinguishing and integrating features of sensory input is essential to human survival and no less paramount in music perception and cognition. Yet, little is known about training-induced plasticity of neural mechanisms for auditory feature integration. This study aimed to contrast the two...

Decontaminate feature for tracking: adaptive tracking via evolutionary feature subset

Science.gov (United States)

Liu, Qiaoyuan; Wang, Yuru; Yin, Minghao; Ren, Jinchang; Li, Ruizhi

2017-11-01

Although various visual tracking algorithms have been proposed in the last 2-3 decades, it remains a challenging problem for effective tracking with fast motion, deformation, occlusion, etc. Under complex tracking conditions, most tracking models are not discriminative and adaptive enough. When the combined feature vectors are inputted to the visual models, this may lead to redundancy causing low efficiency and ambiguity causing poor performance. An effective tracking algorithm is proposed to decontaminate features for each video sequence adaptively, where the visual modeling is treated as an optimization problem from the perspective of evolution. Every feature vector is compared to a biological individual and then decontaminated via classical evolutionary algorithms. With the optimized subsets of features, the "curse of dimensionality" has been avoided while the accuracy of the visual model has been improved. The proposed algorithm has been tested on several publicly available datasets with various tracking challenges and benchmarked with a number of state-of-the-art approaches. The comprehensive experiments have demonstrated the efficacy of the proposed methodology.

Optimized feature subsets for epileptic seizure prediction studies.

Science.gov (United States)

Direito, Bruno; Ventura, Francisco; Teixeira, César; Dourado, António

2011-01-01

The reduction of the number of EEG features to give as inputs to epilepsy seizure predictors is a needed step towards the development of a transportable device for real-time warning. This paper presents a comparative study of three feature selection methods, based on Support Vector Machines. Minimum-Redundancy Maximum-Relevance, Recursive Feature Elimination, Genetic Algorithms, show that, for three patients of the European Database on Epilepsy, the most important univariate features are related to spectral information and statistical moments.

An Algorithm Based on the Self-Organized Maps for the Classification of Facial Features

Directory of Open Access Journals (Sweden)

Gheorghe Gîlcă

2015-12-01

Full Text Available This paper deals with an algorithm based on Self Organized Maps networks which classifies facial features. The proposed algorithm can categorize the facial features defined by the input variables: eyebrow, mouth, eyelids into a map of their grouping. The groups map is based on calculating the distance between each input vector and each output neuron layer , the neuron with the minimum distance being declared winner neuron. The network structure consists of two levels: the first level contains three input vectors, each having forty-one values, while the second level contains the SOM competitive network which consists of 100 neurons. The proposed system can classify facial features quickly and easily using the proposed algorithm based on SOMs.

Supplementary High-Input Impedance Voltage-Mode Universal Biquadratic Filter Using DVCCs

Directory of Open Access Journals (Sweden)

Jitendra Mohan

2012-01-01

Full Text Available To further extend the existing knowledge on voltage-mode universal biquadratic filter, in this paper, a new biquadratic filter circuit with single input and multiple outputs is proposed, employing three differential voltage current conveyors (DVCCs, three resistors, and two grounded capacitors. The proposed circuit realizes all the standard filter functions, that is, high-pass, band-pass, low-pass, notch, and all-pass filters simultaneously. The circuit enjoys the feature of high-input impedance, orthogonal control of resonance angular frequency (o, and quality factor (Q via grounded resistor and the use of grounded capacitors which is ideal for IC implementation.

Penalized feature selection and classification in bioinformatics

OpenAIRE

Ma, Shuangge; Huang, Jian

2008-01-01

In bioinformatics studies, supervised classification with high-dimensional input variables is frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic studies. Feature selection can be employed along with classifier construction to avoid over-fitting, to generate more reliable classifier and to provide more insights into the underlying causal relationships. In this article, we provide a review of several recently developed penalized feature selection and classific...

Construction of an input sensitivity variable CAMAC module for measuring DC voltage

International Nuclear Information System (INIS)

Noda, Nobuaki.

1979-03-01

In on-line experimental data processing systems, the collection of DC voltage data is frequently required. In plasma confinement experiments, for example, the range of input voltage is very wide from over 1 kV applied to photomultiplier tubes to 10 mV full scale of the controller output for ionization vacuum gauges. A DC voltmeter CAMAC module with variable input range, convenient for plasma experiments and inexpensive, has been constructed for trial. The number of input channels is 16, and the input range is changeable in six steps from +-10 mV to +-200 V; these are all set by commands from a computer. The module is actually used for the on-line data processing system for JIPP T-2 experiment. The ideas behind its development, and the functions, features and usage of the module are described in this report. (J.P.N.)

On Input Vector Representation for the SVR model of Reactor Core Loading Pattern Critical Parameters

International Nuclear Information System (INIS)

Trontl, K.; Pevec, D.; Smuc, T.

2008-01-01

Determination and optimization of reactor core loading pattern is an important factor in nuclear power plant operation. The goal is to minimize the amount of enriched uranium (fresh fuel) and burnable absorbers placed in the core, while maintaining nuclear power plant operational and safety characteristics. The usual approach to loading pattern optimization involves high degree of engineering judgment, a set of heuristic rules, an optimization algorithm and a computer code used for evaluating proposed loading patterns. The speed of the optimization process is highly dependent on the computer code used for the evaluation. Recently, we proposed a new method for fast loading pattern evaluation based on general robust regression model relying on the state of the art research in the field of machine learning. We employed Support Vector Regression (SVR) technique. SVR is a supervised learning method in which model parameters are automatically determined by solving a quadratic optimization problem. The preliminary tests revealed a good potential of the SVR method application for fast and accurate reactor core loading pattern evaluation. However, some aspects of model development are still unresolved. The main objective of the work reported in this paper was to conduct additional tests and analyses required for full clarification of the SVR applicability for loading pattern evaluation. We focused our attention on the parameters defining input vector, primarily its structure and complexity, and parameters defining kernel functions. All the tests were conducted on the NPP Krsko reactor core, using MCRAC code for the calculation of reactor core loading pattern critical parameters. The tested input vector structures did not influence the accuracy of the models suggesting that the initially tested input vector, consisted of the number of IFBAs and the k-inf at the beginning of the cycle, is adequate. The influence of kernel function specific parameters (σ for RBF kernel

RECEIVER OPERATING CHARACTERISTICS MEASURE FOR THE RECOGNITION OF STUTTERING DYSFLUENCIES USING LINE SPECTRAL FREQUENCIES

Directory of Open Access Journals (Sweden)

Nahrul Khair Alang Rashid

2017-05-01

Full Text Available Stuttering is a motor-speech disorder, having common features with other motor control disorders such as dystonia, Parkinson’s disease and Tourette’s syndrome. Stuttering results from complex interactions between factors such as motor, language, emotional and genetic. This study used Line Spectral Frequency (LSF for the feature extraction, while using three classifiers for the identification purpose, Multilayer Perceptron (MLP, Recurrent Neural Network (RNN and Radial Basis Function (RBF. The UCLASS (University College London Archive of Stuttered Speech release 1 was used as database in this research. These recordings were from people of ages 12y11m to 19y5m, who were referred to clinics in London for assessment of their stuttering. The performance metrics used for interpreting the results are sensitivity, accuracy, precision and misclassification rate. Only M1 and M2 had below 100% sensitivity for RBF. The sensitivity of M1 was found to be between 40 & 60%, therefore categorized as moderate, while that of M2 falls between 60 & 80%, classed as substantial. Overall, RBF outperforms the two other classifiers, MLP and RNN for all the performance metrics considered.

Combined data mining/NIR spectroscopy for purity assessment of lime juice

Science.gov (United States)

Shafiee, Sahameh; Minaei, Saeid

2018-06-01

This paper reports the data mining study on the NIR spectrum of lime juice samples to determine their purity (natural or synthetic). NIR spectra for 72 pure and synthetic lime juice samples were recorded in reflectance mode. Sample outliers were removed using PCA analysis. Different data mining techniques for feature selection (Genetic Algorithm (GA)) and classification (including the radial basis function (RBF) network, Support Vector Machine (SVM), and Random Forest (RF) tree) were employed. Based on the results, SVM proved to be the most accurate classifier as it achieved the highest accuracy (97%) using the raw spectrum information. The classifier accuracy dropped to 93% when selected feature vector by GA search method was applied as classifier input. It can be concluded that some relevant features which produce good performance with the SVM classifier are removed by feature selection. Also, reduced spectra using PCA do not show acceptable performance (total accuracy of 66% by RBFNN), which indicates that dimensional reduction methods such as PCA do not always lead to more accurate results. These findings demonstrate the potential of data mining combination with near-infrared spectroscopy for monitoring lime juice quality in terms of natural or synthetic nature.

TOPIC: a debugging code for torus geometry input data of Monte Carlo transport code

International Nuclear Information System (INIS)

Iida, Hiromasa; Kawasaki, Hiromitsu.

1979-06-01

TOPIC has been developed for debugging geometry input data of the Monte Carlo transport code. the code has the following features: (1) It debugs the geometry input data of not only MORSE-GG but also MORSE-I capable of treating torus geometry. (2) Its calculation results are shown in figures drawn by Plotter or COM, and the regions not defined or doubly defined are easily detected. (3) It finds a multitude of input data errors in a single run. (4) The input data required in this code are few, so that it is readily usable in a time sharing system of FACOM 230-60/75 computer. Example TOPIC calculations in design study of tokamak fusion reactors (JXFR, INTOR-J) are presented. (author)

IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORK FOR FACE RECOGNITION USING GABOR FEATURE EXTRACTION

Directory of Open Access Journals (Sweden)

Muthukannan K

2013-11-01

Full Text Available Face detection and recognition is the first step for many applications in various fields such as identification and is used as a key to enter into the various electronic devices, video surveillance, and human computer interface and image database management. This paper focuses on feature extraction in an image using Gabor filter and the extracted image feature vector is then given as an input to the neural network. The neural network is trained with the input data. The Gabor wavelet concentrates on the important components of the face including eye, mouth, nose, cheeks. The main requirement of this technique is the threshold, which gives privileged sensitivity. The threshold values are the feature vectors taken from the faces. These feature vectors are given into the feed forward neural network to train the network. Using the feed forward neural network as a classifier, the recognized and unrecognized faces are classified. This classifier attains a higher face deduction rate. By training more input vectors the system proves to be effective. The effectiveness of the proposed method is demonstrated by the experimental results.

Forged Signature Distinction Using Convolutional Neural Network for Feature Extraction

Directory of Open Access Journals (Sweden)

Seungsoo Nam

2018-01-01

Full Text Available This paper proposes a dynamic verification scheme for finger-drawn signatures in smartphones. As a dynamic feature, the movement of a smartphone is recorded with accelerometer sensors in the smartphone, in addition to the moving coordinates of the signature. To extract high-level longitudinal and topological features, the proposed scheme uses a convolution neural network (CNN for feature extraction, and not as a conventional classifier. We assume that a CNN trained with forged signatures can extract effective features (called S-vector, which are common in forging activities such as hesitation and delay before drawing the complicated part. The proposed scheme also exploits an autoencoder (AE as a classifier, and the S-vector is used as the input vector to the AE. An AE has high accuracy for the one-class distinction problem such as signature verification, and is also greatly dependent on the accuracy of input data. S-vector is valuable as the input of AE, and, consequently, could lead to improved verification accuracy especially for distinguishing forged signatures. Compared to the previous work, i.e., the MLP-based finger-drawn signature verification scheme, the proposed scheme decreases the equal error rate by 13.7%, specifically, from 18.1% to 4.4%, for discriminating forged signatures.

Mobile gaze input system for pervasive interaction

DEFF Research Database (Denmark)

2017-01-01

feedback to the user in response to the received command input. The unit provides feedback to the user on how to position the mobile unit in front of his eyes. The gaze tracking unit interacts with one or more controlled devices via wireless or wired communications. Example devices include a lock......, a thermostat, a light or a TV. The connection between the gaze tracking unit may be temporary or longer-lasting. The gaze tracking unit may detect features of the eye that provide information about the identity of the user....

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

Science.gov (United States)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

Science.gov (United States)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

TART input manual

International Nuclear Information System (INIS)

Kimlinger, J.R.; Plechaty, E.F.

1982-01-01

The TART code is a Monte Carlo neutron/photon transport code that is only on the CRAY computer. All the input cards for the TART code are listed, and definitions for all input parameters are given. The execution and limitations of the code are described, and input for two sample problems are given

Top-down contingent feature-specific orienting with and without awareness of the visual input.

Science.gov (United States)

Ansorge, Ulrich; Horstmann, Gernot; Scharlau, Ingrid

2011-01-01

In the present article, the role of endogenous feature-specific orienting for conscious and unconscious vision is reviewed. We start with an overview of orienting. We proceed with a review of masking research, and the definition of the criteria of experimental protocols that demonstrate endogenous and exogenous orienting, respectively. Against this background of criteria, we assess studies of unconscious orienting and come to the conclusion that so far studies of unconscious orienting demonstrated endogenous feature-specific orienting. The review closes with a discussion of the role of unconscious orienting in action control.

Ocularity Feature Contrast Attracts Attention Exogenously

Directory of Open Access Journals (Sweden)

Li Zhaoping

2018-02-01

Full Text Available An eye-of-origin singleton, e.g., a bar shown to the left eye among many other bars shown to the right eye, can capture attention and gaze exogenously or reflexively, even when it appears identical to other visual input items in the scene and when the eye-of-origin feature is irrelevant to the observer’s task. Defining saliency as the strength of exogenous attraction to attention, we say that this eye-of-origin singleton, or its visual location, is salient. Defining the ocularity of a visual input item as the relative difference between its left-eye input and its right-eye input, this paper shows the general case that an ocularity singleton is also salient. For example, a binocular input item among monocular input items is salient, so is a left-eye-dominant input item (e.g., a bar with a higher input contrast to the left eye than to the right eye among right-eye-dominant items. Saliency by unique input ocularity is analogous to saliency by unique input colour (e.g., a red item among green ones, as colour is determined by the relative difference(s between visual inputs to different photoreceptor cones. Just as a smaller colour difference between a colour singleton and background items makes this singleton less salient, so does a smaller ocularity difference between an ocularity singleton and background items. While a salient colour difference is highly visible, a salient ocularity difference is often perceptually invisible in some cases and discouraging gaze shifts towards it in other cases, making its behavioural manifestation not as apparent. Saliency by ocularity contrast provides another support to the idea that the primary visual cortex creates a bottom-up saliency map to guide attention exogenously.

Stereoscopic Feature Tracking System for Retrieving Velocity of Surface Waters

Science.gov (United States)

Zuniga Zamalloa, C. C.; Landry, B. J.

2017-12-01

The present work is concerned with the surface velocity retrieval of flows using a stereoscopic setup and finding the correspondence in the images via feature tracking (FT). The feature tracking provides a key benefit of substantially reducing the level of user input. In contrast to other commonly used methods (e.g., normalized cross-correlation), FT does not require the user to prescribe interrogation window sizes and removes the need for masking when specularities are present. The results of the current FT methodology are comparable to those obtained via Large Scale Particle Image Velocimetry while requiring little to no user input which allowed for rapid, automated processing of imagery.

Integrating support vector machines and random forests to classify crops in time series of Worldview-2 images

Science.gov (United States)

Zafari, A.; Zurita-Milla, R.; Izquierdo-Verdiguier, E.

2017-10-01

Crop maps are essential inputs for the agricultural planning done at various governmental and agribusinesses agencies. Remote sensing offers timely and costs efficient technologies to identify and map crop types over large areas. Among the plethora of classification methods, Support Vector Machine (SVM) and Random Forest (RF) are widely used because of their proven performance. In this work, we study the synergic use of both methods by introducing a random forest kernel (RFK) in an SVM classifier. A time series of multispectral WorldView-2 images acquired over Mali (West Africa) in 2014 was used to develop our case study. Ground truth containing five common crop classes (cotton, maize, millet, peanut, and sorghum) were collected at 45 farms and used to train and test the classifiers. An SVM with the standard Radial Basis Function (RBF) kernel, a RF, and an SVM-RFK were trained and tested over 10 random training and test subsets generated from the ground data. Results show that the newly proposed SVM-RFK classifier can compete with both RF and SVM-RBF. The overall accuracies based on the spectral bands only are of 83, 82 and 83% respectively. Adding vegetation indices to the analysis result in the classification accuracy of 82, 81 and 84% for SVM-RFK, RF, and SVM-RBF respectively. Overall, it can be observed that the newly tested RFK can compete with SVM-RBF and RF classifiers in terms of classification accuracy.

Preclinical Diagnosis of Magnetic Resonance (MR Brain Images via Discrete Wavelet Packet Transform with Tsallis Entropy and Generalized Eigenvalue Proximal Support Vector Machine (GEPSVM

Directory of Open Access Journals (Sweden)

Yudong Zhang

2015-03-01

Full Text Available Background: Developing an accurate computer-aided diagnosis (CAD system of MR brain images is essential for medical interpretation and analysis. In this study, we propose a novel automatic CAD system to distinguish abnormal brains from normal brains in MRI scanning. Methods: The proposed method simplifies the task to a binary classification problem. We used discrete wavelet packet transform (DWPT to extract wavelet packet coefficients from MR brain images. Next, Shannon entropy (SE and Tsallis entropy (TE were harnessed to obtain entropy features from DWPT coefficients. Finally, generalized eigenvalue proximate support vector machine (GEPSVM, and GEPSVM with radial basis function (RBF kernel, were employed as classifier. We tested the four proposed diagnosis methods (DWPT + SE + GEPSVM, DWPT + TE + GEPSVM, DWPT + SE + GEPSVM + RBF, and DWPT + TE + GEPSVM + RBF on three benchmark datasets of Dataset-66, Dataset-160, and Dataset-255. Results: The 10 repetition of K-fold stratified cross validation results showed the proposed DWPT + TE + GEPSVM + RBF method excelled not only other three proposed classifiers but also existing state-of-the-art methods in terms of classification accuracy. In addition, the DWPT + TE + GEPSVM + RBF method achieved accuracy of 100%, 100%, and 99.53% on Dataset-66, Dataset-160, and Dataset-255, respectively. For Dataset-255, the offline learning cost 8.4430s and online prediction cost merely 0.1059s. Conclusions: We have proved the effectiveness of the proposed method, which achieved nearly 100% accuracy over three benchmark datasets.

Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System

Directory of Open Access Journals (Sweden)

Xianming Lang

2017-09-01

Full Text Available Data cannot be shared and leakage cannot be located simultaneously among multiple pipeline leak detection systems. Based on cyber-physical system (CPS architecture, the method for locating leakage for pipelines with multibranch is proposed. The singular point of pressure signals at the ends of pipeline with multibranch is analyzed by wavelet packet analysis, so that the time feature samples could be established. Then, the Fischer-Burmeister function is introduced into the learning process of the twin support vector machine (TWSVM in order to avoid the matrix inversion calculation, and the samples are input into the improved twin support vector machine (ITWSVM to distinguish the pipeline leak location. The simulation results show that the proposed method is more effective than the back propagation (BP neural networks, the radial basis function (RBF neural networks, and the Lagrange twin support vector machine.

Simplifying BRDF input data for optical signature modeling

Science.gov (United States)

Hallberg, Tomas; Pohl, Anna; Fagerström, Jan

2017-05-01

Scene simulations of optical signature properties using signature codes normally requires input of various parameterized measurement data of surfaces and coatings in order to achieve realistic scene object features. Some of the most important parameters are used in the model of the Bidirectional Reflectance Distribution Function (BRDF) and are normally determined by surface reflectance and scattering measurements. Reflectance measurements of the spectral Directional Hemispherical Reflectance (DHR) at various incident angles can normally be performed in most spectroscopy labs, while measuring the BRDF is more complicated or may not be available at all in many optical labs. We will present a method in order to achieve the necessary BRDF data directly from DHR measurements for modeling software using the Sandford-Robertson BRDF model. The accuracy of the method is tested by modeling a test surface by comparing results from using estimated and measured BRDF data as input to the model. These results show that using this method gives no significant loss in modeling accuracy.

A new meshless approach to map electromagnetic loads for FEM analysis on DEMO TF coil system

International Nuclear Information System (INIS)

Biancolini, Marco Evangelos; Brutti, Carlo; Giorgetti, Francesco; Muzzi, Luigi; Turtù, Simonetta; Anemona, Alessandro

2015-01-01

Graphical abstract: - Highlights: • Generation and mapping of magnetic load on DEMO using radial basis function. • Good agreement between RBF interpolation and EM TOSCA computations. • Resultant forces are stable with respect to the target mesh used. • Stress results are robust and accurate even if a coarse cloud is used for RBF interpolation. - Abstract: Demonstration fusion reactors (DEMO) are being envisaged to be able to produce commercial electrical power. The design of the DEMO magnets and of the constituting conductors is a crucial issue in the overall engineering design of such a large fusion machine. In the frame of the EU roadmap of the so-called fast track approach, mechanical studies of preliminary DEMO toroidal field (TF) coil system conceptual designs are being enforced. The magnetic field load acting on the DEMO TF coil conductor has to be evaluated as input in the FEM model mesh, in order to evaluate the stresses on the mechanical structure. To gain flexibility, a novel approach based on the meshless method of radial basis functions (RBF) has been implemented. The present paper describes this original and flexible approach for the generation and mapping of magnetic load on DEMO TF coil system.

Input-output supervisor

International Nuclear Information System (INIS)

Dupuy, R.

1970-01-01

The input-output supervisor is the program which monitors the flow of informations between core storage and peripheral equipments of a computer. This work is composed of three parts: 1 - Study of a generalized input-output supervisor. With sample modifications it looks like most of input-output supervisors which are running now on computers. 2 - Application of this theory on a magnetic drum. 3 - Hardware requirement for time-sharing. (author) [fr

The use of synthetic input sequences in time series modeling

International Nuclear Information System (INIS)

Oliveira, Dair Jose de; Letellier, Christophe; Gomes, Murilo E.D.; Aguirre, Luis A.

2008-01-01

In many situations time series models obtained from noise-like data settle to trivial solutions under iteration. This Letter proposes a way of producing a synthetic (dummy) input, that is included to prevent the model from settling down to a trivial solution, while maintaining features of the original signal. Simulated benchmark models and a real time series of RR intervals from an ECG are used to illustrate the procedure

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest.

Science.gov (United States)

Ma, Suliang; Chen, Mingxuan; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-04-16

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods.

Improving features used for hyper-temporal land cover change detection by reducing the uncertainty in the feature extraction method

CSIR Research Space (South Africa)

Salmon, BP

2017-07-01

Full Text Available the effect which the length of a temporal sliding window has on the success of detecting land cover change. It is shown using a short Fourier transform as a feature extraction method provides meaningful robust input to a machine learning method. In theory...

Feasibility of measuring renal blood flow by phase-contrast magnetic resonance imaging in patients with autosomal dominant polycystic kidney disease

Energy Technology Data Exchange (ETDEWEB)

Spithoven, E.M.; Meijer, E.; Boertien, W.E.; Gaillard, C.A.J.M.; Jong, P.E. de; Gansevoort, R.T. [University of Groningen, Department of Nephrology, Community and Occupational Medicine, University Medical Center Groningen, PO Box 30.001, RB Groningen (Netherlands); Borns, C.; Kappert, P.; Greuter, M.J.W.; Jagt, E. van der [University of Groningen, Department of Radiology, Community and Occupational Medicine, University Medical Center Groningen, Groningen (Netherlands); Vart, P. [University of Groningen, Department of Health Sciences, Community and Occupational Medicine, University Medical Center Groningen, Groningen (Netherlands)

2016-03-15

Renal blood flow (RBF) has been shown to predict disease progression in autosomal dominant polycystic kidney disease (ADPKD). We investigated the feasibility and accuracy of phase-contrast RBF by MRI (RBF{sub MRI}) in ADPKD patients with a wide range of estimated glomerular filtration rate (eGFR) values. First, we validated RBF{sub MRI} measurement using phantoms simulating renal artery hemodynamics. Thereafter, we investigated in a test-set of 21 patients intra- and inter-observer coefficient of variation of RBF{sub MRI}. After validation, we measured RBF{sub MRI} in a cohort of 91 patients and compared the variability explained by characteristics indicative for disease severity for RBF{sub MRI} and RBF measured by continuous hippuran infusion. The correlation in flow measurement using phantoms by phase-contrast MRI was high and fluid collection was high (CCC=0.969). Technical problems that precluded RBF{sub MRI} measurement occurred predominantly in patients with a lower eGFR (34% vs. 16%). In subjects with higher eGFRs, variability in RBF explained by disease characteristics was similar for RBF{sub MRI} compared to RBF{sub Hip,} whereas in subjects with lower eGFRs, this was significantly less for RBF{sub MRI}. Our study shows that RBF can be measured accurately in ADPKD patients by phase-contrast, but this technique may be less feasible in subjects with a lower eGFR. (orig.)

Feature Selection for Audio Surveillance in Urban Environment

Directory of Open Access Journals (Sweden)

KIKTOVA Eva

2014-05-01

Full Text Available This paper presents the work leading to the acoustic event detection system, which is designed to recognize two types of acoustic events (shot and breaking glass in urban environment. For this purpose, a huge front-end processing was performed for the effective parametric representation of an input sound. MFCC features and features computed during their extraction (MELSPEC and FBANK, then MPEG-7 audio descriptors and other temporal and spectral characteristics were extracted. High dimensional feature sets were created and in the next phase reduced by the mutual information based selection algorithms. Hidden Markov Model based classifier was applied and evaluated by the Viterbi decoding algorithm. Thus very effective feature sets were identified and also the less important features were found.

RIP INPUT TABLES FROM WAPDEG FOR LA DESIGN SELECTION: HIGHER THERMAL LOADING

International Nuclear Information System (INIS)

K. Mon

1999-01-01

The purpose of this calculation is to document (1) the Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b. Software Routine Report for WAPDEG (Version 3.09)) simulations used to analyze waste package degradation and failure under the repository exposure conditions characterized by the higher thermal loading repository design feature and, (2) post-processing of these results into tables of waste package degradation time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems version 5.19.01 (RIP) computer program (Golder Associates 1998). Specifically, the WAPDEG simulations discussed in this calculation correspond to waste package emplacement conditions (repository environment and design) defined in the Total System Performance Assessment-Viability Assessment (TSPA-VA), with the exception that the higher thermal loading Design Feature (Design Feature 26) of the License Application Design Selection (LADS) analysis was analyzed. Higher thermal loading would keep the drift temperature above the boiling point of water for a longer period of time, thereby minimizing moisture around the waste packages during a longer post-closure period. The higher thermal loading would also affect the surrounding rock, which may have adverse effects. The only failure mechanism of this feature would be if the effects on the surrounding rock were determined to be unacceptable. As a result of the change in waste package placement relative to the TSPA-VA base-case design, different temperature and relative humidity time histories at the waste package surface are calculated (input to the WAPDEG simulations), and consequently different waste package failure histories (as calculated by WAPDEG) result

Noise guidelines across Canada : a practical look at the key inputs

International Nuclear Information System (INIS)

Marshall, J.

2010-01-01

Methods of applying noise guidelines in Canada to wind turbine siting plans were discussed. A noise impact analysis is a critical feature of wind turbine siting. However, noise impacts at the receptor (dBA) and their relation to the sound power levels emitted from wind turbines are not well-understood by wind power operators. Decibel and perceived sound levels were discussed, and issues related to noise modelling at the basic component level were reviewed. The inputs defined by different noise guidelines across Canada were outlined in order to determine the impact that inputs may have on the results of noise modelling studies. Various Canadian noise models were evaluated and compared. Noise modelling techniques were also discussed in relation to constraint maps and turbine siting strategies. tabs., figs.

Network and neuronal membrane properties in hybrid networks reciprocally regulate selectivity to rapid thalamocortical inputs.

Science.gov (United States)

Pesavento, Michael J; Pinto, David J

2012-11-01

Rapidly changing environments require rapid processing from sensory inputs. Varying deflection velocities of a rodent's primary facial vibrissa cause varying temporal neuronal activity profiles within the ventral posteromedial thalamic nucleus. Local neuron populations in a single somatosensory layer 4 barrel transform sparsely coded input into a spike count based on the input's temporal profile. We investigate this transformation by creating a barrel-like hybrid network with whole cell recordings of in vitro neurons from a cortical slice preparation, embedding the biological neuron in the simulated network by presenting virtual synaptic conductances via a conductance clamp. Utilizing the hybrid network, we examine the reciprocal network properties (local excitatory and inhibitory synaptic convergence) and neuronal membrane properties (input resistance) by altering the barrel population response to diverse thalamic input. In the presence of local network input, neurons are more selective to thalamic input timing; this arises from strong feedforward inhibition. Strongly inhibitory (damping) network regimes are more selective to timing and less selective to the magnitude of input but require stronger initial input. Input selectivity relies heavily on the different membrane properties of excitatory and inhibitory neurons. When inhibitory and excitatory neurons had identical membrane properties, the sensitivity of in vitro neurons to temporal vs. magnitude features of input was substantially reduced. Increasing the mean leak conductance of the inhibitory cells decreased the network's temporal sensitivity, whereas increasing excitatory leak conductance enhanced magnitude sensitivity. Local network synapses are essential in shaping thalamic input, and differing membrane properties of functional classes reciprocally modulate this effect.

ColloInputGenerator

DEFF Research Database (Denmark)

2013-01-01

This is a very simple program to help you put together input files for use in Gries' (2007) R-based collostruction analysis program. It basically puts together a text file with a frequency list of lexemes in the construction and inserts a column where you can add the corpus frequencies. It requires...... it as input for basic collexeme collostructional analysis (Stefanowitsch & Gries 2003) in Gries' (2007) program. ColloInputGenerator is, in its current state, based on programming commands introduced in Gries (2009). Projected updates: Generation of complete work-ready frequency lists....

Effect of Heat Input on Inclusion Evolution Behavior in Heat-Affected Zone of EH36 Shipbuilding Steel

Science.gov (United States)

Sun, Jincheng; Zou, Xiaodong; Matsuura, Hiroyuki; Wang, Cong

2018-03-01

The effects of heat input parameters on inclusion and microstructure characteristics have been investigated using welding thermal simulations. Inclusion features from heat-affected zones (HAZs) were profiled. It was found that, under heat input of 120 kJ/cm, Al-Mg-Ti-O-(Mn-S) composite inclusions can act effectively as nucleation sites for acicular ferrites. However, this ability disappears when the heat input is increased to 210 kJ/cm. In addition, confocal scanning laser microscopy (CSLM) was used to document possible inclusion-microstructure interactions, shedding light on how inclusions assist beneficial transformations toward property enhancement.

Using RBF to Enable Circuit Emulation Service over Internet%采用RBF来支撑互联网络上的电路模拟服务

Institute of Scientific and Technical Information of China (English)

金涬; 张斌; 赵阳; 王庆波; 陈滢

2009-01-01

Circuit Emulation Service(CES)aims to enable packet switched networks to provide guaranteed services with comparable qualities of circuit switched networks.Our paper addresses the key issue of QoS of CES flows over Internet.Enlightened by the time division idea popularly used in circuit switched networks,we propose a time division based control mechanism to provide guaranteed QoS for the constant-rate CES flows.The control mechanism is able to estimate the arrival times of the coming packets in CES flows,and reserve the time slots for them.ACCOrdingly.it enables the packets to consume the reserved time slots of their own,so the CES flows are guaranteed to be processed.Refreshing Bloom Filter(RBF),an efficient data representation structure,is proposed to support the time division control mechanism.It consists of multiple bloom filters,and can efficiently record the arrival time slots of millions of packets.The proposed control system model could be a practical tool to support Circuit Emulation Services over Intemet.

Microstructural characterization of the HAZ of the AISI 439 with different heat input

International Nuclear Information System (INIS)

Silva, Lorena de Azevedo; Lima, Luciana Iglesias Lourenco; Campos, Wagner Reis da Costa

2007-01-01

Ferritic stainless steels have certain useful corrosion properties, such as resistance to chloride, corrosion in oxidizing aqueous media, oxidation at high temperatures, etc. It is suitable for the aqueous chloride environments, heat transfer applications, condenser tubing for fresh water power plants, industrial buildings, and recently, the ferritic stainless steels have also received attention owing to its superior performance under irradiation. Sometimes in these applications the use of welding processes is necessary. The object of the present work was to research the relationship between microstructure and microhardness in the heat affect zone (HAZ) of the AISI 439, for two different heat input. The base metal shows a random distribution of the precipitates. The HAZ size, grain size, and the amount of precipitates had increased to the bigger heat input weld. The precipitation occurred in bigger amount in the sample with greater heat input, had increased the microhardness. It was observed that the grain size is related with heat input, and that the microhardness is more strong related with other feature, as carbides and nitrites precipitation. (author)

Entorhinal-CA3 Dual-Input Control of Spike Timing in the Hippocampus by Theta-Gamma Coupling.

Science.gov (United States)

Fernández-Ruiz, Antonio; Oliva, Azahara; Nagy, Gergő A; Maurer, Andrew P; Berényi, Antal; Buzsáki, György

2017-03-08

Theta-gamma phase coupling and spike timing within theta oscillations are prominent features of the hippocampus and are often related to navigation and memory. However, the mechanisms that give rise to these relationships are not well understood. Using high spatial resolution electrophysiology, we investigated the influence of CA3 and entorhinal inputs on the timing of CA1 neurons. The theta-phase preference and excitatory strength of the afferent CA3 and entorhinal inputs effectively timed the principal neuron activity, as well as regulated distinct CA1 interneuron populations in multiple tasks and behavioral states. Feedback potentiation of distal dendritic inhibition by CA1 place cells attenuated the excitatory entorhinal input at place field entry, coupled with feedback depression of proximal dendritic and perisomatic inhibition, allowing the CA3 input to gain control toward the exit. Thus, upstream inputs interact with local mechanisms to determine theta-phase timing of hippocampal neurons to support memory and spatial navigation. Copyright © 2017 Elsevier Inc. All rights reserved.

Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.

Science.gov (United States)

Echegaray, Sebastian; Bakr, Shaimaa; Rubin, Daniel L; Napel, Sandy

2017-10-06

The aim of this study was to develop an open-source, modular, locally run or server-based system for 3D radiomics feature computation that can be used on any computer system and included in existing workflows for understanding associations and building predictive models between image features and clinical data, such as survival. The QIFE exploits various levels of parallelization for use on multiprocessor systems. It consists of a managing framework and four stages: input, pre-processing, feature computation, and output. Each stage contains one or more swappable components, allowing run-time customization. We benchmarked the engine using various levels of parallelization on a cohort of CT scans presenting 108 lung tumors. Two versions of the QIFE have been released: (1) the open-source MATLAB code posted to Github, (2) a compiled version loaded in a Docker container, posted to DockerHub, which can be easily deployed on any computer. The QIFE processed 108 objects (tumors) in 2:12 (h/mm) using 1 core, and 1:04 (h/mm) hours using four cores with object-level parallelization. We developed the Quantitative Image Feature Engine (QIFE), an open-source feature-extraction framework that focuses on modularity, standards, parallelism, provenance, and integration. Researchers can easily integrate it with their existing segmentation and imaging workflows by creating input and output components that implement their existing interfaces. Computational efficiency can be improved by parallelizing execution at the cost of memory usage. Different parallelization levels provide different trade-offs, and the optimal setting will depend on the size and composition of the dataset to be processed.

Total dose induced increase in input offset voltage in JFET input operational amplifiers

International Nuclear Information System (INIS)

Pease, R.L.; Krieg, J.; Gehlhausen, M.; Black, J.

1999-01-01

Four different types of commercial JFET input operational amplifiers were irradiated with ionizing radiation under a variety of test conditions. All experienced significant increases in input offset voltage (Vos). Microprobe measurement of the electrical characteristics of the de-coupled input JFETs demonstrates that the increase in Vos is a result of the mismatch of the degraded JFETs. (authors)

OFFSCALE: A PC input processor for the SCALE code system. The ORIGNATE processor for ORIGEN-S

International Nuclear Information System (INIS)

Bowman, S.M.

1994-11-01

OFFSCALE is a suite of personal computer input processor programs developed at Oak Ridge National Laboratory to provide an easy-to-use interface for modules in the SCALE-4 code system. ORIGNATE is a program in the OFFSCALE suite that serves as a user-friendly interface for the ORIGEN-S isotopic generation and depletion code. It is designed to assist an ORIGEN-S user in preparing an input file for execution of light-water-reactor (LWR) fuel depletion and decay cases. ORIGNATE generates an input file that may be used to execute ORIGEN-S in SCALE-4. ORIGNATE features a pulldown menu system that accesses sophisticated data entry screens. The program allows the user to quickly set up an ORIGEN-S input file and perform error checking. This capability increases productivity and decreases the chance of user error

Conceptual Design of GRIG (GUI Based RETRAN Input Generator)

International Nuclear Information System (INIS)

Lee, Gyung Jin; Hwang, Su Hyun; Hong, Soon Joon; Lee, Byung Chul; Jang, Chan Su; Um, Kil Sup

2007-01-01

and archival of results. But it has no capability to interconnect database of NPP design material. RETRANUI (RETRAN User Interface) developed by Computer Simulation and Analysis, Inc. is a PC-based graphical user interface designed to assist the RETRAN analyst with execution of the RETRAN computer programs and to provide convenient automated editing and plotting features. The RETRAN calculation is monitored and controlled by the RETRANUI. Once the analysis is complete, the results can be conveniently plotted or the output file viewed by selecting the appropriate RETRANUI toolbar button. But the function is limited to post-processing. Therefore, GRIG (Graphical User Interface based RETRAN Input Generator) is being developed to generate the basic input of transient analysis code from the database of NPP design manual, to minimize the faults induced in the progress of input generation, and to enhance the user convenience. The methodology of GRIG interconnecting the input generator with the database and calculation note is new approach that has never been tried until now

Enhancing MINIX 3 Input/Output performance using a virtual machine approach

OpenAIRE

Pessolani, Pablo Andrés; González, César Daniel

2010-01-01

MINIX 3 is an open-source operating system designed to be highly reliable, flexible, and secure. The kernel is extremely small and user processes, specialized servers and device drivers run as user-mode insulated processes. These features, the tiny amount of kernel code, and other aspects greatly enhance system reliability. The drawbacks of running device drivers in usermode are the performance penalties on input/output ports access, kernel data structures access, interrupt indirect manage...

Classifying three imaginary states of the same upper extremity using time-domain features.

Directory of Open Access Journals (Sweden)

Mojgan Tavakolan

Full Text Available Brain-computer interface (BCI allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM with a radial basis kernel function (RBF. An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP, filter bank CSP (FBCSP, and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

Science.gov (United States)

Mensi, Skander; Hagens, Olivier; Gerstner, Wulfram; Pozzorini, Christian

2016-02-01

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.

Posterior Inferotemporal Cortex Cells Use Multiple Input Pathways for Shape Encoding.

Science.gov (United States)

Ponce, Carlos R; Lomber, Stephen G; Livingstone, Margaret S

2017-05-10

In the macaque monkey brain, posterior inferior temporal (PIT) cortex cells contribute to visual object recognition. They receive concurrent inputs from visual areas V4, V3, and V2. We asked how these different anatomical pathways shape PIT response properties by deactivating them while monitoring PIT activity in two male macaques. We found that cooling of V4 or V2|3 did not lead to consistent changes in population excitatory drive; however, population pattern analyses showed that V4-based pathways were more important than V2|3-based pathways. We did not find any image features that predicted decoding accuracy differences between both interventions. Using the HMAX hierarchical model of visual recognition, we found that different groups of simulated "PIT" units with different input histories (lacking "V2|3" or "V4" input) allowed for comparable levels of object-decoding performance and that removing a large fraction of "PIT" activity resulted in similar drops in performance as in the cooling experiments. We conclude that distinct input pathways to PIT relay similar types of shape information, with V1-dependent V4 cells providing more quantitatively useful information for overall encoding than cells in V2 projecting directly to PIT. SIGNIFICANCE STATEMENT Convolutional neural networks are the best models of the visual system, but most emphasize input transformations across a serial hierarchy akin to the primary "ventral stream" (V1 → V2 → V4 → IT). However, the ventral stream also comprises parallel "bypass" pathways: V1 also connects to V4, and V2 to IT. To explore the advantages of mixing long and short pathways in the macaque brain, we used cortical cooling to silence inputs to posterior IT and compared the findings with an HMAX model with parallel pathways. Copyright © 2017 the authors 0270-6474/17/375019-16$15.00/0.

Feature-aware natural texture synthesis

KAUST Repository

Wu, Fuzhang

2014-12-04

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis quality can be affected adversely if the texture elements in an example display spatially varied patterns, such as perspective distortion, the composition of different sub-textures, and variations in global color pattern as a result of complex illumination. This issue is common in natural textures and is a fundamental challenge for previously developed methods. Thus, we address it from a feature point of view and propose a feature-aware approach to synthesize natural textures. The synthesis process is guided by a feature map that represents the visual characteristics of the input texture. Moreover, we present a novel adaptive initialization algorithm that can effectively avoid the repeat and verbatim copying artifacts. Our approach improves texture synthesis in many images that cannot be handled effectively with traditional technologies.

RIP Input Tables From WAPDEG for LA Design Selection: Continuous Pre-Closure Ventilation

International Nuclear Information System (INIS)

K.G. Mon

1999-01-01

The purpose of this calculation is to document the creation of .tables for input into Integrated Probabilistic Simulator for Environmental Systems (RIP) version 5.19.01 (Golder Associates 1998) from Waste Package Degradation (WAPDEG) version 3.09 (CRWMS M and O 1998b. ''Software Routine Report for WAPDEG'' (Version 3.09)) simulations. This calculation details the creation of the RIP input tables (representing waste package corrosion degradation over time) for the License Application Design Selection (LADS) analysis of the effects of continuous pre-closure ventilation. Ventilation during the operational phase of the repository could remove considerable water from the system, as well as reduce temperatures. Pre-closure ventilation is LADS Design Feature 7

Higher order visual input to the mushroom bodies in the bee, Bombus impatiens.

Science.gov (United States)

Paulk, Angelique C; Gronenberg, Wulfila

2008-11-01

To produce appropriate behaviors based on biologically relevant associations, sensory pathways conveying different modalities are integrated by higher-order central brain structures, such as insect mushroom bodies. To address this function of sensory integration, we characterized the structure and response of optic lobe (OL) neurons projecting to the calyces of the mushroom bodies in bees. Bees are well known for their visual learning and memory capabilities and their brains possess major direct visual input from the optic lobes to the mushroom bodies. To functionally characterize these visual inputs to the mushroom bodies, we recorded intracellularly from neurons in bumblebees (Apidae: Bombus impatiens) and a single neuron in a honeybee (Apidae: Apis mellifera) while presenting color and motion stimuli. All of the mushroom body input neurons were color sensitive while a subset was motion sensitive. Additionally, most of the mushroom body input neurons would respond to the first, but not to subsequent, presentations of repeated stimuli. In general, the medulla or lobula neurons projecting to the calyx signaled specific chromatic, temporal, and motion features of the visual world to the mushroom bodies, which included sensory information required for the biologically relevant associations bees form during foraging tasks.

Peripheral Sensory Deprivation Restores Critical-Period-like Plasticity to Adult Somatosensory Thalamocortical Inputs

Directory of Open Access Journals (Sweden)

Seungsoo Chung

2017-06-01

Full Text Available Recent work has shown that thalamocortical (TC inputs can be plastic after the developmental critical period has closed, but the mechanism that enables re-establishment of plasticity is unclear. Here, we find that long-term potentiation (LTP at TC inputs is transiently restored in spared barrel cortex following either a unilateral infra-orbital nerve (ION lesion, unilateral whisker trimming, or unilateral ablation of the rodent barrel cortex. Restoration of LTP is associated with increased potency at TC input and reactivates anatomical map plasticity induced by whisker follicle ablation. The reactivation of TC LTP is accompanied by reappearance of silent synapses. Both LTP and silent synapse formation are preceded by transient re-expression of synaptic GluN2B-containing N-methyl-D-aspartate (NMDA receptors, which are required for the reappearance of TC plasticity. These results clearly demonstrate that peripheral sensory deprivation reactivates synaptic plasticity in the mature layer 4 barrel cortex with features similar to the developmental critical period.

Input and execution

International Nuclear Information System (INIS)

Carr, S.; Lane, G.; Rowling, G.

1986-11-01

This document describes the input procedures, input data files and operating instructions for the SYVAC A/C 1.03 computer program. SYVAC A/C 1.03 simulates the groundwater mediated movement of radionuclides from underground facilities for the disposal of low and intermediate level wastes to the accessible environment, and provides an estimate of the subsequent radiological risk to man. (author)

What to measure next to improve decision making? On top-down task driven feature saliency

DEFF Research Database (Denmark)

Hansen, Lars Kai; Karadogan, Seliz; Marchegiani, Letizia

2011-01-01

Top-down attention is modeled as decision making based on incomplete information. We consider decisions made in a sequential measurement situation where initially only an incomplete input feature vector is available, however, where we are given the possibility to acquire additional input values...... among the missing features. The procecure thus poses the question what to do next? We take an information theoretical approach implemented for generality in a generative mixture model. The framework allows us reduce the decision about what to measure next in a classification problem to the estimation...

Safety analysis code input automation using the Nuclear Plant Data Bank

International Nuclear Information System (INIS)

Kopp, H.; Leung, J.; Tajbakhsh, A.; Viles, F.

1985-01-01

The Nuclear Plant Data Bank (NPDB) is a computer-based system that organizes a nuclear power plant's technical data, providing mechanisms for data storage, retrieval, and computer-aided engineering analysis. It has the specific objective to describe thermohydraulic systems in order to support: rapid information retrieval and display, and thermohydraulic analysis modeling. The Nuclear Plant Data Bank (NPBD) system fully automates the storage and analysis based on this data. The system combines the benefits of a structured data base system and computer-aided modeling with links to large scale codes for engineering analysis. Emphasis on a friendly and very graphically oriented user interface facilitates both initial use and longer term efficiency. Specific features are: organization and storage of thermohydraulic data items, ease in locating specific data items, graphical and tabular display capabilities, interactive model construction, organization and display of model input parameters, input deck construction for TRAC and RELAP analysis programs, and traceability of plant data, user model assumptions, and codes used in the input deck construction process. The major accomplishments of this past year were the development of a RELAP model generation capability and the development of a CRAY version of the code

Boost Half-Bridge DC-DC Converter with Reconfigurable Rectifier for Ultra-Wide Input Voltage Range Applications

DEFF Research Database (Denmark)

Vinnikov, Dmitri; Chub, Andrii; Liivik, Elizaveta

2018-01-01

This paper introduces a novel galvanically isolated boost half-bridge dc-dc converter intended for modern power electronic applications where ultra-wide input voltage regulation range is needed. A reconfigurable output rectifier stage performs a transition between the voltage doubler and the full......-bridge diode rectifiers and, by this means, extends the regulation range significantly. The converter features a low number of components and resonant soft switching of semiconductors, which result in high power conversion efficiency over a wide input voltage and load range. The paper presents the operating...

A low-voltage Op Amp with rail-to-rail constant-gm input stage and a class AB rail-to-rail output stage

NARCIS (Netherlands)

Botma, J.H.; Wassenaar, R.F.; Wiegerink, Remco J.

1993-01-01

In this paper a low-voltage two-stage Op Amp is presented. The Op Amp features rail-to-rail operation and has an @put stage with a constant transconductance (%) over the entire common-mode input range. The input stage consists of an n- and a PMOS differential pair connected in parallel. The constant

OFFSCALE: A PC input processor for the SCALE code system. The CSASIN processor for the criticality sequences

International Nuclear Information System (INIS)

Bowman, S.M.

1994-11-01

OFFSCALE is a suite of personal computer input processor programs developed at Oak Ridge National Laboratory to provide an easy-to-use interface for modules in the SCALE-4 code system. CSASIN (formerly known as OFFSCALE) is a program in the OFFSCALE suite that serves as a user-friendly interface for the Criticality Safety Analysis Sequences (CSAS) available in SCALE-4. It is designed to assist a SCALE-4 user in preparing an input file for execution of criticality safety problems. Output from CSASIN generates an input file that may be used to execute the CSAS control module in SCALE-4. CSASIN features a pulldown menu system that accesses sophisticated data entry screens. The program allows the user to quickly set up a CSAS input file and perform data checking. This capability increases productivity and decreases the chance of user error

The UK waste input-output table: Linking waste generation to the UK economy.

Science.gov (United States)

Salemdeeb, Ramy; Al-Tabbaa, Abir; Reynolds, Christian

2016-10-01

In order to achieve a circular economy, there must be a greater understanding of the links between economic activity and waste generation. This study introduces the first version of the UK waste input-output table that could be used to quantify both direct and indirect waste arisings across the supply chain. The proposed waste input-output table features 21 industrial sectors and 34 waste types and is for the 2010 time-period. Using the waste input-output table, the study results quantitatively confirm that sectors with a long supply chain (i.e. manufacturing and services sectors) have higher indirect waste generation rates compared with industrial primary sectors (e.g. mining and quarrying) and sectors with a shorter supply chain (e.g. construction). Results also reveal that the construction, mining and quarrying sectors have the highest waste generation rates, 742 and 694 tonne per £1m of final demand, respectively. Owing to the aggregated format of the first version of the waste input-output, the model does not address the relationship between waste generation and recycling activities. Therefore, an updated version of the waste input-output table is expected be developed considering this issue. Consequently, the expanded model would lead to a better understanding of waste and resource flows in the supply chain. © The Author(s) 2016.

PLEXOS Input Data Generator

Energy Technology Data Exchange (ETDEWEB)

2017-02-01

The PLEXOS Input Data Generator (PIDG) is a tool that enables PLEXOS users to better version their data, automate data processing, collaborate in developing inputs, and transfer data between different production cost modeling and other power systems analysis software. PIDG can process data that is in a generalized format from multiple input sources, including CSV files, PostgreSQL databases, and PSS/E .raw files and write it to an Excel file that can be imported into PLEXOS with only limited manual intervention.

Event-related potentials reveal the relations between feature representations at different levels of abstraction.

Science.gov (United States)

Hannah, Samuel D; Shedden, Judith M; Brooks, Lee R; Grundy, John G

2016-11-01

In this paper, we use behavioural methods and event-related potentials (ERPs) to explore the relations between informational and instantiated features, as well as the relation between feature abstraction and rule type. Participants are trained to categorize two species of fictitious animals and then identify perceptually novel exemplars. Critically, two groups are given a perfectly predictive counting rule that, according to Hannah and Brooks (2009. Featuring familiarity: How a familiar feature instantiation influences categorization. Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 63, 263-275. Retrieved from http://doi.org/10.1037/a0017919), should orient them to using abstract informational features when categorizing the novel transfer items. A third group is taught a feature list rule, which should orient them to using detailed instantiated features. One counting-rule group were taught their rule before any exposure to the actual stimuli, and the other immediately after training, having learned the instantiations first. The feature-list group were also taught their rule after training. The ERP results suggest that at test, the two counting-rule groups processed items differently, despite their identical rule. This not only supports the distinction that informational and instantiated features are qualitatively different feature representations, but also implies that rules can readily operate over concrete inputs, in contradiction to traditional approaches that assume that rules necessarily act on abstract inputs.

A Simplified Whole-Organ CT Perfusion Technique with Biphasic Acquisition: Preliminary Investigation of Accuracy and Protocol Feasibility in Kidneys.

Science.gov (United States)

Yuan, XiaoDong; Zhang, Jing; Quan, ChangBin; Tian, Yuan; Li, Hong; Ao, GuoKun

2016-04-01

To determine the feasibility and accuracy of a protocol for calculating whole-organ renal perfusion (renal blood flow [RBF]) and regional perfusion on the basis of biphasic computed tomography (CT), with concurrent dynamic contrast material-enhanced (DCE) CT perfusion serving as the reference standard. This prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Biphasic CT of the kidneys, including precontrast and arterial phase imaging, was integrated with a first-pass dynamic volume CT protocol and performed and analyzed in 23 patients suspected of having renal artery stenosis. The perfusion value derived from biphasic CT was calculated as CT number enhancement divided by the area under the arterial input function and compared with the DCE CT perfusion data by using the paired t test, correlation analysis, and Bland-Altman plots. Correlation analysis was made between the RBF and the extent of renal artery stenosis. All postprocessing was independently performed by two observers and then averaged as the final result. Mean ± standard deviation biphasic and DCE CT perfusion data for RBF were 425.62 mL/min ± 124.74 and 419.81 mL/min ± 121.13, respectively (P = .53), and for regional perfusion they were 271.15 mL/min per 100 mL ± 82.21 and 266.33 mL/min per 100 mL ± 74.40, respectively (P = .31). Good correlation and agreement were shown between biphasic and DCE CT perfusion for RBF (r = 0.93; ±10% variation from mean perfusion data [P < .001]) and for regional perfusion (r = 0.90; ±13% variation from mean perfusion data [P < .001]). The extent of renal artery stenosis was negatively correlated with RBF with biphasic CT perfusion (r = -0.81, P = .012). Biphasic CT perfusion is clinically feasible and provides perfusion data comparable to DCE CT perfusion data at both global and regional levels in the kidney. Online supplemental material is available for this article.

Design Features of Modern Mechanical Ventilators.

Science.gov (United States)

MacIntyre, Neil

2016-12-01

A positive-pressure breath ideally should provide a V T that is adequate for gas exchange and appropriate muscle unloading while minimizing any risk for injury or discomfort. The latest generation of ventilators uses sophisticated feedback systems to sculpt positive-pressure breaths according to patient effort and respiratory system mechanics. Currently, however, these new control strategies are not totally closed-loop systems. This is because the automatic input variables remain limited, some clinician settings are still required, and the specific features of the perfect breath design still are not entirely clear. Despite these limitations, there are some rationale for many of these newer feedback features. Copyright © 2016 Elsevier Inc. All rights reserved.

Low-Level Color and Texture Feature Extraction of Coral Reef Components

Directory of Open Access Journals (Sweden)

Ma. Sheila Angeli Marcos

2003-06-01

Full Text Available The purpose of this study is to develop a computer-based classifier that automates coral reef assessmentfrom digitized underwater video. We extract low-level color and texture features from coral images toserve as input to a high-level classifier. Low-level features for color were labeled blue, green, yellow/brown/orange, and gray/white, which are described by the normalized chromaticity histograms of thesemajor colors. The color matching capability of these features was determined through a technique called“Histogram Backprojection”. The low-level texture feature marks a region as coarse or fine dependingon the gray-level variance of the region.

Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: simulation and verification with in vivo data

International Nuclear Information System (INIS)

Fang, Yu-Hua; Kao, Tsair; Liu, Ren-Shyan; Wu, Liang-Chih

2004-01-01

A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13±8.85 and 16.60±9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification. (orig.)

Effect of dominant features on neural network performance in the classification of mammographic lesions

International Nuclear Information System (INIS)

Zhimin Huo; Giger, M.L.; Metz, C.E.

1999-01-01

Two different classifiers, an artificial neural network (Ann) and a hybrid system (one step rule-based method followed by an artificial neural network) have been investigated to merge computer-extracted features in the task of differentiating between malignant and benign masses. A database consisting of 65 cases (38 malignant and 26 benign) was used in the study. A total of four computer-extracted features - spiculation, margin sharpness and two density-related measures - was used to characterize these masses. Results from our previous study showed that the hybrid system performed better than the ANN classifier. In our current study, to understand the difference between the two classifiers, we investigated their learning and decision-making processes by studying the relationships between the input features and the outputs. A correlation study showed that the outputs from the ANN-alone method correlated strongly with one of the input features (spiculation), yielding a correlation coefficient of 0.91, whereas the correlation coefficients (absolute value) for the other features ranged from 0.19 to 0.40. This strong correlation between the ANN output and spiculation measure indicates that the learning and decision-making processes of the ANN-alone method were dominated by the spiculation measure. Three-dimensional plots of the computer output as functions of the input features demonstrate that the ANN-alone method did not learn as effectively as the hybrid system in differentiating non-spiculated malignant masses from benign masses, thus resulting in an inferior performance at the high sensitivity levels. We found that with a limited database it is detrimental for an ANN to learn the significance of other features in the presence of a dominant feature. The hybrid system, which initially applied a rule concerning the value of the spiculation measure prior to employing an ANN, prevents over-learning from the dominant feature and performed better than the ANN-alone method

An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: Application for isolated sites in Algeria

International Nuclear Information System (INIS)

Mellit, A.; Benghanem, M.; Hadj Arab, A.; Guessoum, G.

2004-07-01

In this paper we investigate, by using an adaptive Artificial Neural Network (ANN), in order to find a suitable model for sizing Stand-Alone Photovoltaic (SAPV) systems, based on a minimum of input data. This model combines Radial Basis Function (RBF) network and Infinite Impulse Response (IIR) filter in order to accelerate the convergence of the network. For the sizing of a photovoltaic (PV) system, we need to determine the optimal sizing coefficients (K PV , K B . These coefficients allow us to determine the number of solar panels and storage batteries necessary to satisfy a given consumption, especially in isolated sites where the global solar radiation data is not always available and which are considered the most important parameters for sizing a PV system. Obtained results by classical models (analytical, numerical, analytical- numerical, B-spline function) and new models like feed-forward (MLP), radial basis function (RBF), MLP-IIR and RBF-IIR have been compared with experimental sizing coefficients in order to illustrate the accuracy of the results of the new developed model. This model has been trained by using 200 known optimal sizing coefficients corresponding to 200 locations in Algeria. In this way, the adaptive model was trained to accept and even handle a number of unusual cases, the unknown validation sizing coefficients set produced very set accurate estimation and a correlation coefficient of 98% was obtained between the calculated and that estimated by the RBF-IIR model. This result indicates that the proposed method can be successfully used for the estimation of optimal sizing coefficients of SAPV systems for any locations in Algeria, but the methodology can be generalized using different locations over the world. (author)

Microlens array processor with programmable weight mask and direct optical input

Science.gov (United States)

Schmid, Volker R.; Lueder, Ernst H.; Bader, Gerhard; Maier, Gert; Siegordner, Jochen

1999-03-01

We present an optical feature extraction system with a microlens array processor. The system is suitable for online implementation of a variety of transforms such as the Walsh transform and DCT. Operating with incoherent light, our processor accepts direct optical input. Employing a sandwich- like architecture, we obtain a very compact design of the optical system. The key elements of the microlens array processor are a square array of 15 X 15 spherical microlenses on acrylic substrate and a spatial light modulator as transmissive mask. The light distribution behind the mask is imaged onto the pixels of a customized a-Si image sensor with adjustable gain. We obtain one output sample for each microlens image and its corresponding weight mask area as summation of the transmitted intensity within one sensor pixel. The resulting architecture is very compact and robust like a conventional camera lens while incorporating a high degree of parallelism. We successfully demonstrate a Walsh transform into the spatial frequency domain as well as the implementation of a discrete cosine transform with digitized gray values. We provide results showing the transformation performance for both synthetic image patterns and images of natural texture samples. The extracted frequency features are suitable for neural classification of the input image. Other transforms and correlations can be implemented in real-time allowing adaptive optical signal processing.

Bioremediation of petroleum hydrocarbon contaminated soil by Rhodobacter sphaeroides biofertilizer and plants.

Science.gov (United States)

Jiao, Haihua; Luo, Jinxue; Zhang, Yiming; Xu, Shengjun; Bai, Zhihui; Huang, Zhanbin

2015-09-01

Bio-augmentation is a promising technique for remediation of polluted soils. This study aimed to evaluate the bio-augmentation effect of Rhodobacter sphaeroides biofertilizer (RBF) on the bioremediation of total petroleum hydrocarbons (TPH) contaminated soil. A greenhouse pot experiment was conducted over a period of 120 days, three methods for enhancing bio-augmentation were tested on TPH contaminated soils, including single addition RBF, planting, and combining of RBF and three crop species, such as wheat (W), cabbage (C) and spinach (S), respectively. The results demonstrated that the best removal of TPH from contaminated soil in the RBF bio-augmentation rhizosphere soils was found to be 46.2%, 65.4%, 67.5% for W+RBF, C+RBF, S+RBF rhizosphere soils respectively. RBF supply impacted on the microbial community diversity (phospholipid fatty acids, PLFA) and the activity of soil enzymes, such as dehydrogenase (DH), alkaline phosphatase (AP) and urease (UR). There were significant difference among the soil only containing crude oil (CK), W, C and S rhizosphere soils and RBF bio-augmentation soils. Moreover, the changes were significantly distinct depended on crops species. It was concluded that the RBF is a valuable material for improving effect of remediation of TPH polluted soils.

SSYST-3. Input description

International Nuclear Information System (INIS)

Meyder, R.

1983-12-01

The code system SSYST-3 is designed to analyse the thermal and mechanical behaviour of a fuel rod during a LOCA. The report contains a complete input-list for all modules and several tested inputs for a LOCA analysis. (orig.)

Design and Implementation of Kana-Input Navigation System for Kids based on the Cyber Assistant

Directory of Open Access Journals (Sweden)

Hiroshi Matsuda

2004-02-01

Full Text Available In Japan, it has increased the opportunity for young children to experience the personal computer in elementary schools. However, in order to use computer, many domestic barriers have confronted young children (Kids because they cannot read difficult Kanji characters and had not learnt Roman alphabet yet. As a result, they cannot input text strings by JIS Kana keyboard. In this research, we developed Kana-Input NaVigation System for kids (KINVS based on the Cyber Assistant System (CAS. CAS is a Human-Style Software Robot based on the 3D-CG real-time animation and voice synthesis technology. KINVS enables to input Hiragana/Katakana characters by mouse operation only (without keyboard operation and CAS supports them by using speaking, facial expression, body action and sound effects. KINVS displays the 3D-Stage like a classroom. In this room, Blackboard, Interactive parts to input Kana-characters, and CAS are placed. As some results of preliminary experiments, it is definitely unfit for Kids to double-click objects quickly or to move the Scrollbar by mouse dragging. So, mouse input method of KINVS are designed to use only single click and wheeler rotation. To input characters, Kids clicks or rotates the Interactive Parts. KINVS reports all information by voice speaking and Kana subtitles instead of Kanji text. Furthermore, to verify the functional feature of KINVS, we measured how long Kids had taken to input long text by using KINVS.

Material input of nuclear fuel

International Nuclear Information System (INIS)

Rissanen, S.; Tarjanne, R.

2001-01-01

The Material Input (MI) of nuclear fuel, expressed in terms of the total amount of natural material needed for manufacturing a product, is examined. The suitability of the MI method for assessing the environmental impacts of fuels is also discussed. Material input is expressed as a Material Input Coefficient (MIC), equalling to the total mass of natural material divided by the mass of the completed product. The material input coefficient is, however, only an intermediate result, which should not be used as such for the comparison of different fuels, because the energy contents of nuclear fuel is about 100 000-fold compared to the energy contents of fossil fuels. As a final result, the material input is expressed in proportion to the amount of generated electricity, which is called MIPS (Material Input Per Service unit). Material input is a simplified and commensurable indicator for the use of natural material, but because it does not take into account the harmfulness of materials or the way how the residual material is processed, it does not alone express the amount of environmental impacts. The examination of the mere amount does not differentiate between for example coal, natural gas or waste rock containing usually just sand. Natural gas is, however, substantially more harmful for the ecosystem than sand. Therefore, other methods should also be used to consider the environmental load of a product. The material input coefficient of nuclear fuel is calculated using data from different types of mines. The calculations are made among other things by using the data of an open pit mine (Key Lake, Canada), an underground mine (McArthur River, Canada) and a by-product mine (Olympic Dam, Australia). Furthermore, the coefficient is calculated for nuclear fuel corresponding to the nuclear fuel supply of Teollisuuden Voima (TVO) company in 2001. Because there is some uncertainty in the initial data, the inaccuracy of the final results can be even 20-50 per cent. The value

A High-Performance FPGA-Based Image Feature Detector and Matcher Based on the FAST and BRIEF Algorithms

Directory of Open Access Journals (Sweden)

Michał Fularz

2015-10-01

Full Text Available Image feature detection and matching is a fundamental operation in image processing. As the detected and matched features are used as input data for high-level computer vision algorithms, the matching accuracy directly influences the quality of the results of the whole computer vision system. Moreover, as the algorithms are frequently used as a part of a real-time processing pipeline, the speed at which the input image data are handled is also a concern. The paper proposes an embedded system architecture for feature detection and matching. The architecture implements the FAST feature detector and the BRIEF feature descriptor and is capable of establishing key point correspondences in the input image data stream coming from either an external sensor or memory at a speed of hundreds of frames per second, so that it can cope with most demanding applications. Moreover, the proposed design is highly flexible and configurable, and facilitates the trade-off between the processing speed and programmable logic resource utilization. All the designed hardware blocks are designed to use standard, widely adopted hardware interfaces based on the AMBA AXI4 interface protocol and are connected using an underlying direct memory access (DMA architecture, enabling bottleneck-free inter-component data transfers.

[INVITED] Evaluation of process observation features for laser metal welding

Science.gov (United States)

Tenner, Felix; Klämpfl, Florian; Nagulin, Konstantin Yu.; Schmidt, Michael

2016-06-01

In the present study we show how fast the fluid dynamics change when changing the laser power for different feed rates during laser metal welding. By the use of two high-speed cameras and a data acquisition system we conclude how fast we have to image the process to measure the fluid dynamics with a very high certainty. Our experiments show that not all process features which can be measured during laser welding do represent the process behavior similarly well. Despite the good visibility of the vapor plume the monitoring of its movement is less suitable as an input signal for a closed-loop control. The features measured inside the keyhole show a good correlation with changes of process parameters. Due to its low noise, the area of the keyhole opening is well suited as an input signal for a closed-loop control of the process.

Design of a 300-Watt Isolated Power Supply with Minimized Circuit Input-to-Output Parasitic Capacitance

DEFF Research Database (Denmark)

Nguyen-Duy, Khiem; Petersen, Lars Press; Knott, Arnold

2014-01-01

This paper presents the design of a 300-Watt isolated power supply for MOS gate driver circuit in medium and high voltage applications. The key feature of the developed power supply is having a very low circuit input-to-output parasitic capacitance, thus maximizing its noise immunity. This makes...

Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis

DEFF Research Database (Denmark)

Jiang, Jiuchuan; Jaeger, Manfred

2015-01-01

distribution is defined by the model from numerical input variables that are only used for conditioning the distribution of discrete response variables. We show how numerical input relations can very easily be used in the Relational Bayesian Network framework, and that existing inference and learning methods......Most work in the area of statistical relational learning (SRL) is focussed on discrete data, even though a few approaches for hybrid SRL models have been proposed that combine numerical and discrete variables. In this paper we distinguish numerical random variables for which a probability...... use the augmented RBN framework to define probabilistic models for multi-relational (social) networks in which the probability of a link between two nodes depends on numeric latent feature vectors associated with the nodes. A generic learning procedure can be used to obtain a maximum-likelihood fit...

Non parametric, self organizing, scalable modeling of spatiotemporal inputs: the sign language paradigm.

Science.gov (United States)

Caridakis, G; Karpouzis, K; Drosopoulos, A; Kollias, S

2012-12-01

Modeling and recognizing spatiotemporal, as opposed to static input, is a challenging task since it incorporates input dynamics as part of the problem. The vast majority of existing methods tackle the problem as an extension of the static counterpart, using dynamics, such as input derivatives, at feature level and adopting artificial intelligence and machine learning techniques originally designed for solving problems that do not specifically address the temporal aspect. The proposed approach deals with temporal and spatial aspects of the spatiotemporal domain in a discriminative as well as coupling manner. Self Organizing Maps (SOM) model the spatial aspect of the problem and Markov models its temporal counterpart. Incorporation of adjacency, both in training and classification, enhances the overall architecture with robustness and adaptability. The proposed scheme is validated both theoretically, through an error propagation study, and experimentally, on the recognition of individual signs, performed by different, native Greek Sign Language users. Results illustrate the architecture's superiority when compared to Hidden Markov Model techniques and variations both in terms of classification performance and computational cost. Copyright © 2012 Elsevier Ltd. All rights reserved.

Graphical user interface for input output characterization of single variable and multivariable highly nonlinear systems

Directory of Open Access Journals (Sweden)

Shahrukh Adnan Khan M. D.

2017-01-01

Full Text Available This paper presents a Graphical User Interface (GUI software utility for the input/output characterization of single variable and multivariable nonlinear systems by obtaining the sinusoidal input describing function (SIDF of the plant. The software utility is developed on MATLAB R2011a environment. The developed GUI holds no restriction on the nonlinearity type, arrangement and system order; provided that output(s of the system is obtainable either though simulation or experiments. An insight to the GUI and its features are presented in this paper and example problems from both single variable and multivariable cases are demonstrated. The formulation of input/output behavior of the system is discussed and the nucleus of the MATLAB command underlying the user interface has been outlined. Some of the industries that would benefit from this software utility includes but not limited to aerospace, defense technology, robotics and automotive.

Enhanced Input in LCTL Pedagogy

Directory of Open Access Journals (Sweden)

Marilyn S. Manley

2009-08-01

Full Text Available Language materials for the more-commonly-taught languages (MCTLs often include visual input enhancement (Sharwood Smith 1991, 1993 which makes use of typographical cues like bolding and underlining to enhance the saliency of targeted forms. For a variety of reasons, this paper argues that the use of enhanced input, both visual and oral, is especially important as a tool for the lesscommonly-taught languages (LCTLs. As there continues to be a scarcity of teaching resources for the LCTLs, individual teachers must take it upon themselves to incorporate enhanced input into their own self-made materials. Specific examples of how to incorporate both visual and oral enhanced input into language teaching are drawn from the author’s own experiences teaching Cuzco Quechua. Additionally, survey results are presented from the author’s Fall 2010 semester Cuzco Quechua language students, supporting the use of both visual and oral enhanced input.

Enhanced Input in LCTL Pedagogy

Directory of Open Access Journals (Sweden)

Marilyn S. Manley

2010-08-01

Full Text Available Language materials for the more-commonly-taught languages (MCTLs often include visual input enhancement (Sharwood Smith 1991, 1993 which makes use of typographical cues like bolding and underlining to enhance the saliency of targeted forms. For a variety of reasons, this paper argues that the use of enhanced input, both visual and oral, is especially important as a tool for the lesscommonly-taught languages (LCTLs. As there continues to be a scarcity of teaching resources for the LCTLs, individual teachers must take it upon themselves to incorporate enhanced input into their own self-made materials. Specific examples of how to incorporate both visual and oral enhanced input into language teaching are drawn from the author’s own experiences teaching Cuzco Quechua. Additionally, survey results are presented from the author’s Fall 2010 semester Cuzco Quechua language students, supporting the use of both visual and oral enhanced input.

Using Economic Input/Output Tables to Predict a Country's Nuclear Status

International Nuclear Information System (INIS)

Weimar, Mark R.; Daly, Don S.; Wood, Thomas W.

2010-01-01

Both nuclear power and nuclear weapons programs should have (related) economic signatures which are detectible at some scale. We evaluated this premise in a series of studies using national economic input/output (IO) data. Statistical discrimination models using economic IO tables predict with a high probability whether a country with an unknown predilection for nuclear weapons proliferation is in fact engaged in nuclear power development or nuclear weapons proliferation. We analyzed 93 IO tables, spanning the years 1993 to 2005 for 37 countries that are either members or associates of the Organization for Economic Cooperation and Development (OECD). The 2009 OECD input/output tables featured 48 industrial sectors based on International Standard Industrial Classification (ISIC) Revision 3, and described the respective economies in current country-of-origin valued currency. We converted and transformed these reported values to US 2005 dollars using appropriate exchange rates and implicit price deflators, and addressed discrepancies in reported industrial sectors across tables. We then classified countries with Random Forest using either the adjusted or industry-normalized values. Random Forest, a classification tree technique, separates and categorizes countries using a very small, select subset of the 2304 individual cells in the IO table. A nation's efforts in nuclear power, be it for electricity or nuclear weapons, are an enterprise with a large economic footprint -- an effort so large that it should discernibly perturb coarse country-level economics data such as that found in yearly input-output economic tables. The neoclassical economic input-output model describes a country's or region's economy in terms of the requirements of industries to produce the current level of economic output. An IO table row shows the distribution of an industry's output to the industrial sectors while a table column shows the input required of each industrial sector by a given

Fault diagnosis and performance evaluation for high current LIA based on radial basis function neural network

International Nuclear Information System (INIS)

Yang Xinglin; Wang Huacen; Chen Nan; Dai Wenhua; Li Jin

2006-01-01

High current linear induction accelerator (LIA) is a complicated experimental physics device. It is difficult to evaluate and predict its performance. this paper presents a method which combines wavelet packet transform and radial basis function (RBF) neural network to build fault diagnosis and performance evaluation in order to improve reliability of high current LIA. The signal characteristics vectors which are extracted based on energy parameters of wavelet packet transform can well present the temporal and steady features of pulsed power signal, and reduce data dimensions effectively. The fault diagnosis system for accelerating cell and the trend classification system for the beam current based on RBF networks can perform fault diagnosis and evaluation, and provide predictive information for precise maintenance of high current LIA. (authors)

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

Science.gov (United States)

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

2006-11-01

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

The Effects of Audiovisual Inputs on Solving the Cocktail Party Problem in the Human Brain: An fMRI Study.

Science.gov (United States)

Li, Yuanqing; Wang, Fangyi; Chen, Yongbin; Cichocki, Andrzej; Sejnowski, Terrence

2017-09-25

At cocktail parties, our brains often simultaneously receive visual and auditory information. Although the cocktail party problem has been widely investigated under auditory-only settings, the effects of audiovisual inputs have not. This study explored the effects of audiovisual inputs in a simulated cocktail party. In our fMRI experiment, each congruent audiovisual stimulus was a synthesis of 2 facial movie clips, each of which could be classified into 1 of 2 emotion categories (crying and laughing). Visual-only (faces) and auditory-only stimuli (voices) were created by extracting the visual and auditory contents from the synthesized audiovisual stimuli. Subjects were instructed to selectively attend to 1 of the 2 objects contained in each stimulus and to judge its emotion category in the visual-only, auditory-only, and audiovisual conditions. The neural representations of the emotion features were assessed by calculating decoding accuracy and brain pattern-related reproducibility index based on the fMRI data. We compared the audiovisual condition with the visual-only and auditory-only conditions and found that audiovisual inputs enhanced the neural representations of emotion features of the attended objects instead of the unattended objects. This enhancement might partially explain the benefits of audiovisual inputs for the brain to solve the cocktail party problem. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

MDS MIC Catalog Inputs

Science.gov (United States)

Johnson-Throop, Kathy A.; Vowell, C. W.; Smith, Byron; Darcy, Jeannette

2006-01-01

This viewgraph presentation reviews the inputs to the MDS Medical Information Communique (MIC) catalog. The purpose of the group is to provide input for updating the MDS MIC Catalog and to request that MMOP assign Action Item to other working groups and FSs to support the MITWG Process for developing MIC-DDs.

PRE-CASKETSS: an input data generation computer program for thermal and structural analysis of nuclear fuel shipping casks

International Nuclear Information System (INIS)

Ikushima, Takeshi

1988-12-01

A computer program PRE-CASKETSS has been developed for the purpose of input data generation for thermal and structural analysis computer code system CASKETSS (CASKETSS means a modular code system for CASK Evaluation code system for Thermal and Structural Safety). Main features of PRE-CASKETSS are as follow; (1) Function of input data generation for thermal and structural analysis computer programs is provided in the program. (2) Two- and three-dimensional mesh generation for finite element and finite difference programs are available in the program. (3) The capacity of the material input data generation are provided in the program. (4) The boundary conditions, the load conditions and the initial conditions are capable in the program. (5) This computer program operate both the time shearing system and the batch system. In the paper, brief illustration of calculation method, input data and sample calculations are presented. (author)

A linear-RBF multikernel SVM to classify big text corpora.

Science.gov (United States)

Romero, R; Iglesias, E L; Borrajo, L

2015-01-01

Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

Electricity market price spike analysis by a hybrid data model and feature selection technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2010-01-01

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

Model, analysis, and evaluation of the effects of analog VLSI arithmetic on linear subspace-based image recognition.

Science.gov (United States)

Carvajal, Gonzalo; Figueroa, Miguel

2014-07-01

Typical image recognition systems operate in two stages: feature extraction to reduce the dimensionality of the input space, and classification based on the extracted features. Analog Very Large Scale Integration (VLSI) is an attractive technology to achieve compact and low-power implementations of these computationally intensive tasks for portable embedded devices. However, device mismatch limits the resolution of the circuits fabricated with this technology. Traditional layout techniques to reduce the mismatch aim to increase the resolution at the transistor level, without considering the intended application. Relating mismatch parameters to specific effects in the application level would allow designers to apply focalized mismatch compensation techniques according to predefined performance/cost tradeoffs. This paper models, analyzes, and evaluates the effects of mismatched analog arithmetic in both feature extraction and classification circuits. For the feature extraction, we propose analog adaptive linear combiners with on-chip learning for both Least Mean Square (LMS) and Generalized Hebbian Algorithm (GHA). Using mathematical abstractions of analog circuits, we identify mismatch parameters that are naturally compensated during the learning process, and propose cost-effective guidelines to reduce the effect of the rest. For the classification, we derive analog models for the circuits necessary to implement Nearest Neighbor (NN) approach and Radial Basis Function (RBF) networks, and use them to emulate analog classifiers with standard databases of face and hand-writing digits. Formal analysis and experiments show how we can exploit adaptive structures and properties of the input space to compensate the effects of device mismatch at the application level, thus reducing the design overhead of traditional layout techniques. Results are also directly extensible to multiple application domains using linear subspace methods. Copyright © 2014 Elsevier Ltd. All rights

Input filter compensation for switching regulators

Science.gov (United States)

Lee, F. C.; Kelkar, S. S.

1982-01-01

The problems caused by the interaction between the input filter, output filter, and the control loop are discussed. The input filter design is made more complicated because of the need to avoid performance degradation and also stay within the weight and loss limitations. Conventional input filter design techniques are then dicussed. The concept of pole zero cancellation is reviewed; this concept is the basis for an approach to control the peaking of the output impedance of the input filter and thus mitigate some of the problems caused by the input filter. The proposed approach for control of the peaking of the output impedance of the input filter is to use a feedforward loop working in conjunction with feedback loops, thus forming a total state control scheme. The design of the feedforward loop for a buck regulator is described. A possible implementation of the feedforward loop design is suggested.

Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features

Directory of Open Access Journals (Sweden)

Vishal eVaingankar

2012-12-01

Full Text Available All visual signals the cortex receives are influenced by the perigeniculate sector of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped centre-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate nonselective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate, using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-nonlinear models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs nonlinearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream

Neurons in the thalamic reticular nucleus are selective for diverse and complex visual features

Science.gov (United States)

Vaingankar, Vishal; Soto-Sanchez, Cristina; Wang, Xin; Sommer, Friedrich T.; Hirsch, Judith A.

2012-01-01

All visual signals the cortex receives are influenced by the perigeniculate sector (PGN) of the thalamic reticular nucleus, which receives input from relay cells in the lateral geniculate and provides feedback inhibition in return. Relay cells have been studied in quantitative depth; they behave in a roughly linear fashion and have receptive fields with a stereotyped center-surround structure. We know far less about reticular neurons. Qualitative studies indicate they simply pool ascending input to generate non-selective gain control. Yet the perigeniculate is complicated; local cells are densely interconnected and fire lengthy bursts. Thus, we employed quantitative methods to explore the perigeniculate using relay cells as controls. By adapting methods of spike-triggered averaging and covariance analysis for bursts, we identified both first and second order features that build reticular receptive fields. The shapes of these spatiotemporal subunits varied widely; no stereotyped pattern emerged. Companion experiments showed that the shape of the first but not second order features could be explained by the overlap of On and Off inputs to a given cell. Moreover, we assessed the predictive power of the receptive field and how much information each component subunit conveyed. Linear-non-linear (LN) models including multiple subunits performed better than those made with just one; further each subunit encoded different visual information. Model performance for reticular cells was always lesser than for relay cells, however, indicating that reticular cells process inputs non-linearly. All told, our results suggest that the perigeniculate encodes diverse visual features to selectively modulate activity transmitted downstream. PMID:23269915

7 CFR 3430.607 - Stakeholder input.

Science.gov (United States)

2010-01-01

... 7 Agriculture 15 2010-01-01 2010-01-01 false Stakeholder input. 3430.607 Section 3430.607 Agriculture Regulations of the Department of Agriculture (Continued) COOPERATIVE STATE RESEARCH, EDUCATION... § 3430.607 Stakeholder input. CSREES shall seek and obtain stakeholder input through a variety of forums...

Maximum entropy methods for extracting the learned features of deep neural networks.

Science.gov (United States)

Finnegan, Alex; Song, Jun S

2017-10-01

New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

World Input-Output Network.

Directory of Open Access Journals (Sweden)

Federica Cerina

Full Text Available Production systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD is one of the first efforts to construct the global multi-regional input-output (GMRIO tables. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we analyze respectively the global, regional, and local network properties of the so-called world input-output network (WION and document its evolution over time. At global level, we find that the industries are highly but asymmetrically connected, which implies that micro shocks can lead to macro fluctuations. At regional level, we find that the world production is still operated nationally or at most regionally as the communities detected are either individual economies or geographically well defined regions. Finally, at local level, for each industry we compare the network-based measures with the traditional methods of backward linkages. We find that the network-based measures such as PageRank centrality and community coreness measure can give valuable insights into identifying the key industries.

7 CFR 3430.15 - Stakeholder input.

Science.gov (United States)

2010-01-01

... 7 Agriculture 15 2010-01-01 2010-01-01 false Stakeholder input. 3430.15 Section 3430.15... Stakeholder input. Section 103(c)(2) of the Agricultural Research, Extension, and Education Reform Act of 1998... RFAs for competitive programs. CSREES will provide instructions for submission of stakeholder input in...

Input description for BIOPATH

International Nuclear Information System (INIS)

Marklund, J.E.; Bergstroem, U.; Edlund, O.

1980-01-01

The computer program BIOPATH describes the flow of radioactivity within a given ecosystem after a postulated release of radioactive material and the resulting dose for specified population groups. The present report accounts for the input data necessary to run BIOPATH. The report also contains descriptions of possible control cards and an input example as well as a short summary of the basic theory.(author)

Superpixel-Based Feature for Aerial Image Scene Recognition

Directory of Open Access Journals (Sweden)

Hongguang Li

2018-01-01

Full Text Available Image scene recognition is a core technology for many aerial remote sensing applications. Different landforms are inputted as different scenes in aerial imaging, and all landform information is regarded as valuable for aerial image scene recognition. However, the conventional features of the Bag-of-Words model are designed using local points or other related information and thus are unable to fully describe landform areas. This limitation cannot be ignored when the aim is to ensure accurate aerial scene recognition. A novel superpixel-based feature is proposed in this study to characterize aerial image scenes. Then, based on the proposed feature, a scene recognition method of the Bag-of-Words model for aerial imaging is designed. The proposed superpixel-based feature that utilizes landform information establishes top-task superpixel extraction of landforms to bottom-task expression of feature vectors. This characterization technique comprises the following steps: simple linear iterative clustering based superpixel segmentation, adaptive filter bank construction, Lie group-based feature quantification, and visual saliency model-based feature weighting. Experiments of image scene recognition are carried out using real image data captured by an unmanned aerial vehicle (UAV. The recognition accuracy of the proposed superpixel-based feature is 95.1%, which is higher than those of scene recognition algorithms based on other local features.

Wire Finishing Mill Rolling Bearing Fault Diagnosis Based on Feature Extraction and BP Neural Network

Directory of Open Access Journals (Sweden)

Hong-Yu LIU

2014-10-01

Full Text Available Rolling bearing is main part of rotary machine. It is frail section of rotary machine. Its running status affects entire mechanical equipment system performance directly. Vibration acceleration signals of the third finishing mill of Anshan Steel and Iron Group wire plant were collected in this paper. Fourier analysis, power spectrum analysis and wavelet transform were made on collected signals. Frequency domain feature extraction and wavelet transform feature extraction were made on collected signals. BP neural network fault diagnosis model was adopted. Frequency domain feature values and wavelet transform feature values were treated as neural network input values. Various typical fault models were treated as neural network output values. Corresponding relations between feature vector and fault omen were utilized. BP neural network model of typical wire plant finishing mill rolling bearing fault was constructed by training many groups sample data. After inputting sample needed to be diagnosed, wire plant finishing mill rolling bearing fault can be diagnosed. This research has important practical significance on enhancing rolling bearing fault diagnosis precision, repairing rolling bearing duly, decreasing stop time, enhancing equipment running efficiency and enhancing economic benefits.

Modeling and generating input processes

Energy Technology Data Exchange (ETDEWEB)

Johnson, M.E.

1987-01-01

This tutorial paper provides information relevant to the selection and generation of stochastic inputs to simulation studies. The primary area considered is multivariate but much of the philosophy at least is relevant to univariate inputs as well. 14 refs.

Comparison of l₁-Norm SVR and Sparse Coding Algorithms for Linear Regression.

Science.gov (United States)

Zhang, Qingtian; Hu, Xiaolin; Zhang, Bo

2015-08-01

Support vector regression (SVR) is a popular function estimation technique based on Vapnik's concept of support vector machine. Among many variants, the l1-norm SVR is known to be good at selecting useful features when the features are redundant. Sparse coding (SC) is a technique widely used in many areas and a number of efficient algorithms are available. Both l1-norm SVR and SC can be used for linear regression. In this brief, the close connection between the l1-norm SVR and SC is revealed and some typical algorithms are compared for linear regression. The results show that the SC algorithms outperform the Newton linear programming algorithm, an efficient l1-norm SVR algorithm, in efficiency. The algorithms are then used to design the radial basis function (RBF) neural networks. Experiments on some benchmark data sets demonstrate the high efficiency of the SC algorithms. In particular, one of the SC algorithms, the orthogonal matching pursuit is two orders of magnitude faster than a well-known RBF network designing algorithm, the orthogonal least squares algorithm.

Inputs to the dorsal striatum of the mouse reflect the parallel circuit architecture of the forebrain.

Science.gov (United States)

Pan, Weixing X; Mao, Tianyi; Dudman, Joshua T

2010-01-01

The basal ganglia play a critical role in the regulation of voluntary action in vertebrates. Our understanding of the function of the basal ganglia relies heavily upon anatomical information, but continued progress will require an understanding of the specific functional roles played by diverse cell types and their connectivity. An increasing number of mouse lines allow extensive identification, characterization, and manipulation of specified cell types in the basal ganglia. Despite the promise of genetically modified mice for elucidating the functional roles of diverse cell types, there is relatively little anatomical data obtained directly in the mouse. Here we have characterized the retrograde labeling obtained from a series of tracer injections throughout the dorsal striatum of adult mice. We found systematic variations in input along both the medial-lateral and anterior-posterior neuraxes in close agreement with canonical features of basal ganglia anatomy in the rat. In addition to the canonical features we have provided experimental support for the importance of non-canonical inputs to the striatum from the raphe nuclei and the amygdala. To look for organization at a finer scale we have analyzed the correlation structure of labeling intensity across our entire dataset. Using this analysis we found substantial local heterogeneity within the large-scale order. From this analysis we conclude that individual striatal sites receive varied combinations of cortical and thalamic input from multiple functional areas, consistent with some earlier studies in the rat that have suggested the presence of a combinatorial map.

Wave energy input into the Ekman layer

Institute of Scientific and Technical Information of China (English)

2008-01-01

This paper is concerned with the wave energy input into the Ekman layer, based on 3 observational facts that surface waves could significantly affect the profile of the Ekman layer. Under the assumption of constant vertical diffusivity, the analytical form of wave energy input into the Ekman layer is derived. Analysis of the energy balance shows that the energy input to the Ekman layer through the wind stress and the interaction of the Stokes-drift with planetary vorticity can be divided into two kinds. One is the wind energy input, and the other is the wave energy input which is dependent on wind speed, wave characteristics and the wind direction relative to the wave direction. Estimates of wave energy input show that wave energy input can be up to 10% in high-latitude and high-wind speed areas and higher than 20% in the Antarctic Circumpolar Current, compared with the wind energy input into the classical Ekman layer. Results of this paper are of significance to the study of wave-induced large scale effects.

Characterizing the Input-Output Function of the Olfactory-Limbic Pathway in the Guinea Pig

Directory of Open Access Journals (Sweden)

Gian Luca Breschi

2015-01-01

Full Text Available Nowadays the neuroscientific community is taking more and more advantage of the continuous interaction between engineers and computational neuroscientists in order to develop neuroprostheses aimed at replacing damaged brain areas with artificial devices. To this end, a technological effort is required to develop neural network models which can be fed with the recorded electrophysiological patterns to yield the correct brain stimulation to recover the desired functions. In this paper we present a machine learning approach to derive the input-output function of the olfactory-limbic pathway in the in vitro whole brain of guinea pig, less complex and more controllable than an in vivo system. We first experimentally characterized the neuronal pathway by delivering different sets of electrical stimuli from the lateral olfactory tract (LOT and by recording the corresponding responses in the lateral entorhinal cortex (l-ERC. As a second step, we used information theory to evaluate how much information output features carry about the input. Finally we used the acquired data to learn the LOT-l-ERC “I/O function,” by means of the kernel regularized least squares method, able to predict l-ERC responses on the basis of LOT stimulation features. Our modeling approach can be further exploited for brain prostheses applications.

Improvement of Meteorological Inputs for TexAQS-II Air Quality Simulations

Science.gov (United States)

Ngan, F.; Byun, D.; Kim, H.; Cheng, F.; Kim, S.; Lee, D.

2008-12-01

An air quality forecasting system (UH-AQF) for Eastern Texas, which is in operation by the Institute for Multidimensional Air Quality Studies (IMAQS) at the University of Houston, uses the Fifth-Generation PSU/NCAR Mesoscale Model MM5 model as the meteorological driver for modeling air quality with the Community Multiscale Air Quality (CMAQ) model. While the forecasting system was successfully used for the planning and implementation of various measurement activities, evaluations of the forecasting results revealed a few systematic problems in the numerical simulations. From comparison with observations, we observe some times over-prediction of northerly winds caused by inaccurate synoptic inputs and other times too strong southerly winds caused by local sea breeze development. Discrepancies in maximum and minimum temperature are also seen for certain days. Precipitation events, as well as clouds, are simulated at the incorrect locations and times occasionally. Model simulatednrealistic thunderstorms are simulated, causing sometimes cause unrealistically strong outflows. To understand physical and chemical processes influencing air quality measures, a proper description of real world meteorological conditions is essential. The objective of this study is to generate better meteorological inputs than the AQF results to support the chemistry modeling. We utilized existing objective analysis and nudging tools in the MM5 system to develop the MUltiscale Nest-down Data Assimilation System (MUNDAS), which incorporates extensive meteorological observations available in the simulated domain for the retrospective simulation of the TexAQS-II period. With the re-simulated meteorological input, we are able to better predict ozone events during TexAQS-II period. In addition, base datasets in MM5 such as land use/land cover, vegetation fraction, soil type and sea surface temperature are updated by satellite data to represent the surface features more accurately. They are key

Development of an Input Suite for an Orthotropic Composite Material Model

Science.gov (United States)

Hoffarth, Canio; Shyamsunder, Loukham; Khaled, Bilal; Rajan, Subramaniam; Goldberg, Robert K.; Carney, Kelly S.; Dubois, Paul; Blankenhorn, Gunther

2017-01-01

An orthotropic three-dimensional material model suitable for use in modeling impact tests has been developed that has three major components elastic and inelastic deformations, damage and failure. The material model has been implemented as MAT213 into a special version of LS-DYNA and uses tabulated data obtained from experiments. The prominent features of the constitutive model are illustrated using a widely-used aerospace composite the T800S3900-2B[P2352W-19] BMS8-276 Rev-H-Unitape fiber resin unidirectional composite. The input for the deformation model consists of experimental data from 12 distinct experiments at a known temperature and strain rate: tension and compression along all three principal directions, shear in all three principal planes, and off axis tension or compression tests in all three principal planes, along with other material constants. There are additional input associated with the damage and failure models. The steps in using this model are illustrated composite characterization tests, verification tests and a validation test. The results show that the developed and implemented model is stable and yields acceptably accurate results.

Fast region-based object detection and tracking using correlation of features

CSIR Research Space (South Africa)

Senekal, F

2010-11-01

Full Text Available and track a target object (or objects) over a series of digital images. Visual target tracking can be accomplished by feature-based or region-based approaches. In feature-based approaches, interest points are calculated in a digital image, and a local...-time performance based on the computational power that is available on a specific platform. To further reduce the computational requirements, process- ing is restricted to the region of interest (ROI). The region of interest is provided as an input parameter...

Ex vivo dissection of optogenetically activated mPFC and hippocampal inputs to neurons in the basolateral amygdala: implications for fear and emotional memory

Directory of Open Access Journals (Sweden)

Cora eHübner

2014-03-01

Full Text Available Many lines of evidence suggest that a reciprocally interconnected network comprising the amygdala, ventral hippocampus (vHC, and medial prefrontal cortex (mPFC participates in different aspects of the acquisition and extinction of conditioned fear responses and fear behavior. This could at least in part be mediated by direct connections from mPFC or vHC to amygdala to control amygdala activity and output. However, currently the interactions between mPFC and vHC afferents and their specific targets in the amygdala are still poorly understood. Here, we use an ex-vivo optogenetic approach to dissect synaptic properties of inputs from mPFC and vHC to defined neuronal populations in the basal amygdala (BA, the area that we identify as a major target of these projections. We find that BA principal neurons (PNs and local BA interneurons (INs receive monosynaptic excitatory inputs from mPFC and vHC. In addition, both these inputs also recruit GABAergic feedforward inhibition in a substantial fraction of PNs, in some neurons this also comprises a slow GABAB-component. Amongst the innervated PNs we identify neurons that project back to subregions of the mPFC, indicating a loop between neurons in mPFC and BA, and a pathway from vHC to mPFC via BA. Interestingly, mPFC inputs also recruit feedforward inhibition in a fraction of INs, suggesting that these inputs can activate dis-inhibitory circuits in the BA. A general feature of both mPFC and vHC inputs to local INs is that excitatory inputs display faster rise and decay kinetics than in PNs, which would enable temporally precise signaling. However, mPFC and vHC inputs to both PNs and INs differ in their presynaptic release properties, in that vHC inputs are more depressing. In summary, our data describe novel wiring, and features of synaptic connections from mPFC and vHC to amygdala that could help to interpret functions of these interconnected brain areas at the network level.

Ex vivo dissection of optogenetically activated mPFC and hippocampal inputs to neurons in the basolateral amygdala: implications for fear and emotional memory

Science.gov (United States)

Hübner, Cora; Bosch, Daniel; Gall, Andrea; Lüthi, Andreas; Ehrlich, Ingrid

2014-01-01

Many lines of evidence suggest that a reciprocally interconnected network comprising the amygdala, ventral hippocampus (vHC), and medial prefrontal cortex (mPFC) participates in different aspects of the acquisition and extinction of conditioned fear responses and fear behavior. This could at least in part be mediated by direct connections from mPFC or vHC to amygdala to control amygdala activity and output. However, currently the interactions between mPFC and vHC afferents and their specific targets in the amygdala are still poorly understood. Here, we use an ex-vivo optogenetic approach to dissect synaptic properties of inputs from mPFC and vHC to defined neuronal populations in the basal amygdala (BA), the area that we identify as a major target of these projections. We find that BA principal neurons (PNs) and local BA interneurons (INs) receive monosynaptic excitatory inputs from mPFC and vHC. In addition, both these inputs also recruit GABAergic feedforward inhibition in a substantial fraction of PNs, in some neurons this also comprises a slow GABAB-component. Amongst the innervated PNs we identify neurons that project back to subregions of the mPFC, indicating a loop between neurons in mPFC and BA, and a pathway from vHC to mPFC via BA. Interestingly, mPFC inputs also recruit feedforward inhibition in a fraction of INs, suggesting that these inputs can activate dis-inhibitory circuits in the BA. A general feature of both mPFC and vHC inputs to local INs is that excitatory inputs display faster rise and decay kinetics than in PNs, which would enable temporally precise signaling. However, mPFC and vHC inputs to both PNs and INs differ in their presynaptic release properties, in that vHC inputs are more depressing. In summary, our data describe novel wiring, and features of synaptic connections from mPFC and vHC to amygdala that could help to interpret functions of these interconnected brain areas at the network level. PMID:24634648

COLOR IMAGE RETRIEVAL BASED ON FEATURE FUSION THROUGH MULTIPLE LINEAR REGRESSION ANALYSIS

Directory of Open Access Journals (Sweden)

K. Seetharaman

2015-08-01

Full Text Available This paper proposes a novel technique based on feature fusion using multiple linear regression analysis, and the least-square estimation method is employed to estimate the parameters. The given input query image is segmented into various regions according to the structure of the image. The color and texture features are extracted on each region of the query image, and the features are fused together using the multiple linear regression model. The estimated parameters of the model, which is modeled based on the features, are formed as a vector called a feature vector. The Canberra distance measure is adopted to compare the feature vectors of the query and target images. The F-measure is applied to evaluate the performance of the proposed technique. The obtained results expose that the proposed technique is comparable to the other existing techniques.

Role of Mas Receptor Antagonist A799 in Renal Blood Flow Response to Ang 1-7 after Bradykinin Administration in Ovariectomized Estradiol-Treated Rats

Directory of Open Access Journals (Sweden)

Aghdas Dehghani

2015-01-01

Full Text Available Background. The accompanied role of Mas receptor (MasR, bradykinin (BK, and female sex hormone on renal blood flow (RBF response to angiotensin 1-7 is not well defined. We investigated the role of MasR antagonist (A779 and BK on RBF response to Ang 1-7 infusion in ovariectomized estradiol-treated rats. Methods. Ovariectomized Wistar rats received estradiol (OVE or vehicle (OV for two weeks. Catheterized animals were subjected to BK and A799 infusion and mean arterial pressure (MAP, RBF, and renal vascular resistance (RVR responses to Ang 1-7 (0, 100, and 300 ng kg−1 min−1 were determined. Results. Percentage change of RBF (%RBF in response to Ang1-7 infusion increased in a dose-dependent manner. In the presence of BK, when MasR was not blocked, %RBF response to Ang 1-7 in OVE group was greater than OV group significantly (P<0.05. Infusion of 300 ng kg−1 min−1 Ang 1-7 increased RBF by 6.9±1.9% in OVE group versus 0.9±1.8% in OV group. However when MasR was blocked, %RBF response to Ang 1-7 in OV group was greater than OVE group insignificantly. Conclusion. Coadministration of BK and A779 compared to BK alone increased RBF response to Ang 1-7 in vehicle treated rats. Such observation was not seen in estradiol treated rats.

Statistical identification of effective input variables

International Nuclear Information System (INIS)

Vaurio, J.K.

1982-09-01

A statistical sensitivity analysis procedure has been developed for ranking the input data of large computer codes in the order of sensitivity-importance. The method is economical for large codes with many input variables, since it uses a relatively small number of computer runs. No prior judgemental elimination of input variables is needed. The sceening method is based on stagewise correlation and extensive regression analysis of output values calculated with selected input value combinations. The regression process deals with multivariate nonlinear functions, and statistical tests are also available for identifying input variables that contribute to threshold effects, i.e., discontinuities in the output variables. A computer code SCREEN has been developed for implementing the screening techniques. The efficiency has been demonstrated by several examples and applied to a fast reactor safety analysis code (Venus-II). However, the methods and the coding are general and not limited to such applications

Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization

International Nuclear Information System (INIS)

Guerra, Fabio A.; Coelho, Leandro dos S.

2008-01-01

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting

Gestures and multimodal input

OpenAIRE

Keates, Simeon; Robinson, Peter

1999-01-01

For users with motion impairments, the standard keyboard and mouse arrangement for computer access often presents problems. Other approaches have to be adopted to overcome this. In this paper, we will describe the development of a prototype multimodal input system based on two gestural input channels. Results from extensive user trials of this system are presented. These trials showed that the physical and cognitive loads on the user can quickly become excessive and detrimental to the interac...

The Importance of Input and Interaction in SLA

Institute of Scientific and Technical Information of China (English)

党春花

2009-01-01

As is known to us, input and interaction play the crucial roles in second language acquisition (SLA). Different linguistic schools have different explanations to input and interaction Behaviorist theories hold a view that input is composed of stimuli and response, putting more emphasis on the importance of input, while mentalist theories find input is a necessary condition to SLA, not a sufficient condition. At present, social interaction theories, which is one type of cognitive linguistics, suggests that besides input, interaction is also essential to language acquisition. Then, this essay will discuss how input and interaction result in SLA.

A Method to Measure the Bracelet Based on Feature Energy

Science.gov (United States)

Liu, Hongmin; Li, Lu; Wang, Zhiheng; Huo, Zhanqiang

2017-12-01

To measure the bracelet automatically, a novel method based on feature energy is proposed. Firstly, the morphological method is utilized to preprocess the image, and the contour consisting of a concentric circle is extracted. Then, a feature energy function, which is relevant to the distances from one pixel to the edge points, is defined taking into account the geometric properties of the concentric circle. The input image is subsequently transformed to the feature energy distribution map (FEDM) by computing the feature energy of each pixel. The center of the concentric circle is thus located by detecting the maximum on the FEDM; meanwhile, the radii of the concentric circle are determined according to the feature energy function of the center pixel. Finally, with the use of a calibration template, the internal diameter and thickness of the bracelet are measured. The experimental results show that the proposed method can measure the true sizes of the bracelet accurately with the simplicity, directness and robustness compared to the existing methods.

The Effectiveness of Visual Input Enhancement on the Noticing and L2 Development of the Spanish Past Tense

Science.gov (United States)

Loewen, Shawn; Inceoglu, Solène

2016-01-01

Textual manipulation is a common pedagogic tool used to emphasize specific features of a second language (L2) text, thereby facilitating noticing and, ideally, second language development. Visual input enhancement has been used to investigate the effects of highlighting specific grammatical structures in a text. The current study uses a…

The rise of the rats: A growing paediatric issue

Science.gov (United States)

Khatchadourian, Karine; Ovetchkine, Philippe; Minodier, Philippe; Lamarre, Valérie; Lebel, Marc H; Tapiéro, Bruce

2010-01-01

Rat bite fever (RBF), a systemic infection of Streptobacillus moniliformis or Spirillum minus characterized by fever, arthralgias and petechial-purpuric rash on the extremities, carries a mortality rate of 7% to 10% if untreated. In Canada, one adult and two paediatric cases of RBF have been reported since 2000. In recent years, pet rats have become quite popular among children, placing them at an increased risk for RBF. Thus, paediatricians need to be more wary of the potential for RBF in their patients. In the present report, a culture-confirmed case of RBF and two additional cases of suspected infection are described. PMID:21358889

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Directory of Open Access Journals (Sweden)

Hsueh-Hsien Chang

2017-04-01

Full Text Available This paper proposes statistical feature extraction methods combined with artificial intelligence (AI approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

Oversampling the Minority Class in the Feature Space.

Science.gov (United States)

Perez-Ortiz, Maria; Gutierrez, Pedro Antonio; Tino, Peter; Hervas-Martinez, Cesar

2016-09-01

The imbalanced nature of some real-world data is one of the current challenges for machine learning researchers. One common approach oversamples the minority class through convex combination of its patterns. We explore the general idea of synthetic oversampling in the feature space induced by a kernel function (as opposed to input space). If the kernel function matches the underlying problem, the classes will be linearly separable and synthetically generated patterns will lie on the minority class region. Since the feature space is not directly accessible, we use the empirical feature space (EFS) (a Euclidean space isomorphic to the feature space) for oversampling purposes. The proposed method is framed in the context of support vector machines, where the imbalanced data sets can pose a serious hindrance. The idea is investigated in three scenarios: 1) oversampling in the full and reduced-rank EFSs; 2) a kernel learning technique maximizing the data class separation to study the influence of the feature space structure (implicitly defined by the kernel function); and 3) a unified framework for preferential oversampling that spans some of the previous approaches in the literature. We support our investigation with extensive experiments over 50 imbalanced data sets.

Haar-like Features for Robust Real-Time Face Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Moeslund, Thomas B.

2013-01-01

Face recognition is still a very challenging task when the input face image is noisy, occluded by some obstacles, of very low-resolution, not facing the camera, and not properly illuminated. These problems make the feature extraction and consequently the face recognition system unstable....... The proposed system in this paper introduces the novel idea of using Haar-like features, which have commonly been used for object detection, along with a probabilistic classifier for face recognition. The proposed system is simple, real-time, effective and robust against most of the mentioned problems....... Experimental results on public databases show that the proposed system indeed outperforms the state-of-the-art face recognition systems....

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

Directory of Open Access Journals (Sweden)

Tiezhu Shi

2017-05-01

Full Text Available This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF, artificial neural network (ANN, radial basis function- and linear function- based support vector machine (RBF- and LF-SVM were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs. The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value. The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%, ANN (OA = 89%, RBF- (OA = 89% and LF-SVM (OA = 87% had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05. These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies.

Evaluation of Spectral and Prosodic Features of Speech Affected by Orthodontic Appliances Using the Gmm Classifier

Science.gov (United States)

Přibil, Jiří; Přibilová, Anna; Ďuračkoá, Daniela

2014-01-01

The paper describes our experiment with using the Gaussian mixture models (GMM) for classification of speech uttered by a person wearing orthodontic appliances. For the GMM classification, the input feature vectors comprise the basic and the complementary spectral properties as well as the supra-segmental parameters. Dependence of classification correctness on the number of the parameters in the input feature vector and on the computation complexity is also evaluated. In addition, an influence of the initial setting of the parameters for GMM training process was analyzed. Obtained recognition results are compared visually in the form of graphs as well as numerically in the form of tables and confusion matrices for tested sentences uttered using three configurations of orthodontic appliances.

Efficient Topological Localization Using Global and Local Feature Matching

Directory of Open Access Journals (Sweden)

Junqiu Wang

2013-03-01

Full Text Available We present an efficient vision-based global topological localization approach in which different image features are used in a coarse-to-fine matching framework. Orientation Adjacency Coherence Histogram (OACH, a novel image feature, is proposed to improve the coarse localization. The coarse localization results are taken as inputs for the fine localization which is carried out by matching Harris-Laplace interest points characterized by the SIFT descriptor. The computation of OACHs and interest points is efficient due to the fact that these features are computed in an integrated process. The matching of local features is improved by using approximate nearest neighbor searching technique. We have implemented and tested the localization system in real environments. The experimental results demonstrate that our approach is efficient and reliable in both indoor and outdoor environments. This work has also been compared with previous works. The comparison results show that our approach has better performance with higher correct ratio and lower computational complexity.

Methodology for deriving hydrogeological input parameters for safety-analysis models - application to fractured crystalline rocks of Northern Switzerland

International Nuclear Information System (INIS)

Vomvoris, S.; Andrews, R.W.; Lanyon, G.W.; Voborny, O.; Wilson, W.

1996-04-01

Switzerland is one of many nations with nuclear power that is seeking to identify rock types and locations that would be suitable for the underground disposal of nuclear waste. A common challenge among these programs is to provide engineering designers and safety analysts with a reasonably representative hydrogeological input dataset that synthesizes the relevant information from direct field observations as well as inferences and model results derived from those observations. Needed are estimates of the volumetric flux through a volume of rock and the distribution of that flux into discrete pathways between the repository zones and the biosphere. These fluxes are not directly measurable but must be derived based on understandings of the range of plausible hydrogeologic conditions expected at the location investigated. The methodology described in this report utilizes conceptual and numerical models at various scales to derive the input dataset. The methodology incorporates an innovative approach, called the geometric approach, in which field observations and their associated uncertainty, together with a conceptual representation of those features that most significantly affect the groundwater flow regime, were rigorously applied to generate alternative possible realizations of hydrogeologic features in the geosphere. In this approach, the ranges in the output values directly reflect uncertainties in the input values. As a demonstration, the methodology is applied to the derivation of the hydrogeological dataset for the crystalline basement of Northern Switzerland. (author) figs., tabs., refs

Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization

KAUST Repository

Wang, Jim Jing-Yan; Huang, Jianhua Z.; Sun, Yijun; Gao, Xin

2014-01-01

by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant

[Terahertz Spectroscopic Identification with Deep Belief Network].

Science.gov (United States)

Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

2015-12-01

Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

Analysis on relation between safety input and accidents

Institute of Scientific and Technical Information of China (English)

YAO Qing-guo; ZHANG Xue-mu; LI Chun-hui

2007-01-01

The number of safety input directly determines the level of safety, and there exists dialectical and unified relations between safety input and accidents. Based on the field investigation and reliable data, this paper deeply studied the dialectical relationship between safety input and accidents, and acquired the conclusions. The security situation of the coal enterprises was related to the security input rate, being effected little by the security input scale, and build the relationship model between safety input and accidents on this basis, that is the accident model.

Robustness of a Neural Network Model for Power Peak Factor Estimation in Protection Systems

International Nuclear Information System (INIS)

Souza, Rose Mary G.P.; Moreira, Joao M.L.

2006-01-01

This work presents results of robustness verification of artificial neural network correlations that improve the real time prediction of the power peak factor for reactor protection systems. The input variables considered in the correlation are those available in the reactor protection systems, namely, the axial power differences obtained from measured ex-core detectors, and the position of control rods. The correlations, based on radial basis function (RBF) and multilayer perceptron (MLP) neural networks, estimate the power peak factor, without faulty signals, with average errors between 0.13%, 0.19% and 0.15%, and maximum relative error of 2.35%. The robustness verification was performed for three different neural network correlations. The results show that they are robust against signal degradation, producing results with faulty signals with a maximum error of 6.90%. The average error associated to faulty signals for the MLP network is about half of that of the RBF network, and the maximum error is about 1% smaller. These results demonstrate that MLP neural network correlation is more robust than the RBF neural network correlation. The results also show that the input variables present redundant information. The axial power difference signals compensate the faulty signal for the position of a given control rod, and improves the results by about 10%. The results show that the errors in the power peak factor estimation by these neural network correlations, even in faulty conditions, are smaller than the current PWR schemes which may have uncertainties as high as 8%. Considering the maximum relative error of 2.35%, these neural network correlations would allow decreasing the power peak factor safety margin by about 5%. Such a reduction could be used for operating the reactor with a higher power level or with more flexibility. The neural network correlation has to meet requirements of high integrity software that performs safety grade actions. It is shown that the

Measuring Input Thresholds on an Existing Board

Science.gov (United States)

Kuperman, Igor; Gutrich, Daniel G.; Berkun, Andrew C.

2011-01-01

A critical PECL (positive emitter-coupled logic) interface to Xilinx interface needed to be changed on an existing flight board. The new Xilinx input interface used a CMOS (complementary metal-oxide semiconductor) type of input, and the driver could meet its thresholds typically, but not in worst-case, according to the data sheet. The previous interface had been based on comparison with an external reference, but the CMOS input is based on comparison with an internal divider from the power supply. A way to measure what the exact input threshold was for this device for 64 inputs on a flight board was needed. The measurement technique allowed an accurate measurement of the voltage required to switch a Xilinx input from high to low for each of the 64 lines, while only probing two of them. Directly driving an external voltage was considered too risky, and tests done on any other unit could not be used to qualify the flight board. The two lines directly probed gave an absolute voltage threshold calibration, while data collected on the remaining 62 lines without probing gave relative measurements that could be used to identify any outliers. The PECL interface was forced to a long-period square wave by driving a saturated square wave into the ADC (analog to digital converter). The active pull-down circuit was turned off, causing each line to rise rapidly and fall slowly according to the input s weak pull-down circuitry. The fall time shows up as a change in the pulse width of the signal ready by the Xilinx. This change in pulse width is a function of capacitance, pulldown current, and input threshold. Capacitance was known from the different trace lengths, plus a gate input capacitance, which is the same for all inputs. The pull-down current is the same for all inputs including the two that are probed directly. The data was combined, and the Excel solver tool was used to find input thresholds for the 62 lines. This was repeated over different supply voltages and

An Approach to Predict Debris Flow Average Velocity

Directory of Open Access Journals (Sweden)

Chen Cao

2017-03-01

Full Text Available Debris flow is one of the major threats for the sustainability of environmental and social development. The velocity directly determines the impact on the vulnerability. This study focuses on an approach using radial basis function (RBF neural network and gravitational search algorithm (GSA for predicting debris flow velocity. A total of 50 debris flow events were investigated in the Jiangjia gully. These data were used for building the GSA-based RBF approach (GSA-RBF. Eighty percent (40 groups of the measured data were selected randomly as the training database. The other 20% (10 groups of data were used as testing data. Finally, the approach was applied to predict six debris flow gullies velocities in the Wudongde Dam site area, where environmental conditions were similar to the Jiangjia gully. The modified Dongchuan empirical equation and the pulled particle analysis of debris flow (PPA approach were used for comparison and validation. The results showed that: (i the GSA-RBF predicted debris flow velocity values are very close to the measured values, which performs better than those using RBF neural network alone; (ii the GSA-RBF results and the MDEE results are similar in the Jiangjia gully debris flow velocities prediction, and GSA-RBF performs better; (iii in the study area, the GSA-RBF results are validated reliable; and (iv we could consider more variables in predicting the debris flow velocity by using GSA-RBF on the basis of measured data in other areas, which is more applicable. Because the GSA-RBF approach was more accurate, both the numerical simulation and the empirical equation can be taken into consideration for constructing debris flow mitigation works. They could be complementary and verified for each other.

Inputs to the dorsal striatum of the mouse conserve the parallel circuit architecture of the forebrain

Directory of Open Access Journals (Sweden)

Weixing X Pan

2010-12-01

Full Text Available The basal ganglia play a critical role in the regulation of voluntary action in vertebrates. Our understanding of the function of the basal ganglia relies heavily upon anatomical information, but continued progress will require an understanding of the specific functional roles played by diverse cell types and their connectivity. An increasing number of mouse lines allow extensive identification, characterization, and, manipulation of specified cell types in the basal ganglia. Despite the promise of genetically modified mice for elucidating the functional roles of diverse cell types, there is relatively little anatomical data obtained directly in the mouse. Here we have characterized the retrograde labeling obtained from a series of tracer injections throughout the dorsal striatum of adult mice. We found systematic variations in input along both the medial-lateral and anterior-posterior neuraxes in close agreement with canonical features of basal ganglia anatomy in the rat. In addition to the canonical features we have provided experimental support for the importance of non-canonical inputs to the striatum from the raphe nuclei and the amygdala. To look for organization at a finer scale we have analyzed the correlation structure of labeling intensity across our entire dataset. Using this analysis we found substantial local heterogeneity within the large-scale order. From this analysis we conclude that individual striatal sites receive varied combinations of cortical and thalamic input from multiple functional areas, consistent with some earlier studies in the rat that have suggested the presence of a combinatorial map.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

International Nuclear Information System (INIS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-01-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification. (paper)

Mars 2.2 code manual: input requirements

International Nuclear Information System (INIS)

Chung, Bub Dong; Lee, Won Jae; Jeong, Jae Jun; Lee, Young Jin; Hwang, Moon Kyu; Kim, Kyung Doo; Lee, Seung Wook; Bae, Sung Won

2003-07-01

Korea Advanced Energy Research Institute (KAERI) conceived and started the development of MARS code with the main objective of producing a state-of-the-art realistic thermal hydraulic systems analysis code with multi-dimensional analysis capability. MARS achieves this objective by very tightly integrating the one dimensional RELAP5/MOD3 with the multi-dimensional COBRA-TF codes. The method of integration of the two codes is based on the dynamic link library techniques, and the system pressure equation matrices of both codes are implicitly integrated and solved simultaneously. In addition, the Equation-of-State (EOS) for the light water was unified by replacing the EOS of COBRA-TF by that of the RELAP5. This input manual provides a complete list of input required to run MARS. The manual is divided largely into two parts, namely, the one-dimensional part and the multi-dimensional part. The inputs for auxiliary parts such as minor edit requests and graph formatting inputs are shared by the two parts and as such mixed input is possible. The overall structure of the input is modeled on the structure of the RELAP5 and as such the layout of the manual is very similar to that of the RELAP. This similitude to RELAP5 input is intentional as this input scheme will allow minimum modification between the inputs of RELAP5 and MARS. MARS development team would like to express its appreciation to the RELAP5 Development Team and the USNRC for making this manual possible

MARS code manual volume II: input requirements

International Nuclear Information System (INIS)

Chung, Bub Dong; Kim, Kyung Doo; Bae, Sung Won; Jeong, Jae Jun; Lee, Seung Wook; Hwang, Moon Kyu

2010-02-01

Korea Advanced Energy Research Institute (KAERI) conceived and started the development of MARS code with the main objective of producing a state-of-the-art realistic thermal hydraulic systems analysis code with multi-dimensional analysis capability. MARS achieves this objective by very tightly integrating the one dimensional RELAP5/MOD3 with the multi-dimensional COBRA-TF codes. The method of integration of the two codes is based on the dynamic link library techniques, and the system pressure equation matrices of both codes are implicitly integrated and solved simultaneously. In addition, the Equation-Of-State (EOS) for the light water was unified by replacing the EOS of COBRA-TF by that of the RELAP5. This input manual provides a complete list of input required to run MARS. The manual is divided largely into two parts, namely, the one-dimensional part and the multi-dimensional part. The inputs for auxiliary parts such as minor edit requests and graph formatting inputs are shared by the two parts and as such mixed input is possible. The overall structure of the input is modeled on the structure of the RELAP5 and as such the layout of the manual is very similar to that of the RELAP. This similitude to RELAP5 input is intentional as this input scheme will allow minimum modification between the inputs of RELAP5 and MARS3.1. MARS3.1 development team would like to express its appreciation to the RELAP5 Development Team and the USNRC for making this manual possible

Robust Image Hashing Using Radon Transform and Invariant Features

Directory of Open Access Journals (Sweden)

Y.L. Liu

2016-09-01

Full Text Available A robust image hashing method based on radon transform and invariant features is proposed for image authentication, image retrieval, and image detection. Specifically, an input image is firstly converted into a counterpart with a normalized size. Then the invariant centroid algorithm is applied to obtain the invariant feature point and the surrounding circular area, and the radon transform is employed to acquire the mapping coefficient matrix of the area. Finally, the hashing sequence is generated by combining the feature vectors and the invariant moments calculated from the coefficient matrix. Experimental results show that this method not only can resist against the normal image processing operations, but also some geometric distortions. Comparisons of receiver operating characteristic (ROC curve indicate that the proposed method outperforms some existing methods in classification between perceptual robustness and discrimination.

Robust input design for nonlinear dynamic modeling of AUV.

Science.gov (United States)

Nouri, Nowrouz Mohammad; Valadi, Mehrdad

2017-09-01

Input design has a dominant role in developing the dynamic model of autonomous underwater vehicles (AUVs) through system identification. Optimal input design is the process of generating informative inputs that can be used to generate the good quality dynamic model of AUVs. In a problem with optimal input design, the desired input signal depends on the unknown system which is intended to be identified. In this paper, the input design approach which is robust to uncertainties in model parameters is used. The Bayesian robust design strategy is applied to design input signals for dynamic modeling of AUVs. The employed approach can design multiple inputs and apply constraints on an AUV system's inputs and outputs. Particle swarm optimization (PSO) is employed to solve the constraint robust optimization problem. The presented algorithm is used for designing the input signals for an AUV, and the estimate obtained by robust input design is compared with that of the optimal input design. According to the results, proposed input design can satisfy both robustness of constraints and optimality. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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

2018-03-27

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

Six axis force feedback input device

Science.gov (United States)

Ohm, Timothy (Inventor)

1998-01-01

The present invention is a low friction, low inertia, six-axis force feedback input device comprising an arm with double-jointed, tendon-driven revolute joints, a decoupled tendon-driven wrist, and a base with encoders and motors. The input device functions as a master robot manipulator of a microsurgical teleoperated robot system including a slave robot manipulator coupled to an amplifier chassis, which is coupled to a control chassis, which is coupled to a workstation with a graphical user interface. The amplifier chassis is coupled to the motors of the master robot manipulator and the control chassis is coupled to the encoders of the master robot manipulator. A force feedback can be applied to the input device and can be generated from the slave robot to enable a user to operate the slave robot via the input device without physically viewing the slave robot. Also, the force feedback can be generated from the workstation to represent fictitious forces to constrain the input device's control of the slave robot to be within imaginary predetermined boundaries.

Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control.

Science.gov (United States)

Pan, Yongping; Yu, Haoyong

2017-06-01

This brief presents a biomimetic hybrid feedback feedforward neural-network learning control (NNLC) strategy inspired by the human motor learning control mechanism for a class of uncertain nonlinear systems. The control structure includes a proportional-derivative controller acting as a feedback servo machine and a radial-basis-function (RBF) NN acting as a feedforward predictive machine. Under the sufficient constraints on control parameters, the closed-loop system achieves semiglobal practical exponential stability, such that an accurate NN approximation is guaranteed in a local region along recurrent reference trajectories. Compared with the existing NNLC methods, the novelties of the proposed method include: 1) the implementation of an adaptive NN control to guarantee plant states being recurrent is not needed, since recurrent reference signals rather than plant states are utilized as NN inputs, which greatly simplifies the analysis and synthesis of the NNLC and 2) the domain of NN approximation can be determined a priori by the given reference signals, which leads to an easy construction of the RBF-NNs. Simulation results have verified the effectiveness of this approach.

Leaders’ receptivity to subordinates’ creative input: the role of achievement goals and composition of creative input

NARCIS (Netherlands)

Sijbom, R.B.L.; Janssen, O.; van Yperen, N.W.

2015-01-01

We identified leaders’ achievement goals and composition of creative input as important factors that can clarify when and why leaders are receptive to, and supportive of, subordinates’ creative input. As hypothesized, in two experimental studies, we found that relative to mastery goal leaders,

High-frequency matrix converter with square wave input

Science.gov (United States)

Carr, Joseph Alexander; Balda, Juan Carlos

2015-03-31

A device for producing an alternating current output voltage from a high-frequency, square-wave input voltage comprising, high-frequency, square-wave input a matrix converter and a control system. The matrix converter comprises a plurality of electrical switches. The high-frequency input and the matrix converter are electrically connected to each other. The control system is connected to each switch of the matrix converter. The control system is electrically connected to the input of the matrix converter. The control system is configured to operate each electrical switch of the matrix converter converting a high-frequency, square-wave input voltage across the first input port of the matrix converter and the second input port of the matrix converter to an alternating current output voltage at the output of the matrix converter.

Different cortical mechanisms for spatial vs. feature-based attentional selection in visual working memory

Directory of Open Access Journals (Sweden)

Anna Heuer

2016-08-01

Full Text Available The limited capacity of visual working memory necessitates attentional mechanisms that selectively update and maintain only the most task-relevant content. Psychophysical experiments have shown that the retroactive selection of memory content can be based on visual properties such as location or shape, but the neural basis for such differential selection is unknown. For example, it is not known if there are different cortical modules specialized for spatial versus feature-based mnemonic attention, in the same way that has been demonstrated for attention to perceptual input. Here, we used transcranial magnetic stimulation (TMS to identify areas in human parietal and occipital cortex involved in the selection of objects from memory based on cues to their location (spatial information or their shape (featural information. We found that TMS over the supramarginal gyrus (SMG selectively facilitated spatial selection, whereas TMS over the lateral occipital cortex selectively enhanced feature-based selection for remembered objects in the contralateral visual field. Thus, different cortical regions are responsible for spatial vs. feature-based selection of working memory representations. Since the same regions are involved in attention to external events, these new findings indicate overlapping mechanisms for attentional control over perceptual input and mnemonic representations.

The morphology of Ganoderma lucidum mycelium in a repeated-batch fermentation for exopolysaccharide production

Directory of Open Access Journals (Sweden)

Wan Abd Al Qadr Imad Wan-Mohtar

2016-09-01

Full Text Available The morphology of Ganoderma lucidum BCCM 31549 mycelium in a repeated-batch fermentation (RBF was studied for exopolysaccharide (EPS production. RBF was optimised for time to replace and volume to replace. G. lucidum mycelium showed the ability to self-immobilise and exhibited high stability for repeated use in RBF with engulfed pellets. Furthermore, the ovoid and starburst-like pellet morphology was disposed to EPS production in the shake flask and bioreactor, respectively. Seven RBF could be carried out in 500 mL flasks, and five repeated batches were performed in a 2 L bioreactor. Under RBF conditions, autolysis of pellet core in the shake flask and shaving off of the outer hairy region in the bioreactor were observed at the later stages of RBF (R4 for the shake flask and R6 for the bioreactor. The proposed strategy showed that the morphology of G. lucidum mycelium can withstand extended fermentation cycles.

Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

Directory of Open Access Journals (Sweden)

Wei Liu

2013-01-01

Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.

Feature Usage Explorer: Usage Monitoring and Visualization Tool in HTML5 Based Applications

Directory of Open Access Journals (Sweden)

Sarunas Marciuska

2013-10-01

Full Text Available Feature Usage Explorer is a JavaScript library, which automatically detects features in HTML5 based applications and monitors their usage. The collected information can be visualized in a Feature Usage Diagram, which is automatically generated from an input json file. Currently, the users of Feature Usage Explorer have to design their own tool in order to generate the json file from collected usage information. This option remains viable when using the library in order not to constraint the user’s choice of preferred data storage. Feature Usage Explorer can be reused in any HTML5 based applications where an understanding of how users interact with the system is required (i.e. user experience and usability studies, human computer interaction field, or requirement prioritization area.

Textual Enhancement of Input: Issues and Possibilities

Science.gov (United States)

Han, ZhaoHong; Park, Eun Sung; Combs, Charles

2008-01-01

The input enhancement hypothesis proposed by Sharwood Smith (1991, 1993) has stimulated considerable research over the last 15 years. This article reviews the research on textual enhancement of input (TE), an area where the majority of input enhancement studies have aggregated. Methodological idiosyncrasies are the norm of this body of research.…

Efficient speed control of induction motor using RBF based model reference adaptive control method

OpenAIRE

Kilic, Erdal; Ozcalik, Hasan Riza; Yilmaz, Saban

2017-01-01

This paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that ar...

Dependence of renal blood flow on renal artery stenosis measured using CT angiography

Energy Technology Data Exchange (ETDEWEB)

Luedemann, Lutz [Charite - Universitaetsmedizin Berlin (Germany). Dept. of Radiotherapy; Nafz, B.; Persson, P. [Charite - Universitaetsmedizin Berlin (Germany). Inst. for Vegetative Physiology; Elsner, F. [Krankenhaus am Urban, Berlin (Germany). Dept. of Anesthesiology; Grosse-Siestrup, C.; Meissler, M. [Charite - Universitaetsmedizin Berlin (Germany). Experimental Animal Unit; Gutberlet, M. [Charite - Universitaetsmedizin Berlin (Germany). Dept. of Diagnostic and Interventional Radiology; Univ. Leipzig/ Leipzig Heart Center (Germany). Dept. of Diagnostic and Interventional Radiology; Lengsfeld, P.; Voth, M. [Bayer-Schering Pharma AG, Berlin (Germany). Global Medical Affairs Diagnostic Imaging

2011-03-15

The present study investigates the suitability of computed tomography angiography (CTA) depicting the degree of renal artery stenosis for estimating renal blood flow (RBF) in a kidney. Materials and Methods: We investigated renal artery stenosis assessment by CTA in eight adult female hybrid pigs with an ultrasound probe implanted at the renal vein for RBF measurement. An inflatable metal-free cuff was placed around the renal artery to control the RBF. The RBF was then reduced in four steps. For each reduced RBF value and baseline RBF, CTA with a reconstructed slice thickness of 0.625 mm was performed in the arterial phase following injection of 80 ml of nonionic intravenous contrast medium. The radius of the stenotic and non-stenotic renal artery segment was measured in the reconstructed images. Results: A significant linear correlation (p < 0.0001) was found between the relative apparent stenosis (calculated as the ratio of the radii of the actual stenotic segment and a non-stenotic renal artery segment) and RBF. The linear regression yielded a slope of 0.57 and a y-axis of 24.1 %. A significant linear correlation (p < 0.0001) was also found between the relative true stenosis (the ratio of the radii of the actual stenotic segment and a non-stenotic renal artery segment at baseline) and the RBF. The linear regression yielded a slope of 0.67 and a y-axis of 13.8 %. Conclusion: The results show that the relative stenosis apparent on CTA differs from the true degree of renal artery stenosis. Nevertheless, the degree of renal artery stenosis determined by CTA provides a reliable estimate of the resulting RBF reduction. (orig.)

Exercise training attenuates chemoreflex-mediated reductions of renal blood flow in heart failure.

Science.gov (United States)

Marcus, Noah J; Pügge, Carolin; Mediratta, Jai; Schiller, Alicia M; Del Rio, Rodrigo; Zucker, Irving H; Schultz, Harold D

2015-07-15

In chronic heart failure (CHF), carotid body chemoreceptor (CBC) activity is increased and contributes to increased tonic and hypoxia-evoked elevation in renal sympathetic nerve activity (RSNA). Elevated RSNA and reduced renal perfusion may contribute to development of the cardio-renal syndrome in CHF. Exercise training (EXT) has been shown to abrogate CBC-mediated increases in RSNA in experimental heart failure; however, the effect of EXT on CBC control of renal blood flow (RBF) is undetermined. We hypothesized that CBCs contribute to tonic reductions in RBF in CHF, that stimulation of the CBC with hypoxia would result in exaggerated reductions in RBF, and that these responses would be attenuated with EXT. RBF was measured in CHF-sedentary (SED), CHF-EXT, CHF-carotid body denervation (CBD), and CHF-renal denervation (RDNX) groups. We measured RBF at rest and in response to hypoxia (FiO2 10%). All animals exhibited similar reductions in ejection fraction and fractional shortening as well as increases in ventricular systolic and diastolic volumes. Resting RBF was lower in CHF-SED (29 ± 2 ml/min) than in CHF-EXT animals (46 ± 2 ml/min, P < 0.05) or in CHF-CBD animals (42 ± 6 ml/min, P < 0.05). In CHF-SED, RBF decreased during hypoxia, and this was prevented in CHF-EXT animals. Both CBD and RDNX abolished the RBF response to hypoxia in CHF. Mean arterial pressure increased in response to hypoxia in CHF-SED, but was prevented by EXT, CBD, and RDNX. EXT is effective in attenuating chemoreflex-mediated tonic and hypoxia-evoked reductions in RBF in CHF. Copyright © 2015 the American Physiological Society.

Dependence of renal blood flow on renal artery stenosis measured using CT angiography

International Nuclear Information System (INIS)

Luedemann, Lutz; Nafz, B.; Persson, P.; Elsner, F.; Grosse-Siestrup, C.; Meissler, M.; Gutberlet, M.; Univ. Leipzig/ Leipzig Heart Center; Lengsfeld, P.; Voth, M.

2011-01-01

The present study investigates the suitability of computed tomography angiography (CTA) depicting the degree of renal artery stenosis for estimating renal blood flow (RBF) in a kidney. Materials and Methods: We investigated renal artery stenosis assessment by CTA in eight adult female hybrid pigs with an ultrasound probe implanted at the renal vein for RBF measurement. An inflatable metal-free cuff was placed around the renal artery to control the RBF. The RBF was then reduced in four steps. For each reduced RBF value and baseline RBF, CTA with a reconstructed slice thickness of 0.625 mm was performed in the arterial phase following injection of 80 ml of nonionic intravenous contrast medium. The radius of the stenotic and non-stenotic renal artery segment was measured in the reconstructed images. Results: A significant linear correlation (p < 0.0001) was found between the relative apparent stenosis (calculated as the ratio of the radii of the actual stenotic segment and a non-stenotic renal artery segment) and RBF. The linear regression yielded a slope of 0.57 and a y-axis of 24.1 %. A significant linear correlation (p < 0.0001) was also found between the relative true stenosis (the ratio of the radii of the actual stenotic segment and a non-stenotic renal artery segment at baseline) and the RBF. The linear regression yielded a slope of 0.67 and a y-axis of 13.8 %. Conclusion: The results show that the relative stenosis apparent on CTA differs from the true degree of renal artery stenosis. Nevertheless, the degree of renal artery stenosis determined by CTA provides a reliable estimate of the resulting RBF reduction. (orig.)

Automatic target recognition using a feature-based optical neural network

Science.gov (United States)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Learning deep features with adaptive triplet loss for person reidentification

Science.gov (United States)

Li, Zhiqiang; Sang, Nong; Chen, Kezhou; Gao, Changxin; Wang, Ruolin

2018-03-01

Person reidentification (re-id) aims to match a specified person across non-overlapping cameras, which remains a very challenging problem. While previous methods mostly focus on feature extraction or metric learning, this paper makes the attempt in jointly learning both the global full-body and local body-parts features of the input persons with a multichannel convolutional neural network (CNN) model, which is trained by an adaptive triplet loss function that serves to minimize the distance between the same person and maximize the distance between different persons. The experimental results show that our approach achieves very promising results on the large-scale Market-1501 and DukeMTMC-reID datasets.

Robust Automatic Speech Recognition Features using Complex Wavelet Packet Transform Coefficients

Directory of Open Access Journals (Sweden)

TjongWan Sen

2009-11-01

Full Text Available To improve the performance of phoneme based Automatic Speech Recognition (ASR in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA. These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4 from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

Haar-like Rectangular Features for Biometric Recognition

DEFF Research Database (Denmark)

Nasrollahi, Kamal; Moeslund, Thomas B.; Rashidi, Maryam

2013-01-01

Developing a reliable, fast, and robust biometric recognition system is still a challenging task. This is because the inputs to these systems can be noisy, occluded, poorly illuminated, rotated, and of very low-resolutions. This paper proposes a probabilistic classifier using Haar-like features......, which mostly have been used for detection, for biometric recognition. The proposed system has been tested for three different biometrics: ear, iris, and hand vein patterns and it is shown that it is robust against most of the mentioned degradations and it outperforms state-of-the-art systems...

Setting and changing feature priorities in visual short-term memory.

Science.gov (United States)

Kalogeropoulou, Zampeta; Jagadeesh, Akshay V; Ohl, Sven; Rolfs, Martin

2017-04-01

Many everyday tasks require prioritizing some visual features over competing ones, both during the selection from the rich sensory input and while maintaining information in visual short-term memory (VSTM). Here, we show that observers can change priorities in VSTM when, initially, they attended to a different feature. Observers reported from memory the orientation of one of two spatially interspersed groups of black and white gratings. Using colored pre-cues (presented before stimulus onset) and retro-cues (presented after stimulus offset) predicting the to-be-reported group, we manipulated observers' feature priorities independently during stimulus encoding and maintenance, respectively. Valid pre-cues reliably increased observers' performance (reduced guessing, increased report precision) as compared to neutral ones; invalid pre-cues had the opposite effect. Valid retro-cues also consistently improved performance (by reducing random guesses), even if the unexpected group suddenly became relevant (invalid-valid condition). Thus, feature-based attention can reshape priorities in VSTM protecting information that would otherwise be forgotten.

Value-Addes Tax and Shadow Economy : the Role of Input-Output Linkages (revision of CentER Discussion Paper 2013-036)

NARCIS (Netherlands)

Hoseini, Mohammad

2015-01-01

Under the VAT, formal traders report their purchases to the administration for a deduction in their VAT bill. This paper models this third-party reporting feature of the VAT in an input-output economy and quantifies it among different activities using a forward linkages index. The administration can

A neural network model of semantic memory linking feature-based object representation and words.

Science.gov (United States)

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

Non-linear Membrane Properties in Entorhinal Cortical Stellate Cells Reduce Modulation of Input-Output Responses by Voltage Fluctuations

Science.gov (United States)

Fernandez, Fernando R.; Malerba, Paola; White, John A.

2015-01-01

The presence of voltage fluctuations arising from synaptic activity is a critical component in models of gain control, neuronal output gating, and spike rate coding. The degree to which individual neuronal input-output functions are modulated by voltage fluctuations, however, is not well established across different cortical areas. Additionally, the extent and mechanisms of input-output modulation through fluctuations have been explored largely in simplified models of spike generation, and with limited consideration for the role of non-linear and voltage-dependent membrane properties. To address these issues, we studied fluctuation-based modulation of input-output responses in medial entorhinal cortical (MEC) stellate cells of rats, which express strong sub-threshold non-linear membrane properties. Using in vitro recordings, dynamic clamp and modeling, we show that the modulation of input-output responses by random voltage fluctuations in stellate cells is significantly limited. In stellate cells, a voltage-dependent increase in membrane resistance at sub-threshold voltages mediated by Na+ conductance activation limits the ability of fluctuations to elicit spikes. Similarly, in exponential leaky integrate-and-fire models using a shallow voltage-dependence for the exponential term that matches stellate cell membrane properties, a low degree of fluctuation-based modulation of input-output responses can be attained. These results demonstrate that fluctuation-based modulation of input-output responses is not a universal feature of neurons and can be significantly limited by subthreshold voltage-gated conductances. PMID:25909971

Effect of input compression and input frequency response on music perception in cochlear implant users.

Science.gov (United States)

Halliwell, Emily R; Jones, Linor L; Fraser, Matthew; Lockley, Morag; Hill-Feltham, Penelope; McKay, Colette M

2015-06-01

A study was conducted to determine whether modifications to input compression and input frequency response characteristics can improve music-listening satisfaction in cochlear implant users. Experiment 1 compared three pre-processed versions of music and speech stimuli in a laboratory setting: original, compressed, and flattened frequency response. Music excerpts comprised three music genres (classical, country, and jazz), and a running speech excerpt was compared. Experiment 2 implemented a flattened input frequency response in the speech processor program. In a take-home trial, participants compared unaltered and flattened frequency responses. Ten and twelve adult Nucleus Freedom cochlear implant users participated in Experiments 1 and 2, respectively. Experiment 1 revealed a significant preference for music stimuli with a flattened frequency response compared to both original and compressed stimuli, whereas there was a significant preference for the original (rising) frequency response for speech stimuli. Experiment 2 revealed no significant mean preference for the flattened frequency response, with 9 of 11 subjects preferring the rising frequency response. Input compression did not alter music enjoyment. Comparison of the two experiments indicated that individual frequency response preferences may depend on the genre or familiarity, and particularly whether the music contained lyrics.

Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method

Energy Technology Data Exchange (ETDEWEB)

Assareh, Ehsanolah; Poultangari, Iman [Dezful Branch, Islamic Azad University, Dezful (Iran, Islamic Republic of); Tandis, Emad [Mechanical Engineering Department, University of Jundi Shapor, Dezful (Iran, Islamic Republic of); Nedael, Mojtaba [Dept. of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of)

2016-10-15

Enhancing the energy production from wind power in low-wind areas has always been a fundamental subject of research in the field of wind energy industry. In the first phase of this research, an initial investigation was performed to evaluate the potential of wind in south west of Iran. The initial results indicate that the wind potential in the studied location is not sufficient enough and therefore the investigated region is identified as a low wind speed area. In the second part of this study, an advanced optimization model was presented to regulate the torque in the wind generators. For this primary purpose, the torque of wind turbine is adjusted using a Proportional and integral (PI) control system so that at lower speeds of the wind, the power generated by generator is enhanced significantly. The proposed model uses the RBF neural network to adjust the net obtained gains of the PI controller for the purpose of acquiring the utmost electricity which is produced through the generator. Furthermore, in order to edify and instruct the neural network, the optimal data set is obtained by a Hybrid genetic algorithm along with a gravitational search algorithm (HGA-GSA). The proposed method is evaluated by using a 5MW wind turbine manufactured by National Renewable Energy Laboratory (NREL). Final results of this study are indicative of the satisfactory and successful performance of the proposed investigated model.

Observation-based input and dissipation version of WAVEWATCH III

Science.gov (United States)

Zieger, Stefan; Babanin, Alexander; Rogers, Erick; Young, Ian

2013-04-01

Measurements collected at Lake George, Australia, resulted in new insights on the processes of wind wave interaction and white-capping dissipation and consequently new parameterisations of these source terms. The new nonlinear wind input source term accounts for dependence of the growth increment on wave steepness, for airflow separation which leads to a relative reduction of the growth under extreme wind conditions, and for negative growth rate under adverse winds. The new wave breaking and whitecapping dissipation source function features two separate terms: the inherent breaking term and a cumulative dissipation term due to influences of longer waves on wave breaking of shorter waves. Another novel feature of this dissipation is the threshold in terms of spectral density: below this threshold breaking stops and whitecapping becomes zero. In such conditions dissipation due to wave interaction with water turbulence takes over, which regime is particularly relevant for decaying seas and for swell. This paper describes these source terms implemented in WAVEWATCH III and evaluates the performance against existing source terms in duration-limited simulations and against buoy measurements for windsea-dominated conditions. Results show agreement by means of growth curves and integral parameters in the simulations and hindcast. The paper also introduces wave breaking probability as model output, along with standard wind-wave metrics.

Association between exercise intensity and renal blood flow evaluated using ultrasound echo.

Science.gov (United States)

Kawakami, Shotaro; Yasuno, Tetsuhiko; Matsuda, Takuro; Fujimi, Kanta; Ito, Ai; Yoshimura, Saki; Uehara, Yoshinari; Tanaka, Hiroaki; Saito, Takao; Higaki, Yasuki

2018-03-10

High-intensity exercise reduces renal blood flow (RBF) and may transiently exacerbate renal dysfunction. RBF has previously been measured invasively by administration of an indicator material; however, non-invasive measurement is now possible with technological innovations. This study examined variations in RBF at different exercise intensities using ultrasound echo. Eight healthy men with normal renal function (eGFR cys 114 ± 19 mL/min/1.73 m 2 ) participated in this study. Using a bicycle ergometer, participants underwent an incremental exercise test using a ramp protocol (20 W/min) until exhaustion in Study 1 and the lactate acid breaking point (LaBP) was calculated. Participants underwent a multi-stage test at exercise intensities of 60, 80, 100, 120, and 140% LaBP in Study 2. RBF was measured by ultrasound echo at rest and 5 min after exercise in Study 1 and at rest and immediately after each exercise in Study 2. To determine the mechanisms behind RBF decline, a catheter was placed into the antecubital vein to study vasoconstriction dynamics. RBF after maximum exercise decreased by 51% in Study 1. In Study 2, RBF showed no significant decrease until 80% LaBP, and showed a significant decrease (31%) at 100% LaBP compared with at rest (p blood lactate. Reduction in RBF with exercise above the intensity at LaBP was due to decreased cross-sectional area rather than time-averaged flow velocity.

Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

KAUST Repository

AlShahrani, Mona

2015-01-01

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

Towards an Efficient Artificial Neural Network Pruning and Feature Ranking Tool

KAUST Repository

AlShahrani, Mona

2015-05-24

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

Prediction of interface residue based on the features of residue interaction network.

Science.gov (United States)

Jiao, Xiong; Ranganathan, Shoba

2017-11-07

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Compensating Level-Dependent Frequency Representation in Auditory Cortex by Synaptic Integration of Corticocortical Input.

Directory of Open Access Journals (Sweden)

Max F K Happel

Full Text Available Robust perception of auditory objects over a large range of sound intensities is a fundamental feature of the auditory system. However, firing characteristics of single neurons across the entire auditory system, like the frequency tuning, can change significantly with stimulus intensity. Physiological correlates of level-constancy of auditory representations hence should be manifested on the level of larger neuronal assemblies or population patterns. In this study we have investigated how information of frequency and sound level is integrated on the circuit-level in the primary auditory cortex (AI of the Mongolian gerbil. We used a combination of pharmacological silencing of corticocortically relayed activity and laminar current source density (CSD analysis. Our data demonstrate that with increasing stimulus intensities progressively lower frequencies lead to the maximal impulse response within cortical input layers at a given cortical site inherited from thalamocortical synaptic inputs. We further identified a temporally precise intercolumnar synaptic convergence of early thalamocortical and horizontal corticocortical inputs. Later tone-evoked activity in upper layers showed a preservation of broad tonotopic tuning across sound levels without shifts towards lower frequencies. Synaptic integration within corticocortical circuits may hence contribute to a level-robust representation of auditory information on a neuronal population level in the auditory cortex.

Segmental isotope labeling of proteins for NMR structural study using a protein S tag for higher expression and solubility

International Nuclear Information System (INIS)

Kobayashi, Hiroshi; Swapna, G. V. T.; Wu, Kuen-Phon; Afinogenova, Yuliya; Conover, Kenith; Mao, Binchen; Montelione, Gaetano T.; Inouye, Masayori

2012-01-01

A common obstacle to NMR studies of proteins is sample preparation. In many cases, proteins targeted for NMR studies are poorly expressed and/or expressed in insoluble forms. Here, we describe a novel approach to overcome these problems. In the protein S tag-intein (PSTI) technology, two tandem 92-residue N-terminal domains of protein S (PrS 2 ) from Myxococcus xanthus is fused at the N-terminal end of a protein to enhance its expression and solubility. Using intein technology, the isotope-labeled PrS 2 -tag is replaced with non-isotope labeled PrS 2 -tag, silencing the NMR signals from PrS 2 -tag in isotope-filtered 1 H-detected NMR experiments. This method was applied to the E. coli ribosome binding factor A (RbfA), which aggregates and precipitates in the absence of a solubilization tag unless the C-terminal 25-residue segment is deleted (RbfAΔ25). Using the PrS 2 -tag, full-length well-behaved RbfA samples could be successfully prepared for NMR studies. PrS 2 (non-labeled)-tagged RbfA (isotope-labeled) was produced with the use of the intein approach. The well-resolved TROSY-HSQC spectrum of full-length PrS 2 -tagged RbfA superimposes with the TROSY-HSQC spectrum of RbfAΔ25, indicating that PrS 2 -tag does not affect the structure of the protein to which it is fused. Using a smaller PrS-tag, consisting of a single N-terminal domain of protein S, triple resonance experiments were performed, and most of the backbone 1 H, 15 N and 13 C resonance assignments for full-length E. coli RbfA were determined. Analysis of these chemical shift data with the Chemical Shift Index and heteronuclear 1 H– 15 N NOE measurements reveal the dynamic nature of the C-terminal segment of the full-length RbfA protein, which could not be inferred using the truncated RbfAΔ25 construct. CS-Rosetta calculations also demonstrate that the core structure of full-length RbfA is similar to that of the RbfAΔ25 construct.

Day-ahead load forecast using random forest and expert input selection

International Nuclear Information System (INIS)

Lahouar, A.; Ben Hadj Slama, J.

2015-01-01

Highlights: • A model based on random forests for short term load forecast is proposed. • An expert feature selection is added to refine inputs. • Special attention is paid to customers behavior, load profile and special holidays. • The model is flexible and able to handle complex load signal. • A technical comparison is performed to assess the forecast accuracy. - Abstract: The electrical load forecast is getting more and more important in recent years due to the electricity market deregulation and integration of renewable resources. To overcome the incoming challenges and ensure accurate power prediction for different time horizons, sophisticated intelligent methods are elaborated. Utilization of intelligent forecast algorithms is among main characteristics of smart grids, and is an efficient tool to face uncertainty. Several crucial tasks of power operators such as load dispatch rely on the short term forecast, thus it should be as accurate as possible. To this end, this paper proposes a short term load predictor, able to forecast the next 24 h of load. Using random forest, characterized by immunity to parameter variations and internal cross validation, the model is constructed following an online learning process. The inputs are refined by expert feature selection using a set of if–then rules, in order to include the own user specifications about the country weather or market, and to generalize the forecast ability. The proposed approach is tested through a real historical set from the Tunisian Power Company, and the simulation shows accurate and satisfactory results for one day in advance, with an average error exceeding rarely 2.3%. The model is validated for regular working days and weekends, and special attention is paid to moving holidays, following non Gregorian calendar

Renal blood flow after selective injection of different dosages of diatrizoate into the renal artery

International Nuclear Information System (INIS)

Burgener, F.A.; Fischer, H.W.; Weber, D.A.

1975-01-01

The characteristic biphasic renal haemodynamic response to diatrizoate injected into the renal artery was shown in the dog with the 133-xenon washout technique. A brief increase in renal blood flow (RBF) during the first ten seconds is followed by a more prolonged period of diminuished RBF. A dose of 4 ml. diatrizoate 60% resulted in the maximum RBF increase of 43% after ten seconds, but even 1 ml. diatrizoate raised the RBF 24%. The initial vasodilator effect of diatrizoate compares well in its extent with the most potent renal vasodilators. (orig.) [de

Role of the renin-angiotensin system in regulation and autoregulation of renal blood flow

DEFF Research Database (Denmark)

Sørensen, Charlotte Mehlin; Leyssac, Paul Peter; Skøtt, Ole

2000-01-01

The role for ANG II in renal blood flow (RBF) autoregulation is unsettled. The present study was designed to test the effect of clamping plasma ANG II concentrations ([ANG II]) by simultaneous infusion of the angiotensin-converting enzyme inhibitor captopril and ANG II on RBF autoregulation...... in halothane-anesthetized Sprague-Dawley rats. Autoregulation was defined as the RBF response to acute changes in renal perfusion pressure (RPP). Regulation was defined as changes in RBF during long-lasting changes in RPP. The results showed that a prolonged reduction of RPP reset the lower limit...

Modeling Marine Electromagnetic Survey with Radial Basis Function Networks

Directory of Open Access Journals (Sweden)

Agus Arif

2014-11-01

Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network[1].[1] This manuscript is an extended version of our previous paper, entitled Radial Basis Function Networks for Modeling Marine Electromagnetic Survey, which had been presented on 2011 International Conference on Electrical Engineering and Informatics, 17-19 July 2011, Bandung, Indonesia.

Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

Directory of Open Access Journals (Sweden)

Tristan Aumentado-Armstrong

2015-10-01

Full Text Available Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli.

Science.gov (United States)

Aumentado-Armstrong, Tristan; Metzen, Michael G; Sproule, Michael K J; Chacron, Maurice J

2015-10-01

Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

Multiwavelet packet entropy and its application in transmission line fault recognition and classification.

Science.gov (United States)

Liu, Zhigang; Han, Zhiwei; Zhang, Yang; Zhang, Qiaoge

2014-11-01

Multiwavelets possess better properties than traditional wavelets. Multiwavelet packet transformation has more high-frequency information. Spectral entropy can be applied as an analysis index to the complexity or uncertainty of a signal. This paper tries to define four multiwavelet packet entropies to extract the features of different transmission line faults, and uses a radial basis function (RBF) neural network to recognize and classify 10 fault types of power transmission lines. First, the preprocessing and postprocessing problems of multiwavelets are presented. Shannon entropy and Tsallis entropy are introduced, and their difference is discussed. Second, multiwavelet packet energy entropy, time entropy, Shannon singular entropy, and Tsallis singular entropy are defined as the feature extraction methods of transmission line fault signals. Third, the plan of transmission line fault recognition using multiwavelet packet entropies and an RBF neural network is proposed. Finally, the experimental results show that the plan with the four multiwavelet packet energy entropies defined in this paper achieves better performance in fault recognition. The performance with SA4 (symmetric antisymmetric) multiwavelet packet Tsallis singular entropy is the best among the combinations of different multiwavelet packets and the four multiwavelet packet entropies.

A three-phase to three-phase series-resonant power converter with optimal input current waveforms, Part II: implementation and results

NARCIS (Netherlands)

Huisman, H.

1988-01-01

For pt.I see ibid., vol.35, no.2, p.263-8 (1988). A 15 kW three-phase prototype series-resonant power converter is constructed. The converter features sinusoidal output voltage and sinusoidal input currents. The control concepts and necessary electronics, as well as the layout of the power circuit,

Stochastic weather inputs for improved urban water demand forecasting: application of nonlinear input variable selection and machine learning methods

Science.gov (United States)

Quilty, J.; Adamowski, J. F.

2015-12-01

Urban water supply systems are often stressed during seasonal outdoor water use as water demands related to the climate are variable in nature making it difficult to optimize the operation of the water supply system. Urban water demand forecasts (UWD) failing to include meteorological conditions as inputs to the forecast model may produce poor forecasts as they cannot account for the increase/decrease in demand related to meteorological conditions. Meteorological records stochastically simulated into the future can be used as inputs to data-driven UWD forecasts generally resulting in improved forecast accuracy. This study aims to produce data-driven UWD forecasts for two different Canadian water utilities (Montreal and Victoria) using machine learning methods by first selecting historical UWD and meteorological records derived from a stochastic weather generator using nonlinear input variable selection. The nonlinear input variable selection methods considered in this work are derived from the concept of conditional mutual information, a nonlinear dependency measure based on (multivariate) probability density functions and accounts for relevancy, conditional relevancy, and redundancy from a potential set of input variables. The results of our study indicate that stochastic weather inputs can improve UWD forecast accuracy for the two sites considered in this work. Nonlinear input variable selection is suggested as a means to identify which meteorological conditions should be utilized in the forecast.

Generation of Gaussian 09 Input Files for the Computation of 1H and 13C NMR Chemical Shifts of Structures from a Spartan’14 Conformational Search

OpenAIRE

sprotocols

2014-01-01

Authors: Spencer Reisbick & Patrick Willoughby ### Abstract This protocol describes an approach to preparing a series of Gaussian 09 computational input files for an ensemble of conformers generated in Spartan’14. The resulting input files are necessary for computing optimum geometries, relative conformer energies, and NMR shielding tensors using Gaussian. Using the conformational search feature within Spartan’14, an ensemble of conformational isomers was obtained. To convert the str...

Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models

Directory of Open Access Journals (Sweden)

Robert B. Gramacy

2010-02-01

Full Text Available This document describes the new features in version 2.x of the tgp package for R, implementing treed Gaussian process (GP models. The topics covered include methods for dealing with categorical inputs and excluding inputs from the tree or GP part of the model; fully Bayesian sensitivity analysis for inputs/covariates; sequential optimization of black-box functions; and a new Monte Carlo method for inference in multi-modal posterior distributions that combines simulated tempering and importance sampling. These additions extend the functionality of tgp across all models in the hierarchy: from Bayesian linear models, to classification and regression trees (CART, to treed Gaussian processes with jumps to the limiting linear model. It is assumed that the reader is familiar with the baseline functionality of the package, outlined in the first vignette (Gramacy 2007.

Quantifying input uncertainty in an assemble-to-order system simulation with correlated input variables of mixed types

NARCIS (Netherlands)

Akçay, A.E.; Biller, B.

2014-01-01

We consider an assemble-to-order production system where the product demands and the time since the last customer arrival are not independent. The simulation of this system requires a multivariate input model that generates random input vectors with correlated discrete and continuous components. In

Radial basis function interpolation of unstructured, three-dimensional, volumetric particle tracking velocimetry data

International Nuclear Information System (INIS)

Casa, L D C; Krueger, P S

2013-01-01

Unstructured three-dimensional fluid velocity data were interpolated using Gaussian radial basis function (RBF) interpolation. Data were generated to imitate the spatial resolution and experimental uncertainty of a typical implementation of defocusing digital particle image velocimetry. The velocity field associated with a steadily rotating infinite plate was simulated to provide a bounded, fully three-dimensional analytical solution of the Navier–Stokes equations, allowing for robust analysis of the interpolation accuracy. The spatial resolution of the data (i.e. particle density) and the number of RBFs were varied in order to assess the requirements for accurate interpolation. Interpolation constraints, including boundary conditions and continuity, were included in the error metric used for the least-squares minimization that determines the interpolation parameters to explore methods for improving RBF interpolation results. Even spacing and logarithmic spacing of RBF locations were also investigated. Interpolation accuracy was assessed using the velocity field, divergence of the velocity field, and viscous torque on the rotating boundary. The results suggest that for the present implementation, RBF spacing of 0.28 times the boundary layer thickness is sufficient for accurate interpolation, though theoretical error analysis suggests that improved RBF positioning may yield more accurate results. All RBF interpolation results were compared to standard Gaussian weighting and Taylor expansion interpolation methods. Results showed that RBF interpolation improves interpolation results compared to the Taylor expansion method by 60% to 90% based on the average squared velocity error and provides comparable velocity results to Gaussian weighted interpolation in terms of velocity error. RMS accuracy of the flow field divergence was one to two orders of magnitude better for the RBF interpolation compared to the other two methods. RBF interpolation that was applied to

On the Nature of the Input in Optimality Theory

DEFF Research Database (Denmark)

Heck, Fabian; Müller, Gereon; Vogel, Ralf

2002-01-01

The input has two main functions in optimality theory (Prince and Smolensky 1993). First, the input defines the candidate set, in other words it determines which output candidates compete for optimality, and which do not. Second, the input is referred to by faithfulness constraints that prohibit...... output candidates from deviating from specifications in the input. Whereas there is general agreement concerning the relevance of the input in phonology, the nature of the input in syntax is notoriously unclear. In this article, we show that the input should not be taken to define syntactic candidate...... and syntax is due to a basic, irreducible difference between these two components of grammar: Syntax is an information preserving system, phonology is not....

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

Science.gov (United States)

Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

2016-02-01

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Functionality of system components: Conservation of protein function in protein feature space

DEFF Research Database (Denmark)

Jensen, Lars Juhl; Ussery, David; Brunak, Søren

2003-01-01

well on organisms other than the one on which it was trained. We evaluate the performance of such a method, ProtFun, which relies on protein features as its sole input, and show that the method gives similar performance for most eukaryotes and performs much better than anticipated on archaea......Many protein features useful for prediction of protein function can be predicted from sequence, including posttranslational modifications, subcellular localization, and physical/chemical properties. We show here that such protein features are more conserved among orthologs than paralogs, indicating...... they are crucial for protein function and thus subject to selective pressure. This means that a function prediction method based on sequence-derived features may be able to discriminate between proteins with different function even when they have highly similar structure. Also, such a method is likely to perform...

Phasing Out a Polluting Input

OpenAIRE

Eriksson, Clas

2015-01-01

This paper explores economic policies related to the potential conflict between economic growth and the environment. It applies a model with directed technological change and focuses on the case with low elasticity of substitution between clean and dirty inputs in production. New technology is substituted for the polluting input, which results in a gradual decline in pollution along the optimal long-run growth path. In contrast to some recent work, the era of pollution and environmental polic...

Automated Feature Design for Time Series Classification by Genetic Programming

OpenAIRE

Harvey, Dustin Yewell

2014-01-01

Time series classification (TSC) methods discover and exploit patterns in time series and other one-dimensional signals. Although many accurate, robust classifiers exist for multivariate feature sets, general approaches are needed to extend machine learning techniques to make use of signal inputs. Numerous applications of TSC can be found in structural engineering, especially in the areas of structural health monitoring and non-destructive evaluation. Additionally, the fields of process contr...

Performance Improvement and Feature Enhancement of WriteOn

OpenAIRE

Chandrasekar, Samantha

2008-01-01

A Tablet PC is a portable computing device which combines a regular notebook computer with a digitizing screen that interacts with a complementary electronic pen stylus. The pen allows the user to input data by writing on or by tapping the screen. Like a regular notebook computer, the user can also perform tasks using the mouse and keyboard. A Tablet PC gives the users all the features of a regular notebook computer along with the support to recognize, process, and store electronic/digital in...

Structural health monitoring feature design by genetic programming

International Nuclear Information System (INIS)

Harvey, Dustin Y; Todd, Michael D

2014-01-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems. (paper)

Input/Output linearizing control of a nuclear reactor

International Nuclear Information System (INIS)

Perez C, V.

1994-01-01

The feedback linearization technique is an approach to nonlinear control design. The basic idea is to transform, by means of algebraic methods, the dynamics of a nonlinear control system into a full or partial linear system. As a result of this linearization process, the well known basic linear control techniques can be used to obtain some desired dynamic characteristics. When full linearization is achieved, the method is referred to as input-state linearization, whereas when partial linearization is achieved, the method is referred to as input-output linearization. We will deal with the latter. By means of input-output linearization, the dynamics of a nonlinear system can be decomposed into an external part (input-output), and an internal part (unobservable). Since the external part consists of a linear relationship among the output of the plant and the auxiliary control input mentioned above, it is easy to design such an auxiliary control input so that we get the output to behave in a predetermined way. Since the internal dynamics of the system is known, we can check its dynamics behavior on order of to ensure that the internal states are bounded. The linearization method described here can be applied to systems with one-input/one-output, as well as to systems with multiple-inputs/multiple-outputs. Typical control problems such as stabilization and reference path tracking can be solved using this technique. In this work, the input/output linearization theory is presented, as well as the problem of getting the output variable to track some desired trayectories. Further, the design of an input/output control system applied to the nonlinear model of a research nuclear reactor is included, along with the results obtained by computer simulation. (Author)

Feasibility of measuring renal blood flow by phase-contrast magnetic resonance imaging in patients with autosomal dominant polycystic kidney disease.

Science.gov (United States)

Spithoven, E M; Meijer, E; Borns, C; Boertien, W E; Gaillard, C A J M; Kappert, P; Greuter, M J W; van der Jagt, E; Vart, P; de Jong, P E; Gansevoort, R T

2016-03-01

Renal blood flow (RBF) has been shown to predict disease progression in autosomal dominant polycystic kidney disease (ADPKD). We investigated the feasibility and accuracy of phase-contrast RBF by MRI (RBFMRI) in ADPKD patients with a wide range of estimated glomerular filtration rate (eGFR) values. First, we validated RBFMRI measurement using phantoms simulating renal artery hemodynamics. Thereafter, we investigated in a test-set of 21 patients intra- and inter-observer coefficient of variation of RBFMRI. After validation, we measured RBFMRI in a cohort of 91 patients and compared the variability explained by characteristics indicative for disease severity for RBFMRI and RBF measured by continuous hippuran infusion. The correlation in flow measurement using phantoms by phase-contrast MRI was high and fluid collection was high (CCC=0.969). Technical problems that precluded RBFMRI measurement occurred predominantly in patients with a lower eGFR (34% vs. 16%). In subjects with higher eGFRs, variability in RBF explained by disease characteristics was similar for RBFMRI compared to RBFHip, whereas in subjects with lower eGFRs, this was significantly less for RBFMRI. Our study shows that RBF can be measured accurately in ADPKD patients by phase-contrast, but this technique may be less feasible in subjects with a lower eGFR. Renal blood flow (RBF) can be accurately measured by phase-contrast MRI in ADPKD patients. RBF measured by phase-contrast is associated with ADPKD disease severity. RBF measurement by phase-contrast MRI may be less feasible in patients with an impaired eGFR.

WORM: A general-purpose input deck specification language

International Nuclear Information System (INIS)

Jones, T.

1999-01-01

Using computer codes to perform criticality safety calculations has become common practice in the industry. The vast majority of these codes use simple text-based input decks to represent the geometry, materials, and other parameters that describe the problem. However, the data specified in input files are usually processed results themselves. For example, input decks tend to require the geometry specification in linear dimensions and materials in atom or weight fractions, while the parameter of interest might be mass or concentration. The calculations needed to convert from the item of interest to the required parameter in the input deck are usually performed separately and then incorporated into the input deck. This process of calculating, editing, and renaming files to perform a simple parameter study is tedious at best. In addition, most computer codes require dimensions to be specified in centimeters, while drawings or other materials used to create the input decks might be in other units. This also requires additional calculation or conversion prior to composition of the input deck. These additional calculations, while extremely simple, introduce a source for error in both the calculations and transcriptions. To overcome these difficulties, WORM (Write One, Run Many) was created. It is an easy-to-use programming language to describe input decks and can be used with any computer code that uses standard text files for input. WORM is available, via the Internet, at worm.lanl.gov. A user's guide, tutorials, example models, and other WORM-related materials are also available at this Web site. Questions regarding WORM should be directed to wormatlanl.gov

Using features of a Creole language to reconstruct population history and cultural evolution: tracing the English origins of Sranan.

Science.gov (United States)

Sherriah, André C; Devonish, Hubert; Thomas, Ewart A C; Creanza, Nicole

2018-04-05

Creole languages are formed in conditions where speakers from distinct languages are brought together without a shared first language, typically under the domination of speakers from one of the languages and particularly in the context of the transatlantic slave trade and European colonialism. One such Creole in Suriname, Sranan, developed around the mid-seventeenth century, primarily out of contact between varieties of English from England, spoken by the dominant group, and multiple West African languages. The vast majority of the basic words in Sranan come from the language of the dominant group, English. Here, we compare linguistic features of modern-day Sranan with those of English as spoken in 313 localities across England. By way of testing proposed hypotheses for the origin of English words in Sranan, we find that 80% of the studied features of Sranan can be explained by similarity to regional dialect features at two distinct input locations within England, a cluster of locations near the port of Bristol and another cluster near Essex in eastern England. Our new hypothesis is supported by the geographical distribution of specific regional dialect features, such as post-vocalic rhoticity and word-initial 'h', and by phylogenetic analysis of these features, which shows evidence favouring input from at least two English dialects in the formation of Sranan. In addition to explicating the dialect features most prominent in the linguistic evolution of Sranan, our historical analyses also provide supporting evidence for two distinct hypotheses about the likely geographical origins of the English speakers whose language was an input to Sranan. The emergence as a likely input to Sranan of the speech forms of a cluster near Bristol is consistent with historical records, indicating that most of the indentured servants going to the Americas between 1654 and 1666 were from Bristol and nearby counties, and that of the cluster near Essex is consistent with documents

Full-order optimal compensators for flow control: the multiple inputs case

Science.gov (United States)

Semeraro, Onofrio; Pralits, Jan O.

2018-03-01

Flow control has been the subject of numerous experimental and theoretical works. We analyze full-order, optimal controllers for large dynamical systems in the presence of multiple actuators and sensors. The full-order controllers do not require any preliminary model reduction or low-order approximation: this feature allows us to assess the optimal performance of an actuated flow without relying on any estimation process or further hypothesis on the disturbances. We start from the original technique proposed by Bewley et al. (Meccanica 51(12):2997-3014, 2016. https://doi.org/10.1007/s11012-016-0547-3), the adjoint of the direct-adjoint (ADA) algorithm. The algorithm is iterative and allows bypassing the solution of the algebraic Riccati equation associated with the optimal control problem, typically infeasible for large systems. In this numerical work, we extend the ADA iteration into a more general framework that includes the design of controllers with multiple, coupled inputs and robust controllers (H_{∞} methods). First, we demonstrate our results by showing the analytical equivalence between the full Riccati solutions and the ADA approximations in the multiple inputs case. In the second part of the article, we analyze the performance of the algorithm in terms of convergence of the solution, by comparing it with analogous techniques. We find an excellent scalability with the number of inputs (actuators), making the method a viable way for full-order control design in complex settings. Finally, the applicability of the algorithm to fluid mechanics problems is shown using the linearized Kuramoto-Sivashinsky equation and the Kármán vortex street past a two-dimensional cylinder.

A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

Directory of Open Access Journals (Sweden)

Kursat Zuhtuogullari

2013-01-01

Full Text Available The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

A soft computing based approach using modified selection strategy for feature reduction of medical systems.

Science.gov (United States)

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

Sound effects: Multimodal input helps infants find displaced objects.

Science.gov (United States)

Shinskey, Jeanne L

2017-09-01

Before 9 months, infants use sound to retrieve a stationary object hidden by darkness but not one hidden by occlusion, suggesting auditory input is more salient in the absence of visual input. This article addresses how audiovisual input affects 10-month-olds' search for displaced objects. In AB tasks, infants who previously retrieved an object at A subsequently fail to find it after it is displaced to B, especially following a delay between hiding and retrieval. Experiment 1 manipulated auditory input by keeping the hidden object audible versus silent, and visual input by presenting the delay in the light versus dark. Infants succeeded more at B with audible than silent objects and, unexpectedly, more after delays in the light than dark. Experiment 2 presented both the delay and search phases in darkness. The unexpected light-dark difference disappeared. Across experiments, the presence of auditory input helped infants find displaced objects, whereas the absence of visual input did not. Sound might help by strengthening object representation, reducing memory load, or focusing attention. This work provides new evidence on when bimodal input aids object processing, corroborates claims that audiovisual processing improves over the first year of life, and contributes to multisensory approaches to studying cognition. Statement of contribution What is already known on this subject Before 9 months, infants use sound to retrieve a stationary object hidden by darkness but not one hidden by occlusion. This suggests they find auditory input more salient in the absence of visual input in simple search tasks. After 9 months, infants' object processing appears more sensitive to multimodal (e.g., audiovisual) input. What does this study add? This study tested how audiovisual input affects 10-month-olds' search for an object displaced in an AB task. Sound helped infants find displaced objects in both the presence and absence of visual input. Object processing becomes more

7 CFR 3431.4 - Solicitation of stakeholder input.

Science.gov (United States)

2010-01-01

... 7 Agriculture 15 2010-01-01 2010-01-01 false Solicitation of stakeholder input. 3431.4 Section... Designation of Veterinarian Shortage Situations § 3431.4 Solicitation of stakeholder input. The Secretary will solicit stakeholder input on the process and procedures used to designate veterinarian shortage situations...

Linguistic labels, dynamic visual features, and attention in infant category learning.

Science.gov (United States)

Deng, Wei Sophia; Sloutsky, Vladimir M

2015-06-01

How do words affect categorization? According to some accounts, even early in development words are category markers and are different from other features. According to other accounts, early in development words are part of the input and are akin to other features. The current study addressed this issue by examining the role of words and dynamic visual features in category learning in 8- to 12-month-old infants. Infants were familiarized with exemplars from one category in a label-defined or motion-defined condition and then tested with prototypes from the studied category and from a novel contrast category. Eye-tracking results indicated that infants exhibited better category learning in the motion-defined condition than in the label-defined condition, and their attention was more distributed among different features when there was a dynamic visual feature compared with the label-defined condition. These results provide little evidence for the idea that linguistic labels are category markers that facilitate category learning. Copyright © 2015 Elsevier Inc. All rights reserved.

FIR signature verification system characterizing dynamics of handwriting features

Science.gov (United States)

Thumwarin, Pitak; Pernwong, Jitawat; Matsuura, Takenobu

2013-12-01

This paper proposes an online signature verification method based on the finite impulse response (FIR) system characterizing time-frequency characteristics of dynamic handwriting features. First, the barycenter determined from both the center point of signature and two adjacent pen-point positions in the signing process, instead of one pen-point position, is used to reduce the fluctuation of handwriting motion. In this paper, among the available dynamic handwriting features, motion pressure and area pressure are employed to investigate handwriting behavior. Thus, the stable dynamic handwriting features can be described by the relation of the time-frequency characteristics of the dynamic handwriting features. In this study, the aforesaid relation can be represented by the FIR system with the wavelet coefficients of the dynamic handwriting features as both input and output of the system. The impulse response of the FIR system is used as the individual feature for a particular signature. In short, the signature can be verified by evaluating the difference between the impulse responses of the FIR systems for a reference signature and the signature to be verified. The signature verification experiments in this paper were conducted using the SUBCORPUS MCYT-100 signature database consisting of 5,000 signatures from 100 signers. The proposed method yielded equal error rate (EER) of 3.21% on skilled forgeries.

Chinese character recognition based on Gabor feature extraction and CNN

Science.gov (United States)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Uncertainty of input data for room acoustic simulations

DEFF Research Database (Denmark)

Jeong, Cheol-Ho; Marbjerg, Gerd; Brunskog, Jonas

2016-01-01

Although many room acoustic simulation models have been well established, simulation results will never be accurate with inaccurate and uncertain input data. This study addresses inappropriateness and uncertainty of input data for room acoustic simulations. Firstly, the random incidence absorption...... and scattering coefficients are insufficient when simulating highly non-diffuse rooms. More detailed information, such as the phase and angle dependence, can greatly improve the simulation results of pressure-based geometrical and wave-based models at frequencies well below the Schroeder frequency. Phase...... summarizes potential advanced absorption measurement techniques that can improve the quality of input data for room acoustic simulations. Lastly, plenty of uncertain input data are copied from unreliable sources. Software developers and users should be careful when spreading such uncertain input data. More...

Combining fine texture and coarse color features for color texture classification

Science.gov (United States)

Wang, Junmin; Fan, Yangyu; Li, Ning

2017-11-01

Color texture classification plays an important role in computer vision applications because texture and color are two fundamental visual features. To classify the color texture via extracting discriminative color texture features in real time, we present an approach of combining the fine texture and coarse color features for color texture classification. First, the input image is transformed from RGB to HSV color space to separate texture and color information. Second, the scale-selective completed local binary count (CLBC) algorithm is introduced to extract the fine texture feature from the V component in HSV color space. Third, both H and S components are quantized at an optimal coarse level. Furthermore, the joint histogram of H and S components is calculated, which is considered as the coarse color feature. Finally, the fine texture and coarse color features are combined as the final descriptor and the nearest subspace classifier is used for classification. Experimental results on CUReT, KTH-TIPS, and New-BarkTex databases demonstrate that the proposed method achieves state-of-the-art classification performance. Moreover, the proposed method is fast enough for real-time applications.

Feature extraction with deep neural networks by a generalized discriminant analysis.

Science.gov (United States)

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.

Feature Selection and Kernel Learning for Local Learning-Based Clustering.

Science.gov (United States)

Zeng, Hong; Cheung, Yiu-ming

2011-08-01

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

A guidance on MELCOR input preparation : An input deck for Ul-Chin 3 and 4 Nuclear Power Plant

Energy Technology Data Exchange (ETDEWEB)

Cho, Song Won

1997-02-01

The objective of this study is to enhance the capability of assessing the severe accident sequence analyses and the containment behavior using MELCOR computer code and to provide the guideline of its efficient use. This report shows the method of the input deck preparation as well as the assessment strategy for the MELCOR code. MELCOR code is a fully integrated, engineering-level computer code that models the progression of severe accidents in light water reactor nuclear power plants. The code is being developed at Sandia National Laboratories for the U.S. NRC as a second generation plant risk assessment tool and the successor to the source term code package. The accident sequence of the reference input deck prepared in this study for Ulchin unit 3 and 4 nuclear power plants, is the total loss of feedwater (TLOFW) without any success of safety systems, which is similar to station blackout (TLMB). It is very useful to simulate a well-known sequence through the best estimated code or experiment, because the results of the simulation before core melt can be compared with the FSAR, but no data is available after core melt. The precalculation for the TLOFW using the reference input deck is performed successfully as expected. The other sequences will be carried out with minor changes in the reference input. This input deck will be improved continually by the adding of the safety systems not included in this input deck, and also through the sensitivity and uncertainty analyses. (author). 19 refs., 10 tabs., 55 figs.

A guidance on MELCOR input preparation : An input deck for Ul-Chin 3 and 4 Nuclear Power Plant

International Nuclear Information System (INIS)

Cho, Song Won.

1997-02-01

The objective of this study is to enhance the capability of assessing the severe accident sequence analyses and the containment behavior using MELCOR computer code and to provide the guideline of its efficient use. This report shows the method of the input deck preparation as well as the assessment strategy for the MELCOR code. MELCOR code is a fully integrated, engineering-level computer code that models the progression of severe accidents in light water reactor nuclear power plants. The code is being developed at Sandia National Laboratories for the U.S. NRC as a second generation plant risk assessment tool and the successor to the source term code package. The accident sequence of the reference input deck prepared in this study for Ulchin unit 3 and 4 nuclear power plants, is the total loss of feedwater (TLOFW) without any success of safety systems, which is similar to station blackout (TLMB). It is very useful to simulate a well-known sequence through the best estimated code or experiment, because the results of the simulation before core melt can be compared with the FSAR, but no data is available after core melt. The precalculation for the TLOFW using the reference input deck is performed successfully as expected. The other sequences will be carried out with minor changes in the reference input. This input deck will be improved continually by the adding of the safety systems not included in this input deck, and also through the sensitivity and uncertainty analyses. (author). 19 refs., 10 tabs., 55 figs

High-Voltage-Input Level Translator Using Standard CMOS

Science.gov (United States)

Yager, Jeremy A.; Mojarradi, Mohammad M.; Vo, Tuan A.; Blalock, Benjamin J.

2011-01-01

proposed integrated circuit would translate (1) a pair of input signals having a low differential potential and a possibly high common-mode potential into (2) a pair of output signals having the same low differential potential and a low common-mode potential. As used here, "low" and "high" refer to potentials that are, respectively, below or above the nominal supply potential (3.3 V) at which standard complementary metal oxide/semiconductor (CMOS) integrated circuits are designed to operate. The input common-mode potential could lie between 0 and 10 V; the output common-mode potential would be 2 V. This translation would make it possible to process the pair of signals by use of standard 3.3-V CMOS analog and/or mixed-signal (analog and digital) circuitry on the same integrated-circuit chip. A schematic of the circuit is shown in the figure. Standard 3.3-V CMOS circuitry cannot withstand input potentials greater than about 4 V. However, there are many applications that involve low-differential-potential, high-common-mode-potential input signal pairs and in which standard 3.3-V CMOS circuitry, which is relatively inexpensive, would be the most appropriate circuitry for performing other functions on the integrated-circuit chip that handles the high-potential input signals. Thus, there is a need to combine high-voltage input circuitry with standard low-voltage CMOS circuitry on the same integrated-circuit chip. The proposed circuit would satisfy this need. In the proposed circuit, the input signals would be coupled into both a level-shifting pair and a common-mode-sensing pair of CMOS transistors. The output of the level-shifting pair would be fed as input to a differential pair of transistors. The resulting differential current output would pass through six standoff transistors to be mirrored into an output branch by four heterojunction bipolar transistors. The mirrored differential current would be converted back to potential by a pair of diode-connected transistors

Impact of Reducing Polarimetric SAR Input on the Uncertainty of Crop Classifications Based on the Random Forests Algorithm

DEFF Research Database (Denmark)

Loosvelt, Lien; Peters, Jan; Skriver, Henning

2012-01-01

Although the use of multidate polarimetric synthetic aperture radar (SAR) data for highly accurate land cover classification has been acknowledged in the literature, the high dimensionality of the data set remains a major issue. This study presents two different strategies to reduce the number...... acquired by the Danish EMISAR on four dates within the period April to July in 1998. The predictive capacity of each feature is analyzed by the importance score generated by random forests (RF). Results show that according to the variation in importance score over time, a distinction can be made between...... general and specific features for crop classification. Based on the importance ranking, features are gradually removed from the single-date data sets in order to construct several multidate data sets with decreasing dimensionality. In the accuracy-oriented and efficiency-oriented reduction, the input...

FPGA implementation of a single-input fuzzy logic controller for boost converter with the absence of an external analog-to-digital converter

DEFF Research Database (Denmark)

Taeed, Fazel; Salam, Z.; Ayob, S.

2012-01-01

converter (ADC). Instead, a simple analog-to-digital conversion scheme is implemented using the FPGA itself. Due to the simplicity of the SIFLC algorithm and the absence of an external ADC, the overall implementation requires only 408 logic elements and five input-output pins of the FPGA.......) and applied on a 50-W boost converter. The SIFLC is compared to the proportional-integral controller; the simulation and practical results indicate that SIFLC exhibits excellent performance for step load and input reference changes. Another feature of this work is the absence of an external analog-to-digital...

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

Science.gov (United States)

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Reaction Decoder Tool (RDT): extracting features from chemical reactions.

Science.gov (United States)

Rahman, Syed Asad; Torrance, Gilliean; Baldacci, Lorenzo; Martínez Cuesta, Sergio; Fenninger, Franz; Gopal, Nimish; Choudhary, Saket; May, John W; Holliday, Gemma L; Steinbeck, Christoph; Thornton, Janet M

2016-07-01

Extracting chemical features like Atom-Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features. This software is implemented in Java, supported on Windows, Linux and Mac OSX, and freely available at https://github.com/asad/ReactionDecoder : asad@ebi.ac.uk or s9asad@gmail.com. © The Author 2016. Published by Oxford University Press.

RANCANG BANGUN APLIKASI PENGENALAN POLA SIDIK JARI

Directory of Open Access Journals (Sweden)

Ryan Wahyudi

2016-04-01

Full Text Available Biometrics is a method of recognition of an identity based on human physical characteristics such as the face, fingerprint, hand geometry, retina, and voice. Biometric identification that commonly used is the fingerprint recognition. Fingerprint identification process can be accelerated by reducing the number of fingerprint comparisons, splitting fingerprint databases into a number of classes based on pre-defined classes, such as fingerprint patterns. Fingerprint patterns are divided into five categories: Whorls, Right Loops, Left Loops, Arch, and Tented Arch. One of the pattern recognition techniques (fingerprint is using neural network. This research developed a RBF (Radial Basis Function neural network, which is known as SLFNs (Single Hidden Layer Feed-forward Neural Networks that reliable in pattern recognition. The use of ELM (Extreme Learning Machine algorithm on RBF network is an alternative to avoid long computation in the absence of adjustment weights during the training process so that the computing time relatively short. OLS (Orthogonal Least Square is used to optimize the weights and RBF network simplification. The preprocessing of fingerprint images are grayscalling, histogram equalization, and image sequences block operation. Feature extraction method that used based on the orientation of the dominant direction of the image. One fingerprint image is represented by a value of 256 dominant angle in radians unit. From the results indicate that the ELM-RBF and OLS system can recognize fingerprint patterns with 100% accuracy on the training process, and 60% accuracy in the testing process. Keywords: Fingerprint Pattern Recognition, Extreme Learning Machine, Radial Basis Function, Orthogonal Least Square Biometrik merupakan metode pengenalan identitas seseorang berdasarkan karakteristik fisik manusia misalnya wajah, sidik jari, struktur telapak tangan, letak retina mata, dan suara. Identifikasi biometrik yang umum digunakan saat ini

Repositioning Recitation Input in College English Teaching

Science.gov (United States)

Xu, Qing

2009-01-01

This paper tries to discuss how recitation input helps overcome the negative influences on the basis of second language acquisition theory and confirms the important role that recitation input plays in improving college students' oral and written English.

Input significance analysis: feature selection through synaptic ...

African Journals Online (AJOL)

Connection Weights (CW) and Garson's Algorithm (GA) and the classifier selected ... from the UCI Machine Learning Repository and executed in an online ... connectionist systems; evolving fuzzy neural network; connection weights; Garson's

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

Science.gov (United States)

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep

ProMC: Input-output data format for HEP applications using varint encoding

Science.gov (United States)

Chekanov, S. V.; May, E.; Strand, K.; Van Gemmeren, P.

2014-10-01

A new data format for Monte Carlo (MC) events, or any structural data, including experimental data, is discussed. The format is designed to store data in a compact binary form using variable-size integer encoding as implemented in the Google's Protocol Buffers package. This approach is implemented in the PROMC library which produces smaller file sizes for MC records compared to the existing input-output libraries used in high-energy physics (HEP). Other important features of the proposed format are a separation of abstract data layouts from concrete programming implementations, self-description and random access. Data stored in PROMC files can be written, read and manipulated in a number of programming languages, such C++, JAVA, FORTRAN and PYTHON.

Remote media vision-based computer input device

Science.gov (United States)

Arabnia, Hamid R.; Chen, Ching-Yi

1991-11-01

In this paper, we introduce a vision-based computer input device which has been built at the University of Georgia. The user of this system gives commands to the computer without touching any physical device. The system receives input through a CCD camera; it is PC- based and is built on top of the DOS operating system. The major components of the input device are: a monitor, an image capturing board, a CCD camera, and some software (developed by use). These are interfaced with a standard PC running under the DOS operating system.

Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

Science.gov (United States)

Liu, Ruian; Liu, Nan; Zeng, Beibei; Chen, Tingting; Yin, Ninghao

2018-03-01

Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.

Consumer input into research: the Australian Cancer Trials website.

Science.gov (United States)

Dear, Rachel F; Barratt, Alexandra L; Crossing, Sally; Butow, Phyllis N; Hanson, Susan; Tattersall, Martin Hn

2011-06-26

The Australian Cancer Trials website (ACTO) was publicly launched in 2010 to help people search for cancer clinical trials recruiting in Australia, provide information about clinical trials and assist with doctor-patient communication about trials. We describe consumer involvement in the design and development of ACTO and report our preliminary patient evaluation of the website. Consumers, led by Cancer Voices NSW, provided the impetus to develop the website. Consumer representative groups were consulted by the research team during the design and development of ACTO which combines a search engine, trial details, general information about trial participation and question prompt lists. Website use was analysed. A patient evaluation questionnaire was completed at one hospital, one week after exposure to the website. ACTO's main features and content reflect consumer input. In February 2011, it covered 1, 042 cancer trials. Since ACTO's public launch in November 2010, until the end of February 2011, the website has had 2, 549 new visits and generated 17, 833 page views. In a sub-study of 47 patient users, 89% found the website helpful for learning about clinical trials and all respondents thought patients should have access to ACTO. The development of ACTO is an example of consumers working with doctors, researchers and policy makers to improve the information available to people whose lives are affected by cancer and to help them participate in their treatment decisions, including consideration of clinical trial enrolment. Consumer input has ensured that the website is informative, targets consumer priorities and is user-friendly. ACTO serves as a model for other health conditions.

Sequence-based classification using discriminatory motif feature selection.

Directory of Open Access Journals (Sweden)

Hao Xiong

Full Text Available Most existing methods for sequence-based classification use exhaustive feature generation, employing, for example, all k-mer patterns. The motivation behind such (enumerative approaches is to minimize the potential for overlooking important features. However, there are shortcomings to this strategy. First, practical constraints limit the scope of exhaustive feature generation to patterns of length ≤ k, such that potentially important, longer (> k predictors are not considered. Second, features so generated exhibit strong dependencies, which can complicate understanding of derived classification rules. Third, and most importantly, numerous irrelevant features are created. These concerns can compromise prediction and interpretation. While remedies have been proposed, they tend to be problem-specific and not broadly applicable. Here, we develop a generally applicable methodology, and an attendant software pipeline, that is predicated on discriminatory motif finding. In addition to the traditional training and validation partitions, our framework entails a third level of data partitioning, a discovery partition. A discriminatory motif finder is used on sequences and associated class labels in the discovery partition to yield a (small set of features. These features are then used as inputs to a classifier in the training partition. Finally, performance assessment occurs on the validation partition. Important attributes of our approach are its modularity (any discriminatory motif finder and any classifier can be deployed and its universality (all data, including sequences that are unaligned and/or of unequal length, can be accommodated. We illustrate our approach on two nucleosome occupancy datasets and a protein solubility dataset, previously analyzed using enumerative feature generation. Our method achieves excellent performance results, with and without optimization of classifier tuning parameters. A Python pipeline implementing the approach is

Noninvasive measurement of renal blood flow by magnetic resonance imaging in rats.

Science.gov (United States)

Romero, Cesar A; Cabral, Glauber; Knight, Robert A; Ding, Guangliang; Peterson, Edward L; Carretero, Oscar A

2018-01-01

Renal blood flow (RBF) provides important information regarding renal physiology and nephropathies. Arterial spin labeling-magnetic resonance imaging (ASL-MRI) is a noninvasive method of measuring blood flow without exogenous contrast media. However, low signal-to-noise ratio and respiratory motion artifacts are challenges for RBF measurements in small animals. Our objective was to evaluate the feasibility and reproducibility of RBF measurements by ASL-MRI using respiratory-gating and navigator correction methods to reduce motion artifacts. ASL-MRI images were obtained from the kidneys of Sprague-Dawley (SD) rats on a 7-Tesla Varian MRI system with a spin-echo imaging sequence. After 4 days, the study was repeated to evaluate its reproducibility. RBF was also measured in animals under unilateral nephrectomy and in renal artery stenosis (RST) to evaluate the sensitivity in high and low RBF models, respectively. RBF was also evaluated in Dahl salt-sensitive (SS) rats and spontaneous hypertensive rats (SHR). In SD rats, the cortical RBFs (cRBF) were 305 ± 59 and 271.8 ± 39 ml·min -1 ·100 g tissue -1 in the right and left kidneys, respectively. Retest analysis revealed no differences ( P = 0.2). The test-retest reliability coefficient was 92 ± 5%. The cRBFs before and after the nephrectomy were 296.8 ± 30 and 428.2 ± 45 ml·min -1 ·100 g tissue -1 ( P = 0.02), respectively. The kidneys with RST exhibited a cRBF decrease compared with sham animals (86 ± 17.6 vs. 198 ± 33.7 ml·min -1 ·100 g tissue -1 ; P < 0.01). The cRBFs in SD, Dahl-SS, and SHR rats were not different ( P = 0.35). We conclude that ASL-MRI performed with navigator correction and respiratory gating is a feasible and reliable noninvasive method for measuring RBF in rats.

Wood Modification at High Temperature and Pressurized Steam: a Relational Model of Mechanical Properties Based on a Neural Network

Directory of Open Access Journals (Sweden)

Hong Yang

2015-07-01

Full Text Available Thermally modified wood has high dimensional stability and biological durability.But if the process parameters of thermal modification are not appropriate, then there will be a decline in the physical properties of wood.A neural network algorithm was employed in this study to establish the relationship between the process parameters of high-temperature and high-pressure thermal modification and the mechanical properties of the wood. Three important parameters: temperature, relative humidity, and treatment time, were considered as the inputs to the neural network. Back propagation (BP neural network and radial basis function (RBF neural network models for prediction were built and compared. The comparison showed that the RBF neural network model had advantages in network structure, convergence speed, and generalization capacity. On this basis, the inverse model, reflecting the relationship between the process parameters and the mechanical properties of wood, was established. Given the desired mechanical properties of the wood, the thermal modification process parameters could be inversely optimized and predicted. The results indicated that the model has good learning ability and generalization capacity. This is of great importance for the theoretical and applicational studies of the thermal modification of wood.

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

Science.gov (United States)

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Response sensitivity of barrel neuron subpopulations to simulated thalamic input.

Science.gov (United States)

Pesavento, Michael J; Rittenhouse, Cynthia D; Pinto, David J

2010-06-01

Our goal is to examine the relationship between neuron- and network-level processing in the context of a well-studied cortical function, the processing of thalamic input by whisker-barrel circuits in rodent neocortex. Here we focus on neuron-level processing and investigate the responses of excitatory and inhibitory barrel neurons to simulated thalamic inputs applied using the dynamic clamp method in brain slices. Simulated inputs are modeled after real thalamic inputs recorded in vivo in response to brief whisker deflections. Our results suggest that inhibitory neurons require more input to reach firing threshold, but then fire earlier, with less variability, and respond to a broader range of inputs than do excitatory neurons. Differences in the responses of barrel neuron subtypes depend on their intrinsic membrane properties. Neurons with a low input resistance require more input to reach threshold but then fire earlier than neurons with a higher input resistance, regardless of the neuron's classification. Our results also suggest that the response properties of excitatory versus inhibitory barrel neurons are consistent with the response sensitivities of the ensemble barrel network. The short response latency of inhibitory neurons may serve to suppress ensemble barrel responses to asynchronous thalamic input. Correspondingly, whereas neurons acting as part of the barrel circuit in vivo are highly selective for temporally correlated thalamic input, excitatory barrel neurons acting alone in vitro are less so. These data suggest that network-level processing of thalamic input in barrel cortex depends on neuron-level processing of the same input by excitatory and inhibitory barrel neurons.

Deep SOMs for automated feature extraction and classification from big data streaming

Science.gov (United States)

Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

2017-03-01

In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

Pathological brain detection based on wavelet entropy and Hu moment invariants.

Science.gov (United States)

Zhang, Yudong; Wang, Shuihua; Sun, Ping; Phillips, Preetha

2015-01-01

With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Feature-size dependent selective edge enhancement of x-ray images

International Nuclear Information System (INIS)

Herman, S.

1988-01-01

Morphological filters are nonlinear signal transformations that operate on a picture directly in the space domain. Such filters are based on the theory of mathematical morphology previously formulated. The filt4er being presented here features a ''mask'' operator (called a ''structuring element'' in some of the literature) which is a function of the two spatial coordinates x and y. The two basic mathematical operations are called ''masked erosion'' and ''masked dilation''. In the case of masked erosion the mask is passed over the input image in a raster pattern. At each position of the mask, the pixel values under the mask are multiplied by the mask pixel values. Then the output pixel value, located at the center position of the mask,is set equal to the minimum of the product of the mask and input values. Similarity, for masked dilation, the output pixel value is the maximum of the product of the input and the mask pixel values. The two basic processes of dilation and erosion can be used to construct the next level of operations the ''positive sieve'' (also called ''opening'') and the ''negative sieve'' (''closing''). The positive sieve modifies the peaks in the image whereas the negative sieve works on image valleys. The positive sieve is implemented by passing the output of the masked erosion step through the masked dilation function. The negative sieve reverses this procedure, using a dilation followed by an erosion. Each such sifting operator is characterized by a ''hole size''. It will be shown that the choice of hole size will select the range of pixel detail sizes which are to be enhanced. The shape of the mask will govern the shape of the enhancement. Finally positive sifting is used to enhance positive-going (peak) features, whereas negative enhances the negative-going (valley) landmarks

Inputs and spatial distribution patterns of Cr in Jiaozhou Bay

Science.gov (United States)

Yang, Dongfang; Miao, Zhenqing; Huang, Xinmin; Wei, Linzhen; Feng, Ming

2018-03-01

Cr pollution in marine bays has been one of the critical environmental issues, and understanding the input and spatial distribution patterns is essential to pollution control. In according to the source strengths of the major pollution sources, the input patterns of pollutants to marine bay include slight, moderate and heavy, and the spatial distribution are corresponding to three block models respectively. This paper analyzed input patterns and distributions of Cr in Jiaozhou Bay, eastern China based on investigation on Cr in surface waters during 1979-1983. Results showed that the input strengths of Cr in Jiaozhou Bay could be classified as moderate input and slight input, and the input strengths were 32.32-112.30 μg L-1 and 4.17-19.76 μg L-1, respectively. The input patterns of Cr included two patterns of moderate input and slight input, and the horizontal distributions could be defined by means of Block Model 2 and Block Model 3, respectively. In case of moderate input pattern via overland runoff, Cr contents were decreasing from the estuaries to the bay mouth, and the distribution pattern was parallel. In case of moderate input pattern via marine current, Cr contents were decreasing from the bay mouth to the bay, and the distribution pattern was parallel to circular. The Block Models were able to reveal the transferring process of various pollutants, and were helpful to understand the distributions of pollutants in marine bay.

Modal Parameter Identification from Responses of General Unknown Random Inputs

DEFF Research Database (Denmark)

Ibrahim, S. R.; Asmussen, J. C.; Brincker, Rune

1996-01-01

Modal parameter identification from ambient responses due to a general unknown random inputs is investigated. Existing identification techniques which are based on assumptions of white noise and or stationary random inputs are utilized even though the inputs conditions are not satisfied....... This is accomplished via adding. In cascade. A force cascade conversion to the structures system under consideration. The input to the force conversion system is white noise and the output of which is the actual force(s) applied to the structure. The white noise input(s) and the structures responses are then used...

Off-line learning from clustered input examples

NARCIS (Netherlands)

Marangi, Carmela; Solla, Sara A.; Biehl, Michael; Riegler, Peter; Marinaro, Maria; Tagliaferri, Roberto

1996-01-01

We analyze the generalization ability of a simple perceptron acting on a structured input distribution for the simple case of two clusters of input data and a linearly separable rule. The generalization ability computed for three learning scenarios: maximal stability, Gibbs, and optimal learning, is

Input reduction for long-term morphodynamic simulations

NARCIS (Netherlands)

Walstra, D.J.R.; Ruessink, G.; Hoekstra, R.; Tonnon, P.K.

2013-01-01

Input reduction is imperative to long-term (> years) morphodynamic simulations to avoid excessive computation times. Here, we discuss the input-reduction framework for wave-dominated coastal settings introduced by Walstra et al. (2013). The framework comprised 4 steps, viz. (1) the selection of the

Input Enhancement and L2 Question Formation.

Science.gov (United States)

White, Lydia; And Others

1991-01-01

Investigated the extent to which form-focused instruction and corrective feedback (i.e., "input enhancement"), provided within a primarily communicative program, contribute to learners' accuracy in question formation. Study results are interpreted as evidence that input enhancement can bring about genuine changes in learners' interlanguage…

Smart-Guard: Defending User Input from Malware

DEFF Research Database (Denmark)

Denzel, Michael; Bruni, Alessandro; Ryan, Mark

2016-01-01

Trusted input techniques can profoundly enhance a variety of scenarios like online banking, electronic voting, Virtual Private Networks, and even commands to a server or Industrial Control System. To protect the system from malware of the sender’s computer, input needs to be reliably authenticated...

Mathematical design of a novel input/instruction device using a moving acoustic emitter

Science.gov (United States)

Wang, Xianchao; Guo, Yukun; Li, Jingzhi; Liu, Hongyu

2017-10-01

This paper is concerned with the mathematical design of a novel input/instruction device using a moving emitter. The emitter acts as a point source and can be installed on a digital pen or worn on the finger of the human being who desires to interact/communicate with the computer. The input/instruction can be recognized by identifying the moving trajectory of the emitter performed by the human being from the collected wave field data. The identification process is modelled as an inverse source problem where one intends to identify the trajectory of a moving point source. There are several salient features of our study which distinguish our result from the existing ones in the literature. First, the point source is moving in an inhomogeneous background medium, which models the human body. Second, the dynamical wave field data are collected in a limited aperture. Third, the reconstruction method is independent of the background medium, and it is totally direct without any matrix inversion. Hence, it is efficient and robust with respect to the measurement noise. Both theoretical justifications and computational experiments are presented to verify our novel findings.

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

Directory of Open Access Journals (Sweden)

Jie-sheng Wang

2014-01-01

Full Text Available For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

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

Science.gov (United States)

Hong, Xia

2006-07-01

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

CREATING INPUT TABLES FROM WAPDEG FOR RIP

International Nuclear Information System (INIS)

K.G. Mon

1998-01-01

The purpose of this calculation is to create tables for input into RIP ver. 5.18 (Integrated Probabilistic Simulator for Environmental Systems) from WAPDEG ver. 3.06 (Waste Package Degradation) output. This calculation details the creation of the RIP input tables for TSPA-VA REV.00

Feature Interactions Enable Decoding of Sensorimotor Transformations for Goal-Directed Movement

Science.gov (United States)

Barany, Deborah A.; Della-Maggiore, Valeria; Viswanathan, Shivakumar; Cieslak, Matthew

2014-01-01

Neurophysiology and neuroimaging evidence shows that the brain represents multiple environmental and body-related features to compute transformations from sensory input to motor output. However, it is unclear how these features interact during goal-directed movement. To investigate this issue, we examined the representations of sensory and motor features of human hand movements within the left-hemisphere motor network. In a rapid event-related fMRI design, we measured cortical activity as participants performed right-handed movements at the wrist, with either of two postures and two amplitudes, to move a cursor to targets at different locations. Using a multivoxel analysis technique with rigorous generalization tests, we reliably distinguished representations of task-related features (primarily target location, movement direction, and posture) in multiple regions. In particular, we identified an interaction between target location and movement direction in the superior parietal lobule, which may underlie a transformation from the location of the target in space to a movement vector. In addition, we found an influence of posture on primary motor, premotor, and parietal regions. Together, these results reveal the complex interactions between different sensory and motor features that drive the computation of sensorimotor transformations. PMID:24828640

Using different classification models in wheat grading utilizing visual features

Science.gov (United States)

Basati, Zahra; Rasekh, Mansour; Abbaspour-Gilandeh, Yousef

2018-04-01

Wheat is one of the most important strategic crops in Iran and in the world. The major component that distinguishes wheat from other grains is the gluten section. In Iran, sunn pest is one of the most important factors influencing the characteristics of wheat gluten and in removing it from a balanced state. The existence of bug-damaged grains in wheat will reduce the quality and price of the product. In addition, damaged grains reduce the enrichment of wheat and the quality of bread products. In this study, after preprocessing and segmentation of images, 25 features including 9 colour features, 10 morphological features, and 6 textual statistical features were extracted so as to classify healthy and bug-damaged wheat grains of Azar cultivar of four levels of moisture content (9, 11.5, 14 and 16.5% w.b.) and two lighting colours (yellow light, the composition of yellow and white lights). Using feature selection methods in the WEKA software and the CfsSubsetEval evaluator, 11 features were chosen as inputs of artificial neural network, decision tree and discriment analysis classifiers. The results showed that the decision tree with the J.48 algorithm had the highest classification accuracy of 90.20%. This was followed by artificial neural network classifier with the topology of 11-19-2 and discrimient analysis classifier at 87.46 and 81.81%, respectively

Three-Class Mammogram Classification Based on Descriptive CNN Features

Directory of Open Access Journals (Sweden)

M. Mohsin Jadoon

2017-01-01

Full Text Available In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases. In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW and convolutional neural network-curvelet transform (CNN-CT. An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE. In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT, while in the second method discrete curvelet transform (DCT is used. In both methods, dense scale invariant feature (DSIFT for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN. Softmax layer and support vector machine (SVM layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

Multiscale registration of remote sensing image using robust SIFT features in Steerable-Domain

Directory of Open Access Journals (Sweden)

Xiangzeng Liu

2011-12-01

Full Text Available This paper proposes a multiscale registration technique using robust Scale Invariant Feature Transform (SIFT features in Steerable-Domain, which can deal with the large variations of scale, rotation and illumination between images. First, a new robust SIFT descriptor is presented, which is invariant under affine transformation. Then, an adaptive similarity measure is developed according to the robust SIFT descriptor and the adaptive normalized cross correlation of feature point’s neighborhood. Finally, the corresponding feature points can be determined by the adaptive similarity measure in Steerable-Domain of the two input images, and the final refined transformation parameters determined by using gradual optimization are adopted to achieve the registration results. Quantitative comparisons of our algorithm with the related methods show a significant improvement in the presence of large scale, rotation changes, and illumination contrast. The effectiveness of the proposed method is demonstrated by the experimental results.

Breast density pattern characterization by histogram features and texture descriptors

Directory of Open Access Journals (Sweden)

Pedro Cunha Carneiro

2017-04-01

Full Text Available Abstract Introduction Breast cancer is the first leading cause of death for women in Brazil as well as in most countries in the world. Due to the relation between the breast density and the risk of breast cancer, in medical practice, the breast density classification is merely visual and dependent on professional experience, making this task very subjective. The purpose of this paper is to investigate image features based on histograms and Haralick texture descriptors so as to separate mammographic images into categories of breast density using an Artificial Neural Network. Methods We used 307 mammographic images from the INbreast digital database, extracting histogram features and texture descriptors of all mammograms and selecting them with the K-means technique. Then, these groups of selected features were used as inputs of an Artificial Neural Network to classify the images automatically into the four categories reported by radiologists. Results An average accuracy of 92.9% was obtained in a few tests using only some of the Haralick texture descriptors. Also, the accuracy rate increased to 98.95% when texture descriptors were mixed with some features based on a histogram. Conclusion Texture descriptors have proven to be better than gray levels features at differentiating the breast densities in mammographic images. From this paper, it was possible to automate the feature selection and the classification with acceptable error rates since the extraction of the features is suitable to the characteristics of the images involving the problem.

INPUT-OUTPUT ANALYSIS : THE NEXT 25 YEARS

NARCIS (Netherlands)

Dietzenbacher, Erik; Lenzen, Manfred; Los, Bart; Guan, Dabo; Lahr, Michael L.; Sancho, Ferran; Suh, Sangwon; Yang, Cuihong; Sancho, S.

2013-01-01

This year marks the 25th anniversary of the International Input-Output Association and the 25th volume of Economic Systems Research. To celebrate this anniversary, a group of eight experts provide their views on the future of input-output. Looking forward, they foresee progress in terms of data

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

Science.gov (United States)

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Design of a Code-Maker Translator Assistive Input Device with a Contest Fuzzy Recognition Algorithm for the Severely Disabled

Directory of Open Access Journals (Sweden)

Chung-Min Wu

2015-01-01

Full Text Available This study developed an assistive system for the severe physical disabilities, named “code-maker translator assistive input device” which utilizes a contest fuzzy recognition algorithm and Morse codes encoding to provide the keyboard and mouse functions for users to access a standard personal computer, smartphone, and tablet PC. This assistive input device has seven features that are small size, easy installing, modular design, simple maintenance, functionality, very flexible input interface selection, and scalability of system functions, when this device combined with the computer applications software or APP programs. The users with severe physical disabilities can use this device to operate the various functions of computer, smartphone, and tablet PCs, such as sending e-mail, Internet browsing, playing games, and controlling home appliances. A patient with a brain artery malformation participated in this study. The analysis result showed that the subject could make himself familiar with operating of the long/short tone of Morse code in one month. In the future, we hope this system can help more people in need.

An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

Directory of Open Access Journals (Sweden)

Yudong Zhang

2013-01-01

Full Text Available Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM with RBF kernel, using particle swarm optimization (PSO to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.

Harmonize input selection for sediment transport prediction

Science.gov (United States)

Afan, Haitham Abdulmohsin; Keshtegar, Behrooz; Mohtar, Wan Hanna Melini Wan; El-Shafie, Ahmed

2017-09-01

In this paper, three modeling approaches using a Neural Network (NN), Response Surface Method (RSM) and response surface method basis Global Harmony Search (GHS) are applied to predict the daily time series suspended sediment load. Generally, the input variables for forecasting the suspended sediment load are manually selected based on the maximum correlations of input variables in the modeling approaches based on NN and RSM. The RSM is improved to select the input variables by using the errors terms of training data based on the GHS, namely as response surface method and global harmony search (RSM-GHS) modeling method. The second-order polynomial function with cross terms is applied to calibrate the time series suspended sediment load with three, four and five input variables in the proposed RSM-GHS. The linear, square and cross corrections of twenty input variables of antecedent values of suspended sediment load and water discharge are investigated to achieve the best predictions of the RSM based on the GHS method. The performances of the NN, RSM and proposed RSM-GHS including both accuracy and simplicity are compared through several comparative predicted and error statistics. The results illustrated that the proposed RSM-GHS is as uncomplicated as the RSM but performed better, where fewer errors and better correlation was observed (R = 0.95, MAE = 18.09 (ton/day), RMSE = 25.16 (ton/day)) compared to the ANN (R = 0.91, MAE = 20.17 (ton/day), RMSE = 33.09 (ton/day)) and RSM (R = 0.91, MAE = 20.06 (ton/day), RMSE = 31.92 (ton/day)) for all types of input variables.

Variance-based sensitivity indices for models with dependent inputs

International Nuclear Information System (INIS)

Mara, Thierry A.; Tarantola, Stefano

2012-01-01

Computational models are intensively used in engineering for risk analysis or prediction of future outcomes. Uncertainty and sensitivity analyses are of great help in these purposes. Although several methods exist to perform variance-based sensitivity analysis of model output with independent inputs only a few are proposed in the literature in the case of dependent inputs. This is explained by the fact that the theoretical framework for the independent case is set and a univocal set of variance-based sensitivity indices is defined. In the present work, we propose a set of variance-based sensitivity indices to perform sensitivity analysis of models with dependent inputs. These measures allow us to distinguish between the mutual dependent contribution and the independent contribution of an input to the model response variance. Their definition relies on a specific orthogonalisation of the inputs and ANOVA-representations of the model output. In the applications, we show the interest of the new sensitivity indices for model simplification setting. - Highlights: ► Uncertainty and sensitivity analyses are of great help in engineering. ► Several methods exist to perform variance-based sensitivity analysis of model output with independent inputs. ► We define a set of variance-based sensitivity indices for models with dependent inputs. ► Inputs mutual contributions are distinguished from their independent contributions. ► Analytical and computational tests are performed and discussed.

Automated input data management in manufacturing process simulation

OpenAIRE

Ettefaghian, Alireza

2015-01-01

Input Data Management (IDM) is a time consuming and costly process for Discrete Event Simulation (DES) projects. Input Data Management is considered as the basis of real-time process simulation (Bergmann, Stelzer and Strassburger, 2011). According to Bengtsson et al. (2009), data input phase constitutes on the average about 31% of the time of an entire simulation project. Moreover, the lack of interoperability between manufacturing applications and simulation software leads to a high cost to ...

Radioactive inputs to the North Sea and the Channel

International Nuclear Information System (INIS)

1984-01-01

The subject is covered in sections: introduction (radioactivity; radioisotopes; discharges from nuclear establishments); data sources (statutory requirements); sources of liquid radioactive waste (figure showing location of principal sources of radioactive discharges; tables listing principal discharges by activity and by nature of radioisotope); Central Electricity Generating Board nuclear power stations; research and industrial establishments; Ministy of Defence establishments; other UK inputs of radioactive waste; total inputs to the North Sea and the Channel (direct inputs; river inputs; adjacent sea areas); conclusions. (U.K.)

Improved features of MARS 1.4 and verification

Energy Technology Data Exchange (ETDEWEB)

Lee, Won Jae; Chung, Bub Don; Jeong, Jae Jun; Ha, Kwi Seok [Korea Atomic Energy Research Institute, Taejon (Korea)

1999-09-01

MARS 1.4 code has been developed as a basic code frame for multi-dimensional thermal-hydraulic analysis of light water reactor transients. This report describes the newly improved features of MARS 1.4 and their verification results. The new features of MARS 1.4 include the implementation of point kinetics model in the 3D module, the coupled heat structure model, the extension of control functions and input check functions in the 3D module, the implementation of new features of RELAP5/MOD3.2.2 -version, the addition of automatic initialization function for fuel 3-D analysis and the unification of material properties and forcing functions, etc. These features have been implemented in the code in order to extend the code modeling capability and to enhance the user friendliness. Among these features, this report describes the implementation of new features of RELAP5/MOD3.3.3-version such as reflood model and critical heat flux models, etc., the automatic initialization function, the unification of material properties and forcing functions and the other code improvements and error corrections, which were not reported in the previous report. Through the verification calculations, the new features of MARS 1.4 have been verified well implemented in the code. In conclusion, MARS 1.4 code has been developed and verified as implemented in the code. In conclusion, MARS 1.4 code has been developed and verified as a multi-dimensional system thermal-hydraulic analysis tool. And, it can play its role as a basic code frame for the future development of a multi-purpose consolidated code, MARS 2.x, for coupled analysis of multi-dimensional system thermal hydraulics, 3D core kinetics, core CHF and containment as well as for further improvement of thermal-hydraulic and numerical models. 4 refs., 10 figs. (Author)

Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters

Directory of Open Access Journals (Sweden)

Hongshan Zhao

2012-05-01

Full Text Available Short-term solar irradiance forecasting (STSIF is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV, and the Levenberg-Marquardt algorithm (LMA is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS, and the results indicated that the forecast accuracy is obviously improved under variable weather conditions.

Arterial Transit Time-corrected Renal Blood Flow Measurement with Pulsed Continuous Arterial Spin Labeling MR Imaging.

Science.gov (United States)

Shimizu, Kazuhiro; Kosaka, Nobuyuki; Fujiwara, Yasuhiro; Matsuda, Tsuyoshi; Yamamoto, Tatsuya; Tsuchida, Tatsuro; Tsuchiyama, Katsuki; Oyama, Nobuyuki; Kimura, Hirohiko

2017-01-10

The importance of arterial transit time (ATT) correction for arterial spin labeling MRI has been well debated in neuroimaging, but it has not been well evaluated in renal imaging. The purpose of this study was to evaluate the feasibility of pulsed continuous arterial spin labeling (pcASL) MRI with multiple post-labeling delay (PLD) acquisition for measuring ATT-corrected renal blood flow (ATC-RBF). A total of 14 volunteers were categorized into younger (n = 8; mean age, 27.0 years) and older groups (n = 6; 64.8 years). Images of pcASL were obtained at three different PLDs (0.5, 1.0, and 1.5 s), and ATC-RBF and ATT were calculated using a single-compartment model. To validate ATC-RBF, a comparative study of effective renal plasma flow (ERPF) measured by 99m Tc-MAG3 scintigraphy was performed. ATC-RBF was corrected by kidney volume (ATC-cRBF) for comparison with ERPF. The younger group showed significantly higher ATC-RBF (157.68 ± 38.37 mL/min/100 g) and shorter ATT (961.33 ± 260.87 ms) than the older group (117.42 ± 24.03 mL/min/100 g and 1227.94 ± 226.51 ms, respectively; P renal ASL-MRI as debated in brain imaging.

Fault Features Extraction and Identification based Rolling Bearing Fault Diagnosis

International Nuclear Information System (INIS)

Qin, B; Sun, G D; Zhang L Y; Wang J G; HU, J

2017-01-01

For the fault classification model based on extreme learning machine (ELM), the diagnosis accuracy and stability of rolling bearing is greatly influenced by a critical parameter, which is the number of nodes in hidden layer of ELM. An adaptive adjustment strategy is proposed based on vibrational mode decomposition, permutation entropy, and nuclear kernel extreme learning machine to determine the tunable parameter. First, the vibration signals are measured and then decomposed into different fault feature models based on variation mode decomposition. Then, fault feature of each model is formed to a high dimensional feature vector set based on permutation entropy. Second, the ELM output function is expressed by the inner product of Gauss kernel function to adaptively determine the number of hidden layer nodes. Finally, the high dimension feature vector set is used as the input to establish the kernel ELM rolling bearing fault classification model, and the classification and identification of different fault states of rolling bearings are carried out. In comparison with the fault classification methods based on support vector machine and ELM, the experimental results show that the proposed method has higher classification accuracy and better generalization ability. (paper)

Non-perturbative inputs for gluon distributions in the hadrons

International Nuclear Information System (INIS)

Ermolaev, B.I.; Troyan, S.I.

2017-01-01

Description of hadronic reactions at high energies is conventionally done in the framework of QCD factorization. All factorization convolutions comprise non-perturbative inputs mimicking non-perturbative contributions and perturbative evolution of those inputs. We construct inputs for the gluon-hadron scattering amplitudes in the forward kinematics and, using the optical theorem, convert them into inputs for gluon distributions in the hadrons, embracing the cases of polarized and unpolarized hadrons. In the first place, we formulate mathematical criteria which any model for the inputs should obey and then suggest a model satisfying those criteria. This model is based on a simple reasoning: after emitting an active parton off the hadron, the remaining set of spectators becomes unstable and therefore it can be described through factors of the resonance type, so we call it the resonance model. We use it to obtain non-perturbative inputs for gluon distributions in unpolarized and polarized hadrons for all available types of QCD factorization: basic, K_T-and collinear factorizations. (orig.)

SO2 policy and input substitution under spatial monopoly

International Nuclear Information System (INIS)

Gerking, Shelby; Hamilton, Stephen F.

2010-01-01

Following the U.S. Clean Air Act Amendments of 1990, electric utilities dramatically increased their utilization of low-sulfur coal from the Powder River Basin (PRB). Recent studies indicate that railroads hauling PRB coal exercise a substantial degree of market power and that relative price changes in the mining and transportation sectors were contributing factors to the observed pattern of input substitution. This paper asks the related question: To what extent does more stringent SO 2 policy stimulate input substitution from high-sulfur coal to low-sulfur coal when railroads hauling low-sulfur coal exercise spatial monopoly power? The question underpins the effectiveness of incentive-based environmental policies given the essential role of market performance in input, output, and abatement markets in determining the social cost of regulation. Our analysis indicates that environmental regulation leads to negligible input substitution effects when clean and dirty inputs are highly substitutable and the clean input market is mediated by a spatial monopolist. (author)

Non-perturbative inputs for gluon distributions in the hadrons

Energy Technology Data Exchange (ETDEWEB)

Ermolaev, B.I. [Ioffe Physico-Technical Institute, Saint Petersburg (Russian Federation); Troyan, S.I. [St. Petersburg Institute of Nuclear Physics, Gatchina (Russian Federation)

2017-03-15

Description of hadronic reactions at high energies is conventionally done in the framework of QCD factorization. All factorization convolutions comprise non-perturbative inputs mimicking non-perturbative contributions and perturbative evolution of those inputs. We construct inputs for the gluon-hadron scattering amplitudes in the forward kinematics and, using the optical theorem, convert them into inputs for gluon distributions in the hadrons, embracing the cases of polarized and unpolarized hadrons. In the first place, we formulate mathematical criteria which any model for the inputs should obey and then suggest a model satisfying those criteria. This model is based on a simple reasoning: after emitting an active parton off the hadron, the remaining set of spectators becomes unstable and therefore it can be described through factors of the resonance type, so we call it the resonance model. We use it to obtain non-perturbative inputs for gluon distributions in unpolarized and polarized hadrons for all available types of QCD factorization: basic, K{sub T}-and collinear factorizations. (orig.)

CBM first-level event selector input interface

Energy Technology Data Exchange (ETDEWEB)

Hutter, Dirk [Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt (Germany); Collaboration: CBM-Collaboration

2016-07-01

The CBM First-level Event Selector (FLES) is the central event selection system of the upcoming CBM experiment at FAIR. Designed as a high-performance computing cluster, its task is an online analysis of the physics data at a total data rate exceeding 1 TByte/s. To allow efficient event selection, the FLES performs timeslice building, which combines the data from all given input links to self-contained, overlapping processing intervals and distributes them to compute nodes. Partitioning the input data streams into specialized containers allows to perform this task very efficiently. The FLES Input Interface defines the linkage between FEE and FLES data transport framework. Utilizing a custom FPGA board, it receives data via optical links, prepares them for subsequent timeslice building, and transfers the data via DMA to the PC's memory. An accompanying HDL module implements the front-end logic interface and FLES link protocol in the front-end FPGAs. Prototypes of all Input Interface components have been implemented and integrated into the FLES framework. In contrast to earlier prototypes, which included components to work without a FPGA layer between FLES and FEE, the structure matches the foreseen final setup. This allows the implementation and evaluation of the final CBM read-out chain. An overview of the FLES Input Interface as well as studies on system integration and system start-up are presented.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems.

Science.gov (United States)

Cho, Ming-Yuan; Hoang, Thi Thom

2017-01-01

Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems

Directory of Open Access Journals (Sweden)

Ming-Yuan Cho

2017-01-01

Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.

Multi-Input Convolutional Neural Network for Flower Grading

Directory of Open Access Journals (Sweden)

Yu Sun

2017-01-01

Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

Condition monitoring of face milling tool using K-star algorithm and histogram features of vibration signal

Directory of Open Access Journals (Sweden)

C.K. Madhusudana

2016-09-01

Full Text Available This paper deals with the fault diagnosis of the face milling tool based on machine learning approach using histogram features and K-star algorithm technique. Vibration signals of the milling tool under healthy and different fault conditions are acquired during machining of steel alloy 42CrMo4. Histogram features are extracted from the acquired signals. The decision tree is used to select the salient features out of all the extracted features and these selected features are used as an input to the classifier. K-star algorithm is used as a classifier and the output of the model is utilised to study and classify the different conditions of the face milling tool. Based on the experimental results, K-star algorithm is provided a better classification accuracy in the range from 94% to 96% with histogram features and is acceptable for fault diagnosis.

Consumer input into research: the Australian Cancer Trials website

Directory of Open Access Journals (Sweden)

Butow Phyllis N

2011-06-01

Full Text Available Abstract Background The Australian Cancer Trials website (ACTO was publicly launched in 2010 to help people search for cancer clinical trials recruiting in Australia, provide information about clinical trials and assist with doctor-patient communication about trials. We describe consumer involvement in the design and development of ACTO and report our preliminary patient evaluation of the website. Methods Consumers, led by Cancer Voices NSW, provided the impetus to develop the website. Consumer representative groups were consulted by the research team during the design and development of ACTO which combines a search engine, trial details, general information about trial participation and question prompt lists. Website use was analysed. A patient evaluation questionnaire was completed at one hospital, one week after exposure to the website. Results ACTO's main features and content reflect consumer input. In February 2011, it covered 1, 042 cancer trials. Since ACTO's public launch in November 2010, until the end of February 2011, the website has had 2, 549 new visits and generated 17, 833 page views. In a sub-study of 47 patient users, 89% found the website helpful for learning about clinical trials and all respondents thought patients should have access to ACTO. Conclusions The development of ACTO is an example of consumers working with doctors, researchers and policy makers to improve the information available to people whose lives are affected by cancer and to help them participate in their treatment decisions, including consideration of clinical trial enrolment. Consumer input has ensured that the website is informative, targets consumer priorities and is user-friendly. ACTO serves as a model for other health conditions.

The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water.

Science.gov (United States)

Roshani, G H; Karami, A; Salehizadeh, A; Nazemi, E

2017-11-01

The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the 152 Eu and 137 Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Input Shaping to Reduce Solar Array Structural Vibrations

Science.gov (United States)

Doherty, Michael J.; Tolson, Robert J.

1998-01-01

Structural vibrations induced by actuators can be minimized using input shaping. Input shaping is a feedforward method in which actuator commands are convolved with shaping functions to yield a shaped set of commands. These commands are designed to perform the maneuver while minimizing the residual structural vibration. In this report, input shaping is extended to stepper motor actuators. As a demonstration, an input-shaping technique based on pole-zero cancellation was used to modify the Solar Array Drive Assembly (SADA) actuator commands for the Lewis satellite. A series of impulses were calculated as the ideal SADA output for vibration control. These impulses were then discretized for use by the SADA stepper motor actuator and simulated actuator outputs were used to calculate the structural response. The effectiveness of input shaping is limited by the accuracy of the knowledge of the modal frequencies. Assuming perfect knowledge resulted in significant vibration reduction. Errors of 10% in the modal frequencies caused notably higher levels of vibration. Controller robustness was improved by incorporating additional zeros in the shaping function. The additional zeros did not require increased performance from the actuator. Despite the identification errors, the resulting feedforward controller reduced residual vibrations to the level of the exactly modeled input shaper and well below the baseline cases. These results could be easily applied to many other vibration-sensitive applications involving stepper motor actuators.

Plasticity of the cis-regulatory input function of a gene.

Directory of Open Access Journals (Sweden)

Avraham E Mayo

2006-04-01

Full Text Available The transcription rate of a gene is often controlled by several regulators that bind specific sites in the gene's cis-regulatory region. The combined effect of these regulators is described by a cis-regulatory input function. What determines the form of an input function, and how variable is it with respect to mutations? To address this, we employ the well-characterized lac operon of Escherichia coli, which has an elaborate input function, intermediate between Boolean AND-gate and OR-gate logic. We mapped in detail the input function of 12 variants of the lac promoter, each with different point mutations in the regulator binding sites, by means of accurate expression measurements from living cells. We find that even a few mutations can significantly change the input function, resulting in functions that resemble Pure AND gates, OR gates, or single-input switches. Other types of gates were not found. The variant input functions can be described in a unified manner by a mathematical model. The model also lets us predict which functions cannot be reached by point mutations. The input function that we studied thus appears to be plastic, in the sense that many of the mutations do not ruin the regulation completely but rather result in new ways to integrate the inputs.

Computer Generated Inputs for NMIS Processor Verification

International Nuclear Information System (INIS)

J. A. Mullens; J. E. Breeding; J. A. McEvers; R. W. Wysor; L. G. Chiang; J. R. Lenarduzzi; J. T. Mihalczo; J. K. Mattingly

2001-01-01

Proper operation of the Nuclear Identification Materials System (NMIS) processor can be verified using computer-generated inputs [BIST (Built-In-Self-Test)] at the digital inputs. Preselected sequences of input pulses to all channels with known correlation functions are compared to the output of the processor. These types of verifications have been utilized in NMIS type correlation processors at the Oak Ridge National Laboratory since 1984. The use of this test confirmed a malfunction in a NMIS processor at the All-Russian Scientific Research Institute of Experimental Physics (VNIIEF) in 1998. The NMIS processor boards were returned to the U.S. for repair and subsequently used in NMIS passive and active measurements with Pu at VNIIEF in 1999

Online feature selection with streaming features.

Science.gov (United States)

Wu, Xindong; Yu, Kui; Ding, Wei; Wang, Hao; Zhu, Xingquan

2013-05-01

We propose a new online feature selection framework for applications with streaming features where the knowledge of the full feature space is unknown in advance. We define streaming features as features that flow in one by one over time whereas the number of training examples remains fixed. This is in contrast with traditional online learning methods that only deal with sequentially added observations, with little attention being paid to streaming features. The critical challenges for Online Streaming Feature Selection (OSFS) include 1) the continuous growth of feature volumes over time, 2) a large feature space, possibly of unknown or infinite size, and 3) the unavailability of the entire feature set before learning starts. In the paper, we present a novel Online Streaming Feature Selection method to select strongly relevant and nonredundant features on the fly. An efficient Fast-OSFS algorithm is proposed to improve feature selection performance. The proposed algorithms are evaluated extensively on high-dimensional datasets and also with a real-world case study on impact crater detection. Experimental results demonstrate that the algorithms achieve better compactness and higher prediction accuracy than existing streaming feature selection algorithms.

Fault feature extraction method based on local mean decomposition Shannon entropy and improved kernel principal component analysis model

Directory of Open Access Journals (Sweden)

Jinlu Sheng

2016-07-01

Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.

Optical implementation of a feature-based neural network with application to automatic target recognition

Science.gov (United States)

Chao, Tien-Hsin; Stoner, William W.

1993-01-01

An optical neural network based on the neocognitron paradigm is introduced. A novel aspect of the architecture design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by feeding back the ouput of the feature correlator interatively to the input spatial light modulator and by updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intraclass fault tolerance and interclass discrimination is achieved. A detailed system description is provided. Experimental demonstrations of a two-layer neural network for space-object discrimination is also presented.

Modeling inputs to computer models used in risk assessment

International Nuclear Information System (INIS)

Iman, R.L.

1987-01-01

Computer models for various risk assessment applications are closely scrutinized both from the standpoint of questioning the correctness of the underlying mathematical model with respect to the process it is attempting to model and from the standpoint of verifying that the computer model correctly implements the underlying mathematical model. A process that receives less scrutiny, but is nonetheless of equal importance, concerns the individual and joint modeling of the inputs. This modeling effort clearly has a great impact on the credibility of results. Model characteristics are reviewed in this paper that have a direct bearing on the model input process and reasons are given for using probabilities-based modeling with the inputs. The authors also present ways to model distributions for individual inputs and multivariate input structures when dependence and other constraints may be present

KENO V.a Primer: A Primer for Criticality Calculations with SCALE/KENO V.a Using CSPAN for Input

International Nuclear Information System (INIS)

Busch, R.D.

2003-01-01

The SCALE (Standardized Computer Analyses for Licensing Evaluation) computer software system developed at Oak Ridge National Laboratory (ORNL) is widely used and accepted around the world for criticality safety analyses. The well-known KENO V.a three-dimensional Monte Carlo criticality computer code is the primary criticality safety analysis tool in SCALE. The KENO V.a primer is designed to help a new user understand and use the SCALE/KENO V.a Monte Carlo code for nuclear criticality safety analyses. It assumes that the user has a college education in a technical field. There is no assumption of familiarity with Monte Carlo codes in general or with SCALE/KENO V.a in particular. The primer is designed to teach by example, with each example illustrating two or three features of SCALE/KENO V.a that are useful in criticality analyses. The primer is based on SCALE 4.4a, which includes the Criticality Safety Processor for Analysis (CSPAN) input processor for Windows personal computers (PCs). A second edition of the primer, which uses the new KENO Visual Editor, is currently under development at ORNL and is planned for publication in late 2003. Each example in this first edition of the primer uses CSPAN to provide the framework for data input. Starting with a Quickstart section, the primer gives an overview of the basic requirements for SCALE/KENO V.a input and allows the user to quickly run a simple criticality problem with SCALE/KENO V.a. The sections that follow Quickstart include a list of basic objectives at the beginning that identifies the goal of the section and the individual SCALE/KENO V.a features which are covered in detail in the example problems in that section. Upon completion of the primer, a new user should be comfortable using CSPAN to set up criticality problems in SCALE/KENO V.a

Understanding Legacy Features with Featureous

DEFF Research Database (Denmark)

Olszak, Andrzej; Jørgensen, Bo Nørregaard

2011-01-01

Java programs called Featureous that addresses this issue. Featureous allows a programmer to easily establish feature-code traceability links and to analyze their characteristics using a number of visualizations. Featureous is an extension to the NetBeans IDE, and can itself be extended by third...

Remote input/output station

CERN Multimedia

1972-01-01

A general view of the remote input/output station installed in building 112 (ISR) and used for submitting jobs to the CDC 6500 and 6600. The card reader on the left and the line printer on the right are operated by programmers on a self-service basis.

Subject-based feature extraction by using fisher WPD-CSP in brain-computer interfaces.

Science.gov (United States)

Yang, Banghua; Li, Huarong; Wang, Qian; Zhang, Yunyuan

2016-06-01

Feature extraction of electroencephalogram (EEG) plays a vital role in brain-computer interfaces (BCIs). In recent years, common spatial pattern (CSP) has been proven to be an effective feature extraction method. However, the traditional CSP has disadvantages of requiring a lot of input channels and the lack of frequency information. In order to remedy the defects of CSP, wavelet packet decomposition (WPD) and CSP are combined to extract effective features. But WPD-CSP method considers less about extracting specific features that are fitted for the specific subject. So a subject-based feature extraction method using fisher WPD-CSP is proposed in this paper. The idea of proposed method is to adapt fisher WPD-CSP to each subject separately. It mainly includes the following six steps: (1) original EEG signals from all channels are decomposed into a series of sub-bands using WPD; (2) average power values of obtained sub-bands are computed; (3) the specified sub-bands with larger values of fisher distance according to average power are selected for that particular subject; (4) each selected sub-band is reconstructed to be regarded as a new EEG channel; (5) all new EEG channels are used as input of the CSP and a six-dimensional feature vector is obtained by the CSP. The subject-based feature extraction model is so formed; (6) the probabilistic neural network (PNN) is used as the classifier and the classification accuracy is obtained. Data from six subjects are processed by the subject-based fisher WPD-CSP, the non-subject-based fisher WPD-CSP and WPD-CSP, respectively. Compared with non-subject-based fisher WPD-CSP and WPD-CSP, the results show that the proposed method yields better performance (sensitivity: 88.7±0.9%, and specificity: 91±1%) and the classification accuracy from subject-based fisher WPD-CSP is increased by 6-12% and 14%, respectively. The proposed subject-based fisher WPD-CSP method can not only remedy disadvantages of CSP by WPD but also discriminate

Effect of heat input on the microstructure, residual stresses and corrosion resistance of 304L austenitic stainless steel weldments

Energy Technology Data Exchange (ETDEWEB)

Unnikrishnan, Rahul, E-mail: rahulunnikrishnannair@gmail.com [Department of Metallurgical and Materials Engineering, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, Maharashtra (India); Idury, K.S.N. Satish, E-mail: satishidury@gmail.com [Department of Metallurgical and Materials Engineering, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, Maharashtra (India); Ismail, T.P., E-mail: tpisma@gmail.com [Department of Metallurgical and Materials Engineering, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, Maharashtra (India); Bhadauria, Alok, E-mail: alokbhadauria1@gmail.com [Department of Metallurgical and Materials Engineering, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, Maharashtra (India); Shekhawat, S.K., E-mail: satishshekhawat@gmail.com [Department of Metallurgical Engineering and Materials Science, Indian Institute of Technology Bombay (IITB), Powai, Mumbai 400076, Maharashtra (India); Khatirkar, Rajesh K., E-mail: rajesh.khatirkar@gmail.com [Department of Metallurgical and Materials Engineering, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, Maharashtra (India); Sapate, Sanjay G., E-mail: sgsapate@yahoo.com [Department of Metallurgical and Materials Engineering, Visvesvaraya National Institute of Technology (VNIT), South Ambazari Road, Nagpur 440010, Maharashtra (India)

2014-07-01

Austenitic stainless steels are widely used in high performance pressure vessels, nuclear, chemical, process and medical industry due to their very good corrosion resistance and superior mechanical properties. However, austenitic stainless steels are prone to sensitization when subjected to higher temperatures (673 K to 1173 K) during the manufacturing process (e.g. welding) and/or certain applications (e.g. pressure vessels). During sensitization, chromium in the matrix precipitates out as carbides and intermetallic compounds (sigma, chi and Laves phases) decreasing the corrosion resistance and mechanical properties. In the present investigation, 304L austenitic stainless steel was subjected to different heat inputs by shielded metal arc welding process using a standard 308L electrode. The microstructural developments were characterized by using optical microscopy and electron backscattered diffraction, while the residual stresses were measured by X-ray diffraction using the sin{sup 2}ψ method. It was observed that even at the highest heat input, shielded metal arc welding process does not result in significant precipitation of carbides or intermetallic phases. The ferrite content and grain size increased with increase in heat input. The grain size variation in the fusion zone/heat affected zone was not effectively captured by optical microscopy. This study shows that electron backscattered diffraction is necessary to bring out changes in the grain size quantitatively in the fusion zone/heat affected zone as it can consider twin boundaries as a part of grain in the calculation of grain size. The residual stresses were compressive in nature for the lowest heat input, while they were tensile at the highest heat input near the weld bead. The significant feature of the welded region and the base metal was the presence of a very strong texture. The texture in the heat affected zone was almost random. - Highlights: • Effect of heat input on microstructure, residual

Towards a Highly Efficient Meshfree Simulation of Non-Newtonian Free Surface Ice Flow: Application to the Haut Glacier d'Arolla

Science.gov (United States)

Shcherbakov, V.; Ahlkrona, J.

2016-12-01

In this work we develop a highly efficient meshfree approach to ice sheet modeling. Traditionally mesh based methods such as finite element methods are employed to simulate glacier and ice sheet dynamics. These methods are mature and well developed. However, despite of numerous advantages these methods suffer from some drawbacks such as necessity to remesh the computational domain every time it changes its shape, which significantly complicates the implementation on moving domains, or a costly assembly procedure for nonlinear problems. We introduce a novel meshfree approach that frees us from all these issues. The approach is built upon a radial basis function (RBF) method that, thanks to its meshfree nature, allows for an efficient handling of moving margins and free ice surface. RBF methods are also accurate and easy to implement. Since the formulation is stated in strong form it allows for a substantial reduction of the computational cost associated with the linear system assembly inside the nonlinear solver. We implement a global RBF method that defines an approximation on the entire computational domain. This method exhibits high accuracy properties. However, it suffers from a disadvantage that the coefficient matrix is dense, and therefore the computational efficiency decreases. In order to overcome this issue we also implement a localized RBF method that rests upon a partition of unity approach to subdivide the domain into several smaller subdomains. The radial basis function partition of unity method (RBF-PUM) inherits high approximation characteristics form the global RBF method while resulting in a sparse system of equations, which essentially increases the computational efficiency. To demonstrate the usefulness of the RBF methods we model the velocity field of ice flow in the Haut Glacier d'Arolla. We assume that the flow is governed by the nonlinear Blatter-Pattyn equations. We test the methods for different basal conditions and for a free moving

Comparison of Linear Microinstability Calculations of Varying Input Realism

International Nuclear Information System (INIS)

Rewoldt, G.

2003-01-01

The effect of varying ''input realism'' or varying completeness of the input data for linear microinstability calculations, in particular on the critical value of the ion temperature gradient for the ion temperature gradient mode, is investigated using gyrokinetic and gyrofluid approaches. The calculations show that varying input realism can have a substantial quantitative effect on the results

Comparison of linear microinstability calculations of varying input realism

International Nuclear Information System (INIS)

Rewoldt, G.; Kinsey, J.E.

2004-01-01

The effect of varying 'input realism' or varying completeness of the input data for linear microinstability calculations, in particular on the critical value of the ion temperature gradient for the ion temperature gradient mode, is investigated using gyrokinetic and gyrofluid approaches. The calculations show that varying input realism can have a substantial quantitative effect on the results

Feature extraction from high resolution satellite imagery as an input to the development and rapid update of a METRANS geographic information system (GIS).

Science.gov (United States)

2011-06-01

This report describes an accuracy assessment of extracted features derived from three : subsets of Quickbird pan-sharpened high resolution satellite image for the area of the : Port of Los Angeles, CA. Visual Learning Systems Feature Analyst and D...

Echolalic responses by a child with autism to four experimental conditions of sociolinguistic input.

Science.gov (United States)

Violette, J; Swisher, L

1992-02-01

Studies of the immediate verbal imitations (IVIs) of subjects with echolalia report that features of linguistic or social input alone affect the number of IVIs elicited. This experimental study of a child with echolalia and autism controlled each of these variables while introducing a systematic change in the other. The subject produced more (p less than .05) IVIs in response to unknown lexical words presented with a high degree of directiveness (Condition D) than in response to three other conditions of stimulus presentation (e.g., unknown lexical words, minimally directive style.) Thus, an interaction between the effects of linguistic and social input was demonstrated. IVIs were produced across all conditions, primarily during first presentations of lexical stimuli. Only the IVIs elicited by first presentations of the lexical stimuli during Condition D differed significantly (p less than .05) from the number of IVIs elicited by first presentations of lexical stimuli in other conditions. These findings viewed together suggest that the occurrence of IVIs was related, at least for this child, to an uncertain or informative event and that this response was significantly greater when the lexical stimuli were unknown and presented in a highly directive style.

Smart mobility solution with multiple input Output interface.

Science.gov (United States)

Sethi, Aartika; Deb, Sujay; Ranjan, Prabhat; Sardar, Arghya

2017-07-01

Smart wheelchairs are commonly used to provide solution for mobility impairment. However their usage is limited primarily due to high cost owing from sensors required for giving input, lack of adaptability for different categories of input and limited functionality. In this paper we propose a smart mobility solution using smartphone with inbuilt sensors (accelerometer, camera and speaker) as an input interface. An Emotiv EPOC+ is also used for motor imagery based input control synced with facial expressions in cases of extreme disability. Apart from traction, additional functions like home security and automation are provided using Internet of Things (IoT) and web interfaces. Although preliminary, our results suggest that this system can be used as an integrated and efficient solution for people suffering from mobility impairment. The results also indicate a decent accuracy is obtained for the overall system.

Contour interpolated radial basis functions with spline boundary correction for fast 3D reconstruction of the human articular cartilage from MR images

Energy Technology Data Exchange (ETDEWEB)

Javaid, Zarrar; Unsworth, Charles P., E-mail: c.unsworth@auckland.ac.nz [Department of Engineering Science, The University of Auckland, Auckland 1010 (New Zealand); Boocock, Mark G.; McNair, Peter J. [Health and Rehabilitation Research Center, Auckland University of Technology, Auckland 1142 (New Zealand)

2016-03-15

Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhances volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume

Contour interpolated radial basis functions with spline boundary correction for fast 3D reconstruction of the human articular cartilage from MR images

International Nuclear Information System (INIS)

Javaid, Zarrar; Unsworth, Charles P.; Boocock, Mark G.; McNair, Peter J.

2016-01-01

Purpose: The aim of this work is to demonstrate a new image processing technique that can provide a “near real-time” 3D reconstruction of the articular cartilage of the human knee from MR images which is user friendly. This would serve as a point-of-care 3D visualization tool which would benefit a consultant radiologist in the visualization of the human articular cartilage. Methods: The authors introduce a novel fusion of an adaptation of the contour method known as “contour interpolation (CI)” with radial basis functions (RBFs) which they describe as “CI-RBFs.” The authors also present a spline boundary correction which further enhances volume estimation of the method. A subject cohort consisting of 17 right nonpathological knees (ten female and seven male) is assessed to validate the quality of the proposed method. The authors demonstrate how the CI-RBF method dramatically reduces the number of data points required for fitting an implicit surface to the entire cartilage, thus, significantly improving the speed of reconstruction over the comparable RBF reconstruction method of Carr. The authors compare the CI-RBF method volume estimation to a typical commercial package (3D DOCTOR), Carr’s RBF method, and a benchmark manual method for the reconstruction of the femoral, tibial, and patellar cartilages. Results: The authors demonstrate how the CI-RBF method significantly reduces the number of data points (p-value < 0.0001) required for fitting an implicit surface to the cartilage, by 48%, 31%, and 44% for the patellar, tibial, and femoral cartilages, respectively. Thus, significantly improving the speed of reconstruction (p-value < 0.0001) by 39%, 40%, and 44% for the patellar, tibial, and femoral cartilages over the comparable RBF model of Carr providing a near real-time reconstruction of 6.49, 8.88, and 9.43 min for the patellar, tibial, and femoral cartilages, respectively. In addition, it is demonstrated how the CI-RBF method matches the volume

Input data required for specific performance assessment codes

International Nuclear Information System (INIS)

Seitz, R.R.; Garcia, R.S.; Starmer, R.J.; Dicke, C.A.; Leonard, P.R.; Maheras, S.J.; Rood, A.S.; Smith, R.W.

1992-02-01

The Department of Energy's National Low-Level Waste Management Program at the Idaho National Engineering Laboratory generated this report on input data requirements for computer codes to assist States and compacts in their performance assessments. This report gives generators, developers, operators, and users some guidelines on what input data is required to satisfy 22 common performance assessment codes. Each of the codes is summarized and a matrix table is provided to allow comparison of the various input required by the codes. This report does not determine or recommend which codes are preferable

Structured perceptual input imposes an egocentric frame of reference-pointing, imagery, and spatial self-consciousness.

Science.gov (United States)

Marcel, Anthony; Dobel, Christian

2005-01-01

Perceptual input imposes and maintains an egocentric frame of reference, which enables orientation. When blindfolded, people tended to mistake the assumed intrinsic axes of symmetry of their immediate environment (a room) for their own egocentric relation to features of the room. When asked to point to the door and window, known to be at mid-points of facing (or adjacent) walls, they pointed with their arms at 180 degrees (or 90 degrees) angles, irrespective of where they thought they were in the room. People did the same when requested to imagine the situation. They justified their responses (inappropriately) by logical necessity or a structural description of the room rather than (appropriately) by relative location of themselves and the reference points. In eight experiments, we explored the effect on this in perception and imagery of: perceptual input (without perceptibility of the target reference points); imaging oneself versus another person; aids to explicit spatial self-consciousness; order of questions about self-location; and the relation of targets to the axes of symmetry of the room. The results indicate that, if one is deprived of structured perceptual input, as well as losing one's bearings, (a) one is likely to lose one's egocentric frame of reference itself, and (b) instead of pointing to reference points, one demonstrates their structural relation by adopting the intrinsic axes of the environment as one's own. This is prevented by providing noninformative perceptual input or by inducing subjects to imagine themselves from the outside, which makes explicit the fact of their being located relative to the world. The role of perceptual contact with a structured world is discussed in relation to sensory deprivation and imagery, appeal is made to Gibson's theory of joint egoreception and exteroception, and the data are related to recent theories of spatial memory and navigation.

Synaptic inputs compete during rapid formation of the calyx of Held: a new model system for neural development.

Science.gov (United States)

Holcomb, Paul S; Hoffpauir, Brian K; Hoyson, Mitchell C; Jackson, Dakota R; Deerinck, Thomas J; Marrs, Glenn S; Dehoff, Marlin; Wu, Jonathan; Ellisman, Mark H; Spirou, George A

2013-08-07

Hallmark features of neural circuit development include early exuberant innervation followed by competition and pruning to mature innervation topography. Several neural systems, including the neuromuscular junction and climbing fiber innervation of Purkinje cells, are models to study neural development in part because they establish a recognizable endpoint of monoinnervation of their targets and because the presynaptic terminals are large and easily monitored. We demonstrate here that calyx of Held (CH) innervation of its target, which forms a key element of auditory brainstem binaural circuitry, exhibits all of these characteristics. To investigate CH development, we made the first application of serial block-face scanning electron microscopy to neural development with fine temporal resolution and thereby accomplished the first time series for 3D ultrastructural analysis of neural circuit formation. This approach revealed a growth spurt of added apposed surface area (ASA)>200 μm2/d centered on a single age at postnatal day 3 in mice and an initial rapid phase of growth and competition that resolved to monoinnervation in two-thirds of cells within 3 d. This rapid growth occurred in parallel with an increase in action potential threshold, which may mediate selection of the strongest input as the winning competitor. ASAs of competing inputs were segregated on the cell body surface. These data suggest mechanisms to select "winning" inputs by regional reinforcement of postsynaptic membrane to mediate size and strength of competing synaptic inputs.

Distributed Input and State Estimation Using Local Information in Heterogeneous Sensor Networks

Directory of Open Access Journals (Sweden)

Dzung Tran

2017-07-01

Full Text Available A new distributed input and state estimation architecture is introduced and analyzed for heterogeneous sensor networks. Specifically, nodes of a given sensor network are allowed to have heterogeneous information roles in the sense that a subset of nodes can be active (that is, subject to observations of a process of interest and the rest can be passive (that is, subject to no observation. Both fixed and varying active and passive roles of sensor nodes in the network are investigated. In addition, these nodes are allowed to have non-identical sensor modalities under the common underlying assumption that they have complimentary properties distributed over the sensor network to achieve collective observability. The key feature of our framework is that it utilizes local information not only during the execution of the proposed distributed input and state estimation architecture but also in its design in that global uniform ultimate boundedness of error dynamics is guaranteed once each node satisfies given local stability conditions independent from the graph topology and neighboring information of these nodes. As a special case (e.g., when all nodes are active and a positive real condition is satisfied, the asymptotic stability can be achieved with our algorithm. Several illustrative numerical examples are further provided to demonstrate the efficacy of the proposed architecture.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Science.gov (United States)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

Science.gov (United States)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

The CBM first-level event selector input interface

Energy Technology Data Exchange (ETDEWEB)

Hutter, Dirk; Lindenstruth, Volker [Frankfurt Institute for Advanced Studies, Goethe University, Frankfurt (Germany); Collaboration: CBM-Collaboration

2015-07-01

The CBM First-level Event Selector (FLES) is the central event selection system of the upcoming CBM experiment at FAIR. Designed as a high-performance computing cluster, its task is an online analysis of the physics data at a total data rate exceeding 1 TByte/s. To allow efficient event selection, the FLES has to combine the data from all given input links to self-contained, overlapping processing intervals and distribute them to compute nodes. This task can be performed efficiently by partitioning the detector data streams into specialized containers. The FLES Interface Board (FLIB), implemented as a custom FPGA board, receives these containers via optical links, prepares them for subsequent interval building, and transfers the data via DMA to the PC's memory. A prototype of the FLIB has been implemented. The inclusion of features foreseen for other parts of the CBM read-out chain allows the evaluation of the interval building concept. Performance studies demonstrated high read-out bandwidth with low overhead. In addition, the FLIB has been used successfully as a readout device in test-beams and lab setups. An overview of the FLES Interface Board as well as results from latest studies is presented.

On the Role of L1 Markedness and L2 Input Robustness in Determining Potentially Fossilizable Language Forms in Iranian EFL Learners' Writing

Science.gov (United States)

Nushi, Musa

2016-01-01

Han's (2009, 2013) selective fossilization hypothesis (SFH) claims that L1 markedness and L2 input robustness determine the fossilizability (and learnability) of an L2 feature. To test the validity of the model, a pseudo-longitudinal study was designed in which the errors in the argumentative essays of 52 Iranian EFL learners were identified and…

Hydraulic and hydrogeochemical characteristics of a riverbank filtration site in rural India.

Science.gov (United States)

Boving, T B; Choudri, B S; Cady, P; Cording, A; Patil, K; Reddy, Veerabaswant

2014-07-01

A riverbank filtration (RBF) system was tested along the Kali River in rural part of the state of Karnataka in India. The polluted river and water from open wells served the local population as their principal irrigation water resource and some used it for drinking. Four RBF wells (up to 25 m deep) were installed. The mean hydraulic conductivity of the well field is 6.3 x 10(-3) cm/s and, based on Darcy's law, the water travel time from the river to the principal RBF well (MW3) is 45.2 days. A mixing model based on dissolved silica concentrations indicated that, depending on the distance from the river and closeness to irrigated rice fields, approximately 27 to 73% of the well water originated from groundwater. Stable isotopic data indicates that a fraction of the water was drawn in from the nearby rice fields that were irrigated with river water. Relative to preexisting drinking water sources (Kali River and an open well), RBF well water showed lower concentration of dissolved metals (60.1% zinc, 27.8% cadmium, 83.9% lead, 75.5% copper, 100% chromium). This study demonstrates that RBF technology can produce high-quality water from low-quality surface water sources in a rural, tropical setting typical for many emerging economies. Further, in parts of the world where flood irrigation is common, RBF well water may draw in infiltrated irrigation water, which possibly alters its geochemical composition. A combination of more than one mixing model, silica together with stable isotopes, was shown to be useful explaining the origin of the RBF water at this study site.

Sensitivity analysis of complex models: Coping with dynamic and static inputs

International Nuclear Information System (INIS)

Anstett-Collin, F.; Goffart, J.; Mara, T.; Denis-Vidal, L.

2015-01-01

In this paper, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the one of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen–Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient sampling-based strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a building energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house. - Highlights: • Sensitivity analysis of models with uncertain static and dynamic inputs is performed. • Karhunen–Loève (KL) decomposition of the spatio/temporal inputs is performed. • The influence of the dynamic inputs is studied through the modes of the KL expansion. • The proposed approach is applied to a building energy model. • Impact of weather data and material properties on performance of real house is given

MARS input data for steady-state calculation of ATLAS

International Nuclear Information System (INIS)

Park, Hyun Sik; Euh, D. J.; Choi, K. Y.; Kwon, T. S.; Jeong, J. J.; Baek, W. P.

2004-12-01

An integral effect test loop for Pressurized Water Reactors (PWRs), the ATLAS (Advanced Thermal-hydraulic Test Loop for Accident Simulation), is under construction by Thermal-Hydraulics Safety Research Division in Korea Atomic Energy Research Institute (KAERI). This report includes calculation sheets of the input for the best-estimate system analysis code, the MARS code, based on the ongoing design features of ATLAS. The ATLAS facility has been designed to have the length scale of 1/2 and area scale of 1/144 compared with the reference plant, APR1400. The contents of this report are divided into three parts: (1) core and reactor vessel, (2) steam generator and steam line, and (3) primary piping, pressurizer and reactor coolant pump. The steady-state analysis for the ATLAS facility will be performed based on these calculation sheets, and its results will be applied to the detailed design of ATLAS. Additionally, the calculation results will contribute to getting optimum test conditions and preliminary operational test conditions for the steady-state and transient experiments

Comparative effects of riboflavin, nicotinamide and folic acid on alveolar bone loss: A morphometric and histopathologic study in rats

Directory of Open Access Journals (Sweden)

Akpınar Aysun

2016-01-01

Full Text Available Introduction. Periodontitis is a chronic inflammatory and osteolytic disease. Vitamin B complex is a class of water-soluble vitamins that play important roles in cell metabolism. Objective. The aim of this study was to evaluate the effects of riboflavin (RBF, nicotinamide (NA, and folic acid (FA on alveolar bone loss in experimental periodontitis rat model. Methods. Sixty-four male Wistar rats were randomly divided into the following eight groups: Control, Ligated, RBF50 (RBF, 50 mg/kg daily, NA50 (NA, 50 mg/kg daily, FA50 (FA, 50 mg/kg daily, RBF100 (RBF, 100 mg/kg daily, NA100 (NA, 100 mg/kg daily, and FA100 (FA, 100 mg/kg daily. Periodontitis was induced using silk ligature around the right first mandibular molar. After 11 days the rats were sacrificed. Mandible and serum samples were collected. Changes in alveolar bone levels were measured clinically, and periodontal tissues were examined histopathologically. Serum IL-1β (pg/ml levels were analyzed by using ELISA. Results. Mean alveolar bone loss in the mandibular first molar tooth revealed to be significantly lower in RBF100 group than in the Control group. In the Ligated group, alveolar bone loss was significantly higher than in all other groups. The ratio of presence of inflammatory cell infiltration in the Ligated group was significantly higher than in the Control group. The differences in the serum IL-1β levels between the groups were not statistically significant. Osteoclasts that were observed in the Ligated group were significantly higher than those of the Control and FA100 groups. The osteoblastic activity in the Ligated group, RBF100, and NA100 groups were shown to be significantly higher than those in the Control group. Conclusion. This study has demonstrated that systemic administration of RBF, NA, and FA in different dosages (50-100 mg/kg reduced alveolar bone loss in periodontal disease in rats.

Molecular structure input on the web

Directory of Open Access Journals (Sweden)

Ertl Peter

2010-02-01

Full Text Available Abstract A molecule editor, that is program for input and editing of molecules, is an indispensable part of every cheminformatics or molecular processing system. This review focuses on a special type of molecule editors, namely those that are used for molecule structure input on the web. Scientific computing is now moving more and more in the direction of web services and cloud computing, with servers scattered all around the Internet. Thus a web browser has become the universal scientific user interface, and a tool to edit molecules directly within the web browser is essential. The review covers a history of web-based structure input, starting with simple text entry boxes and early molecule editors based on clickable maps, before moving to the current situation dominated by Java applets. One typical example - the popular JME Molecule Editor - will be described in more detail. Modern Ajax server-side molecule editors are also presented. And finally, the possible future direction of web-based molecule editing, based on technologies like JavaScript and Flash, is discussed.

A strategy for integrated low-input potato production

NARCIS (Netherlands)

Vereijken, P.H.; Loon, van C.D.

1991-01-01

Current systems of potato growing use large amounts of pesticides and fertilizers; these inputs are costly and cause environmental problems. In this paper a strategy for integrated low-input potato production is developed with the aim of reducing costs, improving product quality and reducing

Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies

Directory of Open Access Journals (Sweden)

Bo Liu

2014-01-01

Full Text Available Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.

The ART of representation: Memory reduction and noise tolerance in a neural network vision system

Science.gov (United States)

Langley, Christopher S.

The Feature Cerebellar Model Arithmetic Computer (FCMAC) is a multiple-input-single-output neural network that can provide three-degree-of-freedom (3-DOF) pose estimation for a robotic vision system. The FCMAC provides sufficient accuracy to enable a manipulator to grasp an object from an arbitrary pose within its workspace. The network learns an appearance-based representation of an object by storing coarsely quantized feature patterns. As all unique patterns are encoded, the network size grows uncontrollably. A new architecture is introduced herein, which combines the FCMAC with an Adaptive Resonance Theory (ART) network. The ART module categorizes patterns observed during training into a set of prototypes that are used to build the FCMAC. As a result, the network no longer grows without bound, but constrains itself to a user-specified size. Pose estimates remain accurate since the ART layer tends to discard the least relevant information first. The smaller network performs recall faster, and in some cases is better for generalization, resulting in a reduction of error at recall time. The ART-Under-Constraint (ART-C) algorithm is extended to include initial filling with randomly selected patterns (referred to as ART-F). In experiments using a real-world data set, the new network performed equally well using less than one tenth the number of coarse patterns as a regular FCMAC. The FCMAC is also extended to include real-valued input activations. As a result, the network can be tuned to reject a variety of types of noise in the image feature detection. A quantitative analysis of noise tolerance was performed using four synthetic noise algorithms, and a qualitative investigation was made using noisy real-world image data. In validation experiments, the FCMAC system outperformed Radial Basis Function (RBF) networks for the 3-DOF problem, and had accuracy comparable to that of Principal Component Analysis (PCA) and superior to that of Shape Context Matching (SCM), both

Canonical multi-valued input Reed-Muller trees and forms

Science.gov (United States)

Perkowski, M. A.; Johnson, P. D.

1991-01-01

There is recently an increased interest in logic synthesis using EXOR gates. The paper introduces the fundamental concept of Orthogonal Expansion, which generalizes the ring form of the Shannon expansion to the logic with multiple-valued (mv) inputs. Based on this concept we are able to define a family of canonical tree circuits. Such circuits can be considered for binary and multiple-valued input cases. They can be multi-level (trees and DAG's) or flattened to two-level AND-EXOR circuits. Input decoders similar to those used in Sum of Products (SOP) PLA's are used in realizations of multiple-valued input functions. In the case of the binary logic the family of flattened AND-EXOR circuits includes several forms discussed by Davio and Green. For the case of the logic with multiple-valued inputs, the family of the flattened mv AND-EXOR circuits includes three expansions known from literature and two new expansions.

The Input-Output Relationship of the Cholinergic Basal Forebrain

Directory of Open Access Journals (Sweden)

Matthew R. Gielow

2017-02-01

Full Text Available Basal forebrain cholinergic neurons influence cortical state, plasticity, learning, and attention. They collectively innervate the entire cerebral cortex, differentially controlling acetylcholine efflux across different cortical areas and timescales. Such control might be achieved by differential inputs driving separable cholinergic outputs, although no input-output relationship on a brain-wide level has ever been demonstrated. Here, we identify input neurons to cholinergic cells projecting to specific cortical regions by infecting cholinergic axon terminals with a monosynaptically restricted viral tracer. This approach revealed several circuit motifs, such as central amygdala neurons synapsing onto basolateral amygdala-projecting cholinergic neurons or strong somatosensory cortical input to motor cortex-projecting cholinergic neurons. The presence of input cells in the parasympathetic midbrain nuclei contacting frontally projecting cholinergic neurons suggest that the network regulating the inner eye muscles are additionally regulating cortical state via acetylcholine efflux. This dataset enables future circuit-level experiments to identify drivers of known cortical cholinergic functions.

Residual β-Cell Function 3 to 6 Years After Onset of Type 1 Diabetes Reduces Risk of Severe Hypoglycemia in Children and Adolescents

DEFF Research Database (Denmark)

Sorensen, Jesper Sand; Johannesen, Jesper; Pociot, Flemming

2013-01-01

OBJECTIVETo determine the prevalence of residual -cell function (RBF) in children after 3-6 years of type 1 diabetes, and to examine the association between RBF and incidence of severe hypoglycemia, glycemic control, and insulin requirements.RESEARCH DESIGN AND METHODSA total of 342 children (173....../mol]; P 0.2 nmol/L (mean +/- SE: 1.07 +/- 0.02 vs. 0.93 +/- 0.07 units/kg/day; P children after 3-6 years of type 1 diabetes. Children with RBF...... boys) 4.8-18.9 years of age with type 1 diabetes for 3-6 years were included. RBF was assessed by testing meal-stimulated C-peptide concentrations. Information regarding severe hypoglycemia within the past year, current HbA(1c), and daily insulin requirements was retrieved from the medical records...

Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

International Nuclear Information System (INIS)

Amjady, Nima; Keynia, Farshid

2009-01-01

With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

Input or intimacy

Directory of Open Access Journals (Sweden)

Judit Navracsics

2014-01-01

Full Text Available According to the critical period hypothesis, the earlier the acquisition of a second language starts, the better. Owing to the plasticity of the brain, up until a certain age a second language can be acquired successfully according to this view. Early second language learners are commonly said to have an advantage over later ones especially in phonetic/phonological acquisition. Native-like pronunciation is said to be most likely to be achieved by young learners. However, there is evidence of accentfree speech in second languages learnt after puberty as well. Occasionally, on the other hand, a nonnative accent may appear even in early second (or third language acquisition. Cross-linguistic influences are natural in multilingual development, and we would expect the dominant language to have an impact on the weaker one(s. The dominant language is usually the one that provides the largest amount of input for the child. But is it always the amount that counts? Perhaps sometimes other factors, such as emotions, ome into play? In this paper, data obtained from an EnglishPersian-Hungarian trilingual pair of siblings (under age 4 and 3 respectively is analyzed, with a special focus on cross-linguistic influences at the phonetic/phonological levels. It will be shown that beyond the amount of input there are more important factors that trigger interference in multilingual development.

Locally linear approximation for Kernel methods : the Railway Kernel

OpenAIRE

Muñoz, Alberto; González, Javier

2008-01-01

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capab...

Multifunction input-output board for the IBM AT/XT (Lab-Master)

Energy Technology Data Exchange (ETDEWEB)

Pilyar, A V

1996-12-31

Multifunction input-output board for the IBM PC AT/XT is described. It consists of a CMOS analog input multiplexer, programmable amplifier, a fast 12-bit ADC, four 10-bit DAC and two 8-bit digital input-output registers. Specifications of analog input and output are given. 6 refs.

DOG -II input generator program for DOT3.5 code

International Nuclear Information System (INIS)

Hayashi, Katsumi; Handa, Hiroyuki; Yamada, Koubun; Kamogawa, Susumu; Takatsu, Hideyuki; Koizumi, Kouichi; Seki, Yasushi

1992-01-01

DOT3.5 is widely used for radiation transport analysis of fission reactors, fusion experimental facilities and particle accelerators. We developed the input generator program for DOT3.5 code in aim to prepare input data effectively. Formar program DOG was developed and used internally in Hitachi Engineering Company. In this new version DOG-II, limitation for R-Θ geometry was removed. All the input data is created by interactive method in front of color display without using DOT3.5 manual. Also the geometry related input are easily created without calculation of precise curved mesh point. By using DOG-II, reliable input data for DOT3.5 code is obtained easily and quickly

Double input converters for different voltage sources with isolated charger

Directory of Open Access Journals (Sweden)

Chalash Sattayarak

2014-09-01

Full Text Available This paper presents the double input converters for different voltage input sources with isolated charger coils. This research aims to increase the performance of the battery charger circuit. In the circuit, there are the different voltage levels of input source. The operating modes of the switch in the circuit use the microcontroller to control the battery charge and to control discharge mode automatically when the input voltage sources are lost from the system. The experimental result of this research shows better performance for charging at any time period of the switch, while the voltage input sources work together. Therefore, this research can use and develop to battery charger for present or future.

Input shaping control with reentry commands of prescribed duration

Directory of Open Access Journals (Sweden)

Valášek M.

2008-12-01

Full Text Available Control of flexible mechanical structures often deals with the problem of unwanted vibration. The input shaping is a feedforward method based on modification of the input signal so that the output performs the demanded behaviour. The presented approach is based on a finite-time Laplace transform. It leads to no-vibration control signal without any limitations on its time duration because it is not strictly connected to the system resonant frequency. This idea used for synthesis of control input is extended to design of dynamical shaper with reentry property that transform an arbitrary input signal to the signal that cause no vibration. All these theoretical tasks are supported by the results of simulation experiments.

Looking for myself: current multisensory input alters self-face recognition.

Science.gov (United States)

Tsakiris, Manos

2008-01-01

How do I know the person I see in the mirror is really me? Is it because I know the person simply looks like me, or is it because the mirror reflection moves when I move, and I see it being touched when I feel touch myself? Studies of face-recognition suggest that visual recognition of stored visual features inform self-face recognition. In contrast, body-recognition studies conclude that multisensory integration is the main cue to selfhood. The present study investigates for the first time the specific contribution of current multisensory input for self-face recognition. Participants were stroked on their face while they were looking at a morphed face being touched in synchrony or asynchrony. Before and after the visuo-tactile stimulation participants performed a self-recognition task. The results show that multisensory signals have a significant effect on self-face recognition. Synchronous tactile stimulation while watching another person's face being similarly touched produced a bias in recognizing one's own face, in the direction of the other person included in the representation of one's own face. Multisensory integration can update cognitive representations of one's body, such as the sense of ownership. The present study extends this converging evidence by showing that the correlation of synchronous multisensory signals also updates the representation of one's face. The face is a key feature of our identity, but at the same time is a source of rich multisensory experiences used to maintain or update self-representations.

GARFEM input deck description

Energy Technology Data Exchange (ETDEWEB)

Zdunek, A.; Soederberg, M. (Aeronautical Research Inst. of Sweden, Bromma (Sweden))

1989-01-01

The input card deck for the finite element program GARFEM version 3.2 is described in this manual. The program includes, but is not limited to, capabilities to handle the following problems: * Linear bar and beam element structures, * Geometrically non-linear problems (bar and beam), both static and transient dynamic analysis, * Transient response dynamics from a catalog of time varying external forcing function types or input function tables, * Eigenvalue solution (modes and frequencies), * Multi point constraints (MPC) for the modelling of mechanisms and e.g. rigid links. The MPC definition is used only in the geometrically linearized sense, * Beams with disjunct shear axis and neutral axis, * Beams with rigid offset. An interface exist that connects GARFEM with the program GAROS. GAROS is a program for aeroelastic analysis of rotating structures. Since this interface was developed GARFEM now serves as a preprocessor program in place of NASTRAN which was formerly used. Documentation of the methods applied in GARFEM exists but is so far limited to the capacities in existence before the GAROS interface was developed.

A comprehensive analysis of earthquake damage patterns using high dimensional model representation feature selection

Science.gov (United States)

Taşkin Kaya, Gülşen

2013-10-01

Recently, earthquake damage assessment using satellite images has been a very popular ongoing research direction. Especially with the availability of very high resolution (VHR) satellite images, a quite detailed damage map based on building scale has been produced, and various studies have also been conducted in the literature. As the spatial resolution of satellite images increases, distinguishability of damage patterns becomes more cruel especially in case of using only the spectral information during classification. In order to overcome this difficulty, textural information needs to be involved to the classification to improve the visual quality and reliability of damage map. There are many kinds of textural information which can be derived from VHR satellite images depending on the algorithm used. However, extraction of textural information and evaluation of them have been generally a time consuming process especially for the large areas affected from the earthquake due to the size of VHR image. Therefore, in order to provide a quick damage map, the most useful features describing damage patterns needs to be known in advance as well as the redundant features. In this study, a very high resolution satellite image after Iran, Bam earthquake was used to identify the earthquake damage. Not only the spectral information, textural information was also used during the classification. For textural information, second order Haralick features were extracted from the panchromatic image for the area of interest using gray level co-occurrence matrix with different size of windows and directions. In addition to using spatial features in classification, the most useful features representing the damage characteristic were selected with a novel feature selection method based on high dimensional model representation (HDMR) giving sensitivity of each feature during classification. The method called HDMR was recently proposed as an efficient tool to capture the input

Explaining Support Vector Machines: A Color Based Nomogram.

Directory of Open Access Journals (Sweden)

Vanya Van Belle

Full Text Available Support vector machines (SVMs are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied with, a large variety of data can be analysed using these tools. Machine learning thanks its popularity to the good performance of the resulting models. However, interpreting the models is far from obvious, especially when non-linear kernels are used. Hence, the methods are used as black boxes. As a consequence, the use of SVMs is less supported in areas where interpretability is important and where people are held responsible for the decisions made by models.In this work, we investigate whether SVMs using linear, polynomial and RBF kernels can be explained such that interpretations for model-based decisions can be provided. We further indicate when SVMs can be explained and in which situations interpretation of SVMs is (hitherto not possible. Here, explainability is defined as the ability to produce the final decision based on a sum of contributions which depend on one single or at most two input variables.Our experiments on simulated and real-life data show that explainability of an SVM depends on the chosen parameter values (degree of polynomial kernel, width of RBF kernel and regularization constant. When several combinations of parameter values yield the same cross-validation performance, combinations with a lower polynomial degree or a larger kernel width have a higher chance of being explainable.This work summarizes SVM classifiers obtained with linear, polynomial and RBF kernels in a single plot. Linear and polynomial kernels up to the second degree are represented exactly. For other kernels an indication of the reliability of the approximation is presented. The complete methodology is available as an R package and two apps and a movie are provided to illustrate the possibilities offered by the method.

Feature Extraction in the North Sinai Desert Using Spaceborne Synthetic Aperture Radar: Potential Archaeological Applications

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Christopher Stewart

2016-10-01

Full Text Available Techniques were implemented to extract anthropogenic features in the desert region of North Sinai using data from the first- and second-generation Phased Array type L-band Synthetic Aperture Radar (PALSAR-1 and 2. To obtain a synoptic view over the study area, a mosaic of average, multitemporal (De Grandi filtered PALSAR-1 σ° backscatter of North Sinai was produced. Two subset regions were selected for further analysis. The first included an area of abundant linear features of high relative backscatter in a strategic, but sparsely developed area between the Wadi Tumilat and Gebel Maghara. The second included an area of low backscatter anomaly features in a coastal sabkha around the archaeological sites of Tell el-Farama, Tell el-Mahzan, and Tell el-Kanais. Over the subset region between the Wadi Tumilat and Gebel Maghara, algorithms were developed to extract linear features and convert them to vector format to facilitate interpretation. The algorithms were based on mathematical morphology, but to distinguish apparent man-made features from sand dune ridges, several techniques were applied. The first technique took as input the average σ° backscatter and used a Digital Elevation Model (DEM derived Local Incidence Angle (LAI mask to exclude sand dune ridges. The second technique, which proved more effective, used the average interferometric coherence as input. Extracted features were compared with other available information layers and in some cases revealed partially buried roads. Over the coastal subset region a time series of PALSAR-2 spotlight data were processed. The coefficient of variation (CoV of De Grandi filtered imagery clearly revealed anomaly features of low CoV. These were compared with the results of an archaeological field walking survey carried out previously. The features generally correspond with isolated areas identified in the field survey as having a higher density of archaeological finds, and interpreted as possible

Input Manipulation, Enhancement and Processing: Theoretical Views and Empirical Research

Science.gov (United States)

Benati, Alessandro

2016-01-01

Researchers in the field of instructed second language acquisition have been examining the issue of how learners interact with input by conducting research measuring particular kinds of instructional interventions (input-oriented and meaning-based). These interventions include such things as input flood, textual enhancement and processing…

Seismic signal auto-detecing from different features by using Convolutional Neural Network

Science.gov (United States)

Huang, Y.; Zhou, Y.; Yue, H.; Zhou, S.

2017-12-01

We try Convolutional Neural Network to detect some features of seismic data and compare their efficience. The features include whether a signal is seismic signal or noise and the arrival time of P and S phase and each feature correspond to a Convolutional Neural Network. We first use traditional STA/LTA to recongnize some events and then use templete matching to find more events as training set for the Neural Network. To make the training set more various, we add some noise to the seismic data and make some synthetic seismic data and noise. The 3-component raw signal and time-frequancy ananlyze are used as the input data for our neural network. Our Training is performed on GPUs to achieve efficient convergence. Our method improved the precision in comparison with STA/LTA and template matching. We will move to recurrent neural network to see if this kind network is better in detect P and S phase.

Self-Structured Organizing Single-Input CMAC Control for Robot Manipulator

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ThanhQuyen Ngo

2011-09-01

Full Text Available This paper represents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of proposed control system so that the stability of the system can be guaranteed. The simulation results of robot manipulator are provided to verify the effectiveness of the proposed control methodology.

The Economic Impact of Tourism. An Input-Output Analysis

OpenAIRE

Camelia SURUGIU

2009-01-01

The paper presents an Input-Output Analysis for Romania, an important source of information for the investigation of the inter-relations existing among different industries. The Input-Output Analysis is used to determine the role and importance of different economic value added, incomes and employment and it analyses the existing connection in an economy. This paper is focused on tourism and the input-output analysis is finished for the Hotels and Restaurants Sector.

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks

Institute of Scientific and Technical Information of China (English)

Ling Bai; Ping Guo; Zhan-Yi Hu

2005-01-01

An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification.Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.

Soil-Related Input Parameters for the Biosphere Model

International Nuclear Information System (INIS)

Smith, A. J.

2004-01-01

This report presents one of the analyses that support the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN). The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the details of the conceptual model as well as the mathematical model and the required input parameters. The biosphere model is one of a series of process models supporting the postclosure Total System Performance Assessment (TSPA) for the Yucca Mountain repository. A schematic representation of the documentation flow for the Biosphere input to TSPA is presented in Figure 1-1. This figure shows the evolutionary relationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the biosphere abstraction products for TSPA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (TWP) (BSC 2004 [DIRS 169573]). This figure is included to provide an understanding of how this analysis report contributes to biosphere modeling in support of the license application, and is not intended to imply that access to the listed documents is required to understand the contents of this report. This report, ''Soil-Related Input Parameters for the Biosphere Model'', is one of the five analysis reports that develop input parameters for use in the ERMYN model. This report is the source documentation for the six biosphere parameters identified in Table 1-1. The purpose of this analysis was to develop the biosphere model parameters associated with the accumulation and depletion of radionuclides in the soil. These parameters support the calculation of radionuclide concentrations in soil from on-going irrigation or ash deposition and, as a direct consequence, radionuclide concentration in other environmental media that are affected by radionuclide concentrations in soil. The analysis was performed in accordance with the TWP (BSC 2004 [DIRS 169573]) where the governing procedure was defined as AP-SIII.9Q, ''Scientific Analyses''. This

Outsourcing, public Input provision and policy cooperation

OpenAIRE

Aronsson, Thomas; Koskela, Erkki

2009-01-01

This paper concerns public input provision as an instrument for redistribution under international outsourcing by using a model-economy comprising two countries, North and South, where firms in the North may outsource part of their low-skilled labor intensive production to the South. We consider two interrelated issues: (i) the incentives for each country to modify the provision of public input goods in response to international outsourcing, and (ii) whether international outsourcing justifie...

A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

Directory of Open Access Journals (Sweden)

Hanlun Li

2016-01-01

Full Text Available In the past few years, many multispectral systems which consist of several identical monochrome cameras equipped with different bandpass filters have been developed. However, due to the significant difference in the intensity between different band images, image registration becomes very difficult. Considering the common structural characteristic of the multispectral systems, this paper proposes an effective method for registering different band images. First we use the phase correlation method to calculate the parameters of a coarse-offset relationship between different band images. Then we use the scale invariant feature transform (SIFT to detect the feature points. For every feature point in a reference image, we can use the coarse-offset parameters to predict the location of its matching point. We only need to compare the feature point in the reference image with the several near feature points from the predicted location instead of the feature points all over the input image. Our experiments show that this method does not only avoid false matches and increase correct matches, but also solve the matching problem between an infrared band image and a visible band image in cases lacking man-made objects.

Impacts and managerial implications for sewer systems due to recent changes to inputs in domestic wastewater - A review.

Science.gov (United States)

Mattsson, Jonathan; Hedström, Annelie; Ashley, Richard M; Viklander, Maria

2015-09-15

Ever since the advent of major sewer construction in the 1850s, the issue of increased solids deposition in sewers due to changes in domestic wastewater inputs has been frequently debated. Three recent changes considered here are the introduction of kitchen sink food waste disposers (FWDs); rising levels of inputs of fat, oil and grease (FOG); and the installation of low-flush toilets (LFTs). In this review these changes have been examined with regard to potential solids depositional impacts on sewer systems and the managerial implications. The review indicates that each of the changes has the potential to cause an increase in solids deposition in sewers and this is likely to be more pronounced for the upstream reaches of networks that serve fewer households than the downstream parts and for specific sewer features such as sags. The review has highlighted the importance of educational campaigns directed to the public to mitigate deposition as many of the observed problems have been linked to domestic behaviour in regard to FOGs, FWDs and toilet flushing. A standardized monitoring procedure of repeat sewer blockage locations can also be a means to identify depositional hot-spots. Interactions between the various changes in inputs in the studies reviewed here indicated an increased potential for blockage formation, but this would need to be further substantiated. As the precise nature of these changes in inputs have been found to be variable, depending on lifestyles and type of installation, the additional problems that may arise pose particular challenges to sewer operators and managers because of the difficulty in generalizing the nature of the changes, particularly where retrofitting projects in households are being considered. The three types of changes to inputs reviewed here highlight the need to consider whether or not more or less solid waste from households should be diverted into sewers. Copyright © 2015 Elsevier Ltd. All rights reserved.

Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification

Science.gov (United States)

Liu, Yongbin; He, Bing; Liu, Fang; Lu, Siliang; Zhao, Yilei

2016-12-01

Fault pattern identification is a crucial step for the intelligent fault diagnosis of real-time health conditions in monitoring a mechanical system. However, many challenges exist in extracting the effective feature from vibration signals for fault recognition. A new feature fusion method is proposed in this study to extract new features using kernel joint approximate diagonalization of eigen-matrices (KJADE). In the method, the input space that is composed of original features is mapped into a high-dimensional feature space by nonlinear mapping. Then, the new features can be estimated through the eigen-decomposition of the fourth-order cumulative kernel matrix obtained from the feature space. Therefore, the proposed method could be used to reduce data redundancy because it extracts the inherent pattern structure of different fault classes as it is nonlinear by nature. The integration evaluation factor of between-class and within-class scatters (SS) is employed to depict the clustering performance quantitatively, and the new feature subset extracted by the proposed method is fed into a multi-class support vector machine for fault pattern identification. Finally, the effectiveness of the proposed method is verified by experimental vibration signals with different bearing fault types and severities. Results of several cases show that the KJADE algorithm is efficient in feature fusion for bearing fault identification.

Improving permafrost distribution modelling using feature selection algorithms

Science.gov (United States)

Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail

2016-04-01

The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its

Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

Directory of Open Access Journals (Sweden)

German Ignacio Parisi

2015-06-01

Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

An electronic system for simulation of neural networks with a micro-second real time constraint

International Nuclear Information System (INIS)

Chorti, Arsenia; Granado, Bertrand; Denby, Bruce; Garda, Patrick

2001-01-01

Neural networks implemented in hardware can perform pattern recognition very quickly, and as such have been used to advantage in the triggering systems of certain high energy physics experiments. Typically, time constants of the order of a few microseconds are required. In this paper, we present a new system. MAHARADJA, for evaluating MLP and RBF neural network paradigms in real time. The system is tested on a possible ATLAS muon triggering application suggested by the Tel Aviv ATLAS group, consisting of a 4-8-8-4 MLP which must be evaluated in 10 microseconds. The inputs to the net are dx/dz, x(z=0), dy/dz, and y(z=0), whereas the outputs give pt, tan(phi), sin(theta), and q, the charge. With a 10 MHz clock, MAHARADJA calculates the result in 6.8 microseconds; at 20 MHz, which is readily attainable, this would be reduced to only 3.4 microseconds. The system can also handle RBF networks with 3 different distance metrics (Euclidean, Manhattan and Mahalanobis), and can simulate any MLP of 10 hidden layers or less. The electronic implementation is with FPGA's, which can be optimized for a specific neural network because the number of processing elements can be modified

Novel approach for dam break flow modeling using computational intelligence

Science.gov (United States)

Seyedashraf, Omid; Mehrabi, Mohammad; Akhtari, Ali Akbar

2018-04-01

A new methodology based on the computational intelligence (CI) system is proposed and tested for modeling the classic 1D dam-break flow problem. The reason to seek for a new solution lies in the shortcomings of the existing analytical and numerical models. This includes the difficulty of using the exact solutions and the unwanted fluctuations, which arise in the numerical results. In this research, the application of the radial-basis-function (RBF) and multi-layer-perceptron (MLP) systems is detailed for the solution of twenty-nine dam-break scenarios. The models are developed using seven variables, i.e. the length of the channel, the depths of the up-and downstream sections, time, and distance as the inputs. Moreover, the depths and velocities of each computational node in the flow domain are considered as the model outputs. The models are validated against the analytical, and Lax-Wendroff and MacCormack FDM schemes. The findings indicate that the employed CI models are able to replicate the overall shape of the shock- and rarefaction-waves. Furthermore, the MLP system outperforms RBF and the tested numerical schemes. A new monolithic equation is proposed based on the best fitting model, which can be used as an efficient alternative to the existing piecewise analytic equations.

Sensory Synergy as Environmental Input Integration

Directory of Open Access Journals (Sweden)

Fady eAlnajjar

2015-01-01

Full Text Available The development of a method to feed proper environmental inputs back to the central nervous system (CNS remains one of the challenges in achieving natural movement when part of the body is replaced with an artificial device. Muscle synergies are widely accepted as a biologically plausible interpretation of the neural dynamics between the CNS and the muscular system. Yet the sensorineural dynamics of environmental feedback to the CNS has not been investigated in detail. In this study, we address this issue by exploring the concept of sensory synergy. In contrast to muscle synergy, we hypothesize that sensory synergy plays an essential role in integrating the overall environmental inputs to provide low-dimensional information to the CNS. We assume that sensor synergy and muscle synergy communicate using these low-dimensional signals. To examine our hypothesis, we conducted posture control experiments involving lateral disturbance with 9 healthy participants. Proprioceptive information represented by the changes on muscle lengths were estimated by using the musculoskeletal model analysis software SIMM. Changes on muscles lengths were then used to compute sensory synergies. The experimental results indicate that the environmental inputs were translated into the two dimensional signals and used to move the upper limb to the desired position immediately after the lateral disturbance. Participants who showed high skill in posture control were found to be likely to have a strong correlation between sensory and muscle signaling as well as high coordination between the utilized sensory synergies. These results suggest the importance of integrating environmental inputs into suitable low-dimensional signals before providing them to the CNS. This mechanism should be essential when designing the prosthesis’ sensory system to make the controller simpler

Sensory synergy as environmental input integration.

Science.gov (United States)

Alnajjar, Fady; Itkonen, Matti; Berenz, Vincent; Tournier, Maxime; Nagai, Chikara; Shimoda, Shingo

2014-01-01

The development of a method to feed proper environmental inputs back to the central nervous system (CNS) remains one of the challenges in achieving natural movement when part of the body is replaced with an artificial device. Muscle synergies are widely accepted as a biologically plausible interpretation of the neural dynamics between the CNS and the muscular system. Yet the sensorineural dynamics of environmental feedback to the CNS has not been investigated in detail. In this study, we address this issue by exploring the concept of sensory synergy. In contrast to muscle synergy, we hypothesize that sensory synergy plays an essential role in integrating the overall environmental inputs to provide low-dimensional information to the CNS. We assume that sensor synergy and muscle synergy communicate using these low-dimensional signals. To examine our hypothesis, we conducted posture control experiments involving lateral disturbance with nine healthy participants. Proprioceptive information represented by the changes on muscle lengths were estimated by using the musculoskeletal model analysis software SIMM. Changes on muscles lengths were then used to compute sensory synergies. The experimental results indicate that the environmental inputs were translated into the two dimensional signals and used to move the upper limb to the desired position immediately after the lateral disturbance. Participants who showed high skill in posture control were found to be likely to have a strong correlation between sensory and muscle signaling as well as high coordination between the utilized sensory synergies. These results suggest the importance of integrating environmental inputs into suitable low-dimensional signals before providing them to the CNS. This mechanism should be essential when designing the prosthesis' sensory system to make the controller simpler.

Multi-Feature Based Multiple Landmine Detection Using Ground Penetration Radar

Directory of Open Access Journals (Sweden)

S. Park

2014-06-01

Full Text Available This paper presents a novel method for detection of multiple landmines using a ground penetrating radar (GPR. Conventional algorithms mainly focus on detection of a single landmine, which cannot linearly extend to the multiple landmine case. The proposed algorithm is composed of four steps; estimation of the number of multiple objects buried in the ground, isolation of each object, feature extraction and detection of landmines. The number of objects in the GPR signal is estimated by using the energy projection method. Then signals for the objects are extracted by using the symmetry filtering method. Each signal is then processed for features, which are given as input to the support vector machine (SVM for landmine detection. Three landmines buried in various ground conditions are considered for the test of the proposed method. They demonstrate that the proposed method can successfully detect multiple landmines.

Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing

Directory of Open Access Journals (Sweden)

Marwa Farouk Ibrahim Ibrahim

2018-01-01

Full Text Available Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and

RIP Input Tables From WAPDEG for LA Design Selection: Repository Horizon Elevation - 2-Level AML 50% and Near Maximum

International Nuclear Information System (INIS)

B.E. Bullard

1999-01-01

The purpose of this calculation is to document the WAPDEG version 3.09 (CRWMS M and O 1998b). Software Routine Report for WAPDEG (Version 3.09) simulations used to analyze waste package degradation and failure under the repository exposure conditions characterized by a two-tier thermal loading repository design. Also documented is the post-processing of these results into tables of waste-package-degradation-time histories suitable for use as input into the Integrated Probabilistic Simulator for Environmental Systems (RIP) version 5.19.01 (Golder Associates 1998) computer program. Specifically, the WAPDEG simulations discussed in this calculation correspond to waste package emplacement conditions (repository environment and design) as defined in the Total System Performance Assessment-Viability Assessment (CRWMS M and O 1998a). Total System Performance Assessment-Viability Assessment (TSPA-VA) Analyses Technical Basis Document--Chapter 5, Waste Package Degradation Modeling And Abstraction, pp. 5-27 to 5-29, with the exception that a two-tier thermal loading design feature as specified in the License Application Design Selection (LADS) study was analyzed. The particular design feature evaluated in this report is a modification of the repository horizon elevation and layout within the Topopah Springs Member of Yucca Mountain. Specifically, the modification consists of adding a second level, 50-m above the base case repository layout. Two options were considered, representing two variations in thermal loading. In Design Feature 25e (designated DF25e), each level has an Areal Mass Loading (AML) of 42.5 MTU/acre (i.e., half the VA base case). In Design Feature 25f (designated DF25), each level has an AML of 64MTU/acre. As a result of the change in waste package placement relative to the TSPA-VA base-case design, different temperature and relative humidity time histories at the waste package surface are calculated (input to the WAPDEG simulations), and consequently

Real-time transient stabilization and voltage regulation of power generators with unknown mechanical power input

International Nuclear Information System (INIS)

Kenne, Godpromesse; Goma, Raphael; Nkwawo, Homere; Lamnabhi-Lagarrigue, Francoise; Arzande, Amir; Vannier, Jean Claude

2010-01-01

A nonlinear adaptive excitation controller is proposed to enhance the transient stability and voltage regulation of synchronous generators with unknown power angle and mechanical power input. The proposed method is based on a standard third-order model of a synchronous generator which requires only information about the physical available measurements of relative angular speed, active electric power, infinite bus and generator terminal voltages. The operating conditions are computed online using the above physical available measurements, the terminal voltage reference value and the estimate of the mechanical power input. The proposed design is therefore capable of providing satisfactory voltage in the presence of unknown variations of the power system operating conditions. Using the concept of sliding mode equivalent control techniques, a robust decentralized adaptive controller which insures the exponential convergence of the outputs to the desired ones, is obtained. Real-time experimental results are reported, comparing the performance of the proposed adaptive nonlinear control scheme to one of the conventional AVR/PSS controller. The high simplicity of the overall adaptive control scheme and its robustness with respect to line impedance variation including critical unbalanced operating condition and temporary turbine fault, constitute the main positive features of the proposed approach.

Real-time transient stabilization and voltage regulation of power generators with unknown mechanical power input

Energy Technology Data Exchange (ETDEWEB)

Kenne, Godpromesse, E-mail: gokenne@yahoo.co [Laboratoire d' Automatique et d' Informatique Appliquee (LAIA), Departement de Genie Electrique, Universite de Dschang, B.P. 134 Bandjoun (Cameroon); Goma, Raphael, E-mail: raphael.goma@lss.supelec.f [Laboratoire des Signaux et Systemes (L2S), CNRS-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Nkwawo, Homere, E-mail: homere.nkwawo@iutv.univ-paris13.f [Departement GEII, Universite Paris XIII, 99 Avenue Jean Baptiste Clement, 93430 Villetaneuse (France); Lamnabhi-Lagarrigue, Francoise, E-mail: lamnabhi@lss.supelec.f [Laboratoire des Signaux et Systemes (L2S), CNRS-SUPELEC, Universite Paris XI, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Arzande, Amir, E-mail: Amir.arzande@supelec.f [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France); Vannier, Jean Claude, E-mail: Jean-claude.vannier@supelec.f [Departement Energie, Ecole Superieure d' Electricite-SUPELEC, 3 Rue Joliot Curie, 91192 Gif-sur-Yvette (France)

2010-01-15

A nonlinear adaptive excitation controller is proposed to enhance the transient stability and voltage regulation of synchronous generators with unknown power angle and mechanical power input. The proposed method is based on a standard third-order model of a synchronous generator which requires only information about the physical available measurements of relative angular speed, active electric power, infinite bus and generator terminal voltages. The operating conditions are computed online using the above physical available measurements, the terminal voltage reference value and the estimate of the mechanical power input. The proposed design is therefore capable of providing satisfactory voltage in the presence of unknown variations of the power system operating conditions. Using the concept of sliding mode equivalent control techniques, a robust decentralized adaptive controller which insures the exponential convergence of the outputs to the desired ones, is obtained. Real-time experimental results are reported, comparing the performance of the proposed adaptive nonlinear control scheme to one of the conventional AVR/PSS controller. The high simplicity of the overall adaptive control scheme and its robustness with respect to line impedance variation including critical unbalanced operating condition and temporary turbine fault, constitute the main positive features of the proposed approach.

Does Input Enhancement Work for Learning Politeness Strategies?

Science.gov (United States)

Khatib, Mohammad; Safari, Mahmood

2013-01-01

The present study investigated the effect of input enhancement on the acquisition of English politeness strategies by intermediate EFL learners. Two groups of freshman English majors were randomly assigned to the experimental (enhanced input) group and the control (mere exposure) group. Initially, a TOEFL test and a discourse completion test (DCT)…

Change in CO2 emission and its transmissions between Korea and Japan using international input-output analysis

International Nuclear Information System (INIS)

Rhee, Hae-Chun; Chung, Hyun-Sik

2006-01-01

This paper is intended to analyze CO 2 transmission between Japan and South Korea through international trade based on 1990 and 1995 international input-output data. It applied a residual-free structural decomposition method proposed by Chung and Rhee [Chung, H.S., Rhee, H.C., 2001. A residual-free decomposition of the sources of carbon dioxide emissions: a case of the Korean industries. Energy 26 (1), 15-30] to emission-related international input-output analysis for the first time in the decomposition studies. This paper is a case study regarding the manner and the extent to which CO 2 emissions are influenced by international trade between Japan (an Annex I country) and South Korea (a non-Annex I country), which is of particular interest for the carbon leakage issue. In this paper, we attempted to show which factors contributed to the changes in emission of the major greenhouse gas in South Korea and Japan. The changes in emission are analyzed in terms of emission intensity, input techniques, demand composition, and trade structures. According to our analysis, South Korea, a non-Annex I country, has more energy-intensive production structures than Japan, an Annex I country. South Korea's trade pattern with Japan reflects these production features, resulting in the Korea's comparative advantage in emission intensive products, though the degree has somewhat mitigated in 1995 compared to 1990. (author)

Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

Science.gov (United States)

Morelli, Eugene A.; Klein, Vladislav

1990-01-01

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Diffuse optical characterization of an exercising patient group with peripheral artery disease

Science.gov (United States)

Putt, Mary; Chandra, Malavika; Yu, Guoqiang; Xing, Xiaoman; Han, Sung Wan; Lech, Gwen; Shang, Yu; Durduran, Turgut; Zhou, Chao; Yodh, Arjun G.; Mohler, Emile R.

2013-01-01

Abstract. Peripheral artery disease (PAD) is a common condition with high morbidity. While measurement of tissue oxygen saturation (StO2) has been demonstrated, this is the first study to assess both StO2 and relative blood flow (rBF) in the extremities of PAD patients. Diffuse optics is employed to measure hemodynamic response to treadmill and pedal exercises in 31 healthy controls and 26 patients. For StO2, mild and moderate/severe PAD groups show pronounced differences compared with controls. Pre-exercise mean StO2 is lower in PAD groups by 9.3% to 10.6% compared with means of 63.5% to 66.2% in controls. For pedal, relative rate of return of StO2 to baseline is more rapid in controls (p<0.05). Patterns of rBF also differ among groups. After both exercises, rBF tend to occur at depressed levels among severe PAD patients compared with healthy (p<0.05); post-treadmill, rBF tend to occur at elevated levels among healthy compared with severe PAD patients (p<0.05). Additionally, relative rate of return to baseline StO2 is more rapid among subjects with reduced levels of depression in rBF (p=0.041), even after adjustment for ankle brachial index. This suggests a physiologic connection between rBF and oxygenation that can be measured using diffuse optics, and potentially employed as an evaluative tool in further studies. PMID:23708193

A parallel input composite transimpedance amplifier

Science.gov (United States)

Kim, D. J.; Kim, C.

2018-01-01

A new approach to high performance current to voltage preamplifier design is presented. The design using multiple operational amplifiers (op-amps) has a parasitic capacitance compensation network and a composite amplifier topology for fast, precision, and low noise performance. The input stage consisting of a parallel linked JFET op-amps and a high-speed bipolar junction transistor (BJT) gain stage driving the output in the composite amplifier topology, cooperating with the capacitance compensation feedback network, ensures wide bandwidth stability in the presence of input capacitance above 40 nF. The design is ideal for any two-probe measurement, including high impedance transport and scanning tunneling microscopy measurements.

Image Feature Types and Their Predictions of Aesthetic Preference and Naturalness

Directory of Open Access Journals (Sweden)

Marc G. Berman

2017-04-01

Full Text Available Previous research has investigated ways to quantify visual information of a scene in terms of a visual processing hierarchy, i.e., making sense of visual environment by segmentation and integration of elementary sensory input. Guided by this research, studies have developed categories for low-level visual features (e.g., edges, colors, high-level visual features (scene-level entities that convey semantic information such as objects, and how models of those features predict aesthetic preference and naturalness. For example, in Kardan et al. (2015a, 52 participants provided aesthetic preference and naturalness ratings, which are used in the current study, for 307 images of mixed natural and urban content. Kardan et al. (2015a then developed a model using low-level features to predict aesthetic preference and naturalness and could do so with high accuracy. What has yet to be explored is the ability of higher-level visual features (e.g., horizon line position relative to viewer, geometry of building distribution relative to visual access to predict aesthetic preference and naturalness of scenes, and whether higher-level features mediate some of the association between the low-level features and aesthetic preference or naturalness. In this study we investigated these relationships and found that low- and high- level features explain 68.4% of the variance in aesthetic preference ratings and 88.7% of the variance in naturalness ratings. Additionally, several high-level features mediated the relationship between the low-level visual features and aaesthetic preference. In a multiple mediation analysis, the high-level feature mediators accounted for over 50% of the variance in predicting aesthetic preference. These results show that high-level visual features play a prominent role predicting aesthetic preference, but do not completely eliminate the predictive power of the low-level visual features. These strong predictors provide powerful insights for

Integrate-and-fire vs Poisson models of LGN input to V1 cortex: noisier inputs reduce orientation selectivity.

Science.gov (United States)

Lin, I-Chun; Xing, Dajun; Shapley, Robert

2012-12-01

One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.

Effect of imidazole and indomethacin on hemodynamics of the obstructed canine kidney

International Nuclear Information System (INIS)

Balint, P.; Laszlo, K.

1985-01-01

In the anesthetized dog renal blood flow (RBF) and its intrarenal distribution were investigated by the radioactive microsphere technique 24 hr after bilateral (BUL) and unilateral (UUL) ureteral ligation. In the control series indomethacin (IM) led to a decrease in RBF with outward shifting of zonal perfusions; imidazole (IA) did not cause significant changes in renal hemodynamics. In the BUL series there was a sharp drop in RBF with a proportional decrease in outer (OC) and inner (IC) cortical perfusion; IM treatment resulted in a further decrease in overall and zonal perfusions. IA, a selective inhibitor of thromboxane synthetase, relieved IC vasoconstriction. In the ligated kidney of the UUL preparations decrease in RBF was due to OC vasoconstriction, while IC perfusion equalled controls. IM led to an overall vasoconstriction in all cortical layers; IA did not influence either total RBF or its distribution. It was concluded that BUL ''unmasked'' TXA2 production in the IC layers, while IM treatment, by inhibiting the production of PGE2, PGI2, and TXA2, resulted in an overall vasoconstriction both in controls and the BUL and UUL preparations