Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Incomplete convolutions in production and inventory models
Houtum, van G.J.J.A.N.; Zijm, W.H.M.
1997-01-01
In this paper, we study incomplete convolutions of continuous distribution functions, as they appear in the analysis of (multi-stage) production and inventory systems. Three example systems are discussed where these incomplete convolutions naturally arise. We derive explicit, nonrecursive formulae
Spherical convolutions and their application in molecular modelling
DEFF Research Database (Denmark)
Boomsma, Wouter; Frellsen, Jes
2017-01-01
Convolutional neural networks are increasingly used outside the domain of image analysis, in particular in various areas of the natural sciences concerned with spatial data. Such networks often work out-of-the box, and in some cases entire model architectures from image analysis can be carried over...... to other problem domains almost unaltered. Unfortunately, this convenience does not trivially extend to data in non-euclidean spaces, such as spherical data. In this paper, we introduce two strategies for conducting convolutions on the sphere, using either a spherical-polar grid or a grid based...... of spherical convolutions in the context of molecular modelling, by considering structural environments within proteins. We show that the models are capable of learning non-trivial functions in these molecular environments, and that our spherical convolutions generally outperform standard 3D convolutions...
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
Edgeworth Expansion Based Model for the Convolutional Noise pdf
Directory of Open Access Journals (Sweden)
Yonatan Rivlin
2014-01-01
Full Text Available Recently, the Edgeworth expansion up to order 4 was used to represent the convolutional noise probability density function (pdf in the conditional expectation calculations where the source pdf was modeled with the maximum entropy density approximation technique. However, the applied Lagrange multipliers were not the appropriate ones for the chosen model for the convolutional noise pdf. In this paper we use the Edgeworth expansion up to order 4 and up to order 6 to model the convolutional noise pdf. We derive the appropriate Lagrange multipliers, thus obtaining new closed-form approximated expressions for the conditional expectation and mean square error (MSE as a byproduct. Simulation results indicate hardly any equalization improvement with Edgeworth expansion up to order 4 when using optimal Lagrange multipliers over a nonoptimal set. In addition, there is no justification for using the Edgeworth expansion up to order 6 over the Edgeworth expansion up to order 4 for the 16QAM and easy channel case. However, Edgeworth expansion up to order 6 leads to improved equalization performance compared to the Edgeworth expansion up to order 4 for the 16QAM and hard channel case as well as for the case where the 64QAM is sent via an easy channel.
A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network
Directory of Open Access Journals (Sweden)
Zhen Wang
2018-05-01
Full Text Available Conventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC, estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN is proposed to recognize the GPS acquisition correlation envelope in this article. The proposed method is a double dwell acquisition in which a short integration is adopted in the first dwell and a long integration is applied in the second one. To reduce the search space for parameters, BCNN detects the possible envelope which contains the auto-correlation peak in the first dwell to compress the initial search space to 1/1023. Although there is a long integration in the second dwell, the acquisition computation overhead is still low due to the compressed search space. Comprehensively, the total computation overhead of the proposed method is only 1/5 of conventional ones. Experiments show that the proposed double dwell/correlation envelope identification (DD/CEI neural network achieves 2 dB improvement when compared with the MAX/TC under the same specification.
Accurate lithography simulation model based on convolutional neural networks
Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki
2017-07-01
Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.
Model selection for convolutive ICA with an application to spatiotemporal analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2007-01-01
We present a new algorithm for maximum likelihood convolutive independent component analysis (ICA) in which components are unmixed using stable autoregressive filters determined implicitly by estimating a convolutive model of the mixing process. By introducing a convolutive mixing model...... for the components, we show how the order of the filters in the model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving a subspace of independent components in electroencephalography (EEG). Initial results suggest that in some cases, convolutive mixing may...
An effective convolutional neural network model for Chinese sentiment analysis
Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong
2017-06-01
Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.
A staggered-grid convolutional differentiator for elastic wave modelling
Sun, Weijia; Zhou, Binzhong; Fu, Li-Yun
2015-11-01
The computation of derivatives in governing partial differential equations is one of the most investigated subjects in the numerical simulation of physical wave propagation. An analytical staggered-grid convolutional differentiator (CD) for first-order velocity-stress elastic wave equations is derived in this paper by inverse Fourier transformation of the band-limited spectrum of a first derivative operator. A taper window function is used to truncate the infinite staggered-grid CD stencil. The truncated CD operator is almost as accurate as the analytical solution, and as efficient as the finite-difference (FD) method. The selection of window functions will influence the accuracy of the CD operator in wave simulation. We search for the optimal Gaussian windows for different order CDs by minimizing the spectral error of the derivative and comparing the windows with the normal Hanning window function for tapering the CD operators. It is found that the optimal Gaussian window appears to be similar to the Hanning window function for tapering the same CD operator. We investigate the accuracy of the windowed CD operator and the staggered-grid FD method with different orders. Compared to the conventional staggered-grid FD method, a short staggered-grid CD operator achieves an accuracy equivalent to that of a long FD operator, with lower computational costs. For example, an 8th order staggered-grid CD operator can achieve the same accuracy of a 16th order staggered-grid FD algorithm but with half of the computational resources and time required. Numerical examples from a homogeneous model and a crustal waveguide model are used to illustrate the superiority of the CD operators over the conventional staggered-grid FD operators for the simulation of wave propagations.
A deep convolutional neural network model to classify heartbeats.
Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San
2017-10-01
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Craig, Tim; Battista, Jerry; Van Dyk, Jake
2003-01-01
Convolution methods have been used to model the effect of geometric uncertainties on dose delivery in radiation therapy. Convolution assumes shift invariance of the dose distribution. Internal inhomogeneities and surface curvature lead to violations of this assumption. The magnitude of the error resulting from violation of shift invariance is not well documented. This issue is addressed by comparing dose distributions calculated using the Convolution method with dose distributions obtained by Direct Simulation. A comparison of conventional Static dose distributions was also made with Direct Simulation. This analysis was performed for phantom geometries and several clinical tumor sites. A modification to the Convolution method to correct for some of the inherent errors is proposed and tested using example phantoms and patients. We refer to this modified method as the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over different beam arrangements in the various phantom examples) was 21% with the Static dose calculation, 9% with Convolution, and reduced to 5% with the Corrected Convolution. The average maximum dose error in the calculated volume (averaged over four clinical examples) was 9% for the Static method, 13% for Convolution, and 3% for Corrected Convolution. While Convolution can provide a superior estimate of the dose delivered when geometric uncertainties are present, the violation of shift invariance can result in substantial errors near the surface of the patient. The proposed Corrected Convolution modification reduces errors near the surface to 3% or less
Digital Tomosynthesis System Geometry Analysis Using Convolution-Based Blur-and-Add (BAA) Model.
Wu, Meng; Yoon, Sungwon; Solomon, Edward G; Star-Lack, Josh; Pelc, Norbert; Fahrig, Rebecca
2016-01-01
Digital tomosynthesis is a three-dimensional imaging technique with a lower radiation dose than computed tomography (CT). Due to the missing data in tomosynthesis systems, out-of-plane structures in the depth direction cannot be completely removed by the reconstruction algorithms. In this work, we analyzed the impulse responses of common tomosynthesis systems on a plane-to-plane basis and proposed a fast and accurate convolution-based blur-and-add (BAA) model to simulate the backprojected images. In addition, the analysis formalism describing the impulse response of out-of-plane structures can be generalized to both rotating and parallel gantries. We implemented a ray tracing forward projection and backprojection (ray-based model) algorithm and the convolution-based BAA model to simulate the shift-and-add (backproject) tomosynthesis reconstructions. The convolution-based BAA model with proper geometry distortion correction provides reasonably accurate estimates of the tomosynthesis reconstruction. A numerical comparison indicates that the simulated images using the two models differ by less than 6% in terms of the root-mean-squared error. This convolution-based BAA model can be used in efficient system geometry analysis, reconstruction algorithm design, out-of-plane artifacts suppression, and CT-tomosynthesis registration.
Convolution product construction of interactions in probabilistic physical models
International Nuclear Information System (INIS)
Ratsimbarison, H.M.; Raboanary, R.
2007-01-01
This paper aims to give a probabilistic construction of interactions which may be relevant for building physical theories such as interacting quantum field theories. We start with the path integral definition of partition function in quantum field theory which recall us the probabilistic nature of this physical theory. From a Gaussian law considered as free theory, an interacting theory is constructed by nontrivial convolution product between the free theory and an interacting term which is also a probability law. The resulting theory, again a probability law, exhibits two proprieties already present in nowadays theories of interactions such as Gauge theory : the interaction term does not depend on the free term, and two different free theories can be implemented with the same interaction.
Directory of Open Access Journals (Sweden)
Yuankun Li
2018-02-01
Full Text Available Although correlation filter (CF-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.
Optimized parallel convolutions for non-linear fluid models of tokamak ηi turbulence
International Nuclear Information System (INIS)
Milovich, J.L.; Tomaschke, G.; Kerbel, G.D.
1993-01-01
Non-linear computational fluid models of plasma turbulence based on spectral methods typically spend a large fraction of the total computing time evaluating convolutions. Usually these convolutions arise from an explicit or semi implicit treatment of the convective non-linearities in the problem. Often the principal convective velocity is perpendicular to magnetic field lines allowing a reduction of the convolution to two dimensions in an appropriate geometry, but beyond this, different models vary widely in the particulars of which mode amplitudes are selectively evolved to get the most efficient representation of the turbulence. As the number of modes in the problem, N, increases, the amount of computation required for this part of the evolution algorithm then scales as N 2 /timestep for a direct or analytic method and N ln N/timestep for a pseudospectral method. The constants of proportionality depend on the particulars of mode selection and determine the size problem for which the method will perform equally. For large enough N, the pseudospectral method performance is always superior, though some problems do not require correspondingly high resolution. Further, the Courant condition for numerical stability requires that the timestep size must decrease proportionately as N increases, thus accentuating the need to have fast methods for larger N problems. The authors have developed a package for the Cray system which performs these convolutions for a rather arbitrary mode selection scheme using either method. The package is highly optimized using a combination of macro and microtasking techniques, as well as vectorization and in some cases assembly coded routines. Parts of the package have also been developed and optimized for the CM200 and CM5 system. Performance comparisons with respect to problem size, parallelization, selection schemes and architecture are presented
Maximal monotone model with delay term of convolution
Directory of Open Access Journals (Sweden)
Claude-Henri Lamarque
2005-01-01
Full Text Available Mechanical models are governed either by partial differential equations with boundary conditions and initial conditions (e.g., in the frame of continuum mechanics or by ordinary differential equations (e.g., after discretization via Galerkin procedure or directly from the model description with the initial conditions. In order to study dynamical behavior of mechanical systems with a finite number of degrees of freedom including nonsmooth terms (e.g., friction, we consider here problems governed by differential inclusions. To describe effects of particular constitutive laws, we add a delay term. In contrast to previous papers, we introduce delay via a Volterra kernel. We provide existence and uniqueness results by using an Euler implicit numerical scheme; then convergence with its order is established. A few numerical examples are given.
Alidoost, F.; Arefi, H.
2016-06-01
In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings' roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings' roofs automatically considering the complementary nature of height and RGB information.
The Gaussian streaming model and convolution Lagrangian effective field theory
Energy Technology Data Exchange (ETDEWEB)
Vlah, Zvonimir [Stanford Institute for Theoretical Physics and Department of Physics, Stanford University, Stanford, CA 94306 (United States); Castorina, Emanuele; White, Martin, E-mail: zvlah@stanford.edu, E-mail: ecastorina@berkeley.edu, E-mail: mwhite@berkeley.edu [Department of Physics, University of California, Berkeley, CA 94720 (United States)
2016-12-01
We update the ingredients of the Gaussian streaming model (GSM) for the redshift-space clustering of biased tracers using the techniques of Lagrangian perturbation theory, effective field theory (EFT) and a generalized Lagrangian bias expansion. After relating the GSM to the cumulant expansion, we present new results for the real-space correlation function, mean pairwise velocity and pairwise velocity dispersion including counter terms from EFT and bias terms through third order in the linear density, its leading derivatives and its shear up to second order. We discuss the connection to the Gaussian peaks formalism. We compare the ingredients of the GSM to a suite of large N-body simulations, and show the performance of the theory on the low order multipoles of the redshift-space correlation function and power spectrum. We highlight the importance of a general biasing scheme, which we find to be as important as higher-order corrections due to non-linear evolution for the halos we consider on the scales of interest to us.
Supervised Convolutional Sparse Coding
Affara, Lama Ahmed
2018-04-08
Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.
Convolution Model of a Queueing System with the cFIFO Service Discipline
Directory of Open Access Journals (Sweden)
Sławomir Hanczewski
2016-01-01
Full Text Available This article presents an approximate convolution model of a multiservice queueing system with the continuous FIFO (cFIFO service discipline. The model makes it possible to service calls sequentially with variable bit rate, determined by unoccupied (free resources of the multiservice server. As compared to the FIFO discipline, the cFIFO queue utilizes the resources of a multiservice server more effectively. The assumption in the model is that the queueing system is offered a mixture of independent multiservice Bernoulli-Poisson-Pascal (BPP call streams. The article also discusses the results of modelling a number of queueing systems to which different, non-Poissonian, call streams are offered. To verify the accuracy of the model, the results of the analytical calculations are compared with the results of simulation experiments for a number of selected queueing systems. The study has confirmed the accuracy of all adopted theoretical assumptions for the proposed analytical model.
Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.
Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus
2017-01-01
Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.
Directory of Open Access Journals (Sweden)
Yea-Hoon Kwon
2018-04-01
Full Text Available The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG and galvanic skin response (GSR signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.
Dispersion-convolution model for simulating peaks in a flow injection system.
Pai, Su-Cheng; Lai, Yee-Hwong; Chiao, Ling-Yun; Yu, Tiing
2007-01-12
A dispersion-convolution model is proposed for simulating peak shapes in a single-line flow injection system. It is based on the assumption that an injected sample plug is expanded due to a "bulk" dispersion mechanism along the length coordinate, and that after traveling over a distance or a period of time, the sample zone will develop into a Gaussian-like distribution. This spatial pattern is further transformed to a temporal coordinate by a convolution process, and finally a temporal peak image is generated. The feasibility of the proposed model has been examined by experiments with various coil lengths, sample sizes and pumping rates. An empirical dispersion coefficient (D*) can be estimated by using the observed peak position, height and area (tp*, h* and At*) from a recorder. An empirical temporal shift (Phi*) can be further approximated by Phi*=D*/u2, which becomes an important parameter in the restoration of experimental peaks. Also, the dispersion coefficient can be expressed as a second-order polynomial function of the pumping rate Q, for which D*(Q)=delta0+delta1Q+delta2Q2. The optimal dispersion occurs at a pumping rate of Qopt=sqrt[delta0/delta2]. This explains the interesting "Nike-swoosh" relationship between the peak height and pumping rate. The excellent coherence of theoretical and experimental peak shapes confirms that the temporal distortion effect is the dominating reason to explain the peak asymmetry in flow injection analysis.
Glue detection based on teaching points constraint and tracking model of pixel convolution
Geng, Lei; Ma, Xiao; Xiao, Zhitao; Wang, Wen
2018-01-01
On-line glue detection based on machine version is significant for rust protection and strengthening in car production. Shadow stripes caused by reflect light and unevenness of inside front cover of car reduce the accuracy of glue detection. In this paper, we propose an effective algorithm to distinguish the edges of the glue and shadow stripes. Teaching points are utilized to calculate slope between the two adjacent points. Then a tracking model based on pixel convolution along motion direction is designed to segment several local rectangular regions using distance. The distance is the height of rectangular region. The pixel convolution along the motion direction is proposed to extract edges of gules in local rectangular region. A dataset with different illumination and complexity shape stripes are used to evaluate proposed method, which include 500 thousand images captured from the camera of glue gun machine. Experimental results demonstrate that the proposed method can detect the edges of glue accurately. The shadow stripes are distinguished and removed effectively. Our method achieves the 99.9% accuracies for the image dataset.
Liu, Dan; Liu, Xuejun; Wu, Yiguang
2018-04-24
This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results.
Directory of Open Access Journals (Sweden)
Dan Liu
2018-04-01
Full Text Available This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN and a continuous pairwise Conditional Random Field (CRF model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results.
Reliability Modeling of Double Beam Bridge Crane
Han, Zhu; Tong, Yifei; Luan, Jiahui; Xiangdong, Li
2018-05-01
This paper briefly described the structure of double beam bridge crane and the basic parameters of double beam bridge crane are defined. According to the structure and system division of double beam bridge crane, the reliability architecture of double beam bridge crane system is proposed, and the reliability mathematical model is constructed.
A scatter model for fast neutron beams using convolution of diffusion kernels
International Nuclear Information System (INIS)
Moyers, M.F.; Horton, J.L.; Boyer, A.L.
1988-01-01
A new model is proposed to calculate dose distributions in materials irradiated with fast neutron beams. Scattered neutrons are transported away from the point of production within the irradiated material in the forward, lateral and backward directions, while recoil protons are transported in the forward and lateral directions. The calculation of dose distributions, such as for radiotherapy planning, is accomplished by convolving a primary attenuation distribution with a diffusion kernel. The primary attenuation distribution may be quickly calculated for any given set of beam and material conditions as it describes only the magnitude and distribution of first interaction sites. The calculation of energy diffusion kernels is very time consuming but must be calculated only once for a given energy. Energy diffusion distributions shown in this paper have been calculated using a Monte Carlo type of program. To decrease beam calculation time, convolutions are performed using a Fast Fourier Transform technique. (author)
Zhang, Wei; Jiang, Ling; Han, Lei
2018-04-01
Convective storm nowcasting refers to the prediction of the convective weather initiation, development, and decay in a very short term (typically 0 2 h) .Despite marked progress over the past years, severe convective storm nowcasting still remains a challenge. With the boom of machine learning, it has been well applied in various fields, especially convolutional neural network (CNN). In this paper, we build a servere convective weather nowcasting system based on CNN and hidden Markov model (HMM) using reanalysis meteorological data. The goal of convective storm nowcasting is to predict if there is a convective storm in 30min. In this paper, we compress the VDRAS reanalysis data to low-dimensional data by CNN as the observation vector of HMM, then obtain the development trend of strong convective weather in the form of time series. It shows that, our method can extract robust features without any artificial selection of features, and can capture the development trend of strong convective storm.
Convolution based profile fitting
International Nuclear Information System (INIS)
Kern, A.; Coelho, A.A.; Cheary, R.W.
2002-01-01
Full text: In convolution based profile fitting, profiles are generated by convoluting functions together to form the observed profile shape. For a convolution of 'n' functions this process can be written as, Y(2θ)=F 1 (2θ)x F 2 (2θ)x... x F i (2θ)x....xF n (2θ). In powder diffractometry the functions F i (2θ) can be interpreted as the aberration functions of the diffractometer, but in general any combination of appropriate functions for F i (2θ) may be used in this context. Most direct convolution fitting methods are restricted to combinations of F i (2θ) that can be convoluted analytically (e.g. GSAS) such as Lorentzians, Gaussians, the hat (impulse) function and the exponential function. However, software such as TOPAS is now available that can accurately convolute and refine a wide variety of profile shapes numerically, including user defined profiles, without the need to convolute analytically. Some of the most important advantages of modern convolution based profile fitting are: 1) virtually any peak shape and angle dependence can normally be described using minimal profile parameters in laboratory and synchrotron X-ray data as well as in CW and TOF neutron data. This is possible because numerical convolution and numerical differentiation is used within the refinement procedure so that a wide range of functions can easily be incorporated into the convolution equation; 2) it can use physically based diffractometer models by convoluting the instrument aberration functions. This can be done for most laboratory based X-ray powder diffractometer configurations including conventional divergent beam instruments, parallel beam instruments, and diffractometers used for asymmetric diffraction. It can also accommodate various optical elements (e.g. multilayers and monochromators) and detector systems (e.g. point and position sensitive detectors) and has already been applied to neutron powder diffraction systems (e.g. ANSTO) as well as synchrotron based
Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping
2018-01-01
Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results
Mano, Tomohiro; Ohtsuki, Tomi
2017-11-01
The three-dimensional Anderson model is a well-studied model of disordered electron systems that shows the delocalization-localization transition. As in our previous papers on two- and three-dimensional (2D, 3D) quantum phase transitions [J. Phys. Soc. Jpn. 85, 123706 (2016), 86, 044708 (2017)], we used an image recognition algorithm based on a multilayered convolutional neural network. However, in contrast to previous papers in which 2D image recognition was used, we applied 3D image recognition to analyze entire 3D wave functions. We show that a full phase diagram of the disorder-energy plane is obtained once the 3D convolutional neural network has been trained at the band center. We further demonstrate that the full phase diagram for 3D quantum bond and site percolations can be drawn by training the 3D Anderson model at the band center.
Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.
Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun
2016-01-01
Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.
Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping
2018-04-26
With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.
[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].
Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei
2017-08-01
The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.
Convolution copula econometrics
Cherubini, Umberto; Mulinacci, Sabrina
2016-01-01
This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes. This approach allows for an arbitrary dependence structure in the increments and provides a generalization with respect to the standard linear independent increments assumption of classical time series models. The book offers a solution to the problem of a general semiparametric approach, which is given by a concept called C-convolution (convolution of dependent variables), and the corresponding theory of convolution-based copulas. Intended for econometrics and statistics scholars with a special interest in time series analysis and copula functions (or other nonparametric approaches), the book is also useful for doctoral students with a basic knowledge of copula functions wanting to learn about the latest research developments in the field.
Li, Shaobo; Liu, Guokai; Tang, Xianghong; Lu, Jianguang; Hu, Jianjun
2017-07-28
Intelligent machine health monitoring and fault diagnosis are becoming increasingly important for modern manufacturing industries. Current fault diagnosis approaches mostly depend on expert-designed features for building prediction models. In this paper, we proposed IDSCNN, a novel bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks and an improved Dempster-Shafer theory based evidence fusion. The convolutional neural networks take the root mean square (RMS) maps from the FFT (Fast Fourier Transformation) features of the vibration signals from two sensors as inputs. The improved D-S evidence theory is implemented via distance matrix from evidences and modified Gini Index. Extensive evaluations of the IDSCNN on the Case Western Reserve Dataset showed that our IDSCNN algorithm can achieve better fault diagnosis performance than existing machine learning methods by fusing complementary or conflicting evidences from different models and sensors and adapting to different load conditions.
International Nuclear Information System (INIS)
Maluckov, Cedomir A.; Karamarkovic, Jugoslav P.; Radovic, Miodrag K.; Pejovic, Momcilo M.
2004-01-01
The convolution-based model of the electrical breakdown time delay distribution is applied for statistical analysis of experimental results obtained in neon-filled diode tube at 6.5 mbar. At first, the numerical breakdown time delay density distributions are obtained by stochastic modeling as the sum of two independent random variables, the electrical breakdown statistical time delay with exponential, and discharge formative time with Gaussian distribution. Then, the single characteristic breakdown time delay distribution is obtained as the convolution of these two random variables with previously determined parameters. These distributions show good correspondence with the experimental distributions, obtained on the basis of 1000 successive and independent measurements. The shape of distributions is investigated, and corresponding skewness and kurtosis are plotted, in order to follow the transition from Gaussian to exponential distribution
International Nuclear Information System (INIS)
Song, William; Battista, Jerry; Van Dyk, Jake
2004-01-01
The convolution method can be used to model the effect of random geometric uncertainties into planned dose distributions used in radiation treatment planning. This is effectively done by linearly adding infinitesimally small doses, each with a particular geometric offset, over an assumed infinite number of fractions. However, this process inherently ignores the radiobiological dose-per-fraction effect since only the summed physical dose distribution is generated. The resultant potential error on predicted radiobiological outcome [quantified in this work with tumor control probability (TCP), equivalent uniform dose (EUD), normal tissue complication probability (NTCP), and generalized equivalent uniform dose (gEUD)] has yet to be thoroughly quantified. In this work, the results of a Monte Carlo simulation of geometric displacements are compared to those of the convolution method for random geometric uncertainties of 0, 1, 2, 3, 4, and 5 mm (standard deviation). The α/β CTV ratios of 0.8, 1.5, 3, 5, and 10 Gy are used to represent the range of radiation responses for different tumors, whereas a single α/β OAR ratio of 3 Gy is used to represent all the organs at risk (OAR). The analysis is performed on a four-field prostate treatment plan of 18 MV x rays. The fraction numbers are varied from 1-50, with isoeffective adjustments of the corresponding dose-per-fractions to maintain a constant tumor control, using the linear-quadratic cell survival model. The average differences in TCP and EUD of the target, and in NTCP and gEUD of the OAR calculated from the convolution and Monte Carlo methods reduced asymptotically as the total fraction number increased, with the differences reaching negligible levels beyond the treatment fraction number of ≥20. The convolution method generally overestimates the radiobiological indices, as compared to the Monte Carlo method, for the target volume, and underestimates those for the OAR. These effects are interconnected and attributed
Mei, Shuang; Wang, Yudan; Wen, Guojun
2018-04-02
Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.
Directory of Open Access Journals (Sweden)
Shuang Mei
2018-04-01
Full Text Available Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality. Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.
Lusch, Bethany; Weholt, Jake; Maia, Pedro D; Kutz, J Nathan
2018-06-01
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular level in vivo. We simulate the effects of neurodegenerative disease and TBI using convolutional neural networks (CNNs) as our model of cognition. We utilize biophysically relevant statistical data on FAS to damage the connections in CNNs in a functionally relevant way. We incorporate energy constraints on the brain by pruning the CNNs to be less over-engineered. Qualitatively, we demonstrate that damage leads to human-like mistakes. Our experiments also provide quantitative assessments of how accuracy is affected by various types and levels of damage. The deficit resulting from a fixed amount of damage greatly depends on which connections are randomly injured, providing intuition for why it is difficult to predict impairments. There is a large degree of subjectivity when it comes to interpreting cognitive deficits from complex systems such as the human brain. However, we provide important insight and a quantitative framework for disorders in which FAS are implicated. Copyright © 2018 Elsevier Inc. All rights reserved.
Wang, C.; Hong, Y.
2017-12-01
Infrared (IR) information from Geostationary satellites can be used to retrieve precipitation at pretty high spatiotemporal resolutions. Traditional artificial intelligence (AI) methodologies, such as artificial neural networks (ANN), have been designed to build the relationship between near-surface precipitation and manually derived IR features in products including PERSIANN and PERSIANN-CCS. This study builds an automatic precipitation detection model based on IR data using Convolutional Neural Network (CNN) which is implemented by the newly developed deep learning framework, Caffe. The model judges whether there is rain or no rain at pixel level. Compared with traditional ANN methods, CNN can extract features inside the raw data automatically and thoroughly. In this study, IR data from GOES satellites and precipitation estimates from the next generation QPE (Q2) over the central United States are used as inputs and labels, respectively. The whole datasets during the study period (June to August in 2012) are randomly partitioned to three sub datasets (train, validation and test) to establish the model at the spatial resolution of 0.08°×0.08° and the temporal resolution of 1 hour. The experiments show great improvements of CNN in rain identification compared to the widely used IR-based precipitation product, i.e., PERSIANN-CCS. The overall gain in performance is about 30% for critical success index (CSI), 32% for probability of detection (POD) and 12% for false alarm ratio (FAR). Compared to other recent IR-based precipitation retrieval methods (e.g., PERSIANN-DL developed by University of California Irvine), our model is simpler with less parameters, but achieves equally or even better results. CNN has been applied in computer vision domain successfully, and our results prove the method is suitable for IR precipitation detection. Future studies can expand the application of CNN from precipitation occurrence decision to precipitation amount retrieval.
Fundamentals of convolutional coding
Johannesson, Rolf
2015-01-01
Fundamentals of Convolutional Coding, Second Edition, regarded as a bible of convolutional coding brings you a clear and comprehensive discussion of the basic principles of this field * Two new chapters on low-density parity-check (LDPC) convolutional codes and iterative coding * Viterbi, BCJR, BEAST, list, and sequential decoding of convolutional codes * Distance properties of convolutional codes * Includes a downloadable solutions manual
Wang, Shuo; Zhou, Mu; Liu, Zaiyi; Liu, Zhenyu; Gu, Dongsheng; Zang, Yali; Dong, Di; Gevaert, Olivier; Tian, Jie
2017-08-01
Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations. We describe this phenomenon by proposing a novel central pooling layer retaining much information on voxel patch center, followed by a multi-scale patch learning strategy. Moreover, we design a weighted sampling to facilitate the model training, where training samples are selected according to their degree of segmentation difficulty. The proposed method has been extensively evaluated on the public LIDC dataset including 893 nodules and an independent dataset with 74 nodules from Guangdong General Hospital (GDGH). We showed that CF-CNN achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively. Moreover, we compared our results with the inter-radiologists consistency on LIDC dataset, showing a difference in average dice score of only 1.98%. Copyright © 2017. Published by Elsevier B.V.
Fast Convolution Module (Fast Convolution Module)
National Research Council Canada - National Science Library
Bierens, L
1997-01-01
This report describes the design and realisation of a real-time range azimuth compression module, the so-called 'Fast Convolution Module', based on the fast convolution algorithm developed at TNO-FEL...
International Nuclear Information System (INIS)
Boellaard, Ronald; Herk, Marcel van; Uiterwaal, Hans; Mijnheer, Ben
1997-01-01
Background and purpose: To determine the accuracy of two-dimensional exit dose measurements with an electronic portal imaging device, EPID, using a convolution model for a variety of clinically relevant situations. Materials and methods: Exit doses were derived from portal dose images, obtained with a liquid-filled EPID at distances of 50 cm or more behind the patient, by using a convolution model. The resulting on- and off-axis exit dose values were first compared with ionization chamber exit dose measurements for homogeneous and inhomogeneous phantoms in open and wedged 4,8 and 18 MV photon beams. The accuracy of the EPID exit dose measurements was then determined for a number of anthropomorphic phantoms (lung and larynx) irradiated under clinical conditions and for a few patients treated in an 8 MV beam. The latter results were compared with in vivo exit dose measurements using diodes. Results: The exit dose can be determined from portal images with an accuracy of 1.2% (1 SD) compared with ionization chamber measurements for open beams and homogeneous phantoms at all tested beam qualities. In the presence of wedges and for inhomogeneous phantoms the average relative accuracy slightly deteriorated to 1.7% (1 SD). For lung phantoms in a 4 MV beam a similar accuracy was obtained after refinement of our convolution model, which requires knowledge of the patient contour. Differences between diode and EPID exit dose measurements for an anthropomorphic lung phantom in an 8 MV beam were 2.5% at most, with an average agreement within 1% (1 SD). For larynx phantoms in a 4 MV beam exit doses obtained with an ionization chamber and EPID agreed within 1.5% (1 SD). Finally, exit doses in a few patients irradiated in an 8 MV beam could be determined with the EPID with an accuracy of 1.1% (1 SD) relative to exit dose measurements using diodes. Conclusions: Portal images, obtained with our EPID and analyzed with our convolution model, can be used to determine the exit dose
Laser alignment measurement model with double beam
Mo, Changtao; Zhang, Lili; Hou, Xianglin; Wang, Ming; Lv, Jia; Du, Xin; He, Ping
2012-10-01
Double LD-Double PSD schedule.employ a symmetric structure and there are a laser and a PSD receiver on each axis. The Double LD-Double PSD is used, and the rectangular coordinate system is set up by use of the relationship of arbitrary two points coordinates, and then the parameter formula is deduced by the knowledge of solid geometry. Using the data acquisition system and the data processing model of laser alignment meter with double laser beam and two detector , basing on the installation parameter of the computer, we can have the state parameter between the two shafts by more complicated calculation and correction. The correcting data of the four under chassis of the adjusted apparatus moving on the level and the vertical plane can be calculated using the computer. This will instruct us to move the apparatus to align the shafts.
International Nuclear Information System (INIS)
Gao Qing; Chen Huafu; Gong Qiyong
2009-01-01
Brain asymmetry is a phenomenon well known for handedness, and has been studied in the motor cortex. However, few studies have quantitatively assessed the asymmetrical cortical activities for handedness in motor areas. In the present study, we systematically and quantitatively investigated asymmetry in the left and right primary motor cortices during sequential finger movements using the Gaussian convolution model approach based on the functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) response. Six right-handed and six left-handed subjects were recruited to perform three types of hand movement tasks. The results for the expected value of the Gaussian convolution model showed that it took the dominant hand a longer average interval of response delay regardless of the handedness and bi- or uni-manual performance. The results for the standard deviation of the Gaussian model suggested that in the mass neurons, these intervals of the dominant hand were much more variable than those of the non-dominant hand. When comparing bi-manual movement conditions with uni-manual movement conditions in the primary motor cortex (PMC), both the expected value and standard deviation in the Gaussian function were significantly smaller (p < 0.05) in the bi-manual conditions, showing that the movement of the non-dominant hand influenced that of the dominant hand.
Energy Technology Data Exchange (ETDEWEB)
Gao Qing [School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054 (China); School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054 (China); Chen Huafu [School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054 (China); School of Applied Mathematics, University of Electronic Science and Technology of China, Chengdu 610054 (China)], E-mail: Chenhf@uestc.edu.cn; Gong Qiyong [Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041 (China)
2009-10-30
Brain asymmetry is a phenomenon well known for handedness, and has been studied in the motor cortex. However, few studies have quantitatively assessed the asymmetrical cortical activities for handedness in motor areas. In the present study, we systematically and quantitatively investigated asymmetry in the left and right primary motor cortices during sequential finger movements using the Gaussian convolution model approach based on the functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) response. Six right-handed and six left-handed subjects were recruited to perform three types of hand movement tasks. The results for the expected value of the Gaussian convolution model showed that it took the dominant hand a longer average interval of response delay regardless of the handedness and bi- or uni-manual performance. The results for the standard deviation of the Gaussian model suggested that in the mass neurons, these intervals of the dominant hand were much more variable than those of the non-dominant hand. When comparing bi-manual movement conditions with uni-manual movement conditions in the primary motor cortex (PMC), both the expected value and standard deviation in the Gaussian function were significantly smaller (p < 0.05) in the bi-manual conditions, showing that the movement of the non-dominant hand influenced that of the dominant hand.
A convolutional approach to reflection symmetry
DEFF Research Database (Denmark)
Cicconet, Marcelo; Birodkar, Vighnesh; Lund, Mads
2017-01-01
We present a convolutional approach to reflection symmetry detection in 2D. Our model, built on the products of complex-valued wavelet convolutions, simplifies previous edge-based pairwise methods. Being parameter-centered, as opposed to feature-centered, it has certain computational advantages w...
Allman, Derek; Reiter, Austin; Bell, Muyinatu
2018-02-01
We previously proposed a method of removing reflection artifacts in photoacoustic images that uses deep learning. Our approach generally relies on using simulated photoacoustic channel data to train a convolutional neural network (CNN) that is capable of distinguishing sources from artifacts based on unique differences in their spatial impulse responses (manifested as depth-based differences in wavefront shapes). In this paper, we directly compare a CNN trained with our previous continuous transducer model to a CNN trained with an updated discrete acoustic receiver model that more closely matches an experimental ultrasound transducer. These two CNNs were trained with simulated data and tested on experimental data. The CNN trained using the continuous receiver model correctly classified 100% of sources and 70.3% of artifacts in the experimental data. In contrast, the CNN trained using the discrete receiver model correctly classified 100% of sources and 89.7% of artifacts in the experimental images. The 19.4% increase in artifact classification accuracy indicates that an acoustic receiver model that closely mimics the experimental transducer plays an important role in improving the classification of artifacts in experimental photoacoustic data. Results are promising for developing a method to display CNN-based images that remove artifacts in addition to only displaying network-identified sources as previously proposed.
Improved double Q2 rescaling model
International Nuclear Information System (INIS)
Gao Yonghua
2001-01-01
The authors present an improved double Q 2 rescaling model. Based on this condition of the nuclear momentum conservation, the authors have found a Q 2 rescaling parameters' formula of the model, where authors have established the connection between the Q 2 rescaling parameter ζ i (i = v, s, g) and the mean binding energy in nucleus. By using this model, the authors coned explain the experimental data of the EMC effect in the whole x region, the nuclear Drell-Yan process and J/Ψ photoproduction process
Inviscid double wake model for stalled airfoils
International Nuclear Information System (INIS)
Marion, L; Ramos-García, N; Sørensen, J N
2014-01-01
An inviscid double wake model based on a steady two-dimensional panel method has been developed to predict aerodynamic loads of wind turbine airfoils in the deep stall region. The separated flow is modelled using two constant vorticity sheets which are released at the trailing edge and at the separation point. A calibration of the code through comparison with experiments has been performed using one set of airfoils. A second set of airfoils has been used for the validation of the calibrated model. Predicted aerodynamic forces for a wide range of angles of attack (0 to 90 deg) are in overall good agreement with wind tunnel measurements
Double beta decay and neutrino mass models
Energy Technology Data Exchange (ETDEWEB)
Helo, J.C. [Universidad Técnica Federico Santa María, Centro-Científico-Tecnológico de Valparaíso, Casilla 110-V, Valparaíso (Chile); Hirsch, M. [AHEP Group, Instituto de Física Corpuscular - C.S.I.C./Universitat de València, Edificio de Institutos de Paterna, Apartado 22085, E-46071 València (Spain); Ota, T. [Department of Physics, Saitama University, Shimo-Okubo 255, 338-8570 Saitama-Sakura (Japan); Santos, F.A. Pereira dos [Departamento de Física, Pontifícia Universidade Católica do Rio de Janeiro,Rua Marquês de São Vicente 225, 22451-900 Gávea, Rio de Janeiro (Brazil)
2015-05-19
Neutrinoless double beta decay allows to constrain lepton number violating extensions of the standard model. If neutrinos are Majorana particles, the mass mechanism will always contribute to the decay rate, however, it is not a priori guaranteed to be the dominant contribution in all models. Here, we discuss whether the mass mechanism dominates or not from the theory point of view. We classify all possible (scalar-mediated) short-range contributions to the decay rate according to the loop level, at which the corresponding models will generate Majorana neutrino masses, and discuss the expected relative size of the different contributions to the decay rate in each class. Our discussion is general for models based on the SM group but does not cover models with an extended gauge. We also work out the phenomenology of one concrete 2-loop model in which both, mass mechanism and short-range diagram, might lead to competitive contributions, in some detail.
Hu, Shuai; Gao, Taichang; Li, Hao; Yang, Bo; Jiang, Zidong; Liu, Lei; Chen, Ming
2017-10-01
The performance of absorbing boundary condition (ABC) is an important factor influencing the simulation accuracy of MRTD (Multi-Resolution Time-Domain) scattering model for non-spherical aerosol particles. To this end, the Convolution Perfectly Matched Layer (CPML), an excellent ABC in FDTD scheme, is generalized and applied to the MRTD scattering model developed by our team. In this model, the time domain is discretized by exponential differential scheme, and the discretization of space domain is implemented by Galerkin principle. To evaluate the performance of CPML, its simulation results are compared with those of BPML (Berenger's Perfectly Matched Layer) and ADE-PML (Perfectly Matched Layer with Auxiliary Differential Equation) for spherical and non-spherical particles, and their simulation errors are analyzed as well. The simulation results show that, for scattering phase matrices, the performance of CPML is better than that of BPML; the computational accuracy of CPML is comparable to that of ADE-PML on the whole, but at scattering angles where phase matrix elements fluctuate sharply, the performance of CPML is slightly better than that of ADE-PML. After orientation averaging process, the differences among the results of different ABCs are reduced to some extent. It also can be found that ABCs have a much weaker influence on integral scattering parameters (such as extinction and absorption efficiencies) than scattering phase matrices, this phenomenon can be explained by the error averaging process in the numerical volume integration.
Karimi, Davood; Samei, Golnoosh; Kesch, Claudia; Nir, Guy; Salcudean, Septimiu E
2018-05-15
Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.
MODELING THE DOUBLE WORM-FACE GEARS
Directory of Open Access Journals (Sweden)
BOLOŞ Codruţa
2015-06-01
Full Text Available The worm-face gear family, invented 60 years ago, contains in its structure several variants which have the following defining elements: tapered worm, reverse tapered worm and cylindrical worm. This type of gear can be realized with a single wheel and also in engagement with the second embodiment of the front worm wheels. This paper presents the matrix - vectorial mathematical model of the double worm-face gear with cylindrical worm and a graphical modeling which is based on the specific geometrical characteristics accomplished by means of the Autodesk Inventor 3D modeling program. The applicability of the study, considering the solutions which it suggests, aims to create opportunities for the use of modern rapid prototyping and analysis of stress FEM technique.
Jozwik, Kamila M.; Kriegeskorte, Nikolaus; Storrs, Katherine R.; Mur, Marieke
2017-01-01
Recent advances in Deep convolutional Neural Networks (DNNs) have enabled unprecedentedly accurate computational models of brain representations, and present an exciting opportunity to model diverse cognitive functions. State-of-the-art DNNs achieve human-level performance on object categorisation, but it is unclear how well they capture human behavior on complex cognitive tasks. Recent reports suggest that DNNs can explain significant variance in one such task, judging object similarity. Here, we extend these findings by replicating them for a rich set of object images, comparing performance across layers within two DNNs of different depths, and examining how the DNNs’ performance compares to that of non-computational “conceptual” models. Human observers performed similarity judgments for a set of 92 images of real-world objects. Representations of the same images were obtained in each of the layers of two DNNs of different depths (8-layer AlexNet and 16-layer VGG-16). To create conceptual models, other human observers generated visual-feature labels (e.g., “eye”) and category labels (e.g., “animal”) for the same image set. Feature labels were divided into parts, colors, textures and contours, while category labels were divided into subordinate, basic, and superordinate categories. We fitted models derived from the features, categories, and from each layer of each DNN to the similarity judgments, using representational similarity analysis to evaluate model performance. In both DNNs, similarity within the last layer explains most of the explainable variance in human similarity judgments. The last layer outperforms almost all feature-based models. Late and mid-level layers outperform some but not all feature-based models. Importantly, categorical models predict similarity judgments significantly better than any DNN layer. Our results provide further evidence for commonalities between DNNs and brain representations. Models derived from visual features
Energy Technology Data Exchange (ETDEWEB)
Campione, Salvatore [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Warne, Larry K. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sainath, Kamalesh [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Basilio, Lorena I. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-10-01
In this report we overview the fundamental concepts for a pair of techniques which together greatly hasten computational predictions of electromagnetic pulse (EMP) excitation of finite-length dissipative conductors over a ground plane. In a time- domain, transmission line (TL) model implementation, predictions are computationally bottlenecked time-wise, either for late-time predictions (about 100ns-10000ns range) or predictions concerning EMP excitation of long TLs (order of kilometers or more ). This is because the method requires a temporal convolution to account for the losses in the ground. Addressing this to facilitate practical simulation of EMP excitation of TLs, we first apply a technique to extract an (approximate) complex exponential function basis-fit to the ground/Earth's impedance function, followed by incorporating this into a recursion-based convolution acceleration technique. Because the recursion-based method only requires the evaluation of the most recent voltage history data (versus the entire history in a "brute-force" convolution evaluation), we achieve necessary time speed- ups across a variety of TL/Earth geometry/material scenarios. Intentionally Left Blank
2.5-D poroelastic wave modelling in double porosity media
Liu, Xu; Greenhalgh, Stewart; Wang, Yanghua
2011-09-01
To approximate seismic wave propagation in double porosity media, the 2.5-D governing equations of poroelastic waves are developed and numerically solved. The equations are obtained by taking a Fourier transform in the strike or medium-invariant direction over all of the field quantities in the 3-D governing equations. The new memory variables from the Zener model are suggested as a way to represent the sum of the convolution integrals for both the solid particle velocity and the macroscopic fluid flux in the governing equations. By application of the memory equations, the field quantities at every time step need not be stored. However, this approximation allows just two Zener relaxation times to represent the very complex double porosity and dual permeability attenuation mechanism, and thus reduce the difficulty. The 2.5-D governing equations are numerically solved by a time-splitting method for the non-stiff parts and an explicit fourth-order Runge-Kutta method for the time integration and a Fourier pseudospectral staggered-grid for handling the spatial derivative terms. The 2.5-D solution has the advantage of producing a 3-D wavefield (point source) for a 2-D model but is much more computationally efficient than the full 3-D solution. As an illustrative example, we firstly show the computed 2.5-D wavefields in a homogeneous single porosity model for which we reformulated an analytic solution. Results for a two-layer, water-saturated double porosity model and a laterally heterogeneous double porosity structure are also presented.
Double diffusivity model under stochastic forcing
Chattopadhyay, Amit K.; Aifantis, Elias C.
2017-05-01
The "double diffusivity" model was proposed in the late 1970s, and reworked in the early 1980s, as a continuum counterpart to existing discrete models of diffusion corresponding to high diffusivity paths, such as grain boundaries and dislocation lines. It was later rejuvenated in the 1990s to interpret experimental results on diffusion in polycrystalline and nanocrystalline specimens where grain boundaries and triple grain boundary junctions act as high diffusivity paths. Technically, the model pans out as a system of coupled Fick-type diffusion equations to represent "regular" and "high" diffusivity paths with "source terms" accounting for the mass exchange between the two paths. The model remit was extended by analogy to describe flow in porous media with double porosity, as well as to model heat conduction in media with two nonequilibrium local temperature baths, e.g., ion and electron baths. Uncoupling of the two partial differential equations leads to a higher-ordered diffusion equation, solutions of which could be obtained in terms of classical diffusion equation solutions. Similar equations could also be derived within an "internal length" gradient (ILG) mechanics formulation applied to diffusion problems, i.e., by introducing nonlocal effects, together with inertia and viscosity, in a mechanics based formulation of diffusion theory. While being remarkably successful in studies related to various aspects of transport in inhomogeneous media with deterministic microstructures and nanostructures, its implications in the presence of stochasticity have not yet been considered. This issue becomes particularly important in the case of diffusion in nanopolycrystals whose deterministic ILG-based theoretical calculations predict a relaxation time that is only about one-tenth of the actual experimentally verified time scale. This article provides the "missing link" in this estimation by adding a vital element in the ILG structure, that of stochasticity, that takes into
Dou, Hao; Sun, Xiao; Li, Bin; Deng, Qianqian; Yang, Xubo; Liu, Di; Tian, Jinwen
2018-03-01
Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.
Convolutive ICA for Spatio-Temporal Analysis of EEG
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, Scott; Hansen, Lars Kai
2007-01-01
in the convolutive model can be correctly detected using Bayesian model selection. We demonstrate a framework for deconvolving an EEG ICA subspace. Initial results suggest that in some cases convolutive mixing may be a more realistic model for EEG signals than the instantaneous ICA model....
Hyper-chaos encryption using convolutional masking and model free unmasking
International Nuclear Information System (INIS)
Qi Guo-Yuan; Matondo Sandra Bazebo
2014-01-01
In this paper, during the masking process the encrypted message is convolved and embedded into a Qi hyper-chaotic system characterizing a high disorder degree. The masking scheme was tested using both Qi hyper-chaos and Lorenz chaos and indicated that Qi hyper-chaos based masking can resist attacks of the filtering and power spectrum analysis, while the Lorenz based scheme fails for high amplitude data. To unmask the message at the receiving end, two methods are proposed. In the first method, a model-free synchronizer, i.e. a multivariable higher-order differential feedback controller between the transmitter and receiver is employed to de-convolve the message embedded in the receiving signal. In the second method, no synchronization is required since the message is de-convolved using the information of the estimated derivative. (general)
Double generalized linear compound poisson models to insurance claims data
DEFF Research Database (Denmark)
Andersen, Daniel Arnfeldt; Bonat, Wagner Hugo
2017-01-01
This paper describes the specification, estimation and comparison of double generalized linear compound Poisson models based on the likelihood paradigm. The models are motivated by insurance applications, where the distribution of the response variable is composed by a degenerate distribution...... implementation and illustrate the application of double generalized linear compound Poisson models using a data set about car insurances....
Evaluating the double Poisson generalized linear model.
Zou, Yaotian; Geedipally, Srinivas Reddy; Lord, Dominique
2013-10-01
The objectives of this study are to: (1) examine the applicability of the double Poisson (DP) generalized linear model (GLM) for analyzing motor vehicle crash data characterized by over- and under-dispersion and (2) compare the performance of the DP GLM with the Conway-Maxwell-Poisson (COM-Poisson) GLM in terms of goodness-of-fit and theoretical soundness. The DP distribution has seldom been investigated and applied since its first introduction two decades ago. The hurdle for applying the DP is related to its normalizing constant (or multiplicative constant) which is not available in closed form. This study proposed a new method to approximate the normalizing constant of the DP with high accuracy and reliability. The DP GLM and COM-Poisson GLM were developed using two observed over-dispersed datasets and one observed under-dispersed dataset. The modeling results indicate that the DP GLM with its normalizing constant approximated by the new method can handle crash data characterized by over- and under-dispersion. Its performance is comparable to the COM-Poisson GLM in terms of goodness-of-fit (GOF), although COM-Poisson GLM provides a slightly better fit. For the over-dispersed data, the DP GLM performs similar to the NB GLM. Considering the fact that the DP GLM can be easily estimated with inexpensive computation and that it is simpler to interpret coefficients, it offers a flexible and efficient alternative for researchers to model count data. Copyright © 2013 Elsevier Ltd. All rights reserved.
Unsteady Double Wake Model for the Simulation of Stalled Airfoils
DEFF Research Database (Denmark)
Ramos García, Néstor; Cayron, Antoine; Sørensen, Jens Nørkær
2015-01-01
In the present work, the recent developed Unsteady Double Wake Model, USDWM, is used to simulate separated flows past a wind turbine airfoil at high angles of attack. The solver is basically an unsteady two-dimensional panel method which uses the unsteady double wake technique to model flow separ...
International Nuclear Information System (INIS)
Cademartiri, Filippo; Palumbo, Alessandro; La Grutta, Ludovico; Runza, Giuseppe; Maffei, Erica; Mollet, Nico R.; Hamers, Ronald; Bruining, Nico; Bartolotta, Tommaso V.; Midiri, Massimo; Somers, Pamela; Knaapen, Michiel; Verheye, Stefan
2007-01-01
Attenuation variability (measured in Hounsfield Units, HU) of human coronary plaques using multislice computed tomography (MSCT) was evaluated in an ex vivo model with increasing convolution kernels. MSCT was performed in seven ex vivo left coronary arteries sunk into oil followingthe instillation of saline (1/∞) and a 1/50 solution of contrast material (400 mgI/ml iomeprol). Scan parameters were: slices/collimation, 16/0.75 mm; rotation time, 375 ms. Four convolution kernels were used: b30f-smooth, b36f-medium smooth, b46f-medium and b60f-sharp. An experienced radiologist scored for the presence of plaques and measured the attenuation in lumen, calcified and noncalcified plaques and the surrounding oil. The results were compared by the ANOVA test and correlated with Pearson's test. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The mean attenuation values were significantly different between the four filters (p < 0.0001) in each structure with both solutions. After clustering for the filter, all of the noncalcified plaque values (20.8 ± 39.1, 14.2 ± 35.8, 14.0 ± 32.0, 3.2 ± 32.4 HU with saline; 74.7 ± 66.6, 68.2 ± 63.3, 66.3 ± 66.5, 48.5 ± 60.0 HU in contrast solution) were significantly different, with the exception of the pair b36f-b46f, for which a moderate-high correlation was generally found. Improved SNRs and CNRs were achieved by b30f and b46f. The use of different convolution filters significantly modified the attenuation values, while sharper filtering increased the calcified plaque attenuation and reduced the noncalcified plaque attenuation. (orig.)
Directory of Open Access Journals (Sweden)
Ammar Guellab
2018-01-01
Full Text Available We propose an efficient finite difference time domain (FDTD method based on the piecewise linear recursive convolution (PLRC technique to evaluate the human body exposure to electromagnetic (EM radiation. The source of radiation considered in this study is a high-power antenna, mounted on a military vehicle, covering a broad band of frequency (100 MHz–3 GHz. The simulation is carried out using a nonhomogeneous human body model which takes into consideration most of the internal body tissues. The human tissues are modeled by a four-pole Debye model which is derived from experimental data by using particle swarm optimization (PSO. The human exposure to EM radiation is evaluated by computing the local and whole-body average specific absorption rate (SAR for each occupant. The higher in-tissue electric field intensity points are localized, and the SAR values are compared with the crew safety standard recommendations. The accuracy of the proposed PLRC-FDTD approach and the matching of the Debye model with the experimental data are verified in this study.
Efficient convolutional sparse coding
Wohlberg, Brendt
2017-06-20
Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.
Multithreaded implicitly dealiased convolutions
Roberts, Malcolm; Bowman, John C.
2018-03-01
Implicit dealiasing is a method for computing in-place linear convolutions via fast Fourier transforms that decouples work memory from input data. It offers easier memory management and, for long one-dimensional input sequences, greater efficiency than conventional zero-padding. Furthermore, for convolutions of multidimensional data, the segregation of data and work buffers can be exploited to reduce memory usage and execution time significantly. This is accomplished by processing and discarding data as it is generated, allowing work memory to be reused, for greater data locality and performance. A multithreaded implementation of implicit dealiasing that accepts an arbitrary number of input and output vectors and a general multiplication operator is presented, along with an improved one-dimensional Hermitian convolution that avoids the loop dependency inherent in previous work. An alternate data format that can accommodate a Nyquist mode and enhance cache efficiency is also proposed.
Heinrich, Mattias P; Blendowski, Max; Oktay, Ozan
2018-05-30
Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU). We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions. Our solution enables the removal of the expensive floating-point matrix multiplications throughout any convolutional neural network and replaces them by energy- and time-preserving binary operators and population counts. We evaluate our approach for the segmentation of the pancreas in CT. Here, our ternary approximation within a fully convolutional network leads to more than 90% memory reductions and high accuracy (without any post-processing) with a Dice overlap of 71.0% that comes close to the one obtained when using networks with high-precision weights and activations. We further provide a concept for sub-second inference without GPUs and demonstrate significant improvements in comparison with binary quantisation and without our proposed ternary hyperbolic tangent continuation. We present a key enabling technique for highly efficient DCNN inference without GPUs that will help to bring the advances of deep learning to practical clinical applications. It has also great promise for improving accuracies in large-scale medical data retrieval.
An exactly soluble model of a shallow double well
Energy Technology Data Exchange (ETDEWEB)
Muñoz-Vega, R., E-mail: rodrigo.munoz@uacm.edu.mx [Universidad Autónoma de la Ciudad de México, Centro Histórico, Fray Servando Teresa de Mier 92, Col. Centro, Del. Cuauhtémoc, México DF, CP 06080 (Mexico); López-Chávez, E., E-mail: elopezc@hotmail.com [Universidad Autónoma de la Ciudad de México, Centro Histórico, Fray Servando Teresa de Mier 92, Col. Centro, Del. Cuauhtémoc, México DF, CP 06080 (Mexico); Salinas-Hernández, E., E-mail: esalinas@ipn.mx [ESCOM-IPN, Av Juan de Dios Bátiz s/n, Unidad Profesional Adolfo López Mateos, Col Lindavista, Del G A Madero, México DF, CP 07738 (Mexico); Flores-Godoy, J.-J., E-mail: job.flores@ibero.mx [Departamento de Física y Matemáticas, Universidad Iberoamericana, Prol. Paseo de la Reforma 880, Col Lomas de Santa Fe, Del A Obregón, México DF, CP 01219 (Mexico); Fernández-Anaya, G., E-mail: guillermo.fernandez@ibero.com [Departamento de Física y Matemáticas, Universidad Iberoamericana, Prol. Paseo de la Reforma 880, Col Lomas de Santa Fe, Del A Obregón, México DF, CP 01219 (Mexico)
2014-06-13
Shallow one-dimensional double-well potentials appear in atomic and molecular physics and other fields. Unlike the “deep” wells of macroscopic quantum coherent systems, shallow double wells need not present low-lying two-level systems. We argue that this feature, the absence of a low-lying two-level system in certain shallow double wells, may allow the finding of new test grounds for quantum mechanics in mesoscopic systems. We illustrate the above ideas with a family of shallow double wells obtained from reflectionless potentials through the Darboux–Bäcklund transform. - Highlights: • We present double wells not conforming to the low-lying two-state system model. • Models similar to ours appear in atomic and molecular physics. • Here there is no classically prohibited region between wells. • The ground probability is peaked at the position of classical unstable equilibrium in this models.
Nuclear norm regularized convolutional Max Pos@Top machine
Li, Qinfeng; Zhou, Xiaofeng; Gu, Aihua; Li, Zonghua; Liang, Ru-Ze
2016-01-01
, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose
Parity doubling in the baryon string model
International Nuclear Information System (INIS)
Khokhlachev, S.B.
1990-01-01
The nature of parity doubling of baryon states with non-zero angular momentum is considered. The idea of explaining this phenomenon lies in the fact that the rotation of the gluon string leads to a centrifugal potential for quarks. The quarks on the string form a quark-diquark system. Quark tunneling from one end of the string to the other is not probable for systems with large angular momentum due to a large centrifugal potential, and the smallness of the underbarrier transition amplitude explains the small mass difference of the states with opposite parity. (orig.)
Symmetry breaking in the double-well hermitian matrix models
Brower, R C; Jain, S; Tan, C I; Brower, Richard C.; Deo, Nevidita; Jain, Sanjay; Tan, Chung-I
1993-01-01
We study symmetry breaking in $Z_2$ symmetric large $N$ matrix models. In the planar approximation for both the symmetric double-well $\\phi^4$ model and the symmetric Penner model, we find there is an infinite family of broken symmetry solutions characterized by different sets of recursion coefficients $R_n$ and $S_n$ that all lead to identical free energies and eigenvalue densities. These solutions can be parameterized by an arbitrary angle $\\theta(x)$, for each value of $x = n/N < 1$. In the double scaling limit, this class reduces to a smaller family of solutions with distinct free energies already at the torus level. For the double-well $\\phi^4$ theory the double scaling string equations are parameterized by a conserved angular momentum parameter in the range $0 \\le l < \\infty$ and a single arbitrary $U(1)$ phase angle.
Directory of Open Access Journals (Sweden)
Giao N. Pham
2018-03-01
Full Text Available With the development of 3D printing, weapons are easily printed without any restriction from the production managers. Therefore, anti-3D weapon model detection is necessary issue in safe 3D printing to prevent the printing of 3D weapon models. In this paper, we would like to propose an anti-3D weapon model detection algorithm to prevent the printing of anti-3D weapon models for safe 3D printing based on the D2 shape distribution and an improved convolutional neural networks (CNNs. The purpose of the proposed algorithm is to detect anti-3D weapon models when they are used in 3D printing. The D2 shape distribution is computed from random points on the surface of a 3D weapon model and their geometric features in order to construct a D2 vector. The D2 vector is then trained by improved CNNs. The CNNs are used to detect anti-3D weapon models for safe 3D printing by training D2 vectors which have been constructed from the D2 shape distribution of 3D weapon models. Experiments with 3D weapon models proved that the D2 shape distribution of 3D weapon models in the same class is the same. Training and testing results also verified that the accuracy of the proposed algorithm is higher than the conventional works. The proposed algorithm is applied in a small application, and it could detect anti-3D weapon models for safe 3D printing.
Analytical Model of Planar Double Split Ring Resonator
DEFF Research Database (Denmark)
Zhurbenko, Vitaliy; Jensen, Thomas; Krozer, Viktor
2007-01-01
This paper focuses on accurate modelling of microstrip double split ring resonators. The impedance matrix representation for coupled lines is applied for the first time to model the SRR, resulting in excellent model accuracy over a wide frequency range. Phase compensation is implemented to take i...
Convolutional Neural Networks - Generalizability and Interpretations
DEFF Research Database (Denmark)
Malmgren-Hansen, David
from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...
Convolution-deconvolution in DIGES
International Nuclear Information System (INIS)
Philippacopoulos, A.J.; Simos, N.
1995-01-01
Convolution and deconvolution operations is by all means a very important aspect of SSI analysis since it influences the input to the seismic analysis. This paper documents some of the convolution/deconvolution procedures which have been implemented into the DIGES code. The 1-D propagation of shear and dilatational waves in typical layered configurations involving a stack of layers overlying a rock is treated by DIGES in a similar fashion to that of available codes, e.g. CARES, SHAKE. For certain configurations, however, there is no need to perform such analyses since the corresponding solutions can be obtained in analytic form. Typical cases involve deposits which can be modeled by a uniform halfspace or simple layered halfspaces. For such cases DIGES uses closed-form solutions. These solutions are given for one as well as two dimensional deconvolution. The type of waves considered include P, SV and SH waves. The non-vertical incidence is given special attention since deconvolution can be defined differently depending on the problem of interest. For all wave cases considered, corresponding transfer functions are presented in closed-form. Transient solutions are obtained in the frequency domain. Finally, a variety of forms are considered for representing the free field motion both in terms of deterministic as well as probabilistic representations. These include (a) acceleration time histories, (b) response spectra (c) Fourier spectra and (d) cross-spectral densities
A Unimodal Model for Double Observer Distance Sampling Surveys.
Directory of Open Access Journals (Sweden)
Earl F Becker
Full Text Available Distance sampling is a widely used method to estimate animal population size. Most distance sampling models utilize a monotonically decreasing detection function such as a half-normal. Recent advances in distance sampling modeling allow for the incorporation of covariates into the distance model, and the elimination of the assumption of perfect detection at some fixed distance (usually the transect line with the use of double-observer models. The assumption of full observer independence in the double-observer model is problematic, but can be addressed by using the point independence assumption which assumes there is one distance, the apex of the detection function, where the 2 observers are assumed independent. Aerially collected distance sampling data can have a unimodal shape and have been successfully modeled with a gamma detection function. Covariates in gamma detection models cause the apex of detection to shift depending upon covariate levels, making this model incompatible with the point independence assumption when using double-observer data. This paper reports a unimodal detection model based on a two-piece normal distribution that allows covariates, has only one apex, and is consistent with the point independence assumption when double-observer data are utilized. An aerial line-transect survey of black bears in Alaska illustrate how this method can be applied.
Convolutional coding techniques for data protection
Massey, J. L.
1975-01-01
Results of research on the use of convolutional codes in data communications are presented. Convolutional coding fundamentals are discussed along with modulation and coding interaction. Concatenated coding systems and data compression with convolutional codes are described.
Hirschman, Isidore Isaac
2005-01-01
In studies of general operators of the same nature, general convolution transforms are immediately encountered as the objects of inversion. The relation between differential operators and integral transforms is the basic theme of this work, which is geared toward upper-level undergraduates and graduate students. It may be read easily by anyone with a working knowledge of real and complex variable theory. Topics include the finite and non-finite kernels, variation diminishing transforms, asymptotic behavior of kernels, real inversion theory, representation theory, the Weierstrass transform, and
Towards dropout training for convolutional neural networks.
Wu, Haibing; Gu, Xiaodong
2015-11-01
Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage. Copyright © 2015 Elsevier Ltd. All rights reserved.
Separating Underdetermined Convolutive Speech Mixtures
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Wang, DeLiang; Larsen, Jan
2006-01-01
a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation...
Strongly-MDS convolutional codes
Gluesing-Luerssen, H; Rosenthal, J; Smarandache, R
Maximum-distance separable (MDS) convolutional codes have the property that their free distance is maximal among all codes of the same rate and the same degree. In this paper, a class of MDS convolutional codes is introduced whose column distances reach the generalized Singleton bound at the
A review of molecular modelling of electric double layer capacitors.
Burt, Ryan; Birkett, Greg; Zhao, X S
2014-04-14
Electric double-layer capacitors are a family of electrochemical energy storage devices that offer a number of advantages, such as high power density and long cyclability. In recent years, research and development of electric double-layer capacitor technology has been growing rapidly, in response to the increasing demand for energy storage devices from emerging industries, such as hybrid and electric vehicles, renewable energy, and smart grid management. The past few years have witnessed a number of significant research breakthroughs in terms of novel electrodes, new electrolytes, and fabrication of devices, thanks to the discovery of innovative materials (e.g. graphene, carbide-derived carbon, and templated carbon) and the availability of advanced experimental and computational tools. However, some experimental observations could not be clearly understood and interpreted due to limitations of traditional theories, some of which were developed more than one hundred years ago. This has led to significant research efforts in computational simulation and modelling, aimed at developing new theories, or improving the existing ones to help interpret experimental results. This review article provides a summary of research progress in molecular modelling of the physical phenomena taking place in electric double-layer capacitors. An introduction to electric double-layer capacitors and their applications, alongside a brief description of electric double layer theories, is presented first. Second, molecular modelling of ion behaviours of various electrolytes interacting with electrodes under different conditions is reviewed. Finally, key conclusions and outlooks are given. Simulations on comparing electric double-layer structure at planar and porous electrode surfaces under equilibrium conditions have revealed significant structural differences between the two electrode types, and porous electrodes have been shown to store charge more efficiently. Accurate electrolyte and
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-12-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup
2017-04-11
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.
Consensus Convolutional Sparse Coding
Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang
2017-01-01
Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
Zhang, Zewang; Sun, Zheng; Liu, Jiaqi; Chen, Jingwen; Huo, Zhao; Zhang, Xiao
2016-01-01
A deep learning approach has been widely applied in sequence modeling problems. In terms of automatic speech recognition (ASR), its performance has significantly been improved by increasing large speech corpus and deeper neural network. Especially, recurrent neural network and deep convolutional neural network have been applied in ASR successfully. Given the arising problem of training speed, we build a novel deep recurrent convolutional network for acoustic modeling and then apply deep resid...
A large deformation viscoelastic model for double-network hydrogels
Mao, Yunwei; Lin, Shaoting; Zhao, Xuanhe; Anand, Lallit
2017-03-01
We present a large deformation viscoelasticity model for recently synthesized double network hydrogels which consist of a covalently-crosslinked polyacrylamide network with long chains, and an ionically-crosslinked alginate network with short chains. Such double-network gels are highly stretchable and at the same time tough, because when stretched the crosslinks in the ionically-crosslinked alginate network rupture which results in distributed internal microdamage which dissipates a substantial amount of energy, while the configurational entropy of the covalently-crosslinked polyacrylamide network allows the gel to return to its original configuration after deformation. In addition to the large hysteresis during loading and unloading, these double network hydrogels also exhibit a substantial rate-sensitive response during loading, but exhibit almost no rate-sensitivity during unloading. These features of large hysteresis and asymmetric rate-sensitivity are quite different from the response of conventional hydrogels. We limit our attention to modeling the complex viscoelastic response of such hydrogels under isothermal conditions. Our model is restricted in the sense that we have limited our attention to conditions under which one might neglect any diffusion of the water in the hydrogel - as might occur when the gel has a uniform initial value of the concentration of water, and the mobility of the water molecules in the gel is low relative to the time scale of the mechanical deformation. We also do not attempt to model the final fracture of such double-network hydrogels.
Period doubling in a model of magnetoconvection with Ohmic heating
International Nuclear Information System (INIS)
Osman, M. B. H.
2000-01-01
In this work it has been studied an idealized model of rotating nonlinear magneto convection to investigate the effects of Ohmic heating. In the over stable region it was found that Ohmic heating can lead to a period-doubling sequence
Double Regge model for non diffractive A1 production
International Nuclear Information System (INIS)
Anjos, J.C.; Endler, A.; Santoro, A.; Simao, F.R.A.
1977-07-01
A Reggeized double-nucleon-exchange model is shown to be able to to reproduce qualitatively the non-diffractive A 1 production recently observed in the reaction K - p → Σ - π + π - π + at 4.15 GeV/c
Dealiased convolutions for pseudospectral simulations
International Nuclear Information System (INIS)
Roberts, Malcolm; Bowman, John C
2011-01-01
Efficient algorithms have recently been developed for calculating dealiased linear convolution sums without the expense of conventional zero-padding or phase-shift techniques. For one-dimensional in-place convolutions, the memory requirements are identical with the zero-padding technique, with the important distinction that the additional work memory need not be contiguous with the input data. This decoupling of data and work arrays dramatically reduces the memory and computation time required to evaluate higher-dimensional in-place convolutions. The memory savings is achieved by computing the in-place Fourier transform of the data in blocks, rather than all at once. The technique also allows one to dealias the n-ary convolutions that arise on Fourier transforming cubic and higher powers. Implicitly dealiased convolutions can be built on top of state-of-the-art adaptive fast Fourier transform libraries like FFTW. Vectorized multidimensional implementations for the complex and centered Hermitian (pseudospectral) cases have already been implemented in the open-source software FFTW++. With the advent of this library, writing a high-performance dealiased pseudospectral code for solving nonlinear partial differential equations has now become a relatively straightforward exercise. New theoretical estimates of computational complexity and memory use are provided, including corrected timing results for 3D pruned convolutions and further consideration of higher-order convolutions.
Symmetry breaking in the double-well hermitian matrix models
International Nuclear Information System (INIS)
Brower, R.C.; Deo, N.; Jain, S.; Tan, C.I.
1993-01-01
We study symmetry breaking in Z 2 symmetric large N matrix models. In the planar approximation for both the symmetric double-well φ 4 model and the symmetric Penner model, we find there is an infinite family of broken symmetry solutions characterized by different sets of recursion coefficients R n and S n that all lead to identical free energies and eigenvalue densities. These solutions can be parameterized by an arbitrary angle θ(x), for each value of x=n/N 4 theory the double scaling string equations are parameterized by a conserved angular momentum parameter in the range 0≤l<∞ and a single arbitrary U(1) phase angle. (orig.)
Prediction of Electricity Usage Using Convolutional Neural Networks
Hansen, Martin
2017-01-01
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other ...
International Nuclear Information System (INIS)
Cademartiri, Filippo; Mollet, Nico R.; Runza, Giuseppe
2005-01-01
Purpose. To assess the variability in attenuation of coronary plaques with multislice CT angiography (MSCT-CA) in an ex-vivo model with varying convolution kernels. Materials and methods. MSCT-CA (Sensation 16, Siemens) was performed in three ex-vivo left coronary arteries after instillation of contrast material solution (Iomeprol 400 mgI/ml, dilution: 1180). The specimens were placed in oil to simulate epicardial fat. Scan parameters: slices 16/0.75 mm, rotation time 375 ms, feed/rotation 3.0 mm, mAs 500, slice thickness 1 mm, and FOV 50 mm. Datasets were reconstructed using 4 different kernels (B30f-smooth, B36f-medium smooth, B46f medium, and B60f-sharp). Each scan was scored for the presence of plaques. Once a plaque was detected, the operator performed attenuation measurements (HU) in coronary lumen, oil, calcified and soft plaque tissue using the same settings in all datasets. The results were compared with T-test and correlated with Pearson's test. Results. Overall, 464 measurements were performed. Significant differences (p [it
Jin, Xiaowei; Cheng, Peng; Chen, Wen-Li; Li, Hui
2018-04-01
A data-driven model is proposed for the prediction of the velocity field around a cylinder by fusion convolutional neural networks (CNNs) using measurements of the pressure field on the cylinder. The model is based on the close relationship between the Reynolds stresses in the wake, the wake formation length, and the base pressure. Numerical simulations of flow around a cylinder at various Reynolds numbers are carried out to establish a dataset capturing the effect of the Reynolds number on various flow properties. The time series of pressure fluctuations on the cylinder is converted into a grid-like spatial-temporal topology to be handled as the input of a CNN. A CNN architecture composed of a fusion of paths with and without a pooling layer is designed. This architecture can capture both accurate spatial-temporal information and the features that are invariant of small translations in the temporal dimension of pressure fluctuations on the cylinder. The CNN is trained using the computational fluid dynamics (CFD) dataset to establish the mapping relationship between the pressure fluctuations on the cylinder and the velocity field around the cylinder. Adam (adaptive moment estimation), an efficient method for processing large-scale and high-dimensional machine learning problems, is employed to implement the optimization algorithm. The trained model is then tested over various Reynolds numbers. The predictions of this model are found to agree well with the CFD results, and the data-driven model successfully learns the underlying flow regimes, i.e., the relationship between wake structure and pressure experienced on the surface of a cylinder is well established.
Semi-doubled sigma models for five-branes
International Nuclear Information System (INIS)
Kimura, Tetsuji
2016-01-01
We study two-dimensional N=(2,2) gauge theory and its dualized system in terms of complex (linear) superfields and their alternatives. Although this technique itself is not new, we can obtain a new model, the so-called “semi-doubled” GLSM. Similar to doubled sigma model, this involves both the original and dual degrees of freedom simultaneously, whilst the latter only contribute to the system via topological interactions. Applying this to the N=(4,4) GLSM for H-monopoles, i.e., smeared NS5-branes, we obtain its T-dualized systems in quite an easy way. As a bonus, we also obtain the semi-doubled GLSM for an exotic 5_2"3-brane whose background is locally nongeometric. In the low energy limit, we construct the semi-doubled NLSM which also generates the conventional string worldsheet sigma models. In the case of the NLSM for 5_2"3-brane, however, we find that the Dirac monopole equation does not make sense any more because the physical information is absorbed into the divergent part via the smearing procedure. This is nothing but the signal which indicates that the nongeometric feature emerges in the considering model.
Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.
2017-08-01
Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and image clustering are discussed. The SLECNS architectures and their spatially invariant equivalent models (SI EMs) using the corresponding matrix-matrix procedures with basic operations of continuous logic and non-linear processing are proposed. These SI EMs have several advantages, such as the ability to recognize image fragments with better efficiency and strong cross correlation. The proposed clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively processing algorithms and to k-average method. The experimental results confirmed that larger images and 2D binary fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an image with dimension of 256x256 (a reference array) and fragments with dimensions of 7x7 and 21x21 for clustering is carried out. The experiments, using the software environment Mathcad, showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar
Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.
2018-03-01
The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference
Double site-bond percolation model for biomaterial implants
Mely, H.; Mathiot, J. -F.
2011-01-01
9 figures - 10 pages; We present a double site-bond percolation model to account, on the one hand, for the vascularization and/or resorption of biomaterial implant in bones, and on the other hand, for its mechanical continuity. The transformation of the implant into osseous material, and the dynamical formation/destruction of this osseous material is accounted for by creation and destruction of links and sites in two, entangled, networks. We identify the relevant parameters to describe the im...
DCMDN: Deep Convolutional Mixture Density Network
D'Isanto, Antonio; Polsterer, Kai Lars
2017-09-01
Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.
Gas Classification Using Deep Convolutional Neural Networks
Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin
2018-01-01
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723
Gas Classification Using Deep Convolutional Neural Networks.
Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin
2018-01-08
In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).
Mobile Stride Length Estimation With Deep Convolutional Neural Networks.
Hannink, Julius; Kautz, Thomas; Pasluosta, Cristian F; Barth, Jens; Schulein, Samuel; GaBmann, Karl-Gunter; Klucken, Jochen; Eskofier, Bjoern M
2018-03-01
Accurate estimation of spatial gait characteristics is critical to assess motor impairments resulting from neurological or musculoskeletal disease. Currently, however, methodological constraints limit clinical applicability of state-of-the-art double integration approaches to gait patterns with a clear zero-velocity phase. We describe a novel approach to stride length estimation that uses deep convolutional neural networks to map stride-specific inertial sensor data to the resulting stride length. The model is trained on a publicly available and clinically relevant benchmark dataset consisting of 1220 strides from 101 geriatric patients. Evaluation is done in a tenfold cross validation and for three different stride definitions. Even though best results are achieved with strides defined from midstance to midstance with average accuracy and precision of , performance does not strongly depend on stride definition. The achieved precision outperforms state-of-the-art methods evaluated on the same benchmark dataset by . Due to the independence of stride definition, the proposed method is not subject to the methodological constrains that limit applicability of state-of-the-art double integration methods. Furthermore, it was possible to improve precision on the benchmark dataset. With more precise mobile stride length estimation, new insights to the progression of neurological disease or early indications might be gained. Due to the independence of stride definition, previously uncharted diseases in terms of mobile gait analysis can now be investigated by retraining and applying the proposed method.
Color encoding in biologically-inspired convolutional neural networks.
Rafegas, Ivet; Vanrell, Maria
2018-05-11
Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. Copyright © 2018 Elsevier Ltd. All rights reserved.
Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex
Directory of Open Access Journals (Sweden)
Mir Jalil Razavi
2017-08-01
Full Text Available Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.
Kumaraswamy autoregressive moving average models for double bounded environmental data
Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme
2017-12-01
In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.
Video Super-Resolution via Bidirectional Recurrent Convolutional Networks.
Huang, Yan; Wang, Wei; Wang, Liang
2018-04-01
Super resolving a low-resolution video, namely video super-resolution (SR), is usually handled by either single-image SR or multi-frame SR. Single-Image SR deals with each video frame independently, and ignores intrinsic temporal dependency of video frames which actually plays a very important role in video SR. Multi-Frame SR generally extracts motion information, e.g., optical flow, to model the temporal dependency, but often shows high computational cost. Considering that recurrent neural networks (RNNs) can model long-term temporal dependency of video sequences well, we propose a fully convolutional RNN named bidirectional recurrent convolutional network for efficient multi-frame SR. Different from vanilla RNNs, 1) the commonly-used full feedforward and recurrent connections are replaced with weight-sharing convolutional connections. So they can greatly reduce the large number of network parameters and well model the temporal dependency in a finer level, i.e., patch-based rather than frame-based, and 2) connections from input layers at previous timesteps to the current hidden layer are added by 3D feedforward convolutions, which aim to capture discriminate spatio-temporal patterns for short-term fast-varying motions in local adjacent frames. Due to the cheap convolutional operations, our model has a low computational complexity and runs orders of magnitude faster than other multi-frame SR methods. With the powerful temporal dependency modeling, our model can super resolve videos with complex motions and achieve well performance.
Xie, Dexuan; Jiang, Yi
2018-05-01
This paper reports a nonuniform ionic size nonlocal Poisson-Fermi double-layer model (nuNPF) and a uniform ionic size nonlocal Poisson-Fermi double-layer model (uNPF) for an electrolyte mixture of multiple ionic species, variable voltages on electrodes, and variable induced charges on boundary segments. The finite element solvers of nuNPF and uNPF are developed and applied to typical double-layer tests defined on a rectangular box, a hollow sphere, and a hollow rectangle with a charged post. Numerical results show that nuNPF can significantly improve the quality of the ionic concentrations and electric fields generated from uNPF, implying that the effect of nonuniform ion sizes is a key consideration in modeling the double-layer structure.
Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L
2018-04-01
In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.
Matrix models, Argyres-Douglas singularities and double scaling limits
International Nuclear Information System (INIS)
Bertoldi, Gaetano
2003-01-01
We construct an N = 1 theory with gauge group U(nN) and degree n+1 tree level superpotential whose matrix model spectral curve develops an Argyres-Douglas singularity. The calculation of the tension of domain walls in the U(nN) theory shows that the standard large-N expansion breaks down at the Argyres-Douglas points, with tension that scales as a fractional power of N. Nevertheless, it is possible to define appropriate double scaling limits which are conjectured to yield the tension of 2-branes in the resulting N = 1 four dimensional non-critical string theories as proposed by Ferrari. (author)
Data Set for Emperical Validation of Double Skin Facade Model
DEFF Research Database (Denmark)
Kalyanova, Olena; Jensen, Rasmus Lund; Heiselberg, Per
2008-01-01
During the recent years the attention to the double skin facade (DSF) concept has greatly increased. Nevertheless, the application of the concept depends on whether a reliable model for simulation of the DSF performance will be developed or pointed out. This is, however, not possible to do, until...... the International Energy Agency (IEA) Task 34 Annex 43. This paper describes the full-scale outdoor experimental test facility ‘the Cube', where the experiments were conducted, the experimental set-up and the measurements procedure for the data sets. The empirical data is composed for the key-functioning modes...
Schematic model studies of double beta decay processes
International Nuclear Information System (INIS)
Civitarese, O.
1996-01-01
Some features of the nuclear matrix elements, for double beta decay transitions to a final ground state and to a final excited one and two-quadrupole phonon states, are presented and discussed in the framework of a schematic model. The competition between spin-flip and non-spin-flip transitions on the relevant nuclear matrix elements, the effects due to proton-neutron pairing correlations and the effects due to the inclusion of exchange terms in the QRPA matrix are discussed. (Author)
Goel, Ekta; Singh, Balraj; Kumar, Sanjay; Singh, Kunal; Jit, Satyabrata
2017-04-01
Two dimensional threshold voltage model of ion-implanted strained-Si double-material double-gate MOSFETs has been done based on the solution of two dimensional Poisson's equation in the channel region using the parabolic approximation method. Novelty of the proposed device structure lies in the amalgamation of the advantages of both the strained-Si channel and double-material double-gate structure with a vertical Gaussian-like doping profile. The effects of different device parameters (such as device channel length, gate length ratios, germanium mole fraction) and doping parameters (such as projected range, straggle parameter) on threshold voltage of the proposed structure have been investigated. It is observed that the subthreshold performance of the device can be improved by simply controlling the doping parameters while maintaining other device parameters constant. The modeling results show a good agreement with the numerical simulation data obtained by using ATLAS™, a 2D device simulator from SILVACO.
Modeling Electric Double-Layers Including Chemical Reaction Effects
DEFF Research Database (Denmark)
Paz-Garcia, Juan Manuel; Johannesson, Björn; Ottosen, Lisbeth M.
2014-01-01
A physicochemical and numerical model for the transient formation of an electric double-layer between an electrolyte and a chemically-active flat surface is presented, based on a finite elements integration of the nonlinear Nernst-Planck-Poisson model including chemical reactions. The model works...... for symmetric and asymmetric multi-species electrolytes and is not limited to a range of surface potentials. Numerical simulations are presented, for the case of a CaCO3 electrolyte solution in contact with a surface with rate-controlled protonation/deprotonation reactions. The surface charge and potential...... are determined by the surface reactions, and therefore they depends on the bulk solution composition and concentration...
Twisted quantum double model of topological order with boundaries
Bullivant, Alex; Hu, Yuting; Wan, Yidun
2017-10-01
We generalize the twisted quantum double model of topological orders in two dimensions to the case with boundaries by systematically constructing the boundary Hamiltonians. Given the bulk Hamiltonian defined by a gauge group G and a 3-cocycle in the third cohomology group of G over U (1 ) , a boundary Hamiltonian can be defined by a subgroup K of G and a 2-cochain in the second cochain group of K over U (1 ) . The consistency between the bulk and boundary Hamiltonians is dictated by what we call the Frobenius condition that constrains the 2-cochain given the 3-cocyle. We offer a closed-form formula computing the ground-state degeneracy of the model on a cylinder in terms of the input data only, which can be naturally generalized to surfaces with more boundaries. We also explicitly write down the ground-state wave function of the model on a disk also in terms of the input data only.
Sound transmission through lightweight double-leaf partitions: theoretical modelling
Wang, J.; Lu, T. J.; Woodhouse, J.; Langley, R. S.; Evans, J.
2005-09-01
This paper presents theoretical modelling of the sound transmission loss through double-leaf lightweight partitions stiffened with periodically placed studs. First, by assuming that the effect of the studs can be replaced with elastic springs uniformly distributed between the sheathing panels, a simple smeared model is established. Second, periodic structure theory is used to develop a more accurate model taking account of the discrete placing of the studs. Both models treat incident sound waves in the horizontal plane only, for simplicity. The predictions of the two models are compared, to reveal the physical mechanisms determining sound transmission. The smeared model predicts relatively simple behaviour, in which the only conspicuous features are associated with coincidence effects with the two types of structural wave allowed by the partition model, and internal resonances of the air between the panels. In the periodic model, many more features are evident, associated with the structure of pass- and stop-bands for structural waves in the partition. The models are used to explain the effects of incidence angle and of the various system parameters. The predictions are compared with existing test data for steel plates with wooden stiffeners, and good agreement is obtained.
Design of convolutional tornado code
Zhou, Hui; Yang, Yao; Gao, Hongmin; Tan, Lu
2017-09-01
As a linear block code, the traditional tornado (tTN) code is inefficient in burst-erasure environment and its multi-level structure may lead to high encoding/decoding complexity. This paper presents a convolutional tornado (cTN) code which is able to improve the burst-erasure protection capability by applying the convolution property to the tTN code, and reduce computational complexity by abrogating the multi-level structure. The simulation results show that cTN code can provide a better packet loss protection performance with lower computation complexity than tTN code.
Double-multiple streamtube model for Darrieus in turbines
Paraschivoiu, I.
1981-01-01
An analytical model is proposed for calculating the rotor performance and aerodynamic blade forces for Darrieus wind turbines with curved blades. The method of analysis uses a multiple-streamtube model, divided into two parts: one modeling the upstream half-cycle of the rotor and the other, the downstream half-cycle. The upwind and downwind components of the induced velocities at each level of the rotor were obtained using the principle of two actuator disks in tandem. Variation of the induced velocities in the two parts of the rotor produces larger forces in the upstream zone and smaller forces in the downstream zone. Comparisons of the overall rotor performance with previous methods and field test data show the important improvement obtained with the present model. The calculations were made using the computer code CARDAA developed at IREQ. The double-multiple streamtube model presented has two major advantages: it requires a much shorter computer time than the three-dimensional vortex model and is more accurate than multiple-streamtube model in predicting the aerodynamic blade loads.
Detecting atrial fibrillation by deep convolutional neural networks.
Xia, Yong; Wulan, Naren; Wang, Kuanquan; Zhang, Henggui
2018-02-01
Atrial fibrillation (AF) is the most common cardiac arrhythmia. The incidence of AF increases with age, causing high risks of stroke and increased morbidity and mortality. Efficient and accurate diagnosis of AF based on the ECG is valuable in clinical settings and remains challenging. In this paper, we proposed a novel method with high reliability and accuracy for AF detection via deep learning. The short-term Fourier transform (STFT) and stationary wavelet transform (SWT) were used to analyze ECG segments to obtain two-dimensional (2-D) matrix input suitable for deep convolutional neural networks. Then, two different deep convolutional neural network models corresponding to STFT output and SWT output were developed. Our new method did not require detection of P or R peaks, nor feature designs for classification, in contrast to existing algorithms. Finally, the performances of the two models were evaluated and compared with those of existing algorithms. Our proposed method demonstrated favorable performances on ECG segments as short as 5 s. The deep convolutional neural network using input generated by STFT, presented a sensitivity of 98.34%, specificity of 98.24% and accuracy of 98.29%. For the deep convolutional neural network using input generated by SWT, a sensitivity of 98.79%, specificity of 97.87% and accuracy of 98.63% was achieved. The proposed method using deep convolutional neural networks shows high sensitivity, specificity and accuracy, and, therefore, is a valuable tool for AF detection. Copyright © 2017 Elsevier Ltd. All rights reserved.
Solutions to Arithmetic Convolution Equations
Czech Academy of Sciences Publication Activity Database
Glöckner, H.; Lucht, L.G.; Porubský, Štefan
2007-01-01
Roč. 135, č. 6 (2007), s. 1619-1629 ISSN 0002-9939 R&D Projects: GA ČR GA201/04/0381 Institutional research plan: CEZ:AV0Z10300504 Keywords : arithmetic functions * Dirichlet convolution * polynomial equations * analytic equations * topological algebras * holomorphic functional calculus Subject RIV: BA - General Mathematics Impact factor: 0.520, year: 2007
Decoding LDPC Convolutional Codes on Markov Channels
Directory of Open Access Journals (Sweden)
Kashyap Manohar
2008-01-01
Full Text Available Abstract This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Decoding LDPC Convolutional Codes on Markov Channels
Directory of Open Access Journals (Sweden)
Chris Winstead
2008-04-01
Full Text Available This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Europan double ridge morphometry as a test of formation models
Dameron, Ashley C.; Burr, Devon M.
2018-05-01
Double ridges on the Jovian satellite Europa consist of two parallel ridges with a central trough. Although these features are nearly ubiquitous on Europa, their formation mechanism(s) is (are) not yet well-understood. Previous hypotheses for their formation can be divided into two groups based on 1) the expected interior slope angles and 2) the magnitude of interior/exterior slope symmetry. The published hypotheses in the first ("fracture") group entail brittle deformation of the crust, either by diapirism, shear heating, or buckling due to compression. Because these mechanisms imply uplift of near-vertical fractures, their predicted interior slopes are steeper than the angle of repose (AOR) with shallower exterior slopes. The second ("flow") group includes cryosedimentary and cryovolcanic processes - explosive or effusive cryovolcanism and tidal squeezing -, which are predicted to form ridge slopes at or below the AOR. Explosive cryovolcanism would form self-symmetric ridges, whereas effusive cryolavas and cryo-sediments deposited during tidal squeezing would likely not exhibit slope symmetry. To distinguish between these two groups of hypothesized formation mechanisms, we derived measurements of interior slope angle and interior/exterior slope symmetry at multiple locations on Europa through analysis of data from the Galileo Solid State Imaging (SSI) camera. Two types of data were used: i) elevation data from five stereo-pair digital elevation models (DEMs) covering four ridges (580 individual measurements), and ii) ridge shadow length measurements taken on individual images over 40 ridges (200 individual measurements). Our results shows that slopes measured on our DEMs, located in the Cilix and Banded Plains regions, typically fall below the AOR, and slope symmetry is dominant. Two different shadow measurement techniques implemented to calculate interior slopes yielded slope angles that also fall below the AOR. The shallow interior slopes derived from both
Double parton correlations in Light-Front constituent quark models
Directory of Open Access Journals (Sweden)
Rinaldi Matteo
2015-01-01
Full Text Available Double parton distribution functions (dPDF represent a tool to explore the 3D proton structure. They can be measured in high energy proton-proton and proton nucleus collisions and encode information on how partons inside a proton are correlated among each other. dPFDs are studied here in the valence quark region, by means of a constituent quark model, where two particle correlations are present without any additional prescription. This framework allows to understand the dynamical origin of the correlations and to clarify which, among the features of the results, are model independent. Use will be made of a relativistic light-front scheme, able to overcome some drawbacks of the previous calculation. Transverse momentum correlations, due to the exact treatment of the boosts, are predicted and analyzed. The role of spin correlations is also shown. Due to the covariance of the approach, some symmetries of the dPDFs are seen unambigously. For the valence sector, also the study of the QCD evolution of the model results, which can be performed safely thanks to the property of good support, has been also completed.
Adaptive Graph Convolutional Neural Networks
Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou
2018-01-01
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...
Review of the convolution algorithm for evaluating service integrated systems
DEFF Research Database (Denmark)
Iversen, Villy Bæk
1997-01-01
In this paper we give a review of the applicability of the convolution algorithm. By this we are able to evaluate communication networks end--to--end with e.g. BPP multi-ratetraffic models insensitive to the holding time distribution. Rearrangement, minimum allocation, and maximum allocation...
Voltage measurements at the vacuum post-hole convolute of the Z pulsed-power accelerator
Directory of Open Access Journals (Sweden)
E. M. Waisman
2014-12-01
Full Text Available Presented are voltage measurements taken near the load region on the Z pulsed-power accelerator using an inductive voltage monitor (IVM. Specifically, the IVM was connected to, and thus monitored the voltage at, the bottom level of the accelerator’s vacuum double post-hole convolute. Additional voltage and current measurements were taken at the accelerator’s vacuum-insulator stack (at a radius of 1.6 m by using standard D-dot and B-dot probes, respectively. During postprocessing, the measurements taken at the stack were translated to the location of the IVM measurements by using a lossless propagation model of the Z accelerator’s magnetically insulated transmission lines (MITLs and a lumped inductor model of the vacuum post-hole convolute. Across a wide variety of experiments conducted on the Z accelerator, the voltage histories obtained from the IVM and the lossless propagation technique agree well in overall shape and magnitude. However, large-amplitude, high-frequency oscillations are more pronounced in the IVM records. It is unclear whether these larger oscillations represent true voltage oscillations at the convolute or if they are due to noise pickup and/or transit-time effects and other resonant modes in the IVM. Results using a transit-time-correction technique and Fourier analysis support the latter. Regardless of which interpretation is correct, both true voltage oscillations and the excitement of resonant modes could be the result of transient electrical breakdowns in the post-hole convolute, though more information is required to determine definitively if such breakdowns occurred. Despite the larger oscillations in the IVM records, the general agreement found between the lossless propagation results and the results of the IVM shows that large voltages are transmitted efficiently through the MITLs on Z. These results are complementary to previous studies [R. D. McBride et al., Phys. Rev. ST Accel. Beams 13, 120401 (2010
Bartlett, Philip L.; Stelbovics, Andris T.
2010-02-01
The propagating exterior complex scaling (PECS) method is extended to all four-body processes in electron impact on helium in an S-wave model. Total and energy-differential cross sections are presented with benchmark accuracy for double ionization, single ionization with excitation, and double excitation (to autoionizing states) for incident-electron energies from threshold to 500 eV. While the PECS three-body cross sections for this model given in the preceding article [Phys. Rev. A 81, 022715 (2010)] are in good agreement with other methods, there are considerable discrepancies for these four-body processes. With this model we demonstrate the suitability of the PECS method for the complete solution of the electron-helium system.
Heat transfer modeling of double-side arc welding
International Nuclear Information System (INIS)
Sun Junsheng; Wu Chuansong
2002-01-01
If a plasma arc and a TIG arc are connected in serial and with the plasma arc placed on the obverse side and the TIG arc on the opposite side of the workpiece, a special double-side arc welding (DSAW) system will be formed, in which the PAW current is forced to flow through the keyhole along the thickness direction so as to compensate the energy consumed for melting the workpiece and improve the penetration capacity of the PAW arc. By considering the mechanics factors which influence the DSAW pool geometric shape, the control equations of the pool surface deformation are derived, and the mathematics mode for DSAW heat transfer is established by using boundary-fitted non-orthogonal coordinate systems. With this model, the difference between DSAW and PAW heat transfer is analyzed and the reason for the increase of DSAW penetration is explained from the point of heat transfer. The welding process experiments show that calculated results are in good agreement with measured ones
Convolution of Distribution-Valued Functions. Applications.
BARGETZ, CHRISTIAN
2011-01-01
In this article we examine products and convolutions of vector-valued functions. For nuclear normal spaces of distributions Proposition 25 in [31,p. 120] yields a vector-valued product or convolution if there is a continuous product or convolution mapping in the range of the vector-valued functions. For specific spaces, we generalize this result to hypocontinuous bilinear maps at the expense of generality with respect to the function space. We consider holomorphic, meromorphic and differentia...
Double porosity models for the description of water infiltration in wood
DEFF Research Database (Denmark)
Krabbenhøft, Kristian; Damkilde, Lars
2004-01-01
In this paper some of the possibilities of applying double porosity and permeability models to the problem of water infiltration in wood are explored. It is shown that the double porosity model can capture a number of commonly reported anomalies including two-stage infiltration...
Applying the Post-Modern Double ABC-X Model to Family Food Insecurity
Hutson, Samantha; Anderson, Melinda; Swafford, Melinda
2015-01-01
This paper develops the argument that using the Double ABC-X model in family and consumer sciences (FCS) curricula is a way to educate nutrition and dietetics students regarding a family's perceptions of food insecurity. The Double ABC-X model incorporates ecological theory as a basis to explain family stress and the resulting adjustment and…
Down image recognition based on deep convolutional neural network
Directory of Open Access Journals (Sweden)
Wenzhu Yang
2018-06-01
Full Text Available Since of the scale and the various shapes of down in the image, it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy, even for the Traditional Convolutional Neural Network (TCNN. To deal with the above problems, a Deep Convolutional Neural Network (DCNN for down image classification is constructed, and a new weight initialization method is proposed. Firstly, the salient regions of a down image were cut from the image using the visual saliency model. Then, these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters, which accord with the statistical characteristics of dataset. At last, a DCNN with Inception module and its variants was constructed. To improve the recognition accuracy, the depth of the network is deepened. The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN, when recognizing the down in the images. The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN. Keywords: Deep convolutional neural network, Weight initialization, Sparse autoencoder, Visual saliency model, Image recognition
A convolutional neural network to filter artifacts in spectroscopic MRI.
Gurbani, Saumya S; Schreibmann, Eduard; Maudsley, Andrew A; Cordova, James Scott; Soher, Brian J; Poptani, Harish; Verma, Gaurav; Barker, Peter B; Shim, Hyunsuk; Cooper, Lee A D
2018-03-09
Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information. A deep learning model was developed that was capable of identifying and filtering out poor quality spectra. The core of the model used a tiled convolutional neural network that analyzed frequency-domain spectra to detect artifacts. When compared with a panel of MRS experts, our convolutional neural network achieved high sensitivity and specificity with an area under the curve of 0.95. A visualization scheme was implemented to better understand how the convolutional neural network made its judgement on single-voxel or multivoxel MRSI, and the convolutional neural network was embedded into a pipeline capable of producing whole-brain spectroscopic MRI volumes in real time. The fully automated method for assessment of spectral quality provides a valuable tool to support clinical MRSI or spectroscopic MRI studies for use in fields such as adaptive radiation therapy planning. © 2018 International Society for Magnetic Resonance in Medicine.
Deep learning for steganalysis via convolutional neural networks
Qian, Yinlong; Dong, Jing; Wang, Wei; Tan, Tieniu
2015-03-01
Current work on steganalysis for digital images is focused on the construction of complex handcrafted features. This paper proposes a new paradigm for steganalysis to learn features automatically via deep learning models. We novelly propose a customized Convolutional Neural Network for steganalysis. The proposed model can capture the complex dependencies that are useful for steganalysis. Compared with existing schemes, this model can automatically learn feature representations with several convolutional layers. The feature extraction and classification steps are unified under a single architecture, which means the guidance of classification can be used during the feature extraction step. We demonstrate the effectiveness of the proposed model on three state-of-theart spatial domain steganographic algorithms - HUGO, WOW, and S-UNIWARD. Compared to the Spatial Rich Model (SRM), our model achieves comparable performance on BOSSbase and the realistic and large ImageNet database.
Experimental study of current loss and plasma formation in the Z machine post-hole convolute
Directory of Open Access Journals (Sweden)
M. R. Gomez
2017-01-01
Full Text Available The Z pulsed-power generator at Sandia National Laboratories drives high energy density physics experiments with load currents of up to 26 MA. Z utilizes a double post-hole convolute to combine the current from four parallel magnetically insulated transmission lines into a single transmission line just upstream of the load. Current loss is observed in most experiments and is traditionally attributed to inefficient convolute performance. The apparent loss current varies substantially for z-pinch loads with different inductance histories; however, a similar convolute impedance history is observed for all load types. This paper details direct spectroscopic measurements of plasma density, temperature, and apparent and actual plasma closure velocities within the convolute. Spectral measurements indicate a correlation between impedance collapse and plasma formation in the convolute. Absorption features in the spectra show the convolute plasma consists primarily of hydrogen, which likely forms from desorbed electrode contaminant species such as H_{2}O, H_{2}, and hydrocarbons. Plasma densities increase from 1×10^{16} cm^{−3} (level of detectability just before peak current to over 1×10^{17} cm^{−3} at stagnation (tens of ns later. The density seems to be highest near the cathode surface, with an apparent cathode to anode plasma velocity in the range of 35–50 cm/μs. Similar plasma conditions and convolute impedance histories are observed in experiments with high and low losses, suggesting that losses are driven largely by load dynamics, which determine the voltage on the convolute.
Critical properties of the double-frequency sine-Gordon model with applications
International Nuclear Information System (INIS)
Fabrizio, M.; Gogolin, A.O.; Nersesyan, A.A.
2000-01-01
We study the properties of the double-frequency sine-Gordon model in the vicinity of the Ising quantum phase transition displayed by this model. Using a mapping onto a generalized lattice quantum Ashkin-Teller model, we obtain critical and nearly-off-critical correlation functions of various operators. We discuss applications of the double-sine-Gordon model to one-dimensional physical systems, like spin chains in a staggered external field and interacting electrons in a staggered potential
Learning Convolutional Text Representations for Visual Question Answering
Wang, Zhengyang; Ji, Shuiwang
2017-01-01
Visual question answering is a recently proposed artificial intelligence task that requires a deep understanding of both images and texts. In deep learning, images are typically modeled through convolutional neural networks, and texts are typically modeled through recurrent neural networks. While the requirement for modeling images is similar to traditional computer vision tasks, such as object recognition and image classification, visual question answering raises a different need for textual...
Maximum likelihood convolutional decoding (MCD) performance due to system losses
Webster, L.
1976-01-01
A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.
A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task
Werfelmann, Robert
2018-01-01
around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions
Symbol synchronization in convolutionally coded systems
Baumert, L. D.; Mceliece, R. J.; Van Tilborg, H. C. A.
1979-01-01
Alternate symbol inversion is sometimes applied to the output of convolutional encoders to guarantee sufficient richness of symbol transition for the receiver symbol synchronizer. A bound is given for the length of the transition-free symbol stream in such systems, and those convolutional codes are characterized in which arbitrarily long transition free runs occur.
The general theory of convolutional codes
Mceliece, R. J.; Stanley, R. P.
1993-01-01
This article presents a self-contained introduction to the algebraic theory of convolutional codes. This introduction is partly a tutorial, but at the same time contains a number of new results which will prove useful for designers of advanced telecommunication systems. Among the new concepts introduced here are the Hilbert series for a convolutional code and the class of compact codes.
Indian Academy of Sciences (India)
President's Address to the Association of Mathematics Teachers of India, December 2011. I am expected to tell you, in 25 minutes, something that should interest you, excite you, pique your curiosity, and make you look for more. It is a tall order, but I will try. The word 'interactive' is in fashion these days. So I will leave a few ...
Improving deep convolutional neural networks with mixed maxout units.
Directory of Open Access Journals (Sweden)
Hui-Zhen Zhao
Full Text Available Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.
Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán
2017-01-01
Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.
A Dewetting Model for Double-Emulsion Droplets
Directory of Open Access Journals (Sweden)
Zhanxiao Kang
2016-11-01
Full Text Available The evolution of double-emulsion droplets is of great importance for the application of microdroplets and microparticles. We study the driving force of the dewetting process, the equilibrium configuration and the dewetting time of double-emulsion droplets. Through energy analysis, we find that the equilibrium configuration of a partial engulfed droplet depends on a dimensionless interfacial tension determined by the three relevant interfacial tensions, and the engulfing part of the inner phase becomes larger as the volume of the outer phase increases. By introducing a dewetting boundary, the dewetting time can be calculated by balancing the driving force, caused by interfacial tensions, and the viscous force. Without considering the momentum change of the continuous phase, the dewetting time is an increasing function against the viscosity of the outer phase and the volume ratio between the outer phase and inner phase.
Double folding model including the Pauli exclusion principle
International Nuclear Information System (INIS)
Gridnev, K.A.; Soubbotin, V.B.; Oertzen, W. von; Bohlen, H.G.; Vinas, X.
2002-01-01
A new method to incorporate the Pauli principle into the double folding approach to the heavy ion potential is proposed. It is shown that in order to take into account the Pauli blocking a redefinition of the density matrices of the free isolated nuclei must be one. A solution to the self-consistent incorporation of the Pauli-blocking effects in the mean-field nucleus-nucleus potential is obtained in the Thomas-Fermi approximation [ru
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Ter¨asvirta (2005) by including...... another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition......, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. The model is applied to a selection of world stock indices, and it is found that time is an important factor affecting...
Adaptive decoding of convolutional codes
Directory of Open Access Journals (Sweden)
K. Hueske
2007-06-01
Full Text Available Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.
Adaptive decoding of convolutional codes
Hueske, K.; Geldmacher, J.; Götze, J.
2007-06-01
Convolutional codes, which are frequently used as error correction codes in digital transmission systems, are generally decoded using the Viterbi Decoder. On the one hand the Viterbi Decoder is an optimum maximum likelihood decoder, i.e. the most probable transmitted code sequence is obtained. On the other hand the mathematical complexity of the algorithm only depends on the used code, not on the number of transmission errors. To reduce the complexity of the decoding process for good transmission conditions, an alternative syndrome based decoder is presented. The reduction of complexity is realized by two different approaches, the syndrome zero sequence deactivation and the path metric equalization. The two approaches enable an easy adaptation of the decoding complexity for different transmission conditions, which results in a trade-off between decoding complexity and error correction performance.
Witoonchart, Peerajak; Chongstitvatana, Prabhas
2017-08-01
In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Deng, Baoqing; Ge, Di; Li, Jiajia; Guo, Yuan; Kim, Chang Nyung
2013-01-01
A double-exponential surface sink model for VOCs sorption on building materials is presented. Here, the diffusion of VOCs in the material is neglected and the material is viewed as a surface sink. The VOCs concentration in the air adjacent to the material surface is introduced and assumed to always maintain equilibrium with the material-phase concentration. It is assumed that the sorption can be described by mass transfer between the room air and the air adjacent to the material surface. The mass transfer coefficient is evaluated from the empirical correlation, and the equilibrium constant can be obtained by linear fitting to the experimental data. The present model is validated through experiments in small and large test chambers. The predicted results accord well with the experimental data in both the adsorption stage and desorption stage. The model avoids the ambiguity of model constants found in other surface sink models and is easy to scale up
Kumar, Amit; Chellappa, Rama
2017-01-01
Recently, Deep Convolution Networks (DCNNs) have been applied to the task of face alignment and have shown potential for learning improved feature representations. Although deeper layers can capture abstract concepts like pose, it is difficult to capture the geometric relationships among the keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution network for facial keypoint detection. Our model predicts the 2D locations of the keypoints and their individual visibility ...
Learning text representation using recurrent convolutional neural network with highway layers
Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun
2016-01-01
Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...
Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao
2018-01-01
Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...
Convolutional Neural Network for Image Recognition
Seifnashri, Sahand
2015-01-01
The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.
Infimal Convolution Regularisation Functionals of BV and Lp Spaces
Burger, Martin
2016-02-03
We study a general class of infimal convolution type regularisation functionals suitable for applications in image processing. These functionals incorporate a combination of the total variation seminorm and Lp norms. A unified well-posedness analysis is presented and a detailed study of the one-dimensional model is performed, by computing exact solutions for the corresponding denoising problem and the case p=2. Furthermore, the dependency of the regularisation properties of this infimal convolution approach to the choice of p is studied. It turns out that in the case p=2 this regulariser is equivalent to the Huber-type variant of total variation regularisation. We provide numerical examples for image decomposition as well as for image denoising. We show that our model is capable of eliminating the staircasing effect, a well-known disadvantage of total variation regularisation. Moreover as p increases we obtain almost piecewise affine reconstructions, leading also to a better preservation of hat-like structures.
Modelling of three-body effects in a double continuum
International Nuclear Information System (INIS)
Tweed, R.J.; Tannous, C.; Marchalant, P.
1993-01-01
Theoretical calculations of double ionisation by electron or photon impact require a final state wavefunction which takes account both of the Coulomb repulsion between the pair of free electrons and of their interaction with the residual ionic core. It is desirable that this should be separable so as to facilitate the introduction of electron-pair correlations in the initial state wavefunction for the collision system. We propose a method for calculating coupled classical trajectories for the free electrons and deducing from these potentials from which their quantum mechanical wavefunctions may be obtained. Test calculations are reported for electron impact single ionisation of hydrogen. (orig.)
Double-folding model including the Pauli exclusion principle
International Nuclear Information System (INIS)
Gridnev, K.A.; Soubbotin, V.B.; Oertzen, W. von; Bohlen, H.G.; Vinas, X.
2002-01-01
A new method for incorporating the Pauli exclusion principle into the double-folding approach to the heavy-ion potential is proposed. The description of the exchange terms at the level of the semiclassical one-body density matrix is used. It is shown that, in order to take into account Pauli blocking properly, the density matrices of free isolated nuclei must be redefined. A solution to the self-consistent incorporation of Pauli blocking effects in the mean-field nucleus-nucleus potential is obtained in the Thomas-Fermi approximation
Improved double-multiple streamtube model for the Darrieus-type vertical axis wind turbine
Berg, D. E.
Double streamtube codes model the curved blade (Darrieus-type) vertical axis wind turbine (VAWT) as a double actuator fish arrangement (one half) and use conservation of momentum principles to determine the forces acting on the turbine blades and the turbine performance. Sandia National Laboratories developed a double multiple streamtube model for the VAWT which incorporates the effects of the incident wind boundary layer, nonuniform velocity between the upwind and downwind sections of the rotor, dynamic stall effects and local blade Reynolds number variations. The theory underlying this VAWT model is described, as well as the code capabilities. Code results are compared with experimental data from two VAWT's and with the results from another double multiple streamtube and a vortex filament code. The effects of neglecting dynamic stall and horizontal wind velocity distribution are also illustrated.
Convolution of large 3D images on GPU and its decomposition
Karas, Pavel; Svoboda, David
2011-12-01
In this article, we propose a method for computing convolution of large 3D images. The convolution is performed in a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of the CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in frequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory consumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on overall computational time. We also study the implementation on multiple GPUs and compare the results between the multi-GPU and multi-CPU implementations.
Multiscale Convolutional Neural Networks for Hand Detection
Directory of Open Access Journals (Sweden)
Shiyang Yan
2017-01-01
Full Text Available Unconstrained hand detection in still images plays an important role in many hand-related vision problems, for example, hand tracking, gesture analysis, human action recognition and human-machine interaction, and sign language recognition. Although hand detection has been extensively studied for decades, it is still a challenging task with many problems to be tackled. The contributing factors for this complexity include heavy occlusion, low resolution, varying illumination conditions, different hand gestures, and the complex interactions between hands and objects or other hands. In this paper, we propose a multiscale deep learning model for unconstrained hand detection in still images. Deep learning models, and deep convolutional neural networks (CNNs in particular, have achieved state-of-the-art performances in many vision benchmarks. Developed from the region-based CNN (R-CNN model, we propose a hand detection scheme based on candidate regions generated by a generic region proposal algorithm, followed by multiscale information fusion from the popular VGG16 model. Two benchmark datasets were applied to validate the proposed method, namely, the Oxford Hand Detection Dataset and the VIVA Hand Detection Challenge. We achieved state-of-the-art results on the Oxford Hand Detection Dataset and had satisfactory performance in the VIVA Hand Detection Challenge.
Modeling of Sulfate Double-Salt in Nuclear Wastes
International Nuclear Information System (INIS)
Toghiani, B.; Lindner, J.S.; Weber, C.F.; Hunt, R.D.
2000-01-01
The Environmental Simulation Program (ESP) continues to adequately predict the solubility of most key chemical systems in the Hanford tank waste. For example, the ESP predictions were in fair agreement with the solubility experiments for the fluoride-phosphate system, although ESP probably underestimates the aqueous amounts. Due to the importance of this system in the formation of pipeline plugs, additional experiments have been made at elevated temperatures, and improvements to the ESP database will be made. ESP encountered problems with sulfate systems because the Public database for ESP does not include anhydrous sodium sulfate in mixed solutions below 32.4 C. This limitation leads to convergence problems and to spurious predictions of solubility near the transition point with sodium sulfate decahydrate when other salts such as sodium nitrate are present. However, ESP was able to make reasonable solubility predictions with a corrected database, demonstrating the need to validate and document the various databases that can be used by ESP. Even though ESP does not include the sulfate-nitrate double salt, this omission does not appear to be a major problem. The solubility predictions with and without the sulfate-nitrate double salt are comparable. In sharp contrast, the sulfate-fluoride double salt is included, but ESP still underestimates solubility in some cases. This problem can misrepresent the ionic strength of the solution, which is an important factor in the formation of pipeline plugs. Solubility tests on the sulfate-fluoride system are planned to provide additional data at higher temperatures and in caustic solutions. These results will be used to improve the range and accuracy of ESP predictions. ESP will continue to provide important predictions for waste processing operations while being evaluated and improved. For example, ESP will be used to determine the amount of water for the saltcake dissolution efforts at Hanford. When ESP underestimates the
A Note on Cubic Convolution Interpolation
Meijering, E.; Unser, M.
2003-01-01
We establish a link between classical osculatory interpolation and modern convolution-based interpolation and use it to show that two well-known cubic convolution schemes are formally equivalent to two osculatory interpolation schemes proposed in the actuarial literature about a century ago. We also discuss computational differences and give examples of other cubic interpolation schemes not previously studied in signal and image processing.
Childhood Epilepsy and Asthma: A Test of an Extension of the Double ABCX Model.
Austin, Joan Kessner
The Double ABCX Model of Family Adjustment and Adaptation, a model that predicts adaptation to chronic stressors on the family, was extended by dividing it into attitudes, coping, and adaptation of parents and child separately, and by including variables relevant to child adaptation to epilepsy or asthma. The extended model was tested on 246…
Convolutional over Recurrent Encoder for Neural Machine Translation
Directory of Open Access Journals (Sweden)
Dakwale Praveen
2017-06-01
Full Text Available Neural machine translation is a recently proposed approach which has shown competitive results to traditional MT approaches. Standard neural MT is an end-to-end neural network where the source sentence is encoded by a recurrent neural network (RNN called encoder and the target words are predicted using another RNN known as decoder. Recently, various models have been proposed which replace the RNN encoder with a convolutional neural network (CNN. In this paper, we propose to augment the standard RNN encoder in NMT with additional convolutional layers in order to capture wider context in the encoder output. Experiments on English to German translation demonstrate that our approach can achieve significant improvements over a standard RNN-based baseline.
International Nuclear Information System (INIS)
Unesaki, Hironobu; Takeda, Toshikazu
1988-01-01
Neutron streaming in a fast breeder reactor fuel assembly caused by the double heterogeneity structure is estimated by double heterogeneous modelling. The conventional pin cell model, a two-region subassembly model and the exact pin cluster model are used to take into account the streaming effect caused by the pin cell structure and the surrounding wrapper tube structure. The heterogeneity of wrapper tube and its surrounding sodium is explicitly considered. The streaming effect is evaluated based on Benoist's diffusion coefficient. The total streaming effect caused by the double heterogeneity structure of a fuel subassembly is found to be -0.2 % dk/kk' for k eff , which is almost twice that obtained from the conventional pin cell model of -0.1 % dk/kk'. (author)
Serang, Oliver
2015-08-01
Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to max-product inference is the max-convolution problem (sometimes presented in log-transformed form and denoted as "infimal convolution," "min-convolution," or "convolution on the tropical semiring"), for which no O(k log(k)) method is currently known. Presented here is an O(k log(k)) numerical method for estimating the max-convolution of two nonnegative vectors (e.g., two probability mass functions), where k is the length of the larger vector. This numerical max-convolution method is then demonstrated by performing fast max-product inference on a convolution tree, a data structure for performing fast inference given information on the sum of n discrete random variables in O(nk log(nk)log(n)) steps (where each random variable has an arbitrary prior distribution on k contiguous possible states). The numerical max-convolution method can be applied to specialized classes of hidden Markov models to reduce the runtime of computing the Viterbi path from nk(2) to nk log(k), and has potential application to the all-pairs shortest paths problem.
AFM tip-sample convolution effects for cylinder protrusions
Shen, Jian; Zhang, Dan; Zhang, Fei-Hu; Gan, Yang
2017-11-01
A thorough understanding about the AFM tip geometry dependent artifacts and tip-sample convolution effect is essential for reliable AFM topographic characterization and dimensional metrology. Using rigid sapphire cylinder protrusions (diameter: 2.25 μm, height: 575 nm) as the model system, a systematic and quantitative study about the imaging artifacts of four types of tips-two different pyramidal tips, one tetrahedral tip and one super sharp whisker tip-is carried out through comparing tip geometry dependent variations in AFM topography of cylinders and constructing the rigid tip-cylinder convolution models. We found that the imaging artifacts and the tip-sample convolution effect are critically related to the actual inclination of the working cantilever, the tip geometry, and the obstructive contacts between the working tip's planes/edges and the cylinder. Artifact-free images can only be obtained provided that all planes and edges of the working tip are steeper than the cylinder sidewalls. The findings reported here will contribute to reliable AFM characterization of surface features of micron or hundreds of nanometers in height that are frequently met in semiconductor, biology and materials fields.
Nuclear norm regularized convolutional Max Pos@Top machine
Li, Qinfeng
2016-11-18
In this paper, we propose a novel classification model for the multiple instance data, which aims to maximize the number of positive instances ranked before the top-ranked negative instances. This method belongs to a recently emerged performance, named as Pos@Top. Our proposed classification model has a convolutional structure that is composed by four layers, i.e., the convolutional layer, the activation layer, the max-pooling layer and the full connection layer. In this paper, we propose an algorithm to learn the convolutional filters and the full connection weights to maximize the Pos@Top measure over the training set. Also, we try to minimize the rank of the filter matrix to explore the low-dimensional space of the instances in conjunction with the classification results. The rank minimization is conducted by the nuclear norm minimization of the filter matrix. In addition, we develop an iterative algorithm to solve the corresponding problem. We test our method on several benchmark datasets. The experimental results show the superiority of our method compared with other state-of-the-art Pos@Top maximization methods.
Double-detonation model of type Ia supernovae with a variable helium layer ignition mass
International Nuclear Information System (INIS)
Zhou Wei-Hong; Zhao Gang; Wang Bo
2014-01-01
Although Type Ia supernovae (SNe Ia) play an important role in the study of cosmology, their progenitors are still poorly understood. Thermonuclear explosions from the helium double-detonation sub-Chandrasekhar mass model have been considered as an alternative method for producing SNe Ia. By adopting the assumption that a double detonation occurs when a He layer with a critical ignition mass accumulates on the surface of a carbon—oxygen white dwarf (CO WD), we perform detailed binary evolution calculations for the He double-detonation model, in which a He layer from a He star accumulates on a CO WD. According to these calculations, we obtain the initial parameter spaces for SNe Ia in the orbital period and secondary mass plane for various initial WD masses. We implement these results into a detailed binary population synthesis approach to calculate SN Ia birthrates and delay times. From this model, the SN Ia birthrate in our Galaxy is ∼0.4 − 1.6 × 10 −3 yr −1 . This indicates that the double-detonation model only produces part of the SNe Ia. The delay times from this model are ∼ 70 – 710 Myr, which contribute to the young population of SNe Ia in the observations. We found that the CO WD + sdB star system CD–30 11223 could produce an SN Ia via the double-detonation model in its future evolution. (research papers)
Patel, Ajay M.; Joshi, Anand Y.
2016-10-01
This paper deals with the nonlinear vibration analysis of a double walled carbon nanotube based mass sensor with curvature factor or waviness, which is doubly clamped at a source and a drain. Nonlinear vibrational behaviour of a double-walled carbon nanotube excited harmonically near its primary resonance is considered. The double walled carbon nanotube is harmonically excited by the addition of an excitation force. The modelling involves stretching of the mid plane and damping as per phenomenon. The equation of motion involves four nonlinear terms for inner and outer tubes of DWCNT due to the curved geometry and the stretching of the central plane due to the boundary conditions. The vibrational behaviour of the double walled carbon nanotube with different surface deviations along its axis is analyzed in the context of the time response, Poincaré maps and Fast Fourier Transformation diagrams. The appearance of instability and chaos in the dynamic response is observed as the curvature factor on double walled carbon nanotube is changed. The phenomenon of Periodic doubling and intermittency are observed as the pathway to chaos. The regions of periodic, sub-harmonic and chaotic behaviour are clearly seen to be dependent on added mass and the curvature factors in the double walled carbon nanotube. Poincaré maps and frequency spectra are used to explicate and to demonstrate the miscellany of the system behaviour. With the increase in the curvature factor system excitations increases and results in an increase of the vibration amplitude with reduction in excitation frequency.
Salient regions detection using convolutional neural networks and color volume
Liu, Guang-Hai; Hou, Yingkun
2018-03-01
Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.
Pion-nucleus double charge exchange and the nuclear shell model
International Nuclear Information System (INIS)
Auerbach, N.; Gibbs, W.R.; Ginocchio, J.N.; Kaufmann, W.B.
1988-01-01
The pion-nucleus double charge exchange reaction is studied with special emphasis on nuclear structure. The reaction mechanism and nuclear structure aspects of the process are separated using both the plane-wave and distorted-wave impulse approximations. Predictions are made employing both the seniority model and a full shell model (with a single active orbit). Transitions to the double analog state and to the ground state of the residual nucleus are computed. The seniority model yields particularly simple relations among double charge exchange cross sections for nuclei within the same shell. Limitations of the seniority model and of the plane-wave impulse approximation are discussed as well as extensions to the generalized seniority scheme. Applications of the foregoing ideas to single charge exchange are also presented
Directory of Open Access Journals (Sweden)
ZHANG Jia-hong
2015-04-01
Full Text Available According to agricultural resource endowment of Jiangsu Province, this paper created kinds of double-chain eco-agricultural model and integrated supporting system based on 'waterfowl, marine lives, aquatic vegetable and paddy rice', 'special food and economic crops with livestock’and‘special food and economic crops with livestock and marine lives’, which were suitable for extension and application in Jiangsu Province. Besides, it set 12 provincial standards and established preliminary technical standard system of‘double-chain’eco-agricultural model. In addition, it explored that‘the leading agricultural enterprises (agricultural co-operatives or family farms+demonstration zones+farmer households’was adopted as operating mechanism of industrialization of eco-agricultural model, which pushed forward rapid development of standardization and industrialization of‘double-chain’eco-agricultural model.
Optimization of the Darrieus wind turbines with double-multiple-streamtube model
International Nuclear Information System (INIS)
Paraschivoiu, I.
1985-01-01
This paper discusses a new improvement of the double-multiple-stream tube model by considering the stream tube expansion effects on the Darrieus wind turbine. These effects, allowing a more realistic modeling of the upwind/downwind flow field asymmetries inherent in the Darrieus rotor, were calculated by using CARDAAX computer code. When the dynamic stall is introduced in the double-multiple-stream tube model, the aerodynamic loads and performance show significant changes in the range of low tip-speed ratio
Directory of Open Access Journals (Sweden)
C. Pfrang
2010-05-01
Full Text Available We present a kinetic double layer model coupling aerosol surface and bulk chemistry (K2-SUB based on the PRA framework of gas-particle interactions (Pöschl-Rudich-Ammann, 2007. K2-SUB is applied to a popular model system of atmospheric heterogeneous chemistry: the interaction of ozone with oleic acid. We show that our modelling approach allows de-convoluting surface and bulk processes, which has been a controversial topic and remains an important challenge for the understanding and description of atmospheric aerosol transformation. In particular, we demonstrate how a detailed treatment of adsorption and reaction at the surface can be coupled to a description of bulk reaction and transport that is consistent with traditional resistor model formulations.
From literature data we have derived a consistent set of kinetic parameters that characterise mass transport and chemical reaction of ozone at the surface and in the bulk of oleic acid droplets. Due to the wide range of rate coefficients reported from different experimental studies, the exact proportions between surface and bulk reaction rates remain uncertain. Nevertheless, the model results suggest an important role of chemical reaction in the bulk and an approximate upper limit of ~10^{−11} cm^{2} s^{−1} for the surface reaction rate coefficient. Sensitivity studies show that the surface accommodation coefficient of the gas-phase reactant has a strong non-linear influence on both surface and bulk chemical reactions. We suggest that K2-SUB may be used to design, interpret and analyse future experiments for better discrimination between surface and bulk processes in the oleic acid-ozone system as well as in other heterogeneous reaction systems of atmospheric relevance.
Algorithm for Financial Derivatives Evaluation in Generalized Double-Heston Model
Directory of Open Access Journals (Sweden)
Tiberiu Socaciu
2010-03-01
Full Text Available This paper shows how can be estimated the value of an option if we assume the double-Heston model on a message-based architecture. For path trace simulation we will discretize continous model with an Euler division of time.
Analytical model for double split ring resonators with arbitrary ring width
DEFF Research Database (Denmark)
Zhurbenko, Vitaliy; Jensen, Thomas; Krozer, Viktor
2008-01-01
For the first time, the analytical model for a double split ring resonator with unequal width rings is developed. The proposed models for the resonators with equal and unequal widths are based on an impedance matrix representation and provide the prediction of performance in a wide frequency range...
Modeling hydrodynamic instabilities of double ablation fronts in inertial confinement fusion
International Nuclear Information System (INIS)
Yanez, C.; Sanz, J.; Olazabal-Loume, M.; Ibanez, L. F.
2013-01-01
A linear Rayleigh-Taylor instability theory of double ablation (DA) fronts is developed for direct-drive inertial confinement fusion. Two approaches are discussed: an analytical discontinuity model for the radiation dominated regime of very steep DA front structure, and a numerical self-consistent model that covers more general hydrodynamic profiles behaviours. Dispersion relation results are compared to 2D simulations. (authors)
Classifying medical relations in clinical text via convolutional neural networks.
He, Bin; Guan, Yi; Dai, Rui
2018-05-16
Deep learning research on relation classification has achieved solid performance in the general domain. This study proposes a convolutional neural network (CNN) architecture with a multi-pooling operation for medical relation classification on clinical records and explores a loss function with a category-level constraint matrix. Experiments using the 2010 i2b2/VA relation corpus demonstrate these models, which do not depend on any external features, outperform previous single-model methods and our best model is competitive with the existing ensemble-based method. Copyright © 2018. Published by Elsevier B.V.
International Nuclear Information System (INIS)
Tran Ngoc, T.D.
2008-07-01
This Ph.D thesis presents the development of the solute transport models in unsaturated double-porosity medium, by using the asymptotic homogenization method. The obtained macroscopic models concern diffusion, diffusion-convection and dispersion-convection, according to the transport regime which is characterized by the non-dimensional numbers. The models consist of two coupled equations that show the local non-equilibrium of concentrations. The double-porosity transport models were numerically implemented using the code COMSOL Multiphysics (finite elements method), and compared with the solution of the same problem at the fine scale. The implementation allows solving the coupled equations in the macro- and micro-porosity domains (two-scale computations). The calculations of the dispersion tensor as a solution of the local boundary value problems, were also conducted. It was shown that the dispersivity depends on the saturation, the physical properties of the macro-porosity domain and the internal structure of the double-porosity medium. Finally, two series of experiments were performed on a physical model of double-porosity that is composed of a periodic assemblage of sintered clay spheres in Hostun sand HN38. The first experiment was a drainage experiment, which was conducted in order to validate the unsaturated flow model. The second series was a dispersion experiment in permanent unsaturated water flow condition (water content measured by gamma ray attenuation technique). A good agreement between the numerical simulations and the experimental observations allows the validation of the developed models. (author)
Do Convolutional Neural Networks Learn Class Hierarchy?
Bilal, Alsallakh; Jourabloo, Amin; Ye, Mao; Liu, Xiaoming; Ren, Liu
2018-01-01
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possibilities of confusion increase. Interestingly, the class confusion patterns follow a hierarchical structure over the classes. We present visual-analytics methods to reveal and analyze this hierarchy of similar classes in relation with CNN-internal data. We found that this hierarchy not only dictates the confusion patterns between the classes, it furthermore dictates the learning behavior of CNNs. In particular, the early layers in these networks develop feature detectors that can separate high-level groups of classes quite well, even after a few training epochs. In contrast, the latter layers require substantially more epochs to develop specialized feature detectors that can separate individual classes. We demonstrate how these insights are key to significant improvement in accuracy by designing hierarchy-aware CNNs that accelerate model convergence and alleviate overfitting. We further demonstrate how our methods help in identifying various quality issues in the training data.
A SPICE model of double-sided Si microstrip detectors
International Nuclear Information System (INIS)
Candelori, A.; Paccagnella, A.; Bonin, F.
1996-01-01
We have developed a SPICE model for the ohmic side of AC-coupled Si microstrip detectors with interstrip isolation via field plates. The interstrip isolation has been measured in various conditions by varying the field plate voltage. Simulations have been compared with experimental data in order to determine the values of the model parameters for different voltages applied to the field plates. The model is able to predict correctly the frequency dependence of the coupling between adjacent strips. Furthermore, we have used such model for the study of the signal propagation along the detector when a current signal is injected in a strip. Only electrical coupling is considered here, without any contribution due to charge sharing derived from carrier diffusion. For this purpose, the AC pads of the strips have been connected to a read-out electronics and the current signal has been injected into a DC pad. Good agreement between measurements and simulations has been reached for the central strip and the first neighbors. Experimental tests and computer simulations have been performed for four different strip and field plate layouts, in order to investigate how the detector geometry affects the parameters of the SPICE model and the signal propagation
Double Layered Sheath in Accurate HV XLPE Cable Modeling
DEFF Research Database (Denmark)
Gudmundsdottir, Unnur Stella; Silva, J. De; Bak, Claus Leth
2010-01-01
This paper discusses modelling of high voltage AC underground cables. For long cables, when crossbonding points are present, not only the coaxial mode of propagation is excited during transient phenomena, but also the intersheath mode. This causes inaccurate simulation results for high frequency...
International Nuclear Information System (INIS)
Bhartia, Mini; Chatterjee, Arun Kumar
2015-01-01
A 2D model for the potential distribution in silicon film is derived for a symmetrical double gate MOSFET in weak inversion. This 2D potential distribution model is used to analytically derive an expression for the subthreshold slope and threshold voltage. A drain current model for lightly doped symmetrical DG MOSFETs is then presented by considering weak and strong inversion regions including short channel effects, series source to drain resistance and channel length modulation parameters. These derived models are compared with the simulation results of the SILVACO (Atlas) tool for different channel lengths and silicon film thicknesses. Lastly, the effect of the fixed oxide charge on the drain current model has been studied through simulation. It is observed that the obtained analytical models of symmetrical double gate MOSFETs are in good agreement with the simulated results for a channel length to silicon film thickness ratio greater than or equal to 2. (paper)
Bhartia, Mini; Chatterjee, Arun Kumar
2015-04-01
A 2D model for the potential distribution in silicon film is derived for a symmetrical double gate MOSFET in weak inversion. This 2D potential distribution model is used to analytically derive an expression for the subthreshold slope and threshold voltage. A drain current model for lightly doped symmetrical DG MOSFETs is then presented by considering weak and strong inversion regions including short channel effects, series source to drain resistance and channel length modulation parameters. These derived models are compared with the simulation results of the SILVACO (Atlas) tool for different channel lengths and silicon film thicknesses. Lastly, the effect of the fixed oxide charge on the drain current model has been studied through simulation. It is observed that the obtained analytical models of symmetrical double gate MOSFETs are in good agreement with the simulated results for a channel length to silicon film thickness ratio greater than or equal to 2.
A Model of High-Frequency Self-Mixing in Double-Barrier Rectifier
Palma, Fabrizio; Rao, R.
2018-03-01
In this paper, a new model of the frequency dependence of the double-barrier THz rectifier is presented. The new structure is of interest because it can be realized by CMOS image sensor technology. Its application in a complex field such as that of THz receivers requires the availability of an analytical model, which is reliable and able to highlight the dependence on the parameters of the physical structure. The model is based on the hydrodynamic semiconductor equations, solved in the small signal approximation. The model depicts the mechanisms of the THz modulation of the charge in the depleted regions of the double-barrier device and explains the self-mixing process, the frequency dependence, and the detection capability of the structure. The model thus substantially improves the analytical models of the THz rectification available in literature, mainly based on lamped equivalent circuits.
Transmission line model for coupled rectangular double split‐ring resonators
DEFF Research Database (Denmark)
Yan, Lei; Tang, Meng; Krozer, Viktor
2011-01-01
In this work, a model based on a coupled transmission line formulation is developed for microstrip rectangular double split‐ring resonators (DSRRs). This model allows using the physical dimensions of the DSRRs as an input avoiding commonly used extraction of equivalent parameters. The model inclu...... simulations of the DSRR structures. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1311–1315, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25988...
Traffic sign classification with dataset augmentation and convolutional neural network
Tang, Qing; Kurnianggoro, Laksono; Jo, Kang-Hyun
2018-04-01
This paper presents a method for traffic sign classification using a convolutional neural network (CNN). In this method, firstly we transfer a color image into grayscale, and then normalize it in the range (-1,1) as the preprocessing step. To increase robustness of classification model, we apply a dataset augmentation algorithm and create new images to train the model. To avoid overfitting, we utilize a dropout module before the last fully connection layer. To assess the performance of the proposed method, the German traffic sign recognition benchmark (GTSRB) dataset is utilized. Experimental results show that the method is effective in classifying traffic signs.
Semantic segmentation of bioimages using convolutional neural networks
CSIR Research Space (South Africa)
Wiehman, S
2016-07-01
Full Text Available Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live...
One weird trick for parallelizing convolutional neural networks
Krizhevsky, Alex
2014-01-01
I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.
Double tracer experiments to evaluate atmospheric transport and dose models
International Nuclear Information System (INIS)
Nielsen, S.P.; Gryning, S.-E.; Thykier-Nielsen, S.; Karlberg, O.; Lyck, E.
1986-05-01
Two tracers, sulphurhexafluoride (SF 6 ) and radioactive noble gases, were released simultaneously from a 110-m stack and detected downwind at distances of 3-4 km. The experiments were made at the Swedish nuclear power plant Ringhals in 1981. The radioactive tracer was routine emissions from unit 1 (BWR). The one-hour measurements yielded crosswind profiles at ground level of SF 6 -concentrations and of gamma radiation from the plume. The measured profiles were compared to profiles calculated with computer models. The comparison showed that the models sometimes underestimate and sometimes overestimate the results, which seems to indicate that the models within their limited accuracy yield unbiased results. The ratios between measured and calculated values range from 0.2 to 3. The measurements revealed a surplus of gamma radiations from the noble gas daughters compared to those from the gases. This was interpreted as due to ground desposition and the estimated deposition velocities range from 2 to 10 cm/s. The meteorological conditions were monitored from a 100-m meteorological tower and from an 11-m mast. Measurements were made of wind speed, wind direction, and temperatures at different heights, and during each experiment a mini-radiosonde was released giving information on a possible inversion layer. The SF 6 -tracer was injected to the stack prior to the experiments. Air-samples were collected downwind in plastic bags by radio-controlled sampling units. The SF 6 -concentrations in the bags were determined with gas chromatography. Measurements of the gamma radiation from the plume were made with ionisation chambers and GM-counters. Furthermore, a few mobile gamma spectrometers were available giving information on the unscattered gamma radiation, thereby permitting identification of the radioactive isotopes. The work was partly financed by the Nuclear Safety Board of the Swedish Utilities and by the Danish association of utilities in Jutland and on Funen, Elsam
Concerning modeling of double-stage water evaporation cooling
Shatskiy, V. P.; Fedulova, L. I.; Gridneva, I. V.
2018-03-01
The matter of need for setting technical norms for production, as well as acceptable microclimate parameters, such as temperature and humidity, at the work place, remains urgent. Use of certain units should be economically sound and that should be taken into account for construction, assembly, operation, technological, and environmental requirements. Water evaporation coolers are simple to maintain, environmentally friendly, and quite cheap, but the development of the most efficient solutions requires mathematical modeling of the heat and mass transfer processes that take place in them.
Double suppression of FCNCs in a supersymmetric model
International Nuclear Information System (INIS)
Kajiyama, Yuji
2004-01-01
A concrete model which can suppress FCNCs and CP violating phenomena is suggested. It is S 3 symmetric extension of MSSM in extra dimensions where only SU(2) and SU(3) gauge multiplet are assumed to propagate in the bulk. They are suppressed due to S 3 flavor symmetry at M SUSY , and the infrared attractive force of gauge interaction in extra dimensions are used to suppress them at the compactification scale. We find that O(1) disorders of the soft parameters are allowed at the cut-off scale to suppress FCNCs and CP violating phenomena. (author)
Double suppression of FCNCs in a supersymmetric model
Energy Technology Data Exchange (ETDEWEB)
Kajiyama, Yuji [Kanazawa Univ., Dept. of Physics, Kanazawa, Ishikawa (Japan)
2004-12-01
A concrete model which can suppress FCNCs and CP violating phenomena is suggested. It is S{sub 3} symmetric extension of MSSM in extra dimensions where only SU(2) and SU(3) gauge multiplet are assumed to propagate in the bulk. They are suppressed due to S{sub 3} flavor symmetry at M{sub SUSY}, and the infrared attractive force of gauge interaction in extra dimensions are used to suppress them at the compactification scale. We find that O(1) disorders of the soft parameters are allowed at the cut-off scale to suppress FCNCs and CP violating phenomena. (author)
Quark-diquark model description for double charm baryons
International Nuclear Information System (INIS)
Majethiya, A.; Patel, B.; Vinodkumar, P. C.
2010-01-01
We report here the mass spectrum and magnetic moments of ccq(q (implied by) u, d, s) systems in the potential model framework by assuming the inter-quark potential as the colour coulomb plus power form with power index ν varying between 0.1 to 2.0. Here the two charm quarks are considered for the diquark states. The conventional one gluon exchange interaction has been employed to get the hyperfine and the fine structure between different states. We have predicted many low-lying states whose experimental verification can exclusively support the quark-diquark structure of the baryons. (authors)
Gradient Flow Convolutive Blind Source Separation
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Nielsen, Chinton Møller
2004-01-01
Experiments have shown that the performance of instantaneous gradient flow beamforming by Cauwenberghs et al. is reduced significantly in reverberant conditions. By expanding the gradient flow principle to convolutive mixtures, separation in a reverberant environment is possible. By use...... of a circular four microphone array with a radius of 5 mm, and applying convolutive gradient flow instead of just applying instantaneous gradient flow, experimental results show an improvement of up to around 14 dB can be achieved for simulated impulse responses and up to around 10 dB for a hearing aid...
Human Face Recognition Using Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Răzvan-Daniel Albu
2009-10-01
Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.
CMOS Compressed Imaging by Random Convolution
Jacques, Laurent; Vandergheynst, Pierre; Bibet, Alexandre; Majidzadeh, Vahid; Schmid, Alexandre; Leblebici, Yusuf
2009-01-01
We present a CMOS imager with built-in capability to perform Compressed Sensing. The adopted sensing strategy is the random Convolution due to J. Romberg. It is achieved by a shift register set in a pseudo-random configuration. It acts as a convolutive filter on the imager focal plane, the current issued from each CMOS pixel undergoing a pseudo-random redirection controlled by each component of the filter sequence. A pseudo-random triggering of the ADC reading is finally applied to comp...
Feedback equivalence of convolutional codes over finite rings
Directory of Open Access Journals (Sweden)
DeCastro-García Noemí
2017-12-01
Full Text Available The approach to convolutional codes from the linear systems point of view provides us with effective tools in order to construct convolutional codes with adequate properties that let us use them in many applications. In this work, we have generalized feedback equivalence between families of convolutional codes and linear systems over certain rings, and we show that every locally Brunovsky linear system may be considered as a representation of a code under feedback convolutional equivalence.
Model of the double-rotor induction motor in terms of electromagnetic differential
Directory of Open Access Journals (Sweden)
Adamczyk Dominik
2016-12-01
Full Text Available The paper presents a concept, a construction, a circuit model and experimental results of the double-rotor induction motor. This type of a motor is to be implemented in the concept of the electromagnetic differential. At the same time it should fulfill the function of differential mechanism and the vehicle drive. One of the motor shafts is coupled to the direction changing mechanical transmission. The windings of the external rotor are powered by slip rings and brushes. The inner rotor has the squirrel-cage windings. The circuit model parameters were calculated based on the 7.5 kW real single-rotor induction motor (2p = 4. Experimental verification of the model was based on comparison between the mentioned single-rotor motor and double-rotor model with the outer rotor blocked. The presented results showed relatively good compliance between the model and real motor.
Ionic diffusion in the double layer at model electrode/molten salt interfaces
International Nuclear Information System (INIS)
Tankeshwar, K.; Tosi, M.P.
1991-08-01
The anisotropic ionic diffusion coefficients in model electrochemical cells in the molten-salt regime for the electrolyte are evaluated from the ionic density profiles reported in simulation work of Grout and coworkers. A local description of the diffusion processes for counterions and coions in the electrical double layer is obtained from the data. (author). 10 refs, 1 fig., 1 tab
Additive quark model and double scattering of pions and protons in deuterium
International Nuclear Information System (INIS)
Bialas, A.; Czyz, W.; Kisielewska, D.
1981-01-01
It is shown that the additive quark model is compatible with the data on double scattering of pions and protons in deuterium. The cross-section for interaction of the hadrons created in the first collision with the second nucleon of the target is determined to be 20-25 mb. (author)
Creating a Double-Spring Model to Teach Chromosome Movement during Mitosis & Meiosis
Luo, Peigao
2012-01-01
The comprehension of chromosome movement during mitosis and meiosis is essential for understanding genetic transmission, but students often find this process difficult to grasp in a classroom setting. I propose a "double-spring model" that incorporates a physical demonstration and can be used as a teaching tool to help students understand this…
Family Adaptation to Stroke: A Metasynthesis of Qualitative Research based on Double ABCX Model
Directory of Open Access Journals (Sweden)
Ali Hesamzadeh, RN, PhD Student of Nursing
2015-09-01
Conclusions: The results of the study are in conformity with the tenets of the Double ABCX Model. Family adaptation is a dynamic process and the present study findings provide rich information on proper assessment and intervention to the practitioners working with families of stroke survivors.
Corpuscular Model of Two-Beam Interference and Double-Slit Experiments with Single Photons
Jin, Fengping; Yuan, Shengjun; De Raedt, Hans; Michielsen, Kristel; Miyashita, Seiji
We introduce an event-based corpuscular simulation model that reproduces the wave mechanical results of single-photon double-slit and two-beam interference experiments and (of a one-to-one copy of an experimental realization) of a single-photon interference experiment with a Fresnel biprism. The
Logical diagnosis model turbojet engine including double-circuit intermittent flow of his injuries
Directory of Open Access Journals (Sweden)
О.П. Стьопушкіна
2007-01-01
Full Text Available In this article is considered question of the change quantitative and qualitative factors of the technical condition constructive element running part of jet engine. As a result called on experimental studies diagnostic sign were definite sign with provision for intermittent damages and on base this is built expert model of the turbojet double-circuit engine.
Method of moments approach to pricing double barrier contracts in polynomial jump-diffusion models
Eriksson, B.; Pistorius, M.
2011-01-01
Abstract: We present a method of moments approach to pricing double barrier contracts when the underlying is modelled by a polynomial jump-diffusion. By general principles the price is linked to certain infinite dimensional linear programming problems. Subsequently approximating these by finite
Efficient airport detection using region-based fully convolutional neural networks
Xin, Peng; Xu, Yuelei; Zhang, Xulei; Ma, Shiping; Li, Shuai; Lv, Chao
2018-04-01
This paper presents a model for airport detection using region-based fully convolutional neural networks. To achieve fast detection with high accuracy, we shared the conv layers between the region proposal procedure and the airport detection procedure and used graphics processing units (GPUs) to speed up the training and testing time. For lack of labeled data, we transferred the convolutional layers of ZF net pretrained by ImageNet to initialize the shared convolutional layers, then we retrained the model using the alternating optimization training strategy. The proposed model has been tested on an airport dataset consisting of 600 images. Experiments show that the proposed method can distinguish airports in our dataset from similar background scenes almost real-time with high accuracy, which is much better than traditional methods.
Schoeftner, J.; Ebner, W.
2017-12-01
Automated and manual transmissions are the main link between engine and powertrain. The technical term when the transmission provides the desired torque during all possible driving conditions is denoted as powertrain matching. Recent developments in the last years show that double-clutch-transmissions (DCTs) are a reasonable compromise in terms of production costs, shifting quality, drivability and fuel efficiency. They have several advantages compared to other automatic transmissions (AT). Most DCTs nowadays consist of a hydraulic actuation control unit, which controls the clutches of the gearbox in order to induce a desired drivetrain torque into the driveline. The main functions of hydraulic systems are manifold: they initiate gear shifts, they provide sufficient oil for lubrication and they control the shift quality by suitably providing a desired oil flow or pressure for the clutch actuation. In this paper, a mathematical model of a passenger car equipped with a DCT is presented. The objective of this contribution is to get an increased understanding for the dynamics of the hydraulic circuit and its coupling to the vehicle drivetrain. The simulation model consists of a hydraulic and a mechanical domain: the hydraulic actuation circuit is described by nonlinear differential equations and includes the dynamics of the line pressure and the proportional valve, as well as the influence of the pressure reducing valve, pipe resistances and accumulator dynamics. The drivetrain with its gear ratios, moments of inertia, torsional stiffness of the rotating shafts and a simple longitudinal vehicle model represent the mechanical domain. The link between hydraulic and mechanical domain is given by the clutch, which combines hydraulic equations and Newton's laws. The presented mathematical model may not only be used as a simulation model for developing the transmission control software, it may also serve as a virtual layout for the design process phase. At the end of this
International Nuclear Information System (INIS)
Fatimah A Noor; Mikrajuddin Abdullah; Sukirno; Khairurrijal
2008-01-01
In this paper, we have derived analytical expression of leakage current through double barriers in Metal Oxide Semiconductor (MOS) capacitor. Initially, electron transmittance through the MOS capacitor was derived by including the coupling between the transverse and longitudinal energies. The transmittance was then employed to obtain leakage current through the double barrier. In this model, we observed the effect of electron velocity due to the coupling effect and the oxide thickness to the leakage current. The calculated results showed that the leakage current decreases as the electron velocity increases. (author)
Discrete convolution-operators and radioactive disintegration. [Numerical solution
Energy Technology Data Exchange (ETDEWEB)
Kalla, S L; VALENTINUZZI, M E [UNIVERSIDAD NACIONAL DE TUCUMAN (ARGENTINA). FACULTAD DE CIENCIAS EXACTAS Y TECNOLOGIA
1975-08-01
The basic concepts of discrete convolution and discrete convolution-operators are briefly described. Then, using the discrete convolution - operators, the differential equations associated with the process of radioactive disintegration are numerically solved. The importance of the method is emphasized to solve numerically, differential and integral equations.
Modeling of anisotropic properties of double quantum rings by the terahertz laser field.
Baghramyan, Henrikh M; Barseghyan, Manuk G; Kirakosyan, Albert A; Ojeda, Judith H; Bragard, Jean; Laroze, David
2018-04-18
The rendering of different shapes of just a single sample of a concentric double quantum ring is demonstrated realizable with a terahertz laser field, that in turn, allows the manipulation of electronic and optical properties of a sample. It is shown that by changing the intensity or frequency of laser field, one can come to a new set of degenerated levels in double quantum rings and switch the charge distribution between the rings. In addition, depending on the direction of an additional static electric field, the linear and quadratic quantum confined Stark effects are observed. The absorption spectrum shifts and the additive absorption coefficient variations affected by laser and electric fields are discussed. Finally, anisotropic electronic and optical properties of isotropic concentric double quantum rings are modeled with the help of terahertz laser field.
Neutrinoless double beta decay in type I+II seesaw models
Energy Technology Data Exchange (ETDEWEB)
Borah, Debasish [Department of Physics, Tezpur University,Tezpur-784028 (India); Dasgupta, Arnab [Institute of Physics, Sachivalaya Marg,Bhubaneshwar-751005 (India)
2015-11-30
We study neutrinoless double beta decay in left-right symmetric extension of the standard model with type I and type II seesaw origin of neutrino masses. Due to the enhanced gauge symmetry as well as extended scalar sector, there are several new physics sources of neutrinoless double beta decay in this model. Ignoring the left-right gauge boson mixing and heavy-light neutrino mixing, we first compute the contributions to neutrinoless double beta decay for type I and type II dominant seesaw separately and compare with the standard light neutrino contributions. We then repeat the exercise by considering the presence of both type I and type II seesaw, having non-negligible contributions to light neutrino masses and show the difference in results from individual seesaw cases. Assuming the new gauge bosons and scalars to be around a TeV, we constrain different parameters of the model including both heavy and light neutrino masses from the requirement of keeping the new physics contribution to neutrinoless double beta decay amplitude below the upper limit set by the GERDA experiment and also satisfying bounds from lepton flavor violation, cosmology and colliders.
A model treating the DNA double-strand break repair inhibition by damage clustering
International Nuclear Information System (INIS)
Rosemann, M.; Abel, H.; Regel, K.
1992-01-01
A microdosimetric model for the interpretation of radiation induced irreparable DNA double-strand breaks was applied to the biological endpoint of chromosomal aberrations. The model explains irreparable DNA double-strand breaks in terms of break clustering in DNA subunits. The model predicts quite good chromosomal aberrations in gamma- and X-ray irradiated V79 cells and human lymphocytes. In the case of α-particle irradiation the presumption had to be made, that only the cells with indirect events in the nucleus (due to delta-electrons) reach the metaphase and are analysed. With the help of this model we are able to explain the peculiar effectiveness of ultrasoft C-X-rays in human lymphocytes. In addition, an interpretation of experiments with accelerated and spatially correlated particles is given. (author)
Double scaling limit in O(N) vector models in D dimensions
International Nuclear Information System (INIS)
Di Vecchia, P.; Kato, M.; Ohta, N.
1991-03-01
Using the standard 1/N expansion, we study O(N) vector models in D dimensions with an arbitrary potential. We limit ourselves to renormalizable theories. We show that there exists a value of the coupling constant corresponding to a critical point and that a double scaling limit can be performed as in D=0 and in the case of matrix models in D=0,1. For D=1 the theory is renormalizable with an arbitrary potential and we find in general a hierarchy of critical theories labelled by an integer k. The universal partition function obtained in the double scaling limit is constructed. Finally we show that the critical behaviour of those models is the same as a branched polymer model recently constructed by Ambjoern, Durhuus and Jonsson. (orig.)
Study on Modelling Standardization of Double-fed Wind Turbine and Its Application
Directory of Open Access Journals (Sweden)
Li Xiang
2016-01-01
Full Text Available Based on the standardized modelling of the International Modelling Team, study on double-fed induction generator (DFIG wind turbine is processed in this paper, aiming at capability of universally and reasonably reflecting key performance related to large scale system analysis. The standardized model proposed is of high degree of structural modularity, easy functional extension and universalization of control strategy and signal. Moreover, it is applicable for wind turbines produced by different manufacturers through model parameter adjustment. The complexity of the model can meet both needs of grid-connected characteristic simulation of wind turbine and large scale power system simulation.
The mass spectrum of double heavy baryons in new potential quark models
Directory of Open Access Journals (Sweden)
Kovalenko Vladimir
2017-01-01
Full Text Available A new approach to study the mass spectrum of double heavy baryons (QQ′q containing strange and charmed quarks is proposed. It is based on the separation of variables in the Schrodinger equation in the prolate spheroidal coordinates. Two nonrelativistic potential models are considered. In the first model, the interaction potential of the quarks is the sum of the Coulomb and non-spherically symmetrical linear confinement potential. In the second model it is assumed that the quark confinement provided by a spherically symmetric harmonic oscillator potential. In both models the mass spectrum is calculated, and a comparison with previous results from other models is performed.
Double-multiple streamtube model for studying vertical-axis wind turbines
Paraschivoiu, Ion
1988-08-01
This work describes the present state-of-the-art in double-multiple streamtube method for modeling the Darrieus-type vertical-axis wind turbine (VAWT). Comparisons of the analytical results with the other predictions and available experimental data show a good agreement. This method, which incorporates dynamic-stall and secondary effects, can be used for generating a suitable aerodynamic-load model for structural design analysis of the Darrieus rotor.
Parallel double-plate capacitive proximity sensor modelling based on effective theory
International Nuclear Information System (INIS)
Li, Nan; Zhu, Haiye; Wang, Wenyu; Gong, Yu
2014-01-01
A semi-analytical model for a double-plate capacitive proximity sensor is presented according to the effective theory. Three physical models are established to derive the final equation of the sensor. Measured data are used to determine the coefficients. The final equation is verified by using measured data. The average relative error of the calculated and the measured sensor capacitance is less than 7.5%. The equation can be used to provide guidance to engineering design of the proximity sensors
Structures of $p$-shell double-$\\Lambda$ hypernuclei studied with microscopic cluster models
Kanada-En'yo, Yoshiko
2018-01-01
$0s$-orbit $\\Lambda$ states in $p$-shell double-$\\Lambda$ hypernuclei ($^{\\ \\,A}_{\\Lambda\\Lambda}Z$), $^{\\ \\,8}_{\\Lambda\\Lambda}\\textrm{Li}$, $^{\\ \\,9}_{\\Lambda\\Lambda}\\textrm{Li}$, $^{10,11,12}_{\\ \\ \\ \\ \\ \\Lambda\\Lambda}\\textrm{Be}$, $^{12,13}_{\\ \\ \\Lambda\\Lambda}\\textrm{B}$, and $^{\\,14}_{\\Lambda\\Lambda}\\textrm{C}$ are investigated. Microscopic cluster models are applied to core nuclear part and a potential model is adopted for $\\Lambda$ particles. The $\\Lambda$-core potential is a folding ...
Deformable image registration using convolutional neural networks
Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.
2018-01-01
Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between
Epileptiform spike detection via convolutional neural networks
DEFF Research Database (Denmark)
Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz
2016-01-01
The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...
Convolutional Neural Networks for SAR Image Segmentation
DEFF Research Database (Denmark)
Malmgren-Hansen, David; Nobel-Jørgensen, Morten
2015-01-01
Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...
A locality aware convolutional neural networks accelerator
Shi, R.; Xu, Z.; Sun, Z.; Peemen, M.C.J.; Li, A.; Corporaal, H.; Wu, D.
2015-01-01
The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time
Estimating the number of sources in a noisy convolutive mixture using BIC
DEFF Research Database (Denmark)
Olsson, Rasmus Kongsgaard; Hansen, Lars Kai
2004-01-01
The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics of the s......The number of source signals in a noisy convolutive mixture is determined based on the exact log-likelihoods of the candidate models. In (Olsson and Hansen, 2004), a novel probabilistic blind source separator was introduced that is based solely on the time-varying second-order statistics...
Alternate symbol inversion for improved symbol synchronization in convolutionally coded systems
Simon, M. K.; Smith, J. G.
1980-01-01
Inverting alternate symbols of the encoder output of a convolutionally coded system provides sufficient density of symbol transitions to guarantee adequate symbol synchronizer performance, a guarantee otherwise lacking. Although alternate symbol inversion may increase or decrease the average transition density, depending on the data source model, it produces a maximum number of contiguous symbols without transition for a particular class of convolutional codes, independent of the data source model. Further, this maximum is sufficiently small to guarantee acceptable symbol synchronizer performance for typical applications. Subsequent inversion of alternate detected symbols permits proper decoding.
Convolute laminations — a theoretical analysis: example of a Pennsylvanian sandstone
Visher, Glenn S.; Cunningham, Russ D.
1981-03-01
Data from an outcropping laminated interval were collected and analyzed to test the applicability of a theoretical model describing instability of layered systems. Rayleigh—Taylor wave perturbations result at the interface between fluids of contrasting density, viscosity, and thickness. In the special case where reverse density and viscosity interlaminations are developed, the deformation response produces a single wave with predictable amplitudes, wavelengths, and amplification rates. Physical measurements from both the outcropping section and modern sediments suggest the usefulness of the model for the interpretation of convolute laminations. Internal characteristics of the stratigraphic interval, and the developmental sequence of convoluted beds, are used to document the developmental history of these structures.
Design and Implementation of Behavior Recognition System Based on Convolutional Neural Network
Directory of Open Access Journals (Sweden)
Yu Bo
2017-01-01
Full Text Available We build a set of human behavior recognition system based on the convolution neural network constructed for the specific human behavior in public places. Firstly, video of human behavior data set will be segmented into images, then we process the images by the method of background subtraction to extract moving foreground characters of body. Secondly, the training data sets are trained into the designed convolution neural network, and the depth learning network is constructed by stochastic gradient descent. Finally, the various behaviors of samples are classified and identified with the obtained network model, and the recognition results are compared with the current mainstream methods. The result show that the convolution neural network can study human behavior model automatically and identify human’s behaviors without any manually annotated trainings.
a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data
Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.
2018-04-01
Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.
Convolutional Dictionary Learning: Acceleration and Convergence
Chun, Il Yong; Fessler, Jeffrey A.
2018-04-01
Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.
Lidar Cloud Detection with Fully Convolutional Networks
Cromwell, E.; Flynn, D.
2017-12-01
The vertical distribution of clouds from active remote sensing instrumentation is a widely used data product from global atmospheric measuring sites. The presence of clouds can be expressed as a binary cloud mask and is a primary input for climate modeling efforts and cloud formation studies. Current cloud detection algorithms producing these masks do not accurately identify the cloud boundaries and tend to oversample or over-represent the cloud. This translates as uncertainty for assessing the radiative impact of clouds and tracking changes in cloud climatologies. The Atmospheric Radiation Measurement (ARM) program has over 20 years of micro-pulse lidar (MPL) and High Spectral Resolution Lidar (HSRL) instrument data and companion automated cloud mask product at the mid-latitude Southern Great Plains (SGP) and the polar North Slope of Alaska (NSA) atmospheric observatory. Using this data, we train a fully convolutional network (FCN) with semi-supervised learning to segment lidar imagery into geometric time-height cloud locations for the SGP site and MPL instrument. We then use transfer learning to train a FCN for (1) the MPL instrument at the NSA site and (2) for the HSRL. In our semi-supervised approach, we pre-train the classification layers of the FCN with weakly labeled lidar data. Then, we facilitate end-to-end unsupervised pre-training and transition to fully supervised learning with ground truth labeled data. Our goal is to improve the cloud mask accuracy and precision for the MPL instrument to 95% and 80%, respectively, compared to the current cloud mask algorithms of 89% and 50%. For the transfer learning based FCN for the HSRL instrument, our goal is to achieve a cloud mask accuracy of 90% and a precision of 80%.
Energy Technology Data Exchange (ETDEWEB)
Bakry, A. [King Abdulaziz University, 80203, Department of Physics, Faculty of Science (Saudi Arabia); Abdulrhmann, S. [Jazan University, 114, Department of Physics, Faculty of Sciences (Saudi Arabia); Ahmed, M., E-mail: mostafa.farghal@mu.edu.eg [King Abdulaziz University, 80203, Department of Physics, Faculty of Science (Saudi Arabia)
2016-06-15
We theoretically model the dynamics of semiconductor lasers subject to the double-reflector feedback. The proposed model is a new modification of the time-delay rate equations of semiconductor lasers under the optical feedback to account for this type of the double-reflector feedback. We examine the influence of adding the second reflector to dynamical states induced by the single-reflector feedback: periodic oscillations, period doubling, and chaos. Regimes of both short and long external cavities are considered. The present analyses are done using the bifurcation diagram, temporal trajectory, phase portrait, and fast Fourier transform of the laser intensity. We show that adding the second reflector attracts the periodic and perioddoubling oscillations, and chaos induced by the first reflector to a route-to-continuous-wave operation. During this operation, the periodic-oscillation frequency increases with strengthening the optical feedback. We show that the chaos induced by the double-reflector feedback is more irregular than that induced by the single-reflector feedback. The power spectrum of this chaos state does not reflect information on the geometry of the optical system, which then has potential for use in chaotic (secure) optical data encryption.
Convolutional neural network architectures for predicting DNA–protein binding
Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.
2016-01-01
Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608
Terahertz double-exponential model for adsorption of volatile organic compounds in active carbon
International Nuclear Information System (INIS)
Zhu, Jing; Zhan, Honglei; Miao, Xinyang; Zhao, Kun; Zhou, Qiong
2017-01-01
In terms of the evaluation of the diffusion-controlled adsorption and diffused rate, a mathematical model was built on the basis of the double-exponential kinetics model and terahertz amplitude in this letter. The double-exponential-THz model described the two-step mechanism controlled by diffusion. A rapid step involves external and internal diffusion, followed by a slow step controlled by intraparticle diffusion. The concentration gradient of the molecules promoted the organic molecules rapidly diffusing to the external surface of adsorbent. The solute molecules then transferred across the liquid film. Intraparticle diffusion began and was determined by the molecular sizes, as well as affinities between organics and activated carbon. (paper)
Modeling nanowire and double-gate junctionless field-effect transistors
Jazaeri, Farzan
2018-01-01
The first book on the topic, this is a comprehensive introduction to the modeling and design of junctionless field effect transistors (FETs). Beginning with a discussion of the advantages and limitations of the technology, the authors also provide a thorough overview of published analytical models for double-gate and nanowire configurations, before offering a general introduction to the EPFL charge-based model of junctionless FETs. Important features are introduced gradually, including nanowire versus double-gate equivalence, technological design space, junctionless FET performances, short channel effects, transcapacitances, asymmetric operation, thermal noise, interface traps, and the junction FET. Additional features compatible with biosensor applications are also discussed. This is a valuable resource for students and researchers looking to understand more about this new and fast developing field.
Schreiber, Shaun; Geldenhuys, Jaco; Villiers, De Hendrik
2017-01-01
Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model
Modeling of Non-WSSUS Double-Rayleigh Fading Channels for Vehicular Communications
Directory of Open Access Journals (Sweden)
Carlos A. Gutiérrez
2017-01-01
Full Text Available This paper deals with the modeling of nonstationary time-frequency (TF dispersive multipath fading channels for vehicle-to-vehicle (V2V communication systems. As a main contribution, the paper presents a novel geometry-based statistical channel model that facilitates the analysis of the nonstationarities of V2V fading channels arising at a small-scale level due to the time-varying nature of the propagation delays. This new geometrical channel model has been formulated following the principles of plane wave propagation (PWP and assuming that the transmitted signal reaches the receiver antenna through double interactions with multiple interfering objects (IOs randomly located in the propagation area. As a consequence of such interactions, the first-order statistics of the channel model’s envelope are shown to follow a worse-than-Rayleigh distribution; specifically, they follow a double-Rayleigh distribution. General expressions are derived for the envelope and phase distributions, four-dimensional (4D TF correlation function (TF-CF, and TF-dependent delay and Doppler profiles of the proposed channel model. Such expressions are valid regardless of the underlying geometry of the propagation area. Furthermore, a closed-form solution of the 4D TF-CF is presented for the particular case of the geometrical two-ring scattering model. The obtained results provide new theoretical insights into the correlation and spectral properties of small-scale nonstationary V2V double-Rayleigh fading channels.
The Substantive Scope of Double Tax Treaties - a Study of Article 2 of the OECD Model Conventions
Brandstetter, Patricia
2010-01-01
Tax treaty protection from international double taxation only goes as far as the treaty's substantive scope. Nations worldwide have adopted the text of Article 2 of the OECD Model Double Taxation Conventions (headed Taxes covered) in concluding bilateral treaties to prevent double taxation in the area of taxes on income and capital and taxes on estates, inheritances, and on gifts. The wording and structure of Article 2 give rise to a host of ambiguities, creating uncertainty for taxpayers reg...
Classifying images using restricted Boltzmann machines and convolutional neural networks
Zhao, Zhijun; Xu, Tongde; Dai, Chenyu
2017-07-01
To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.
Action detection by double hierarchical multi-structure space-time statistical matching model
Han, Jing; Zhu, Junwei; Cui, Yiyin; Bai, Lianfa; Yue, Jiang
2018-03-01
Aimed at the complex information in videos and low detection efficiency, an actions detection model based on neighboring Gaussian structure and 3D LARK features is put forward. We exploit a double hierarchical multi-structure space-time statistical matching model (DMSM) in temporal action localization. First, a neighboring Gaussian structure is presented to describe the multi-scale structural relationship. Then, a space-time statistical matching method is proposed to achieve two similarity matrices on both large and small scales, which combines double hierarchical structural constraints in model by both the neighboring Gaussian structure and the 3D LARK local structure. Finally, the double hierarchical similarity is fused and analyzed to detect actions. Besides, the multi-scale composite template extends the model application into multi-view. Experimental results of DMSM on the complex visual tracker benchmark data sets and THUMOS 2014 data sets show the promising performance. Compared with other state-of-the-art algorithm, DMSM achieves superior performances.
Laser performance and modeling of RE3+:YAG double-clad crystalline fiber waveguides
Li, Da; Lee, Huai-Chuan; Meissner, Stephanie K.; Meissner, Helmuth E.
2018-02-01
We report on laser performance of ceramic Yb:YAG and single crystal Tm:YAG double-clad crystalline fiber waveguide (CFW) lasers towards the goal of demonstrating the design and manufacturing strategy of scaling to high output power. The laser component is a double-clad CFW, with RE3+:YAG (RE = Yb, Tm respectively) core, un-doped YAG inner cladding, and ceramic spinel or sapphire outer cladding. Laser performance of the CFW has been demonstrated with 53.6% slope efficiency and 27.5-W stable output power at 1030-nm for Yb:YAG CFW, and 31.6% slope efficiency and 46.7-W stable output power at 2019-nm for Tm:YAG CFW, respectively. Adhesive-Free Bond (AFB®) technology enables a designable refractive index difference between core and inner cladding, and designable core and inner cladding sizes, which are essential for single transverse mode CFW propagation. To guide further development of CFW designs, we present thermal modeling, power scaling and design of single transverse mode operation of double-clad CFWs and redefine the single-mode operation criterion for the double-clad structure design. The power scaling modeling of double-clad CFW shows that in order to achieve the maximum possible output power limited by the physical properties, including diode brightness, thermal lens effect, and simulated Brillion scattering, the length of waveguide is in the range of 0.5 2 meters. The length of an individual CFW is limited by single crystal growth and doping uniformity to about 100 to 200 mm lengths, and also by availability of starting crystals and manufacturing complexity. To overcome the limitation of CFW lengths, end-to-end proximity-coupling of CFWs is introduced.
No-reference image quality assessment based on statistics of convolution feature maps
Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo
2018-04-01
We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.
Goel, Ekta; Singh, Kunal; Singh, Balraj; Kumar, Sanjay; Jit, Satyabrata
2017-09-01
In this paper, the subthreshold behavior of ion-implanted strained-Si double-material double-gate (DMDG) MOSFETs has been analyzed by means of subthreshold current and subthreshold swing. The surface potential based formulation of subthreshold current and subthreshold swing is done by solving the 2-D Poisson's equations in the channel region using parabolic approximation method. The dependence of subthreshold characteristics on various device parameters such as gate length ratio, Ge mole fraction, peak doping concentration, projected range, straggle parameter etc. has been studied. The modeling results are found to be well matched with the simulation data obtained by a 2-D device simulator, ATLAS™, from SILVACO.
Quantum ratchet effect in a time non-uniform double-kicked model
Chen, Lei; Wang, Zhen-Yu; Hui, Wu; Chu, Cheng-Yu; Chai, Ji-Min; Xiao, Jin; Zhao, Yu; Ma, Jin-Xiang
2017-07-01
The quantum ratchet effect means that the directed transport emerges in a quantum system without a net force. The delta-kicked model is a quantum Hamiltonian model for the quantum ratchet effect. This paper investigates the quantum ratchet effect based on a time non-uniform double-kicked model, in which two flashing potentials alternately act on a particle with a homogeneous initial state of zero momentum, while the intervals between adjacent actions are not equal. The evolution equation of the state of the particle is derived from its Schrödinger equation, and the numerical method to solve the evolution equation is pointed out. The results show that quantum resonances can induce the ratchet effect in this time non-uniform double-kicked model under certain conditions; some quantum resonances, which cannot induce the ratchet effect in previous models, can induce the ratchet effect in this model, and the strengths of the ratchet effect in this model are stronger than those in previous models under certain conditions. These results enrich people’s understanding of the delta-kicked model, and provides a new optional scheme to control the quantum transport of cold atoms in experiment.
Directory of Open Access Journals (Sweden)
Eduard Dyachuk
2015-02-01
Full Text Available The complex unsteady aerodynamics of vertical axis wind turbines (VAWT poses significant challenges to the simulation tools. Dynamic stall is one of the phenomena associated with the unsteady conditions for VAWTs, and it is in the focus of the study. Two dynamic stall models are compared: the widely-used Gormont model and a Leishman–Beddoes-type model. The models are included in a double multiple streamtube model. The effects of flow curvature and flow expansion are also considered. The model results are assessed against the measured data on a Darrieus turbine with curved blades. To study the dynamic stall effects, the comparison of force coefficients between the simulations and experiments is done at low tip speed ratios. Simulations show that the Leishman–Beddoes model outperforms the Gormont model for all tested conditions.
Modeling radiative transfer with the doubling and adding approach in a climate GCM setting
Lacis, A. A.
2017-12-01
The nonlinear dependence of multiply scattered radiation on particle size, optical depth, and solar zenith angle, makes accurate treatment of multiple scattering in the climate GCM setting problematic, due primarily to computational cost issues. In regard to the accurate methods of calculating multiple scattering that are available, their computational cost is far too prohibitive for climate GCM applications. Utilization of two-stream-type radiative transfer approximations may be computationally fast enough, but at the cost of reduced accuracy. We describe here a parameterization of the doubling/adding method that is being used in the GISS climate GCM, which is an adaptation of the doubling/adding formalism configured to operate with a look-up table utilizing a single gauss quadrature point with an extra-angle formulation. It is designed to closely reproduce the accuracy of full-angle doubling and adding for the multiple scattering effects of clouds and aerosols in a realistic atmosphere as a function of particle size, optical depth, and solar zenith angle. With an additional inverse look-up table, this single-gauss-point doubling/adding approach can be adapted to model fractional cloud cover for any GCM grid-box in the independent pixel approximation as a function of the fractional cloud particle sizes, optical depths, and solar zenith angle dependence.
The parametrization of Coulomb barrier heights and positions using the double folding model
International Nuclear Information System (INIS)
Qu, W.W.; Zhang, G.L.; Le, X.Y.
2011-01-01
The Coulomb barrier heights and positions are systematically shown with mass numbers and charge radii of the interacting nuclei. The nuclear potential is calculated by using the double folding model with the density-dependence nucleon-nucleon interaction (CDM3Y6). The pocket formulas are obtained for the Coulomb barrier heights and positions by analyzing several hundreds of heavy-ion systems with mass numbers from light nuclei to heavy nuclei. The parameterized formulas can reproduce the calculated barrier heights and positions by using the double folding model within the accuracy of ±1%. Moreover, the results are agreeable with the experimental data. The relation between the barrier height and the barrier position is also studied.
DEFF Research Database (Denmark)
Valvo, Paolo S.; Sørensen, Bent F.; Toftegaard, Helmuth Langmaack
2015-01-01
A theoretical model of the double cantilever beam tests with bending moments (DCB-UBM) is presented. The specimen is modelled as the assemblage of two laminated beams connected by a cohesive interface. It is assumed that the traction-separation laws – i.e. the relationships between the interfacial...... the cohesive law parameters from experiments. Experimental tests have been conducted on glass fibre reinforced specimens under pure mode I and II loading conditions. The predictions of the theoretical model turn out to be in very good agreement with the experimental results....
Studies on a Double Poisson-Geometric Insurance Risk Model with Interference
Directory of Open Access Journals (Sweden)
Yujuan Huang
2013-01-01
Full Text Available This paper mainly studies a generalized double Poisson-Geometric insurance risk model. By martingale and stopping time approach, we obtain adjustment coefficient equation, the Lundberg inequality, and the formula for the ruin probability. Also the Laplace transformation of the time when the surplus reaches a given level for the first time is discussed, and the expectation and its variance are obtained. Finally, we give the numerical examples.
Neutrinoless double beta decay in an SU(3)L x U(1)N model
International Nuclear Information System (INIS)
Pleitez, V.; Tonasse, M.D.
1993-01-01
A model for the electroweak interactions with SU (3) L x U(1) N gauge symmetry is considered. It is shown that, it is the conservation of F = L + B which forbids massive neutrinos and the neutrinoless double beta decay, (β β) On u. Explicit and spontaneous breaking of F imply that the neutrinos have an arbitrary mass and (β β) On u proceeds also with some contributions that do not depend explicitly on the neutrino mass. (author)
Period-doubling bifurcation and chaos control in a discrete-time mosquito model
Directory of Open Access Journals (Sweden)
Qamar Din
2017-12-01
Full Text Available This article deals with the study of some qualitative properties of a discrete-time mosquito Model. It is shown that there exists period-doubling bifurcation for wide range of bifurcation parameter for the unique positive steady-state of given system. In order to control the bifurcation we introduced a feedback strategy. For further confirmation of complexity and chaotic behavior largest Lyapunov exponents are plotted.
Gajendran, Varun K; Szabo, Robert M; Myo, George K; Curtiss, Shane B
2009-12-01
Open or unstable metacarpal fractures frequently require open reduction and internal fixation. Locking plate technology has improved fixation of unstable fractures in certain settings. In this study, we hypothesized that there would be a difference in strength of fixation using double-row locking plates compared with single- and double-row non-locking plates in comminuted metacarpal fractures. We tested our hypothesis in a gap metacarpal fracture model simulating comminution using fourth-generation, biomechanical testing-grade composite sawbones. The metacarpals were divided into 6 groups of 15 bones each. Groups 1 and 4 were plated with a standard 6-hole, 2.3-mm plate in AO fashion. Groups 2 and 5 were plated with a 6-hole double-row 3-dimensional non-locking plate with bicortical screws aimed for convergence. Groups 3 and 6 were plated with a 6-hole double-row 3-dimensional locking plate with unicortical screws. The plated metacarpals were then tested to failure against cantilever apex dorsal bending (groups 1-3) and torsion (groups 4-6). The loads to failure in groups 1 to 3 were 198 +/- 18, 223 +/- 29, and 203 +/- 19 N, respectively. The torques to failure in groups 4 to 6 were 2,033 +/- 155, 3,190 +/- 235, and 3,161 +/- 268 N mm, respectively. Group 2 had the highest load to failure, whereas groups 5 and 6 shared the highest torques to failure (p row plates had equivalent bending and torsional stiffness, significantly higher than observed for the single-row non-locking plate. No other statistical differences were noted between groups. When subjected to the physiologically relevant forces of apex dorsal bending and torsion in a comminuted metacarpal fracture model, double-row 3-dimensional non-locking plates provided superior stability in bending and equivalent stability in torsion compared with double-row 3-dimensional locking plates, whereas single-row non-locking plates provided the least stability.
An axisymmetric non-hydrostatic model for double-diffusive water systems
Hilgersom, Koen; Zijlema, Marcel; van de Giesen, Nick
2018-02-01
The three-dimensional (3-D) modelling of water systems involving double-diffusive processes is challenging due to the large computation times required to solve the flow and transport of constituents. In 3-D systems that approach axisymmetry around a central location, computation times can be reduced by applying a 2-D axisymmetric model set-up. This article applies the Reynolds-averaged Navier-Stokes equations described in cylindrical coordinates and integrates them to guarantee mass and momentum conservation. The discretized equations are presented in a way that a Cartesian finite-volume model can be easily extended to the developed framework, which is demonstrated by the implementation into a non-hydrostatic free-surface flow model. This model employs temperature- and salinity-dependent densities, molecular diffusivities, and kinematic viscosity. One quantitative case study, based on an analytical solution derived for the radial expansion of a dense water layer, and two qualitative case studies demonstrate a good behaviour of the model for seepage inflows with contrasting salinities and temperatures. Four case studies with respect to double-diffusive processes in a stratified water body demonstrate that turbulent flows are not yet correctly modelled near the interfaces and that an advanced turbulence model is required.
International Nuclear Information System (INIS)
Formosa, Fabien; Badel, Adrien; Lottin, Jacques
2014-01-01
Highlights: • An equivalent electrical network modeling of Stirling engine is proposed. • This model is applied to a membrane low temperate double acting Stirling engine. • The operating conditions (self-startup and steady state behavior) are defined. • An experimental engine is presented and tested. • The model is validated against experimental results. - Abstract: This work presents a network model to simulate the periodic behavior of a double acting free piston type Stirling engine. Each component of the engine is considered independently and its equivalent electrical circuit derived. When assembled in a global electrical network, a global model of the engine is established. Its steady behavior can be obtained by the analysis of the transfer function for one phase from the piston to the expansion chamber. It is then possible to simulate the dynamic (steady state stroke and operation frequency) as well as the thermodynamic performances (output power and efficiency) for given mean pressure, heat source and heat sink temperatures. The motion amplitude especially can be determined by the spring-mass properties of the moving parts and the main nonlinear effects which are taken into account in the model. The thermodynamic features of the model have then been validated using the classical isothermal Schmidt analysis for a given stroke. A three-phase low temperature differential double acting free membrane architecture has been built and tested. The experimental results are compared with the model and a satisfactory agreement is obtained. The stroke and operating frequency are predicted with less than 2% error whereas the output power discrepancy is of about 30%. Finally, some optimization routes are suggested to improve the design and maximize the performances aiming at waste heat recovery applications
The double-degenerate model for the progenitors of Type Ia supernovae
Liu, D.; Wang, B.; Han, Z.
2018-02-01
The double-degenerate (DD) model, involving the merging of massive double carbon-oxygen white dwarfs (CO WDs) driven by gravitational wave radiation, is one of the classical pathways for the formation of Type Ia supernovae (SNe Ia). Recently, it has been proposed that the WD+He subgiant channel has a significant contribution to the production of massive double WDs, in which the primary WD accumulates mass by accreting He-rich matter from an He subgiant. We evolved about 1800 CO WD+He star systems and obtained a large and dense grid for producing SNe Ia through the DD model. We then performed a series of binary population synthesis simulations for the DD model, in which the WD+He subgiant channel is calculated by interpolations in the SN Ia production grid. According to our standard model, the Galactic birth rate of SNe Ia is about 2.4 × 10- 3 yr- 1 for the WD+He subgiant channel of the DD model; the total birth rate is about 3.7 × 10- 3 yr- 1 for all channels, reproducing that of observations. Previous theoretical models still have deficit with the observed SNe Ia with delay times 8 Gyr. After considering the WD+He subgiant channel, we found that the delay time distributions are comparable with the observed results. Additionally, some recent studies proposed that the violent WD mergers are more likely to produce SNe Ia based on the DD model. We estimated that the violent mergers through the DD model may contribute to at most 16 per cent of all SNe Ia.
Anomalous transport in discrete arcs and simulation of double layers in a model auroral circuit
International Nuclear Information System (INIS)
Smith, R.A.
1987-01-01
The evolution and long-time stability of a double layer in a discrete auroral arc requires that the parallel current in the arc, which may be considered uniform at the source, be diverted within the arc to change the flanks of the U-shaped double-layer potential structure. A simple model is presented in which this current re-distribution is effected by anomalous transport based on electrostatic lower hybrid waves driven by the flank structure itself. This process provides the limiting constraint on the double-layer potential. The flank charging may be represented as that of a nonlinear transmission line. A simplified model circuit, in which the transmission line is represented by a nonlinear impedance in parallel with a variable resistor, is incorporated in a 1-d simulation model to give the current density at the DL boundaries. Results are presented for the scaling of the DL potential as a function of the width of the arc and the saturation efficiency of the lower hybrid instability mechanism. (author)
Anomalous transport in discrete arcs and simulation of double layers in a model auroral circuit
Smith, Robert A.
1987-01-01
The evolution and long-time stability of a double layer in a discrete auroral arc requires that the parallel current in the arc, which may be considered uniform at the source, be diverted within the arc to charge the flanks of the U-shaped double-layer potential structure. A simple model is presented in which this current re-distribution is effected by anomalous transport based on electrostatic lower hybrid waves driven by the flank structure itself. This process provides the limiting constraint on the double-layer potential. The flank charging may be represented as that of a nonlinear transmission. A simplified model circuit, in which the transmission line is represented by a nonlinear impedance in parallel with a variable resistor, is incorporated in a 1-d simulation model to give the current density at the DL boundaries. Results are presented for the scaling of the DL potential as a function of the width of the arc and the saturation efficiency of the lower hybrid instability mechanism.
Anomalous transport in discrete arcs and simulation of double layers in a model auroral circuit
International Nuclear Information System (INIS)
Smith, R.A.
1987-01-01
The evolution and long-time stability of a double layer (DL) in a discrete auroral arc requires that the parallel current in the arc, which may be considered uniform at the source, be diverted within the arc to charge the flanks of the U-shaped double layer potential structure. A simple model is presented in which this current redistribution is effected by anomalous transport based on electrostatic lower hybrid waves driven by the flank structure itself. This process provides the limiting constraint on the double layer potential. The flank charging may be represented as that of a nonlinear transmission line. A simplified model circuit, in which the transmission line is represented by a nonlinear impedance in parallel with a variable resistor, is incorporated in a one-dimensional simulation model to give the current density at the DL boundaries. Results are presented for the scaling of the DL potential as a function of the width of the arc and the saturation efficiency of the lower hybrid instability mechanism
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%.
Siamese convolutional networks for tracking the spine motion
Liu, Yuan; Sui, Xiubao; Sun, Yicheng; Liu, Chengwei; Hu, Yong
2017-09-01
Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.
Convolutional neural networks for vibrational spectroscopic data analysis.
Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena
2017-02-15
In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Schulze-Halberg, Axel; Wang, Jie
2015-01-01
We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system
Energy Technology Data Exchange (ETDEWEB)
Schulze-Halberg, Axel, E-mail: axgeschu@iun.edu, E-mail: xbataxel@gmail.com [Department of Mathematics and Actuarial Science and Department of Physics, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States); Wang, Jie, E-mail: wangjie@iun.edu [Department of Computer Information Systems, Indiana University Northwest, 3400 Broadway, Gary, Indiana 46408 (United States)
2015-07-15
We obtain series solutions, the discrete spectrum, and supersymmetric partners for a quantum double-oscillator system. Its potential features a superposition of the one-parameter Mathews-Lakshmanan interaction and a one-parameter harmonic or inverse harmonic oscillator contribution. Furthermore, our results are transferred to a generalized Pöschl-Teller model that is isospectral to the double-oscillator system.
A convolutional neural network neutrino event classifier
International Nuclear Information System (INIS)
Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.
2016-01-01
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.
Marius HERBEI; Sorin DUDAS; Aura COSTEA
2010-01-01
There are more frequent the situations when the same person has a political, economic or social relationship with two or more states , carrying on activities from which they obtain income or owning assets in many states. The double taxation may constitute a real barrier in the way of the economic technical-scientific cooperation, of setting-up of subsidiaries or branches abroad, of the foreign investments of capital and of the external loans, of the development of economic and financial affai...
Quasi-cyclic unit memory convolutional codes
DEFF Research Database (Denmark)
Justesen, Jørn; Paaske, Erik; Ballan, Mark
1990-01-01
Unit memory convolutional codes with generator matrices, which are composed of circulant submatrices, are introduced. This structure facilitates the analysis of efficient search for good codes. Equivalences among such codes and some of the basic structural properties are discussed. In particular......, catastrophic encoders and minimal encoders are characterized and dual codes treated. Further, various distance measures are discussed, and a number of good codes, some of which result from efficient computer search and some of which result from known block codes, are presented...
Campolina, Bruno L.
The prediction of aircraft interior noise involves the vibroacoustic modelling of the fuselage with noise control treatments. This structure is composed of a stiffened metallic or composite panel, lined with a thermal and acoustic insulation layer (glass wool), and structurally connected via vibration isolators to a commercial lining panel (trim). The goal of this work aims at tailoring the noise control treatments taking design constraints such as weight and space optimization into account. For this purpose, a representative aircraft double-wall is modelled using the Statistical Energy Analysis (SEA) method. Laboratory excitations such as diffuse acoustic field and point force are addressed and trends are derived for applications under in-flight conditions, considering turbulent boundary layer excitation. The effect of the porous layer compression is firstly addressed. In aeronautical applications, compression can result from the installation of equipment and cables. It is studied analytically and experimentally, using a single panel and a fibrous uniformly compressed over 100% of its surface. When compression increases, a degradation of the transmission loss up to 5 dB for a 50% compression of the porous thickness is observed mainly in the mid-frequency range (around 800 Hz). However, for realistic cases, the effect should be reduced since the compression rate is lower and compression occurs locally. Then the transmission through structural connections between panels is addressed using a four-pole approach that links the force-velocity pair at each side of the connection. The modelling integrates experimental dynamic stiffness of isolators, derived using an adapted test rig. The structural transmission is then experimentally validated and included in the double-wall SEA model as an equivalent coupling loss factor (CLF) between panels. The tested structures being flat, only axial transmission is addressed. Finally, the dominant sound transmission paths are
Applying Gradient Descent in Convolutional Neural Networks
Cui, Nan
2018-04-01
With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.
Phylogenetic convolutional neural networks in metagenomics.
Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare
2018-03-08
Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.
Image quality assessment using deep convolutional networks
Li, Yezhou; Ye, Xiang; Li, Yong
2017-12-01
This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.
Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks
CSIR Research Space (South Africa)
Gerrand, Jonathan D
2017-07-01
Full Text Available of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5...
J.L. Geluk (Jaap); L. Peng (Liang); C.G. de Vries (Casper)
1999-01-01
textabstractThe paper characterizes first and second order tail behavior of convolutions of i.i.d. heavy tailed random variables with support on the real line. The result is applied to the problem of risk diversification in portfolio analysis and to the estimation of the parameter in a MA(1) model.
Effective field study of ising model on a double perovskite structure
Energy Technology Data Exchange (ETDEWEB)
Ngantso, G. Dimitri; El Amraoui, Y. [LMPHE, (URAC 12), Faculté des Sciences, Université Mohammed V, Rabat (Morocco); Benyoussef, A. [LMPHE, (URAC 12), Faculté des Sciences, Université Mohammed V, Rabat (Morocco); Center of Materials and Nanomaterials, MAScIR, Rabat (Morocco); Hassan II Academy of Science and Technology, Rabat (Morocco); El Kenz, A., E-mail: elkenz@fsr.ac.ma [LMPHE, (URAC 12), Faculté des Sciences, Université Mohammed V, Rabat (Morocco)
2017-02-01
By using the effective field theory (EFT), the mixed spin-1/2 and spin-3/2 Ising ferrimagnetic model adapted to a double perovskite structure has been studied. The EFT calculations have been carried out from Ising Hamiltonian by taking into account first and second nearest-neighbors interactions and the crystal and external magnetic fields. Both first- and second-order phase transitions have been found in phase diagrams of interest. Depending on crystal-field values, the thermodynamic behavior of total magnetization indicated the compensation phenomenon existence. The hysteresis behaviors are studied by investigating the reduced magnetic field dependence of total magnetization and a series of hysteresis loops are shown for different reduced temperatures around the critical one. - Highlights: • Magnetic properties of double perovskite Structure have been studied. • Compensation temperature has been observed below the critical temperature. • Hysteresis behaviors have been studied.
Effective field study of ising model on a double perovskite structure
International Nuclear Information System (INIS)
Ngantso, G. Dimitri; El Amraoui, Y.; Benyoussef, A.; El Kenz, A.
2017-01-01
By using the effective field theory (EFT), the mixed spin-1/2 and spin-3/2 Ising ferrimagnetic model adapted to a double perovskite structure has been studied. The EFT calculations have been carried out from Ising Hamiltonian by taking into account first and second nearest-neighbors interactions and the crystal and external magnetic fields. Both first- and second-order phase transitions have been found in phase diagrams of interest. Depending on crystal-field values, the thermodynamic behavior of total magnetization indicated the compensation phenomenon existence. The hysteresis behaviors are studied by investigating the reduced magnetic field dependence of total magnetization and a series of hysteresis loops are shown for different reduced temperatures around the critical one. - Highlights: • Magnetic properties of double perovskite Structure have been studied. • Compensation temperature has been observed below the critical temperature. • Hysteresis behaviors have been studied.
Neutrino masses and flavor mixing in the extended double Seesaw model with two texture zeros
International Nuclear Information System (INIS)
Hu, Li-Jun; Dulat, Sayipjamal; Ablat, Abduleziz
2011-01-01
We study the light neutrino mass matrix in the extended double Seesaw model (EDSM), and as a result we get its general form. Also we demonstrate that conventional type-I and double seesaw mechanisms can be regarded as two special cases. We analyze the structure of the 9 x 9 neutrino mass matrix in this scenario, and surprisingly we find that EDSM will degenerate to a conventional type-I seesaw mechanism when M R = M S M μ -1 M S T holds exactly. Considering two simple ansaetze in two texture zeros for its 3 x 3 submatrices, we calculate the neutrino masses and flavor mixing angles, in which the θ 13 is a nonzero large angle. (orig.)
Classical mapping for Hubbard operators: Application to the double-Anderson model
Energy Technology Data Exchange (ETDEWEB)
Li, Bin; Miller, William H. [Department of Chemistry and Kenneth S. Pitzer Center for Theoretical Chemistry, University of California, and Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720 (United States); Levy, Tal J.; Rabani, Eran [School of Chemistry, The Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978 (Israel)
2014-05-28
A classical Cartesian mapping for Hubbard operators is developed to describe the nonequilibrium transport of an open quantum system with many electrons. The mapping of the Hubbard operators representing the many-body Hamiltonian is derived by using analogies from classical mappings of boson creation and annihilation operators vis-à-vis a coherent state representation. The approach provides qualitative results for a double quantum dot array (double Anderson impurity model) coupled to fermionic leads for a range of bias voltages, Coulomb couplings, and hopping terms. While the width and height of the conduction peaks show deviations from the master equation approach considered to be accurate in the limit of weak system-leads couplings and high temperatures, the Hubbard mapping captures all transport channels involving transition between many electron states, some of which are not captured by approximate nonequilibrium Green function closures.
Measurement and Modelling of Air Flow Rate in a Naturally Ventilated Double Skin Facade
DEFF Research Database (Denmark)
Heiselberg, Per; Kalyanova, Olena; Jensen, Rasmus Lund
2008-01-01
Air flow rate in a naturally ventilated double skin façade (DSF) is extremely difficult to measure due to the stochastic nature of wind, and as a consequence non-uniform and dynamic flow conditions. This paper describes the results of two different methods to measure the air flow in a full...... by the thermal simulation program, BSim, based on measured weather boundary conditions are compared to the measured air temperature, temperature gradient and mass flow rate in the DSF cavity. The results show that it is possible to predict the temperature distribution and airflow in the DSF although some......-scale outdoor test facility with a naturally ventilated double skin façade. Although both methods are difficult to use under such dynamic air flow conditions, they show reasonable agreement and can be used for experimental validation of numerical models of natural ventilation air flow in DSF. Simulations...
Analysis of adsorption behavior of cations onto quartz surface by electrical double-layer model
International Nuclear Information System (INIS)
Kitamura, Akira; Yamamoto, Tadashi; Fujiwara, Kenso; Nishikawa, Sataro; Moriyama, Hirotake
1999-01-01
In a study of the adsorption behavior of cations onto quartz, the distribution coefficient of a variety of cations was determined using the batch method, and using the titration method, the surface charge densities of quartz in a number of electrolyte solutions. The two values thus determined were analyzed applying the electrical double-layer model, from which optimum parameter values were derived for double-layer electrostatics and intrinsic adsorption equilibrium constants. Based on these parameter values, the mechanism of cation adsorption is discussed: A key factor governing this mechanism proved to be the hydration behavior of cations. Consideration of the Coulomb interaction between the adsorbate ions and adsorbent surface led to the finding of a simple rule governing in common the adsorption equilibrium constants of different metal ions. (author)
Scale model test on a novel 400 kV double-circuit composite pylon
DEFF Research Database (Denmark)
Wang, Qian; Bak, Claus Leth; Silva, Filipe Miguel Faria da
This paper investigates lightning shielding performance of a novel 400 kV double-circuit composite pylon, with the method of scale model test. Lightning strikes to overhead lines were simulated by long-gap discharges between a high voltage electrode with an impulse voltage and equivalent conductors...... around the pylon is discussed. Combined test results and striking distance equation in electro-geometric model, the approximate maximum lightning current that can lead to shielding failure is calculated. Test results verify that the unusual negative shielding angle of - 60° in the composite pylon meets...... requirement and the shielding wires provide acceptable protection from lightning strikes....
Gueddana, Amor; Attia, Moez; Chatta, Rihab
2015-03-01
In this work, we study the error sources standing behind the non-perfect linear optical quantum components composing a non-deterministic quantum CNOT gate model, which performs the CNOT function with a success probability of 4/27 and uses a double encoding technique to represent photonic qubits at the control and the target. We generalize this model to an abstract probabilistic CNOT version and determine the realizability limits depending on a realistic range of the errors. Finally, we discuss physical constraints allowing the implementation of the Asymmetric Partially Polarizing Beam Splitter (APPBS), which is at the heart of correctly realizing the CNOT function.
Multi-Branch Fully Convolutional Network for Face Detection
Bai, Yancheng
2017-07-20
Face detection is a fundamental problem in computer vision. It is still a challenging task in unconstrained conditions due to significant variations in scale, pose, expressions, and occlusion. In this paper, we propose a multi-branch fully convolutional network (MB-FCN) for face detection, which considers both efficiency and effectiveness in the design process. Our MB-FCN detector can deal with faces at all scale ranges with only a single pass through the backbone network. As such, our MB-FCN model saves computation and thus is more efficient, compared to previous methods that make multiple passes. For each branch, the specific skip connections of the convolutional feature maps at different layers are exploited to represent faces in specific scale ranges. Specifically, small faces can be represented with both shallow fine-grained and deep powerful coarse features. With this representation, superior improvement in performance is registered for the task of detecting small faces. We test our MB-FCN detector on two public face detection benchmarks, including FDDB and WIDER FACE. Extensive experiments show that our detector outperforms state-of-the-art methods on all these datasets in general and by a substantial margin on the most challenging among them (e.g. WIDER FACE Hard subset). Also, MB-FCN runs at 15 FPS on a GPU for images of size 640 x 480 with no assumption on the minimum detectable face size.
Transforming Musical Signals through a Genre Classifying Convolutional Neural Network
Geng, S.; Ren, G.; Ogihara, M.
2017-05-01
Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.
Borchers, D L; Langrock, R
2015-12-01
We develop maximum likelihood methods for line transect surveys in which animals go undetected at distance zero, either because they are stochastically unavailable while within view or because they are missed when they are available. These incorporate a Markov-modulated Poisson process model for animal availability, allowing more clustered availability events than is possible with Poisson availability models. They include a mark-recapture component arising from the independent-observer survey, leading to more accurate estimation of detection probability given availability. We develop models for situations in which (a) multiple detections of the same individual are possible and (b) some or all of the availability process parameters are estimated from the line transect survey itself, rather than from independent data. We investigate estimator performance by simulation, and compare the multiple-detection estimators with estimators that use only initial detections of individuals, and with a single-observer estimator. Simultaneous estimation of detection function parameters and availability model parameters is shown to be feasible from the line transect survey alone with multiple detections and double-observer data but not with single-observer data. Recording multiple detections of individuals improves estimator precision substantially when estimating the availability model parameters from survey data, and we recommend that these data be gathered. We apply the methods to estimate detection probability from a double-observer survey of North Atlantic minke whales, and find that double-observer data greatly improve estimator precision here too. © 2015 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Minghua [Stony Brook Univ., NY (United States)
2016-12-08
1. Understanding of the observed variability of ITCZ in the equatorial eastern Pacific. The annual mean precipitation in the eastern Pacific has a maximum zonal band north of the equator in the ITCZ where the maximum SST is located. During the boreal spring (referring to February, March, and April throughout the present paper), because of the accumulated solar radiation heating and oceanic heat transport, a secondary maximum of SST exists in the southeastern equatorial Pacific. Associated with this warm SST is also a seasonal transitional maximum of precipitation in the same region in boreal spring, exhibited as a weak double ITCZ pattern in the equatorial eastern Pacific. This climatological seasonal variation, however, varies greatly from year to year: double ITCZ in the boreal spring occurs in some years but not in other years; when there a single ITCZ, it can appear either north, south or at the equator. Understanding this observed variability is critical to find the ultimate cause of the double ITCZ in climate models. Seasonal variation of ITCZ south of the eastern equatorial Pacific: By analyzing data from satellites, field measurements and atmospheric reanalysis, we have found that in the region where spurious ITCZ in models occurs, there is a “seasonal cloud transition” — from stratocumulus to shallow cumulus and eventually to deep convection —in the South Equatorial Pacific (SEP) from September to April that is similar to the spatial cloud transition from the California coast to the equator. This seasonal transition is associated with increasing sea surface temperature (SST), decreasing lower tropospheric stability and large-scale subsidence. This finding of seasonal cloud transition points to the same source of model errors in the ITCZ simulations as in simulation of stratocumulus-cumulus-deep convection transition. It provides a test for climate models to simulate the relationships between clouds and large-scale atmospheric fields in a region
Ambient modal testing of a double-arch dam: the experimental campaign and model updating
International Nuclear Information System (INIS)
García-Palacios, Jaime H.; Soria, José M.; Díaz, Iván M.; Tirado-Andrés, Francisco
2016-01-01
A finite element model updating of a double-curvature-arch dam (La Tajera, Spain) is carried out hereof using the modal parameters obtained from an operational modal analysis. That is, the system modal dampings, natural frequencies and mode shapes have been identified using output-only identification techniques under environmental loads (wind, vehicles). A finite element model of the dam-reservoir-foundation system was initially created. Then, a testing campaing was then carried out from the most significant test points using high-sensitivity accelerometers wirelessly synchronized. Afterwards, the model updating of the initial model was done using a Monte Carlo based approach in order to match it to the recorded dynamic behaviour. The updated model may be used within a structural health monitoring system for damage detection or, for instance, for the analysis of the seismic response of the arch dam- reservoir-foundation coupled system. (paper)
An analytical drain current model for symmetric double-gate MOSFETs
Directory of Open Access Journals (Sweden)
Fei Yu
2018-04-01
Full Text Available An analytical surface-potential-based drain current model of symmetric double-gate (sDG MOSFETs is described as a SPICE compatible model in this paper. The continuous surface and central potentials from the accumulation to the strong inversion regions are solved from the 1-D Poisson’s equation in sDG MOSFETs. Furthermore, the drain current is derived from the charge sheet model as a function of the surface potential. Over a wide range of terminal voltages, doping concentrations, and device geometries, the surface potential calculation scheme and drain current model are verified by solving the 1-D Poisson’s equation based on the least square method and using the Silvaco Atlas simulation results and experimental data, respectively. Such a model can be adopted as a useful platform to develop the circuit simulator and provide the clear understanding of sDG MOSFET device physics.
An analytical drain current model for symmetric double-gate MOSFETs
Yu, Fei; Huang, Gongyi; Lin, Wei; Xu, Chuanzhong
2018-04-01
An analytical surface-potential-based drain current model of symmetric double-gate (sDG) MOSFETs is described as a SPICE compatible model in this paper. The continuous surface and central potentials from the accumulation to the strong inversion regions are solved from the 1-D Poisson's equation in sDG MOSFETs. Furthermore, the drain current is derived from the charge sheet model as a function of the surface potential. Over a wide range of terminal voltages, doping concentrations, and device geometries, the surface potential calculation scheme and drain current model are verified by solving the 1-D Poisson's equation based on the least square method and using the Silvaco Atlas simulation results and experimental data, respectively. Such a model can be adopted as a useful platform to develop the circuit simulator and provide the clear understanding of sDG MOSFET device physics.
Harmonic Analyzing of the Double PWM Converter in DFIG Based on Mathematical Model
Directory of Open Access Journals (Sweden)
Jing Liu
2017-12-01
Full Text Available Harmonic pollution of double fed induction generators (DFIGs has become a vital concern for its undesirable effects on power quality issues of wind generation systems and grid-connected system, and the double pulse width modulation (PWMconverter is one of the main harmonic sources in DFIGs. Thus the harmonic analysis of the converter in DFIGs is a necessary step to evaluate their harmonic pollution of DFIGs. This paper proposes a detailed harmonic modeling method to discuss the main harmonic components in a converter. In general the harmonic modeling could be divided into the low-order harmonic part (up to 30th order and the high-order harmonic part (greater than order 30 parts in general. The low-order harmonics are produced by the circuit topology and control algorithm, and the harmonic component will be different if the control strategy changes. The high-order harmonics are produced by the modulation of the switching function to the dc variable. In this paper, the low-order harmonic modeling is established according to the directions of power flow under the vector control (VC, and the high-order harmonic modeling is established by the switching function of space vector PWM and dc currents. Meanwhile, the simulations of harmonic a components in a converter are accomplished in a real time digital simulation system. The results indicate that the proposed modeling could effectively show the harmonics distribution of the converter in DFIGs.
Classification of decays involving variable decay chains with convolutional architectures
CERN. Geneva
2018-01-01
Vidyo contribution We present a technique to perform classification of decays that exhibit decay chains involving a variable number of particles, which include a broad class of $B$ meson decays sensitive to new physics. The utility of such decays as a probe of the Standard Model is dependent upon accurate determination of the decay rate, which is challenged by the combinatorial background arising in high-multiplicity decay modes. In our model, each particle in the decay event is represented as a fixed-dimensional vector of feature attributes, forming an $n \\times k$ representation of the event, where $n$ is the number of particles in the event and $k$ is the dimensionality of the feature vector. A convolutional architecture is used to capture dependencies between the embedded particle representations and perform the final classification. The proposed model performs outperforms standard machine learning approaches based on Monte Carlo studies across a range of variable final-state decays with the Belle II det...
Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders
Directory of Open Access Journals (Sweden)
Yang Yu
2017-01-01
Full Text Available Network intrusion detection is one of the most important parts for cyber security to protect computer systems against malicious attacks. With the emergence of numerous sophisticated and new attacks, however, network intrusion detection techniques are facing several significant challenges. The overall objective of this study is to learn useful feature representations automatically and efficiently from large amounts of unlabeled raw network traffic data by using deep learning approaches. We propose a novel network intrusion model by stacking dilated convolutional autoencoders and evaluate our method on two new intrusion detection datasets. Several experiments were carried out to check the effectiveness of our approach. The comparative experimental results demonstrate that the proposed model can achieve considerably high performance which meets the demand of high accuracy and adaptability of network intrusion detection systems (NIDSs. It is quite potential and promising to apply our model in the large-scale and real-world network environments.
Static facial expression recognition with convolution neural networks
Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei
2018-03-01
Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.
An Algorithm for the Convolution of Legendre Series
Hale, Nicholas; Townsend, Alex
2014-01-01
An O(N2) algorithm for the convolution of compactly supported Legendre series is described. The algorithm is derived from the convolution theorem for Legendre polynomials and the recurrence relation satisfied by spherical Bessel functions. Combining with previous work yields an O(N 2) algorithm for the convolution of Chebyshev series. Numerical results are presented to demonstrate the improved efficiency over the existing algorithm. © 2014 Society for Industrial and Applied Mathematics.
The Urbanik generalized convolutions in the non-commutative ...
Indian Academy of Sciences (India)
−sν(dx) < ∞. Now we apply this construction to the Kendall convolution case, starting with the weakly stable measure δ1. Example 1. Let △ be the Kendall convolution, i.e. the generalized convolution with the probability kernel: δ1△δa = (1 − a)δ1 + aπ2 for a ∈ [0, 1] and π2 be the Pareto distribution with the density π2(dx) =.
Modeling and Analysis of Double Stator Slotted Rotor Permanent Magnet Generator
Directory of Open Access Journals (Sweden)
Suhairi Rizuan Che Ahmad
2017-03-01
Full Text Available This paper discusses the modeling and analysis of three phase double stator slotted rotor permanent magnet generator (DSSR-PMG. The use of double stator topology through the double magnetic circuit helps to maximize the usage of flux linkage in the yoke structure of the single stator topology. The analytical computation is done using Permeance Analysis Method (PAM. Finite Element Analysis (FEA is used for numerical verifications and to verify the design structure a prototype laboratory is performed. The analysis is done with various loading conditions to derive the electromagnetic torque, output power and efficiency for the proposed structure. The analytical, numerical and experimental results from the analysis are found to be in good agreement. The maximum power developed by this generator at rated speed of 2000 rpm is of 1 kW with the operational efficiency of 75%. A rectifier bridge circuit is used to make the generated voltage a storage capable constant voltage to make it suitable for mobile applications (such as Direct Current DC generator. The proposed generator structure is highly recommended for applications such as micro-hydro and small renewable plants.
Modelling of the bending behaviour of double floor systems for different contact surfaces
Directory of Open Access Journals (Sweden)
Attila PUSKAS
2014-07-01
Full Text Available In the practice of prefabricated concrete structures considerable surfaces of intermediate floors are constructed using double floor systems with prefabricated bottom layer and upper layer. This second layer is cast on site. The quality of the prefabricated concrete is often of superior class with respect to the monolithic layer. In the service state of the double floor system, important compressive stresses appear in the upper concrete layer. On the other hand, the bond quality between the concrete layers cast in successive stages raises questions especially in the case of hollow core floor units with no connecting reinforcement in-between. The paper presents results of the numerical models prepared for double floor elements having different thicknesses for the top and bottom layers, subjected to bending. Three situations have been studied: stepped top surface of the prefabricated slab with no connecting reinforcement, broom swept tracks on the prefabricated slab with no connecting reinforcement and broom swept tracks on the prefabricated slab with stirrups connecting the concrete layers. For each situation two different ratios of the thicknesses of the layers have been considered. The results are emphasizing the critical regions of the elements, the differences in crack development and in the behaviour resulting from surface preparation and use of connecting reinforcements.
On a Generalized Hankel Type Convolution of Generalized Functions
Indian Academy of Sciences (India)
Generalized Hankel type transformation; Parserval relation; generalized ... The classical generalized Hankel type convolution are defined and extended to a class of generalized functions. ... Proceedings – Mathematical Sciences | News.
Enhanced online convolutional neural networks for object tracking
Zhang, Dengzhuo; Gao, Yun; Zhou, Hao; Li, Tianwen
2018-04-01
In recent several years, object tracking based on convolution neural network has gained more and more attention. The initialization and update of convolution filters can directly affect the precision of object tracking effective. In this paper, a novel object tracking via an enhanced online convolution neural network without offline training is proposed, which initializes the convolution filters by a k-means++ algorithm and updates the filters by an error back-propagation. The comparative experiments of 7 trackers on 15 challenging sequences showed that our tracker can perform better than other trackers in terms of AUC and precision.
International Nuclear Information System (INIS)
Luque, N.B.; Woelki, S.; Henderson, D.; Schmickler, W.
2011-01-01
Highlights: · We augment a double-layer model based on integral equations by calculating the interaction parameters with the electrode from quantum density functional theory · Explicit model calculations for Ag(1 1 1) in aqueous solutions give at least qualitatively good results for the particle profiles · Ours is the only method which allows the calculation of capacity-charge characteristics. · We obtain reasonable values for the Helmholtz (inner-layer) capacity. - Abstract: We have complemented the singlet reference interaction site model for the electric double layer by quantum chemical calculations for the interaction of ions and solvents with an electrode. Specific calculations have been performed for an aqueous solution of NaCl in contact with a Ag(1 1 1) electrode. The particle profiles near the electrode show the specific adsorption of Cl - ions, but not of Na + , and are at least in qualitative agreement with those obtained by molecular dynamics. Including the electronic response of the silver surface into the model results in reasonable capacity-charge characteristics.
Double-gate junctionless transistor model including short-channel effects
International Nuclear Information System (INIS)
Paz, B C; Pavanello, M A; Ávila-Herrera, F; Cerdeira, A
2015-01-01
This work presents a physically based model for double-gate junctionless transistors (JLTs), continuous in all operation regimes. To describe short-channel transistors, short-channel effects (SCEs), such as increase of the channel potential due to drain bias, carrier velocity saturation and mobility degradation due to vertical and longitudinal electric fields, are included in a previous model developed for long-channel double-gate JLTs. To validate the model, an analysis is made by using three-dimensional numerical simulations performed in a Sentaurus Device Simulator from Synopsys. Different doping concentrations, channel widths and channel lengths are considered in this work. Besides that, the series resistance influence is numerically included and validated for a wide range of source and drain extensions. In order to check if the SCEs are appropriately described, besides drain current, transconductance and output conductance characteristics, the following parameters are analyzed to demonstrate the good agreement between model and simulation and the SCEs occurrence in this technology: threshold voltage (V TH ), subthreshold slope (S) and drain induced barrier lowering. (paper)
Directory of Open Access Journals (Sweden)
Haiyang Yu
2014-01-01
Full Text Available This work presents numerical well testing interpretation model and analysis techniques to evaluate formation by using pressure transient data acquired with logging tools in crossflow double-layer reservoirs by polymer flooding. A well testing model is established based on rheology experiments and by considering shear, diffusion, convection, inaccessible pore volume (IPV, permeability reduction, wellbore storage effect, and skin factors. The type curves were then developed based on this model, and parameter sensitivity is analyzed. Our research shows that the type curves have five segments with different flow status: (I wellbore storage section, (II intermediate flow section (transient section, (III mid-radial flow section, (IV crossflow section (from low permeability layer to high permeability layer, and (V systematic radial flow section. The polymer flooding field tests prove that our model can accurately determine formation parameters in crossflow double-layer reservoirs by polymer flooding. Moreover, formation damage caused by polymer flooding can also be evaluated by comparison of the interpreted permeability with initial layered permeability before polymer flooding. Comparison of the analysis of numerical solution based on flow mechanism with observed polymer flooding field test data highlights the potential for the application of this interpretation method in formation evaluation and enhanced oil recovery (EOR.
Cascaded K-means convolutional feature learner and its application to face recognition
Zhou, Daoxiang; Yang, Dan; Zhang, Xiaohong; Huang, Sheng; Feng, Shu
2017-09-01
Currently, considerable efforts have been devoted to devise image representation. However, handcrafted methods need strong domain knowledge and show low generalization ability, and conventional feature learning methods require enormous training data and rich parameters tuning experience. A lightened feature learner is presented to solve these problems with application to face recognition, which shares similar topology architecture as a convolutional neural network. Our model is divided into three components: cascaded convolution filters bank learning layer, nonlinear processing layer, and feature pooling layer. Specifically, in the filters learning layer, we use K-means to learn convolution filters. Features are extracted via convoluting images with the learned filters. Afterward, in the nonlinear processing layer, hyperbolic tangent is employed to capture the nonlinear feature. In the feature pooling layer, to remove the redundancy information and incorporate the spatial layout, we exploit multilevel spatial pyramid second-order pooling technique to pool the features in subregions and concatenate them together as the final representation. Extensive experiments on four representative datasets demonstrate the effectiveness and robustness of our model to various variations, yielding competitive recognition results on extended Yale B and FERET. In addition, our method achieves the best identification performance on AR and labeled faces in the wild datasets among the comparative methods.
Modeling Electric Double-Layer Capacitors Using Charge Variation Methodology in Gibbs Ensemble
Directory of Open Access Journals (Sweden)
Ganeshprasad Pavaskar
2018-01-01
Full Text Available Supercapacitors deliver higher power than batteries and find applications in grid integration and electric vehicles. Recent work by Chmiola et al. (2006 has revealed unexpected increase in the capacitance of porous carbon electrodes using ionic liquids as electrolytes. The work has generated curiosity among both experimentalists and theoreticians. Here, we have performed molecular simulations using a recently developed technique (Punnathanam, 2014 for simulating supercapacitor system. In this technique, the two electrodes (containing electrolyte in slit pore are simulated in two different boxes using the Gibbs ensemble methodology. This reduces the number of particles required and interfacial interactions, which helps in reducing computational load. The method simulates an electric double-layer capacitor (EDLC with macroscopic electrodes with much smaller system sizes. In addition, the charges on individual electrode atoms are allowed to vary in response to movement of electrolyte ions (i.e., electrode is polarizable while ensuring these atoms are at the same electric potential. We also present the application of our technique on EDLCs with the electrodes modeled as slit pores and as complex three-dimensional pore networks for different electrolyte geometries. The smallest pore geometry showed an increase in capacitance toward the potential of 0 charge. This is in agreement with the new understanding of the electrical double layer in regions of dense ionic packing, as noted by Kornyshev’s theoretical model (Kornyshev, 2007, which also showed a similar trend. This is not addressed by the classical Gouy–Chapman theory for the electric double layer. Furthermore, the electrode polarizability simulated in the model improved the accuracy of the calculated capacitance. However, its addition did not significantly alter the capacitance values in the voltage range considered.
Finger vein recognition based on convolutional neural network
Directory of Open Access Journals (Sweden)
Meng Gesi
2017-01-01
Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.
Convolutive Blind Source Separation Methods
DEFF Research Database (Denmark)
Pedersen, Michael Syskind; Larsen, Jan; Kjems, Ulrik
2008-01-01
During the past decades, much attention has been given to the separation of mixed sources, in particular for the blind case where both the sources and the mixing process are unknown and only recordings of the mixtures are available. In several situations it is desirable to recover all sources from...... the recorded mixtures, or at least to segregate a particular source. Furthermore, it may be useful to identify the mixing process itself to reveal information about the physical mixing system. In some simple mixing models each recording consists of a sum of differently weighted source signals. However, in many...... real-world applications, such as in acoustics, the mixing process is more complex. In such systems, the mixtures are weighted and delayed, and each source contributes to the sum with multiple delays corresponding to the multiple paths by which an acoustic signal propagates to a microphone...
Double pendulum model for a tennis stroke including a collision process
Youn, Sun-Hyun
2015-10-01
By means of adding a collision process between the ball and racket in the double pendulum model, we analyzed the tennis stroke. The ball and the racket system may be accelerated during the collision time; thus, the speed of the rebound ball does not simply depend on the angular velocity of the racket. A higher angular velocity sometimes gives a lower rebound ball speed. We numerically showed that the proper time-lagged racket rotation increased the speed of the rebound ball by 20%. We also showed that the elbow should move in the proper direction in order to add the angular velocity of the racket.
Experimental investigation of shock wave diffraction over a single- or double-sphere model
Zhang, L. T.; Wang, T. H.; Hao, L. N.; Huang, B. Q.; Chen, W. J.; Shi, H. H.
2017-01-01
In this study, the unsteady drag produced by the interaction of a shock wave with a single- and a double-sphere model is measured using imbedded accelerometers. The shock wave is generated in a horizontal circular shock tube with an inner diameter of 200 mm. The effect of the shock Mach number and the dimensionless distance between spheres is investigated. The time-history of the drag coefficient is obtained based on Fast Fourier Transformation (FFT) band-block filtering and polynomial fitting of the measured acceleration. The measured peak values of the drag coefficient, with the associated uncertainty, are reported.
Theoretical model for a background noise limited laser-excited optical filter for doubled Nd lasers
Shay, Thomas M.; Garcia, Daniel F.
1990-01-01
A simple theoretical model for the calculation of the dependence of filter quantum efficiency versus laser pump power in an atomic Rb vapor laser-excited optical filter is reported. Calculations for Rb filter transitions that can be used to detect the practical and important frequency-doubled Nd lasers are presented. The results of these calculations show the filter's quantum efficiency versus the laser pump power. The required laser pump powers required range from 2.4 to 60 mW/sq cm of filter aperture.
A variational study of the self-trapped magnetic polaron formation in double-exchange model
International Nuclear Information System (INIS)
Liu Tao; Feng Mang; Wang Kelin
2005-01-01
We study the formation of self-trapped magnetic polaron (STMP) in an antiferro/ferromagnetic double-exchange model semi-analytically by variational solutions. It is shown that the Jahn-Teller effect is not essential to the STMP formation and the STMP forms in the antiferromagnetic material within the region of the order of the lattice constant. We also confirm that no ground state STMP exists in the ferromagnetic background, but the ground state bound MP could appear due to the impurity potential
Corrections to the neutrinoless double-β-decay operator in the shell model
Engel, Jonathan; Hagen, Gaute
2009-06-01
We use diagrammatic perturbation theory to construct an effective shell-model operator for the neutrinoless double-β decay of Se82. The starting point is the same Bonn-C nucleon-nucleon interaction that is used to generate the Hamiltonian for recent shell-model calculations of double-β decay. After first summing high-energy ladder diagrams that account for short-range correlations and then adding diagrams of low order in the G matrix to account for longer-range correlations, we fold the two-body matrix elements of the resulting effective operator with transition densities from the recent shell-model calculation to obtain the overall nuclear matrix element that governs the decay. Although the high-energy ladder diagrams suppress this matrix element at very short distances as expected, they enhance it at distances between one and two fermis, so that their overall effect is small. The corrections due to longer-range physics are large, but cancel one another so that the fully corrected matrix element is comparable to that produced by the bare operator. This cancellation between large and physically distinct low-order terms indicates the importance of a reliable nonperturbative calculation.
Double beta decay - physics beyond the standard model now, and in future (Genius)
Energy Technology Data Exchange (ETDEWEB)
Klapdor-Kleingrothaus, H.V.
1998-08-01
Nuclear double beta decay provides an extraordinarily broad potential to search for beyond standard model physics, probing already now the TeV scale, on which new physics should manifest itself. These possibilities are reviewed here. First, the results of present generation experiments are presented. The most sensitive one of them - the Heidelberg-Moscow experiment in the Gran Sasso - probes the electron mass now in the sub eV region and will reach a limit of {proportional_to}0.1 eV in a few years. Basing to a large extent on the theoretical work of the Heidelberg double beta group in the last two years, results are obtained also for SUSY models (R-parity breaking, sneutrino mass), leptoquarks (leptoquark-Higgs coupling), compositeness, right-handed W boson mass and others. These results are comfortably competitive to corresponding results from high-energy accelerators like TEVATRON, HERA, etc. Second, future perspectives of {beta}{beta} research are discussed. A new Heidelberg experimental proposal (GENIUS) is presented which would allow to increase the sensitivity for Majorana neutrino masses from the present level of at best 0.1 eV down to 0.01 or even 0.001 eV. Its physical potential would be a breakthrough into the multi-TeV range for many beyond standard models. Its sensitivity for neutrino oscillation parameters would be larger than of all present terrestrial neutrino oscillation experiments and of those planned for the future. (orig.)
Double beta decay - physics beyond the standard model now, and in future (Genius)
International Nuclear Information System (INIS)
Klapdor-Kleingrothaus, H.V.
1998-01-01
Nuclear double beta decay provides an extraordinarily broad potential to search for beyond standard model physics, probing already now the TeV scale, on which new physics should manifest itself. These possibilities are reviewed here. First, the results of present generation experiments are presented. The most sensitive one of them - the Heidelberg-Moscow experiment in the Gran Sasso - probes the electron mass now in the sub eV region and will reach a limit of ∝0.1 eV in a few years. Basing to a large extent on the theoretical work of the Heidelberg double beta group in the last two years, results are obtained also for SUSY models (R-parity breaking, sneutrino mass), leptoquarks (leptoquark-Higgs coupling), compositeness, right-handed W boson mass and others. These results are comfortably competitive to corresponding results from high-energy accelerators like TEVATRON, HERA, etc. Second, future perspectives of ββ research are discussed. A new Heidelberg experimental proposal (GENIUS) is presented which would allow to increase the sensitivity for Majorana neutrino masses from the present level of at best 0.1 eV down to 0.01 or even 0.001 eV. Its physical potential would be a breakthrough into the multi-TeV range for many beyond standard models. Its sensitivity for neutrino oscillation parameters would be larger than of all present terrestrial neutrino oscillation experiments and of those planned for the future. (orig.)
International Nuclear Information System (INIS)
Fu, C.Y.
1988-01-01
A unified Hauser-Feshbach/Pre-Equilibrium model is extended and simplified. The extension involves the addition of correlations among states of different total quantum numbers (J and J') and the introduction of consistent level density formulas for the H-F and the P-E parts of the calculation. The simplification, aimed at reducing the computational cost, is achieved mainly by keeping only the off-diagonal terms that involve strongly correlated 2p-1h states. A correlation coefficient is introduced to fit the experimental data. The model has been incorporated into the multistep H-F model code TNG. Calculated double differential (n,xn) cross sections at 14 and 25.7 MeV for iron, niobium, and bismuth are in good agreement with experiments. In use at ORNL and JAERI, the TNG code in various stages of development has been applied with success to the evaluation of double differential (n,xn) cross sections from 1 to 20 MeV for the dominant isotopes of chromium, manganese, iron, nickel, copper, and lead. 11 refs., 2 figs
International Nuclear Information System (INIS)
Munoz-Jaramillo, Andres; Martens, Petrus C. H.; Nandy, Dibyendu; Yeates, Anthony R.
2010-01-01
The emergence of tilted bipolar active regions (ARs) and the dispersal of their flux, mediated via processes such as diffusion, differential rotation, and meridional circulation, is believed to be responsible for the reversal of the Sun's polar field. This process (commonly known as the Babcock-Leighton mechanism) is usually modeled as a near-surface, spatially distributed α-effect in kinematic mean-field dynamo models. However, this formulation leads to a relationship between polar field strength and meridional flow speed which is opposite to that suggested by physical insight and predicted by surface flux-transport simulations. With this in mind, we present an improved double-ring algorithm for modeling the Babcock-Leighton mechanism based on AR eruption, within the framework of an axisymmetric dynamo model. Using surface flux-transport simulations, we first show that an axisymmetric formulation-which is usually invoked in kinematic dynamo models-can reasonably approximate the surface flux dynamics. Finally, we demonstrate that our treatment of the Babcock-Leighton mechanism through double-ring eruption leads to an inverse relationship between polar field strength and meridional flow speed as expected, reconciling the discrepancy between surface flux-transport simulations and kinematic dynamo models.
A double hit model for the distribution of time to AIDS onset
Chillale, Nagaraja Rao
2013-09-01
Incubation time is a key epidemiologic descriptor of an infectious disease. In the case of HIV infection this is a random variable and is probably the longest one. The probability distribution of incubation time is the major determinant of the relation between the incidences of HIV infection and its manifestation to Aids. This is also one of the key factors used for accurate estimation of AIDS incidence in a region. The present article i) briefly reviews the work done, points out uncertainties in estimation of AIDS onset time and stresses the need for its precise estimation, ii) highlights some of the modelling features of onset distribution including immune failure mechanism, and iii) proposes a 'Double Hit' model for the distribution of time to AIDS onset in the cases of (a) independent and (b) dependent time variables of the two markers and examined the applicability of a few standard probability models.
Analytical drift-current threshold voltage model of long-channel double-gate MOSFETs
International Nuclear Information System (INIS)
Shih, Chun-Hsing; Wang, Jhong-Sheng
2009-01-01
This paper presents a new, physical threshold voltage model to solve the ambiguity in determining the threshold voltage of double-gate (DG) MOSFETs. To avoid the difficulties of the conventional 2ψ B model in nearly undoped DG MOSFETs, this study proposes to define the on–off switching based on the actual roles of the drift and diffusion components in the total drain current. The drift current strongly enhances beyond the threshold voltage, while the diffusion current plays a major role in the subthreshold. The threshold voltage is defined as the drift component that exceeds the diffusion counterpart. From the solutions of Poisson's equation, the drift and diffusion currents of DG MOSFETs are separately formulated to derive the analytical expressions of the threshold voltage and associated threshold current. This model provides a comprehensive description of the switching behavior of DG MOSFET devices, and offers a physical onset threshold current to determine the threshold voltage in practical extraction
Simulation Model for Dynamic Operation of Double-Effect Absorption Chillers
Directory of Open Access Journals (Sweden)
Ahmed Mojahid Sid Ahmed Mohammed Salih
2014-07-01
Full Text Available The development in the field of refrigeration and air conditioning systems driven by absorption cycles acquired a considerable importance recently. For commercial absorption chillers, an essential challenge for creating chiller model certainly is the shortage of components technical specifications. These kinds of specifications are usually proprietary for chillers producers. In this paper, a double-effect parallel-flow-type steam absorption chiller model based on thermodynamic and energy equations is presented. The chiller studied is Lithium bromide-water with capacity of 1250 RT (Refrigeration Tons. The governing equations of the dynamic operation of the chiller are developed. From available design information, the values of the overall heat transfer coefficients multiplied by the surface area are computed. The dynamic operation of the absorption chiller is simulated to study the performance of the system. The model is able to provide essential details of the temperature, concentration, and flow rate at each state point in the chiller.
Preliminary results from a four-working space, double-acting piston, Stirling engine controls model
Daniele, C. J.; Lorenzo, C. F.
1980-01-01
A four working space, double acting piston, Stirling engine simulation is being developed for controls studies. The development method is to construct two simulations, one for detailed fluid behavior, and a second model with simple fluid behaviour but containing the four working space aspects and engine inertias, validate these models separately, then upgrade the four working space model by incorporating the detailed fluid behaviour model for all four working spaces. The single working space (SWS) model contains the detailed fluid dynamics. It has seven control volumes in which continuity, energy, and pressure loss effects are simulated. Comparison of the SWS model with experimental data shows reasonable agreement in net power versus speed characteristics for various mean pressure levels in the working space. The four working space (FWS) model was built to observe the behaviour of the whole engine. The drive dynamics and vehicle inertia effects are simulated. To reduce calculation time, only three volumes are used in each working space and the gas temperature are fixed (no energy equation). Comparison of the FWS model predicted power with experimental data shows reasonable agreement. Since all four working spaces are simulated, the unique capabilities of the model are exercised to look at working fluid supply transients, short circuit transients, and piston ring leakage effects.
Convolutional neural networks and face recognition task
Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.
2017-09-01
Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.
Fourier transforms and convolutions for the experimentalist
Jennison, RC
1961-01-01
Fourier Transforms and Convolutions for the Experimentalist provides the experimentalist with a guide to the principles and practical uses of the Fourier transformation. It aims to bridge the gap between the more abstract account of a purely mathematical approach and the rule of thumb calculation and intuition of the practical worker. The monograph springs from a lecture course which the author has given in recent years and for which he has drawn upon a number of sources, including a set of notes compiled by the late Dr. I. C. Browne from a series of lectures given by Mr. J . A. Ratcliffe of t
Target recognition based on convolutional neural network
Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian
2017-11-01
One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.
QCDNUM: Fast QCD evolution and convolution
Botje, M.
2011-02-01
The QCDNUM program numerically solves the evolution equations for parton densities and fragmentation functions in perturbative QCD. Un-polarised parton densities can be evolved up to next-to-next-to-leading order in powers of the strong coupling constant, while polarised densities or fragmentation functions can be evolved up to next-to-leading order. Other types of evolution can be accessed by feeding alternative sets of evolution kernels into the program. A versatile convolution engine provides tools to compute parton luminosities, cross-sections in hadron-hadron scattering, and deep inelastic structure functions in the zero-mass scheme or in generalised mass schemes. Input to these calculations are either the QCDNUM evolved densities, or those read in from an external parton density repository. Included in the software distribution are packages to calculate zero-mass structure functions in un-polarised deep inelastic scattering, and heavy flavour contributions to these structure functions in the fixed flavour number scheme. Program summaryProgram title: QCDNUM version: 17.00 Catalogue identifier: AEHV_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEHV_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence No. of lines in distributed program, including test data, etc.: 45 736 No. of bytes in distributed program, including test data, etc.: 911 569 Distribution format: tar.gz Programming language: Fortran-77 Computer: All Operating system: All RAM: Typically 3 Mbytes Classification: 11.5 Nature of problem: Evolution of the strong coupling constant and parton densities, up to next-to-next-to-leading order in perturbative QCD. Computation of observable quantities by Mellin convolution of the evolved densities with partonic cross-sections. Solution method: Parametrisation of the parton densities as linear or quadratic splines on a discrete grid, and evolution of the spline
Directory of Open Access Journals (Sweden)
Na Liu
2018-03-01
Full Text Available The extraction of activation vectors (or deep features from the fully connected layers of a convolutional neural network (CNN model is widely used for remote sensing image (RSI representation. In this study, we propose to learn discriminative convolution filter (DCF based on class-specific separability criteria for linear transformation of deep features. In particular, two types of pretrained CNN called CaffeNet and VGG-VD16 are introduced to illustrate the generality of the proposed DCF. The activation vectors extracted from the fully connected layers of a CNN are rearranged into the form of an image matrix, from which a spatial arrangement of local patches is extracted using sliding window strategy. DCF learning is then performed on each local patch individually to obtain the corresponding discriminative convolution kernel through generalized eigenvalue decomposition. The proposed DCF learning characterizes that a convolutional kernel with small size (e.g., 3 × 3 pixels can be effectively learned on a small-size local patch (e.g., 8 × 8 pixels, thereby ensuring that the linear transformation of deep features can maintain low computational complexity. Experiments on two RSI datasets demonstrate the effectiveness of DCF in improving the classification performances of deep features without increasing dimensionality.
Spectral-spatial classification of hyperspectral image using three-dimensional convolution network
Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu
2018-01-01
Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.
Ideal flow theory for the double - shearing model as a basis for metal forming design
Alexandrov, S.; Trung, N. T.
2018-02-01
In the case of Tresca’ solids (i.e. solids obeying the Tresca yield criterion and its associated flow rule) ideal flows have been defined elsewhere as solenoidal smooth deformations in which an eigenvector field associated everywhere with the greatest principal stress (and strain rate) is fixed in the material. Under such conditions all material elements undergo paths of minimum plastic work, a condition which is often advantageous for metal forming processes. Therefore, the ideal flow theory is used as the basis of a procedure for the preliminary design of such processes. The present paper extends the theory of stationary planar ideal flow to pressure dependent materials obeying the double shearing model and the double slip and rotation model. It is shown that the original problem of plasticity reduces to a purely geometric problem. The corresponding system of equations is hyperbolic. The characteristic relations are integrated in elementary functions. In regions where one family of characteristics is straight, mapping between the principal lines and Cartesian coordinates is determined by linear ordinary differential equations. An illustrative example is provided.
Mathematical Modeling and Kinematics Analysis of Double Spherical Shell Rotary Docking Skirt
Directory of Open Access Journals (Sweden)
Gong Haixia
2017-01-01
Full Text Available In order to solve the problem of large trim and heel angles of the wrecked submarine, the double spherical shell rotating docking skirt is studied. According to the working principle of the rotating docking skirt, and the fixed skirt, the directional skirt, the angle skirt are simplified as the connecting rod. Therefore, the posture equation and kinematics model of the docking skirt are deduced, and according to the kinematics model, the angle of rotation of the directional skirt and the angle skirt is obtained when the wrecked submarine is in different trim and heel angles. Through the directional skirt and angle skirt with the matching rotation can make docking skirt interface in the 0°~2γ range within the rotation, to complete the docking skirt and the wrecked submarine docking. The MATLAB software is used to visualize the rotation angle of fixed skirt and directional skirt, which lays a good foundation for the development of the control of the double spherical shell rotating docking skirt in future.
Double ionization of atoms by ion impact: two-step models
Energy Technology Data Exchange (ETDEWEB)
Fiori, Marcelo [Departamento de Fisica, Universidad Nacional de Salta, Salta (Argentina); Rocha, A B [Instituto de Quimica, Departamento de FIsico-Quimica, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21949-900, RJ (Brazil); Bielschowsky, C E [Instituto de Quimica, Departamento de FIsico-Quimica, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21949-900, RJ (Brazil); Jalbert, Ginette [Instituto de Fisica, Universidade Federal do Rio de Janeiro, Caixa Postal 68528, Rio de Janeiro, 21941-972, RJ (Brazil); Garibotti, C R [CONICET and Centro Atomico Bariloche, 8400 S. C. Bariloche, RIo Negro (Argentina)
2006-04-14
Total cross sections for the double ionization of He and Li atoms by the impact of H{sup +}, He{sup 2+} and Li{sup 3+} are calculated at intermediate and high energies within two-step models. The double ionization of He by the impact of other bare projectiles at a fixed energy is obtained as well. Single ionization probabilities are calculated within the continuum distorted wave -eikonal-initial-state (CDW-EIS) approximation. The required atomic bound and continuum wave functions are evaluated by numerically solving the atomic wave equation with an optimized potential model (OPM). Correlation between events is introduced by considering ion relaxation. The final state electronic correlation is considered by means of the so-called Gamow factor. We compare the transition probabilities resulting from our approach with those resulting from the use of a Rootham-Hartree-Fock initial state and a Coulomb continuum state with an effective charge. We find that the use of OPM waves gives a better agreement with the experimental results than with Coulomb waves.
Nonresonant Double Hopf Bifurcation in Toxic Phytoplankton-Zooplankton Model with Delay
Yuan, Rui; Jiang, Weihua; Wang, Yong
This paper investigates a toxic phytoplankton-zooplankton model with Michaelis-Menten type phytoplankton harvesting. The model has rich dynamical behaviors. It undergoes transcritical, saddle-node, fold, Hopf, fold-Hopf and double Hopf bifurcation, when the parameters change and go through some of the critical values, the dynamical properties of the system will change also, such as the stability, equilibrium points and the periodic orbit. We first study the stability of the equilibria, and analyze the critical conditions for the above bifurcations at each equilibrium. In addition, the stability and direction of local Hopf bifurcations, and the completion bifurcation set by calculating the universal unfoldings near the double Hopf bifurcation point are given by the normal form theory and center manifold theorem. We obtained that the stable coexistent equilibrium point and stable periodic orbit alternate regularly when the digestion time delay is within some finite value. That is, we derived the pattern for the occurrence, and disappearance of a stable periodic orbit. Furthermore, we calculated the approximation expression of the critical bifurcation curve using the digestion time delay and the harvesting rate as parameters, and determined a large range in terms of the harvesting rate for the phytoplankton and zooplankton to coexist in a long term.
Directory of Open Access Journals (Sweden)
Dongkai Shen
2016-01-01
Full Text Available In recent studies on the dynamic characteristics of ventilation system, it was considered that human had only one lung, and the coupling effect of double lungs on the air flow can not be illustrated, which has been in regard to be vital to life support of patients. In this article, to illustrate coupling effect of double lungs on flow dynamics of mechanical ventilation system, a mathematical model of a mechanical ventilation system, which consists of double lungs and a bi-level positive airway pressure (BIPAP controlled ventilator, was proposed. To verify the mathematical model, a prototype of BIPAP system with a double-lung simulators and a BIPAP ventilator was set up for experimental study. Lastly, the study on the influences of key parameters of BIPAP system on dynamic characteristics was carried out. The study can be referred to in the development of research on BIPAP ventilation treatment and real respiratory diagnostics.
Modified Stieltjes Transform and Generalized Convolutions of Probability Distributions
Directory of Open Access Journals (Sweden)
Lev B. Klebanov
2018-01-01
Full Text Available The classical Stieltjes transform is modified in such a way as to generalize both Stieltjes and Fourier transforms. This transform allows the introduction of new classes of commutative and non-commutative generalized convolutions. A particular case of such a convolution for degenerate distributions appears to be the Wigner semicircle distribution.
Nursing research on a first aid model of double personnel for major burn patients.
Wu, Weiwei; Shi, Kai; Jin, Zhenghua; Liu, Shuang; Cai, Duo; Zhao, Jingchun; Chi, Cheng; Yu, Jiaao
2015-03-01
This study explored the effect of a first aid model employing two nurses on the efficient rescue operation time and the efficient resuscitation time for major burn patients. A two-nurse model of first aid was designed for major burn patients. The model includes a division of labor between the first aid nurses and the re-organization of emergency carts. The clinical effectiveness of the process was examined in a retrospective chart review of 156 cases of major burn patients, experiencing shock and low blood volume, who were admitted to the intensive care unit of the department of burn surgery between November 2009 and June 2013. Of the 156 major burn cases, 87 patients who received first aid using the double personnel model were assigned to the test group and the 69 patients who received first aid using the standard first aid model were assigned to the control group. The efficient rescue operation time and the efficient resuscitation time for the patients were compared between the two groups. Student's t tests were used to the compare the mean difference between the groups. Statistically significant differences between the two groups were found on both measures (P's first aid model based on scientifically validated procedures and a reasonable division of labor can shorten the efficient rescue operation time and the efficient resuscitation time for major burn patients. Given these findings, the model appears to be worthy of clinical application.
An enhanced lumped element electrical model of a double barrier memristive device
International Nuclear Information System (INIS)
Solan, Enver; Ochs, Karlheinz; Dirkmann, Sven; Hansen, Mirko; Kohlstedt, Hermann; Ziegler, Martin; Schroeder, Dietmar; Mussenbrock, Thomas
2017-01-01
The massive parallel approach of neuromorphic circuits leads to effective methods for solving complex problems. It has turned out that resistive switching devices with a continuous resistance range are potential candidates for such applications. These devices are memristive systems—nonlinear resistors with memory. They are fabricated in nanotechnology and hence parameter spread during fabrication may aggravate reproducible analyses. This issue makes simulation models of memristive devices worthwhile. Kinetic Monte-Carlo simulations based on a distributed model of the device can be used to understand the underlying physical and chemical phenomena. However, such simulations are very time-consuming and neither convenient for investigations of whole circuits nor for real-time applications, e.g. emulation purposes. Instead, a concentrated model of the device can be used for both fast simulations and real-time applications, respectively. We introduce an enhanced electrical model of a valence change mechanism (VCM) based double barrier memristive device (DBMD) with a continuous resistance range. This device consists of an ultra-thin memristive layer sandwiched between a tunnel barrier and a Schottky-contact. The introduced model leads to very fast simulations by using usual circuit simulation tools while maintaining physically meaningful parameters. Kinetic Monte-Carlo simulations based on a distributed model and experimental data have been utilized as references to verify the concentrated model. (paper)
A double-layer based model of ion confinement in electron cyclotron resonance ion source
Energy Technology Data Exchange (ETDEWEB)
Mascali, D., E-mail: davidmascali@lns.infn.it; Neri, L.; Celona, L.; Castro, G.; Gammino, S.; Ciavola, G. [Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Sud, via S. Sofia 62, 95123 Catania (Italy); Torrisi, G. [Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Sud, via S. Sofia 62, 95123 Catania (Italy); Università Mediterranea di Reggio Calabria, Dipartimento di Ingegneria dell’Informazione, delle Infrastrutture e dell’Energia Sostenibile, Via Graziella, I-89100 Reggio Calabria (Italy); Sorbello, G. [Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali del Sud, via S. Sofia 62, 95123 Catania (Italy); Università degli Studi di Catania, Dipartimento di Ingegneria Elettrica Elettronica ed Informatica, Viale Andrea Doria 6, 95125 Catania (Italy)
2014-02-15
The paper proposes a new model of ion confinement in ECRIS, which can be easily generalized to any magnetic configuration characterized by closed magnetic surfaces. Traditionally, ion confinement in B-min configurations is ascribed to a negative potential dip due to superhot electrons, adiabatically confined by the magneto-static field. However, kinetic simulations including RF heating affected by cavity modes structures indicate that high energy electrons populate just a thin slab overlapping the ECR layer, while their density drops down of more than one order of magnitude outside. Ions, instead, diffuse across the electron layer due to their high collisionality. This is the proper physical condition to establish a double-layer (DL) configuration which self-consistently originates a potential barrier; this “barrier” confines the ions inside the plasma core surrounded by the ECR surface. The paper will describe a simplified ion confinement model based on plasma density non-homogeneity and DL formation.
Neutrinoless double-beta decay in left-right symmetric models
International Nuclear Information System (INIS)
Picciotto, C.E.; Zahir, M.S.
1982-06-01
Neutrinoless double-beta decay is calculated via doubly charged Higgs, which occur naturally in left-right symmetric models. We find that the comparison with known half-lives yields values of phenomenological parameters which are compatible with earlier analyses of neutral current data. In particular, we obtain a right-handed gauge-boson mass lower bound of the order of 240 GeV. Using this result and expressions for neutrino masses derived in a parity non-conserving left-right symmetric model, we obtain msub(νsub(e)) < 1.5 eV, msub(νsub(μ)) < 0.05 MeV and msub(νsub(tau)) < 18 MeV
Picture book of nucleon--nucleon scattering: amplitudes, models, double- and triple-spin observables
International Nuclear Information System (INIS)
Field, R.D.; Stevens, P.R.
1975-01-01
A comprehensive study of nucleon-nucleon scattering is presented with particular emphasis on the underlying amplitude structure. The five complex NN amplitudes are determined as a function of energy and momentum transfer from existing pp, anti pp, and np elastic scattering data and np and anti pp CHEX data. Some constraints determined from meson-baryon fits are imposed. The resulting amplitudes are used to make predictions of forthcoming double- and triple-spin measurements, and are also compared with the model amplitudes of Kane and Seidl. In addition, the usefulness of transversity amplitudes in NN scattering is discussed, the status of our present knowledge concerning them is examined, and model predictions of these amplitudes are displayed. The paper is presented in a ''picture book'' form so that the reader can get a good overview of NN scattering by studying the figures and reading the tables and figure captions
Modelling of BLDCM with a double 3-phase stator winding and back EMF harmonics
Directory of Open Access Journals (Sweden)
Drozdowski Piotr
2015-03-01
Full Text Available In this paper the mathematical model of the brushless DC motor (BLDCM with a double 3-phase stator winding is analysed. Both the 3-phase windings are mutually displaced by 30 electrical degree. Special care has been sacrificed to influence of higher harmonics of induced electromotive forces (EMF on electromagnetic torque and zero sequence voltages that may be used for sensorless control. The mathematical model has been presented in natural variables and, after transformation to symmetrical components, in a vector form. This allows, from one side, for formulating the equivalent circuit suitable for circuit oriented simulators (e.g.: Spice, SimPowerSystems of Simulink and, from the other point of view, for analysis of higher harmonics influence on control possibilities. These considerations have been illustrated with some results of four quadrant operation obtainded due to simulation at automatic control.
A Hybrid Double-Layer Master-Slave Model For Multicore-Node Clusters
International Nuclear Information System (INIS)
Liu Gang; Schmider, Hartmut; Edgecombe, Kenneth E
2012-01-01
The Double-Layer Master-Slave Model (DMSM) is a suitable hybrid model for executing a workload that consists of multiple independent tasks of varying length on a cluster consisting of multicore nodes. In this model, groups of individual tasks are first deployed to the cluster nodes through an MPI based Master-Slave model. Then, each group is processed by multiple threads on the node through an OpenMP based All-Slave approach. The lack of thread safety of most MPI libraries has to be addressed by a judicious use of OpenMP critical regions and locks. The HPCVL DMSM Library implements this model in Fortran and C. It requires a minimum of user input to set up the framework for the model and to define the individual tasks. Optionally, it supports the dynamic distribution of task-related data and the collection of results at runtime. This library is freely available as source code. Here, we outline the working principles of the library and on a few examples demonstrate its capability to efficiently distribute a workload on a distributed-memory cluster with shared-memory nodes.
Tsehaye, Iyob; Jones, Michael L.; Irwin, Brian J.; Fielder, David G.; Breck, James E.; Luukkonen, David R.
2015-01-01
The proliferation of double-crested cormorants (DCCOs; Phalacrocorax auritus) in North America has raised concerns over their potential negative impacts on game, cultured and forage fishes, island and terrestrial resources, and other colonial water birds, leading to increased public demands to reduce their abundance. By combining fish surplus production and bird functional feeding response models, we developed a deterministic predictive model representing bird–fish interactions to inform an adaptive management process for the control of DCCOs in multiple colonies in Michigan. Comparisons of model predictions with observations of changes in DCCO numbers under management measures implemented from 2004 to 2012 suggested that our relatively simple model was able to accurately reconstruct past DCCO population dynamics. These comparisons helped discriminate among alternative parameterizations of demographic processes that were poorly known, especially site fidelity. Using sensitivity analysis, we also identified remaining critical uncertainties (mainly in the spatial distributions of fish vs. DCCO feeding areas) that can be used to prioritize future research and monitoring needs. Model forecasts suggested that continuation of existing control efforts would be sufficient to achieve long-term DCCO control targets in Michigan and that DCCO control may be necessary to achieve management goals for some DCCO-impacted fisheries in the state. Finally, our model can be extended by accounting for parametric or ecological uncertainty and including more complex assumptions on DCCO–fish interactions as part of the adaptive management process.
Double point source W-phase inversion: Real-time implementation and automated model selection
Nealy, Jennifer; Hayes, Gavin
2015-01-01
Rapid and accurate characterization of an earthquake source is an extremely important and ever evolving field of research. Within this field, source inversion of the W-phase has recently been shown to be an effective technique, which can be efficiently implemented in real-time. An extension to the W-phase source inversion is presented in which two point sources are derived to better characterize complex earthquakes. A single source inversion followed by a double point source inversion with centroid locations fixed at the single source solution location can be efficiently run as part of earthquake monitoring network operational procedures. In order to determine the most appropriate solution, i.e., whether an earthquake is most appropriately described by a single source or a double source, an Akaike information criterion (AIC) test is performed. Analyses of all earthquakes of magnitude 7.5 and greater occurring since January 2000 were performed with extended analyses of the September 29, 2009 magnitude 8.1 Samoa earthquake and the April 19, 2014 magnitude 7.5 Papua New Guinea earthquake. The AIC test is shown to be able to accurately select the most appropriate model and the selected W-phase inversion is shown to yield reliable solutions that match published analyses of the same events.
Modeling Double Subjectivity for Gaining Programmable Insights: Framing the Case of Uber
Directory of Open Access Journals (Sweden)
Loretta Henderson Cheeks
2017-09-01
Full Text Available The Internet is the premier platform that enable the emergence of new technologies. Online news is unstructured narrative text that embeds facts, frames, and amplification that can influence society attitudes about technology adoption. Online news sources are carriers of voluminous amounts of news for reaching significantly large audience and have no geographical or time boundaries. The interplay of complex and dynamical forces among authors and readers allow for progressive emergent and latent properties to exhibit. Our concept of “Double subjectivity” provides a new paradigm for exploring complementary programmable insights of deeply buried meanings in a system. The ability to understand internal embeddedness in a large collection of related articles are beyond the reach of existing computational tools, and are hence left to human readers with unscalable results. This paper uncovers the potential to utilize advanced machine learning in a new way to automate the understanding of implicit structures and their associated latent meanings to give an early human-level insight into emergent technologies, with a concrete example of “Uber”. This paper establishes the new concept of double subjectivity as an instrument for large-scale machining of unstructured text and introduces a social influence model for the discovery of distinct pathways into emerging technology, and hence an insight. The programmable insight reveals early spatial and temporal opinion shift monitoring in complex networks in a structured way for computational treatment and visualization.
Quantitative cellular uptake of double fluorescent core-shelled model submicronic particles
Energy Technology Data Exchange (ETDEWEB)
Leclerc, Lara, E-mail: leclerc@emse.fr [Ecole Nationale Superieure des Mines, CIS-EMSE, LINA (France); Boudard, Delphine [LINA (France); Pourchez, Jeremie; Forest, Valerie [Ecole Nationale Superieure des Mines, CIS-EMSE, LINA (France); Marmuse, Laurence; Louis, Cedric [NANO-H S.A.S (France); Bin, Valerie [LINA (France); Palle, Sabine [Universite Jean Monnet, Centre de Microscopie Confocale Multiphotonique (France); Grosseau, Philippe; Bernache-Assollant, Didier [Ecole Nationale Superieure des Mines, CIS-EMSE, LINA (France); Cottier, Michele [LINA (France)
2012-11-15
The relationship between particles' physicochemical parameters, their uptake by cells and their degree of biological toxicity represent a crucial issue, especially for the development of new technologies such as fabrication of micro- and nanoparticles in the promising field of drug delivery systems. This work was aimed at developing a proof-of-concept for a novel model of double fluorescence submicronic particles that could be spotted inside phagolysosomes. Fluorescein isothiocyanate (FITC) particles were synthesized and then conjugated with a fluorescent pHrodo Trade-Mark-Sign probe, red fluorescence of which increases in acidic conditions such as within lysosomes. After validation in acellular conditions by spectral analysis with confocal microscopy and dynamic light scattering, quantification of phagocytosis was conducted on a macrophage cell line in vitro. The biological impact of pHrodo functionalization (cytotoxicity, inflammatory response, and oxidative stress) was also investigated. Results validate the proof-of-concept of double fluorescent particles (FITC + pHrodo), allowing detection of entirely engulfed pHrodo particles (green and red labeling). Moreover incorporation of pHrodo had no major effects on cytotoxicity compared to particles without pHrodo, making them a powerful tool for micro- and nanotechnologies.
Monte Carlo study of the double and super-exchange model with lattice distortion
Energy Technology Data Exchange (ETDEWEB)
Suarez, J R; Vallejo, E; Navarro, O [Instituto de Investigaciones en Materiales, Universidad Nacional Autonoma de Mexico, Apartado Postal 70-360, 04510 Mexico D. F. (Mexico); Avignon, M, E-mail: jrsuarez@iim.unam.m [Institut Neel, Centre National de la Recherche Scientifique (CNRS) and Universite Joseph Fourier, BP 166, 38042 Grenoble Cedex 9 (France)
2009-05-01
In this work a magneto-elastic phase transition was obtained in a linear chain due to the interplay between magnetism and lattice distortion in a double and super-exchange model. It is considered a linear chain consisting of localized classical spins interacting with itinerant electrons. Due to the double exchange interaction, localized spins tend to align ferromagnetically. This ferromagnetic tendency is expected to be frustrated by anti-ferromagnetic super-exchange interactions between neighbor localized spins. Additionally, lattice parameter is allowed to have small changes, which contributes harmonically to the energy of the system. Phase diagram is obtained as a function of the electron density and the super-exchange interaction using a Monte Carlo minimization. At low super-exchange interaction energy phase transition between electron-full ferromagnetic distorted and electron-empty anti-ferromagnetic undistorted phases occurs. In this case all electrons and lattice distortions were found within the ferromagnetic domain. For high super-exchange interaction energy, phase transition between two site distorted periodic arrangement of independent magnetic polarons ordered anti-ferromagnetically and the electron-empty anti-ferromagnetic undistorted phase was found. For this high interaction energy, Wigner crystallization, lattice distortion and charge distribution inside two-site polarons were obtained.
Directory of Open Access Journals (Sweden)
Enrique Calderín-Ojeda
2017-11-01
Full Text Available Generalized linear models might not be appropriate when the probability of extreme events is higher than that implied by the normal distribution. Extending the method for estimating the parameters of a double Pareto lognormal distribution (DPLN in Reed and Jorgensen (2004, we develop an EM algorithm for the heavy-tailed Double-Pareto-lognormal generalized linear model. The DPLN distribution is obtained as a mixture of a lognormal distribution with a double Pareto distribution. In this paper the associated generalized linear model has the location parameter equal to a linear predictor which is used to model insurance claim amounts for various data sets. The performance is compared with those of the generalized beta (of the second kind and lognorma distributions.
Black, Dolores A.; Robinson, William H.; Wilcox, Ian Z.; Limbrick, Daniel B.; Black, Jeffrey D.
2015-08-01
Single event effects (SEE) are a reliability concern for modern microelectronics. Bit corruptions can be caused by single event upsets (SEUs) in the storage cells or by sampling single event transients (SETs) from a logic path. An accurate prediction of soft error susceptibility from SETs requires good models to convert collected charge into compact descriptions of the current injection process. This paper describes a simple, yet effective, method to model the current waveform resulting from a charge collection event for SET circuit simulations. The model uses two double-exponential current sources in parallel, and the results illustrate why a conventional model based on one double-exponential source can be incomplete. A small set of logic cells with varying input conditions, drive strength, and output loading are simulated to extract the parameters for the dual double-exponential current sources. The parameters are based upon both the node capacitance and the restoring current (i.e., drive strength) of the logic cell.
Double-beta decay: Physics beyond the standard model now and in the future (GENIUS)
International Nuclear Information System (INIS)
Klapdor-Kleingrothaus, H.V.
1998-01-01
Nuclear double beta decay has an extraordinarily broad potential in searches for physics beyond the Standard Model, probing already now the TeV scale, at which new physics is expected to manifest itself. These possibilities are reviewed here. First, the result of present-generation experiments are discussed. The most sensitive one of these, the Heidelberg-Moscow experiment at the Gran Sasso underground laboratory, probes the electron mass now in the sub-eV region and will reach a limit of some 0.1 eV in a few years. On the basis of a large amount of theoretical work done by the Heidelberg Double Beta Group in the last two years, results are obtained also for SUSY models (R-parity breaking and neutrino mass), for leptoquarks (leptoquark-Higgs boson coupling), for compositeness, for right-handed W-boson mass, and for some other problems. These results are comfortably competitive with corresponding results from high-energy accelerators like Tevatron, HERA, etc. Future prospects for ββ research are discussed. A new Heidelberg experimental proposal (GENIUS) is presented, which would make it possible to increase the sensitivity for the Majorana neutrino masses from the present level of at best 0.1 eV down to 0.01 or even 0.001 eV. Its physical potential would be a breakthrough into the multi-TeV range for many models beyond standard. Its sensitivity for neutrino-oscillation parameters would be higher than that of all present terrestrial neutrino-oscillation experiments and that of those planned for the future. It would further, even at a first step, cover almost the full MSSM parameter space for prediction of neutralinos as cold dark matter, making the experimental competitive with LHC in searches for supersymmetry
Double Beta Decay - Physics Beyond the Standard Model Now, and in Future (GENIUS)
Klapdor-Kleingrothaus, H. V.
Nuclear double beta decay provides an extraordinarily broad potential to search for beyond Standard Model physics, probing already now the TeV scale, on which new physics should manifest itself. These possibilities are reviewed here. First, the results of present generation experiments are presented. The most sensitive one of them - the Heidelberg-Moscow experiment in the Gran Sasso - probes the electron mass now in the sub eV region and will reach a limit of ˜ 0.1 eV in a few years. Basing to a large extent on the theoretical work of the Heidelberg Double Beta Group in the last two years, results are obtained also for SUSY models (R-parity breaking, sneutrino mass), leptoquarks (leptoquark-Higgs coupling), com-positeness, right-handed W boson mass and others. These results are comfortably competitive to corresponding results from high-energy accelerators like TEVA-TRON, HERA, etc. Second, future perspectives of ʲʲ research are discussed. A new Heidelberg experimental proposal (GENIUS) is presented which would allow to increase the sensitivity for Majorana neutrino masses from the present level of at best 0.1 eV down to 0.01 or even 0.001 eV. Its physical potential would be a breakthrough into the multi-TeV range for many beyond standard models. Its sensitivity for neutrino oscillation parameters would be larger than of all present terrestrial neutrino oscillation experiments and of those planned for the future. It would further, already in a first step, cover almost the full MSSM parameter space for prediction of neutralinos as cold dark matter, making the experiment competitive to LHC in the search for supersymmetry.
Fully convolutional neural networks improve abdominal organ segmentation
Bobo, Meg F.; Bao, Shunxing; Huo, Yuankai; Yao, Yuang; Virostko, Jack; Plassard, Andrew J.; Lyu, Ilwoo; Assad, Albert; Abramson, Richard G.; Hilmes, Melissa A.; Landman, Bennett A.
2018-03-01
Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1
Zhu, Aichun; Wang, Tian; Snoussi, Hichem
2018-03-01
This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.
Directory of Open Access Journals (Sweden)
Aichun Zhu
2018-03-01
Full Text Available This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN. Firstly, a Relative Mixture Deformable Model (RMDM is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.
The quick convolution of galaxy profiles, with application to power-law intensity distributions
International Nuclear Information System (INIS)
Bailey, M.E.; Sparks, W.B.
1983-01-01
The two-dimensional convolution of a circularly symmetric galaxy model with a Gaussian point-spread function of dispersion σ reduces to a single integral. This is solved analytically for models with power-law intensity distributions and results are given which relate the apparent core radius to σ and the power-law index k. The convolution integral is also simplified for the case of a point-spread function corresponding to a circular aperture. Models of galactic nuclei with stellar density cusps can only be distinguished from alternatives with small core radii if both the brightness and seeing profiles are measured accurately. The results are applied to data on the light distribution at the Galactic Centre. (author)
Directory of Open Access Journals (Sweden)
Abdullah-Al Nahid
2018-01-01
Full Text Available Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a Convolutional Neural Network Raw Image (CNN-I; (b Convolutional Neural Network CT Histogram (CNN-CH; (c Convolutional Neural Network CT LBP (CNN-CL; (d Convolutional
Double-temperature ratchet model and current reversal of coupled Brownian motors
Li, Chen-Pu; Chen, Hong-Bin; Zheng, Zhi-Gang
2017-12-01
On the basis of the transport features and experimental phenomena observed in studies of molecular motors, we propose a double-temperature ratchet model of coupled motors to reveal the dynamical mechanism of cooperative transport of motors with two heads, where the interactions and asynchrony between two motor heads are taken into account. We investigate the collective unidirectional transport of coupled system and find that the direction of motion can be reversed under certain conditions. Reverse motion can be achieved by modulating the coupling strength, coupling free length, and asymmetric coefficient of the periodic potential, which is understood in terms of the effective potential theory. The dependence of the directed current on various parameters is studied systematically. Directed transport of coupled Brownian motors can be manipulated and optimized by adjusting the pulsation period or the phase shift of the pulsation temperature.
Switching LPV Control with Double-Layer LPV Model for Aero-Engines
Tang, Lili; Huang, Jinquan; Pan, Muxuan
2017-11-01
To cover the whole range of operating conditions of aero-engine, a double-layer LPV model is built so as to take into account of the variability due to the flight altitude, Mach number and the rotational speed. With this framework, the problem of designing LPV state-feedback robust controller that guarantees desired bounds on both H_∞ and H_2 performances is considered. Besides this, to reduce the conservativeness caused by a single LPV controller of the whole flight envelope and the common Lyapunov function method, a new method is proposed to design a family of LPV switching controllers. The switching LPV controllers can ensure that the closed-loop system remains stable in the sense of Lyapunov under arbitrary switching logic. Meanwhile, the switching LPV controllers can ensure the parameters change smoothly. The validity and performance of the theoretical results are demonstrated through a numerical example.
Singular solutions for the rigid plastic double slip and rotation model under plane strain
Alexandrov, S.; Lyamina, E.
2018-02-01
In the mechanics of granular and other materials the system of equations comprising the rigid plastic double slip and rotation model together with the stress equilibrium equations under plane strain conditions forms a hyperbolic system. Boundary value problems for this system of equations can involve a frictional interface. An envelope of characteristics may coincide with this interface. In this case, the solution is singular. In particular, some components of the strain rate tensor approach infinity in the vicinity of the frictional interface. Such behavior of solutions is in qualitative agreement with experimental data that show that a narrow layer of localized plastic deformation is often generated near frictional interfaces. The present paper deals with asymptotic analysis of the aforementioned system of equations in the vicinity of an envelope of characteristics. It is shown that the shear strain rate and the spin component in a local coordinate system connected to the envelope follow an inverse square root rule in its vicinity.
A stationary bulk planar ideal flow solution for the double shearing model
Lyamina, E. A.; Kalenova, N. V.; Date, P. P.
2018-04-01
This paper provides a general ideal flow solution for the double shearing model of pressure-dependent plasticity. This new solution is restricted to a special class of stationary planar flows. A distinguished feature of this class of solutions is that one family of characteristic lines is straight. The solution is analytic. The mapping between Cartesian and principal lines based coordinate systems is given in parametric form with characteristic coordinates being the parameters. A simple relation that connects the scale factor for one family of coordinate curves of the principal lines based coordinate system and the magnitude of velocity is derived. The original ideal flow theory is widely used as the basis for inverse methods for the preliminary design of metal forming processes driven by minimum plastic work. The new theory extends this area of application to granular materials.
An Empirical Validation of Building Simulation Software for Modelling of Double-Skin Facade (DSF)
DEFF Research Database (Denmark)
Larsen, Olena Kalyanova; Heiselberg, Per; Felsmann, Clemens
2009-01-01
buildings, but their accuracy might be limited in cases with DSFs because of the complexity of the heat and mass transfer processes within the DSF. To address this problem, an empirical validation of building models with DSF, performed with various building simulation tools (ESP-r, IDA ICE 3.0, VA114......Double-skin facade (DSF) buildings are being built as an attractive, innovative and energy efficient solution. Nowadays, several design tools are used for assessment of thermal and energy performance of DSF buildings. Existing design tools are well-suited for performance assessment of conventional......, TRNSYS-TUD and BSim) was carried out in the framework of IEA SHC Task 34 /ECBCS Annex 43 "Testing and Validation of Building Energy Simulation Tools". The experimental data for the validation was gathered in a full-scale outdoor test facility. The empirical data sets comprise the key-functioning modes...
Double and super-exchange model in one-dimensional systems
International Nuclear Information System (INIS)
Vallejo, E.; Navarro, O.; Avignon, M.
2010-01-01
We present an analytical and numerical study of the competition between double and super-exchange interactions in a one-dimensional model. For low super-exchange interaction energy we find phase separation between ferromagnetic and anti-ferromagnetic phases. When the super-exchange interaction energy gets larger, the conduction electrons are self-trapped within separate small magnetic polarons. These magnetic polarons contain a single electron inside two or three sites depending on the conduction electron density and form a Wigner crystallization. A new phase separation is found between these small polarons and the anti-ferromagnetic phase. Spin-glass behavior is obtained consistent with experimental results of the nickelate one-dimensional compound Y 2-x Ca x BaNiO 5 .
Sagers, Jason D; Leishman, Timothy W; Blotter, Jonathan D
2009-06-01
Low-frequency sound transmission has long plagued the sound isolation performance of lightweight partitions. Over the past 2 decades, researchers have investigated actively controlled structures to prevent sound transmission from a source space into a receiving space. An approach using active segmented partitions (ASPs) seeks to improve low-frequency sound isolation capabilities. An ASP is a partition which has been mechanically and acoustically segmented into a number of small individually controlled modules. This paper provides a theoretical and numerical development of a single ASP module configuration, wherein each panel of the double-panel structure is independently actuated and controlled by an analog feedback controller. A numerical model is developed to estimate frequency response functions for the purpose of controller design, to understand the effects of acoustic coupling between the panels, to predict the transmission loss of the module in both passive and active states, and to demonstrate that the proposed ASP module will produce bidirectional sound isolation.
Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.
Zhong, Jiandan; Lei, Tao; Yao, Guangle
2017-11-24
Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.
Deformable image registration using convolutional neural networks
Eppenhof, Koen A. J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P. W.
2018-03-01
Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between pairs of three-dimensional images. The outputs of the network are three maps for the x, y, and z components of a thin plate spline transformation grid. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application. Training therefore does not require manually annotated ground truth deformation information. The methodology is demonstrated on public data sets of inspiration-expiration lung CT image pairs, which come with annotated corresponding landmarks for evaluation of the registration accuracy. Advantages of this methodology are its fast registration times and its minimal parameterization.
Codeword Structure Analysis for LDPC Convolutional Codes
Directory of Open Access Journals (Sweden)
Hua Zhou
2015-12-01
Full Text Available The codewords of a low-density parity-check (LDPC convolutional code (LDPC-CC are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix H T ( D , while the number of the non-structured ones depends on the particular monomials or polynomials in H T ( D . By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in H T ( D , resulting in improved distance spectrum of the mother code.
A Double Zone Dynamical Model For The Tidal Evolution Of The Obliquity
Damiani, Cilia
2017-10-01
It is debated wether close-in giants planets can form in-situ and if not, which mechanisms are responsible for their migration. One of the observable tests for migration theories is the current value of the obliquity. But after the main migration mechanism has ended, the combined effects of tidal dissipation and the magnetic braking of the star lead to the evolution of both the obliquity and the semi-major axis. The observed correlation between effective temperature and measured projected obliquity has been taken as evidence of such mechanisms being at play. Here I present an improved model for the tidal evolution of the obliquity. It includes all the components of the dynamical tide for circular misaligned systems. It uses an analytical formulation for the frequency-averaged dissipation for each mode, depending only on global stellar parameters, giving a measure of the dissipative properties of the convective zone of the host as it evolves in time. The model also includes the effect of magnetic braking in the framework of the double zone model. This results in the estimation of different tidal evolution timescales for the evolution of the planet's semi-major axis and obliquity depending on the properties of the stellar host. This model can be used to test migration theories, provided that a good determination of stellar radii, masses and ages can be obtained.
Directory of Open Access Journals (Sweden)
Yang Yang
2013-01-01
Full Text Available We investigate the tailed asymptotic behavior of the randomly weighted sums with increments with convolution-equivalent distributions. Our obtained result can be directly applied to a discrete-time insurance risk model with insurance and financial risks and derive the asymptotics for the finite-time probability of the above risk model.
Xu, Z C; Zhu, J
2000-01-01
According to the double-cross mating design and using principles of Cockerham's general genetic model, a genetic model with additive, dominance and epistatic effects (ADAA model) was proposed for the analysis of agronomic traits. Components of genetic effects were derived for different generations. Monte Carlo simulation was conducted for analyzing the ADAA model and its reduced AD model by using different generations. It was indicated that genetic variance components could be estimated without bias by MINQUE(1) method and genetic effects could be predicted effectively by AUP method; at least three generations (including parent, F1 of single cross and F1 of double-cross) were necessary for analyzing the ADAA model and only two generations (including parent and F1 of double-cross) were enough for the reduced AD model. When epistatic effects were taken into account, a new approach for predicting the heterosis of agronomic traits of double-crosses was given on the basis of unbiased prediction of genotypic merits of parents and their crosses. In addition, genotype x environment interaction effects and interaction heterosis due to G x E interaction were discussed briefly.
Data-based mathematical modeling of vectorial transport across double-transfected polarized cells.
Bartholomé, Kilian; Rius, Maria; Letschert, Katrin; Keller, Daniela; Timmer, Jens; Keppler, Dietrich
2007-09-01
Vectorial transport of endogenous small molecules, toxins, and drugs across polarized epithelial cells contributes to their half-life in the organism and to detoxification. To study vectorial transport in a quantitative manner, an in vitro model was used that includes polarized MDCKII cells stably expressing the recombinant human uptake transporter OATP1B3 in their basolateral membrane and the recombinant ATP-driven efflux pump ABCC2 in their apical membrane. These double-transfected cells enabled mathematical modeling of the vectorial transport of the anionic prototype substance bromosulfophthalein (BSP) that has frequently been used to examine hepatobiliary transport. Time-dependent analyses of (3)H-labeled BSP in the basolateral, intracellular, and apical compartments of cells cultured on filter membranes and efflux experiments in cells preloaded with BSP were performed. A mathematical model was fitted to the experimental data. Data-based modeling was optimized by including endogenous transport processes in addition to the recombinant transport proteins. The predominant contributions to the overall vectorial transport of BSP were mediated by OATP1B3 (44%) and ABCC2 (28%). Model comparison predicted a previously unrecognized endogenous basolateral efflux process as a negative contribution to total vectorial transport, amounting to 19%, which is in line with the detection of the basolateral efflux pump Abcc4 in MDCKII cells. Rate-determining steps in the vectorial transport were identified by calculating control coefficients. Data-based mathematical modeling of vectorial transport of BSP as a model substance resulted in a quantitative description of this process and its components. The same systems biology approach may be applied to other cellular systems and to different substances.
An Improved Convolutional Neural Network on Crowd Density Estimation
Directory of Open Access Journals (Sweden)
Pan Shao-Yun
2016-01-01
Full Text Available In this paper, a new method is proposed for crowd density estimation. An improved convolutional neural network is combined with traditional texture feature. The data calculated by the convolutional layer can be treated as a new kind of features.So more useful information of images can be extracted by different features.In the meantime, the size of image has little effect on the result of convolutional neural network. Experimental results indicate that our scheme has adequate performance to allow for its use in real world applications.
Directory of Open Access Journals (Sweden)
Zhixin Yang
Full Text Available The onset of double diffusive convection in a viscoelastic fluid-saturated porous layer is studied when the fluid and solid phase are not in local thermal equilibrium. The modified Darcy model is used for the momentum equation and a two-field model is used for energy equation each representing the fluid and solid phases separately. The effect of thermal non-equilibrium on the onset of double diffusive convection is discussed. The critical Rayleigh number and the corresponding wave number for the exchange of stability and over-stability are obtained, and the onset criterion for stationary and oscillatory convection is derived analytically and discussed numerically.
Directory of Open Access Journals (Sweden)
Jilin Zhang
2017-01-01
Full Text Available With the development of the mobile systems, we gain a lot of benefits and convenience by leveraging mobile devices; at the same time, the information gathered by smartphones, such as location and environment, is also valuable for business to provide more intelligent services for customers. More and more machine learning methods have been used in the field of mobile information systems to study user behavior and classify usage patterns, especially convolutional neural network. With the increasing of model training parameters and data scale, the traditional single machine training method cannot meet the requirements of time complexity in practical application scenarios. The current training framework often uses simple data parallel or model parallel method to speed up the training process, which is why heterogeneous computing resources have not been fully utilized. To solve these problems, our paper proposes a delay synchronization convolutional neural network parallel strategy, which leverages the heterogeneous system. The strategy is based on both synchronous parallel and asynchronous parallel approaches; the model training process can reduce the dependence on the heterogeneous architecture in the premise of ensuring the model convergence, so the convolution neural network framework is more adaptive to different heterogeneous system environments. The experimental results show that the proposed delay synchronization strategy can achieve at least three times the speedup compared to the traditional data parallelism.
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.
Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh
2017-09-01
Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.
A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification
Directory of Open Access Journals (Sweden)
Xiaocong Wei
2017-07-01
Full Text Available Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track the forums, find all of the issues that need resolution and understand their urgency and sentiment. In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically identify whether a post expresses confusion, determine the urgency and classify the polarity of the sentiment. First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation. Second, we learn the post representation from the features extracted through the convolution operation via the LSTM model, which considers the long-term temporal semantic relationships of features. Third, we investigate the possibility of transferring parameters from a model trained on one course to another course and the subsequent fine-tuning. Experiments on three real-world MOOC courses confirm the effectiveness of our framework. This work suggests that our model can potentially significantly increase the effectiveness of monitoring MOOC forums in real time.
Medina, Tait Runnfeldt
The increasing global reach of survey research provides sociologists with new opportunities to pursue theory building and refinement through comparative analysis. However, comparison across a broad array of diverse contexts introduces methodological complexities related to the development of constructs (i.e., measurement modeling) that if not adequately recognized and properly addressed undermine the quality of research findings and cast doubt on the validity of substantive conclusions. The motivation for this dissertation arises from a concern that the availability of cross-national survey data has outpaced sociologists' ability to appropriately analyze and draw meaningful conclusions from such data. I examine the implicit assumptions and detail the limitations of three commonly used measurement models in cross-national analysis---summative scale, pooled factor model, and multiple-group factor model with measurement invariance. Using the orienting lens of the double tension I argue that a new approach to measurement modeling that incorporates important cross-national differences into the measurement process is needed. Two such measurement models---multiple-group factor model with partial measurement invariance (Byrne, Shavelson and Muthen 1989) and the alignment method (Asparouhov and Muthen 2014; Muthen and Asparouhov 2014)---are discussed in detail and illustrated using a sociologically relevant substantive example. I demonstrate that the former approach is vulnerable to an identification problem that arbitrarily impacts substantive conclusions. I conclude that the alignment method is built on model assumptions that are consistent with theoretical understandings of cross-national comparability and provides an approach to measurement modeling and construct development that is uniquely suited for cross-national research. The dissertation makes three major contributions: First, it provides theoretical justification for a new cross-national measurement model and
Double-hit mouse model of cigarette smoke priming for acute lung injury.
Sakhatskyy, Pavlo; Wang, Zhengke; Borgas, Diana; Lomas-Neira, Joanne; Chen, Yaping; Ayala, Alfred; Rounds, Sharon; Lu, Qing
2017-01-01
Epidemiological studies indicate that cigarette smoking (CS) increases the risk and severity of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). The mechanism is not understood, at least in part because of lack of animal models that reproduce the key features of the CS priming process. In this study, using two strains of mice, we characterized a double-hit mouse model of ALI induced by CS priming of injury caused by lipopolysaccharide (LPS). C57BL/6 and AKR mice were preexposed to CS briefly (3 h) or subacutely (3 wk) before intratracheal instillation of LPS and ALI was assessed 18 h after LPS administration by measuring lung static compliance, lung edema, vascular permeability, inflammation, and alveolar apoptosis. We found that as little as 3 h of exposure to CS enhanced LPS-induced ALI in both strains of mice. Similar exacerbating effects were observed after 3 wk of preexposure to CS. However, there was a strain difference in susceptibility to CS priming for ALI, with a greater effect in AKR mice. The key features we observed suggest that 3 wk of CS preexposure of AKR mice is a reproducible, clinically relevant animal model that is useful for studying mechanisms and treatment of CS priming for a second-hit-induced ALI. Our data also support the concept that increased susceptibility to ALI/ARDS is an important adverse health consequence of CS exposure that needs to be taken into consideration when treating critically ill individuals.
Village Building Identification Based on Ensemble Convolutional Neural Networks
Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei
2017-01-01
In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154
Su, Yu-Chuan; Chiu, Tzu-Hsuan; Yeh, Chun-Yen; Huang, Hsin-Fu; Hsu, Winston H.
2014-01-01
Unconstrained video recognition and Deep Convolution Network (DCN) are two active topics in computer vision recently. In this work, we apply DCNs as frame-based recognizers for video recognition. Our preliminary studies, however, show that video corpora with complete ground truth are usually not large and diverse enough to learn a robust model. The networks trained directly on the video data set suffer from significant overfitting and have poor recognition rate on the test set. The same lack-...
Applicability of the Fourier convolution theorem to the analysis of late-type stellar spectra
International Nuclear Information System (INIS)
Bruning, D.H.
1981-01-01
Solar flux and intensity measurements were obtained at Sacramento Peak Observatory to test the validity of the Fourier convolution method as a means of analyzing the spectral line shapes of late-type stars. Analysis of six iron lines near 6200A shows that, in general, the convolution method is not a suitable approximation for the calculation of the flux profile. The convolution method does reasonably reproduce the line shape for some lines which appear not to vary across the disk of the sun, but does not properly calculate the central line depth of these lines. Even if a central depth correction could be found, it is difficult to predict, especially for stars other than the sun, which lines have nearly constant shapes and could be used with the convolution method. Therefore, explicit disk integrations are promoted as the only reliable method of spectral line analysis for late-type stars. Several methods of performing the disk integration are investigated. Although the Abt (1957) prescription appears suitable for the limited case studied, methods using annuli of equal area, equal flux, or equal width (Soberblom, 1980) are considered better models. The model that is the easiest to use and most efficient computationally is the equal area model. Model atmosphere calculations yield values for the microturbulence and macroturbulence similar to those derived by observers. Since the depth dependence of the microturbulence is ignored in the calculations, the intensity profiles at disk center and the limb do not match the observed intensity profiles with only one set of velocity parameters. Use of these incorrectly calculated intensity profiles in the integration procedure to obtain the flux profile leads to incorrect estimates of the solar macroturbulence
Directory of Open Access Journals (Sweden)
Sangram Bana
2016-11-01
Full Text Available In order to predict the performance of a PV system, a reliable and accurate simulation design of PV systems before being installed is a necessity. The present study concerns the development of single and double diode model of solar PV system and ensures the best suited model under specific environmental condition for accurate performance prediction. The information provided in the manufacturers’ data sheet is not sufficient for developing a Simulink based single and double diode models of PV module. These parameters are crucial to predict accurate performance of a PV module. These parameters of the proposed solar PV models have been calculated using an efficient iterative technique. This paper compares the simulation results of both the models with manufacturer’s data sheet to investigate the accuracy and validity. A MATLAB/Simulink based comparative performance analysis of these models under inconsistent atmospheric conditions and the effect of variations in model parameters has been carried out. Despite the simplicity, these models are highly sensitive and respond to a slight variation in temperature and insolation. It is observed that double diode PV model is more accurate under low intensity insolation or shading condition. The performance evaluation of the models under present study will be helpful to understand the I-V curves, which will enable us in predicting the solar PV system power production under variable input conditions.
Chinese Sentence Classification Based on Convolutional Neural Network
Gu, Chengwei; Wu, Ming; Zhang, Chuang
2017-10-01
Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.
High Order Tensor Formulation for Convolutional Sparse Coding
Bibi, Adel Aamer; Ghanem, Bernard
2017-01-01
Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images
Adversarial training and dilated convolutions for brain MRI segmentation
Moeskops, P.; Veta, M.; Lafarge, M.W.; Eppenhof, K.A.J.; Pluim, J.P.W.
2017-01-01
Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to
Classification of urine sediment based on convolution neural network
Pan, Jingjing; Jiang, Cunbo; Zhu, Tiantian
2018-04-01
By designing a new convolution neural network framework, this paper breaks the constraints of the original convolution neural network framework requiring large training samples and samples of the same size. Move and cropping the input images, generate the same size of the sub-graph. And then, the generated sub-graph uses the method of dropout, increasing the diversity of samples and preventing the fitting generation. Randomly select some proper subset in the sub-graphic set and ensure that the number of elements in the proper subset is same and the proper subset is not the same. The proper subsets are used as input layers for the convolution neural network. Through the convolution layer, the pooling, the full connection layer and output layer, we can obtained the classification loss rate of test set and training set. In the red blood cells, white blood cells, calcium oxalate crystallization classification experiment, the classification accuracy rate of 97% or more.
Convolution of second order linear recursive sequences II.
Directory of Open Access Journals (Sweden)
Szakács Tamás
2017-12-01
Full Text Available We continue the investigation of convolutions of second order linear recursive sequences (see the first part in [1]. In this paper, we focus on the case when the characteristic polynomials of the sequences have common root.
FPGA-based digital convolution for wireless applications
Guan, Lei
2017-01-01
This book presents essential perspectives on digital convolutions in wireless communications systems and illustrates their corresponding efficient real-time field-programmable gate array (FPGA) implementations. Covering these digital convolutions from basic concept to vivid simulation/illustration, the book is also supplemented with MS PowerPoint presentations to aid in comprehension. FPGAs or generic all programmable devices will soon become widespread, serving as the “brains” of all types of real-time smart signal processing systems, like smart networks, smart homes and smart cities. The book examines digital convolution by bringing together the following main elements: the fundamental theory behind the mathematical formulae together with corresponding physical phenomena; virtualized algorithm simulation together with benchmark real-time FPGA implementations; and detailed, state-of-the-art case studies on wireless applications, including popular linear convolution in digital front ends (DFEs); nonlinear...
A new hybrid double divisor ratio spectra method for the analysis of ternary mixtures
Youssef, Rasha M.; Maher, Hadir M.
2008-10-01
A new spectrophotometric method was developed for the simultaneous determination of ternary mixtures, without prior separation steps. This method is based on convolution of the double divisor ratio spectra, obtained by dividing the absorption spectrum of the ternary mixture by a standard spectrum of two of the three compounds in the mixture, using combined trigonometric Fourier functions. The magnitude of the Fourier function coefficients, at either maximum or minimum points, is related to the concentration of each drug in the mixture. The mathematical explanation of the procedure is illustrated. The method was applied for the assay of a model mixture consisting of isoniazid (ISN), rifampicin (RIF) and pyrazinamide (PYZ) in synthetic mixtures, commercial tablets and human urine samples. The developed method was compared with the double divisor ratio spectra derivative method (DDRD) and derivative ratio spectra-zero-crossing method (DRSZ). Linearity, validation, accuracy, precision, limits of detection, limits of quantitation, and other aspects of analytical validation are included in the text.
Probing flavor models with {sup 76}Ge-based experiments on neutrinoless double-β decay
Energy Technology Data Exchange (ETDEWEB)
Agostini, Matteo [Technische Universitaet Muenchen, Physik Department and Excellence Cluster Universe, Munich (Germany); Gran Sasso Science Institute (INFN), L' Aquila (Italy); Merle, Alexander [Max-Planck-Institut fuer Physik (Werner-Heisenberg-Institut), Munich (Germany); Zuber, Kai [Technische Universitaet Dresden, Institute for Nuclear and Particle Physics, Dresden (Germany)
2016-04-15
The physics impact of a staged approach for double-β decay experiments based on {sup 76}Ge is studied. The scenario considered relies on realistic time schedules envisioned by the Gerda and the Majorana collaborations, which are jointly working towards the realization of a future larger scale {sup 76}Ge experiment. Intermediate stages of the experiments are conceived to perform quasi background-free measurements, and different data sets can be reliably combined to maximize the physics outcome. The sensitivity for such a global analysis is presented, with focus on how neutrino flavor models can be probed already with preliminary phases of the experiments. The synergy between theory and experiment yields strong benefits for both sides: the model predictions can be used to sensibly plan the experimental stages, and results from intermediate stages can be used to constrain whole groups of theoretical scenarios. This strategy clearly generates added value to the experimental efforts, while at the same time it allows to achieve valuable physics results as early as possible. (orig.)
Indo-Pacific ENSO modes in a double-basin Zebiak-Cane model
Wieners, Claudia; de Ruijter, Will; Dijkstra, Henk
2016-04-01
We study Indo-Pacific interactions on ENSO timescales in a double-basin version of the Zebiak-Cane ENSO model, employing both time integrations and bifurcation analysis (continuation methods). The model contains two oceans (the Indian and Pacific Ocean) separated by a meridional wall. Interaction between the basins is possible via the atmosphere overlaying both basins. We focus on the effect of the Indian Ocean (both its mean state and its variability) on ENSO stability. In addition, inspired by analysis of observational data (Wieners et al, Coherent tropical Indo-Pacific interannual climate variability, in review), we investigate the effect of state-dependent atmospheric noise. Preliminary results include the following: 1) The background state of the Indian Ocean stabilises the Pacific ENSO (i.e. the Hopf bifurcation is shifted to higher values of the SST-atmosphere coupling), 2) the West Pacific cooling (warming) co-occurring with El Niño (La Niña) is essential to simulate the phase relations between Pacific and Indian SST anomalies, 3) a non-linear atmosphere is needed to simulate the effect of the Indian Ocean variability onto the Pacific ENSO that is suggested by observations.
Double porosity model to describe both permeability change and dissolution processes
International Nuclear Information System (INIS)
Niibori, Yuichi; Usui, Hideo; Chida, Taiji
2015-01-01
Cement is a practical material for constructing the geological disposal system of radioactive wastes. The dynamic behavior of both permeability change and dissolution process caused by a high pH groundwater was explained using a double porosity model assuming that each packed particle consists of the sphere-shaped aggregation of smaller particles. This model assumes two kinds of porosities between the particle clusters and between the particles, where the former porosity change mainly controls the permeability change of the bed, and the latter porosity change controls the diffusion of OH"- ions inducing the dissolution of silica. The fundamental equations consist of a diffusion equation of spherical coordinates of OH"- ions including the first-order reaction term and some equations describing the size changes of both the particles and the particle clusters with time. The change of over-all permeability of the packed bed is evaluated by Kozeny-Carman equation and the calculated radii of particle clusters. The calculated result well describes the experimental result of both permeability change and dissolution processes. (author)
Stability considerations and a double-diffusive convection model for solar ponds
Energy Technology Data Exchange (ETDEWEB)
Lin, E.I.H.; Sha, W.T.; Soo, S.L.
1979-04-01
A brief survey is made on the basic principles, current designs and economic advantages of salinity-gradient solar ponds as solar collectors and reservoirs. Solar ponds are well-suited for various AIPH (agricultural and industrial process heat) applications, and as annual storage devices for space heating and cooling. The benefit of an efficient pond is demonstrated via a preliminary economic analysis which suggests the idea of energy farming as a profitable alternative for land usage in the face of rising fuel cost. The economy and reliability of solar-pond operation depend crucially on the stability of the nonconvective gradient zone against disturbances such as generated by a severe weather condition. Attention is focused on the subject of stability, and pertinent existing results are summarized and discussed. Details of the derivation of three-dimensional stability criteria for thermohaline convection with linear gradients are presented. Ten key questions pertaining to stability are posed, whose answers must be sought through extensive analytical and numerical studies. Possible methods of approach toward enhancing solar-pond stability are also discussed. For the numerical studies of pond behavior and stability characteristics, a double-diffusive convection model is proposed. The model can be constructed by extending the three-dimensional thermohydrodynamic computer code COMMIX-SA, following the necessary steps outlined; computational plans are described. Similarities exist between the halothermocline and the thermocline storage systems, and an extended COMMIX-SA will be a valuable tool for the investigation of both.
Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung
2018-04-23
In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.
Traffic sign recognition with deep convolutional neural networks
Karamatić, Boris
2016-01-01
The problem of detection and recognition of traffic signs is becoming an important problem when it comes to the development of self driving cars and advanced driver assistance systems. In this thesis we will develop a system for detection and recognition of traffic signs. For the problem of detection we will use aggregate channel features and for the problem of recognition we will use a deep convolutional neural network. We will describe how convolutional neural networks work, how they are co...
Convolutional Codes with Maximum Column Sum Rank for Network Streaming
Mahmood, Rafid; Badr, Ahmed; Khisti, Ashish
2015-01-01
The column Hamming distance of a convolutional code determines the error correction capability when streaming over a class of packet erasure channels. We introduce a metric known as the column sum rank, that parallels column Hamming distance when streaming over a network with link failures. We prove rank analogues of several known column Hamming distance properties and introduce a new family of convolutional codes that maximize the column sum rank up to the code memory. Our construction invol...
Efficient and Invariant Convolutional Neural Networks for Dense Prediction
Gao, Hongyang; Ji, Shuiwang
2017-01-01
Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods...
Research of convolutional neural networks for traffic sign recognition
Stadalnikas, Kasparas
2017-01-01
In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...
On the Fresnel sine integral and the convolution
Directory of Open Access Journals (Sweden)
Adem Kılıçman
2003-01-01
Full Text Available The Fresnel sine integral S(x, the Fresnel cosine integral C(x, and the associated functions S+(x, S−(x, C+(x, and C−(x are defined as locally summable functions on the real line. Some convolutions and neutrix convolutions of the Fresnel sine integral and its associated functions with x+r, xr are evaluated.
Improved Reactive Flow Modeling of the LX-17 Double Shock Experiments
Rehagen, Thomas J.; Vitello, Peter
2017-06-01
Over driven double shock experiments provide a measurement of the properties of the reaction product states of the insensitive high explosive LX-17 (92.5% TATB and 7.5% Kel-F by weight). These experiments used two flyer materials mounted on the end of a projectile to send an initial shock through the LX-17, followed by a second shock of a higher magnitude into the detonation products. In the experiments, the explosive was initially driven by the flyer plate to pressures above the Chapman-Jouguet state. The particle velocity history was recorded by Photonic Doppler Velocimetry (PDV) probes pointing at an aluminum foil coated LiF window. The PDV data shows a sharp initial shock and decay, followed by a rounded second shock. Here, the experimental results are compared to 2D and 3D Cheetah reactive flow modeling. Our default Cheetah reactive flow model fails to accurately reproduce the decay of the first shock or the curvature or strength of the second shock. A new model is proposed in which the carbon condensate produced in the reaction zone is controlled by a kinetic rate. This allows the carbon condensate to be initially out of chemical equilibrium with the product gas. This new model reproduces the initial detonation peak and decay, and matches the curvature of the second shock, however, it still over-predicts the strength of the second shock. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract No. DE-AC52-07NA27344.
Convolution-based estimation of organ dose in tube current modulated CT
Tian, Xiaoyu; Segars, W. Paul; Dixon, Robert L.; Samei, Ehsan
2016-05-01
Estimating organ dose for clinical patients requires accurate modeling of the patient anatomy and the dose field of the CT exam. The modeling of patient anatomy can be achieved using a library of representative computational phantoms (Samei et al 2014 Pediatr. Radiol. 44 460-7). The modeling of the dose field can be challenging for CT exams performed with a tube current modulation (TCM) technique. The purpose of this work was to effectively model the dose field for TCM exams using a convolution-based method. A framework was further proposed for prospective and retrospective organ dose estimation in clinical practice. The study included 60 adult patients (age range: 18-70 years, weight range: 60-180 kg). Patient-specific computational phantoms were generated based on patient CT image datasets. A previously validated Monte Carlo simulation program was used to model a clinical CT scanner (SOMATOM Definition Flash, Siemens Healthcare, Forchheim, Germany). A practical strategy was developed to achieve real-time organ dose estimation for a given clinical patient. CTDIvol-normalized organ dose coefficients ({{h}\\text{Organ}} ) under constant tube current were estimated and modeled as a function of patient size. Each clinical patient in the library was optimally matched to another computational phantom to obtain a representation of organ location/distribution. The patient organ distribution was convolved with a dose distribution profile to generate {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} values that quantified the regional dose field for each organ. The organ dose was estimated by multiplying {{≤ft(\\text{CTD}{{\\text{I}}\\text{vol}}\\right)}\\text{organ, \\text{convolution}}} with the organ dose coefficients ({{h}\\text{Organ}} ). To validate the accuracy of this dose estimation technique, the organ dose of the original clinical patient was estimated using Monte Carlo program with TCM profiles explicitly modeled. The
Directory of Open Access Journals (Sweden)
H. A. Ahmed
2012-08-01
Full Text Available The main goal of this paper is to introduce a new platform for the implementation and simulation of communication systems. SCILAB/SCICOS is an open source software for conducting communication system related experiments, aiming to provide an experimentation platform for research on communication theories. Double Sideband Suppressed Carrier (DSB-SC Modulator is modeled and simulated using this platform.
The Double ABCX Model of Adaptation in Racially Diverse Families with a School-Age Child with Autism
Manning, Margaret M.; Wainwright, Laurel; Bennett, Jillian
2011-01-01
In this study, the Double ABCX model of family adaptation was used to explore the impact of severity of autism symptoms, behavior problems, social support, religious coping, and reframing, on outcomes related to family functioning and parental distress. The sample included self-report measures collected from 195 families raising school-age…
Modeling a Naturally Ventilated Double Skin Façade with a Building Thermal Simulation Program
DEFF Research Database (Denmark)
Jensen, Rasmus Lund; Kalyanova, Olena; Heiselberg, Per
2008-01-01
to predict. This is manly due to the very transient and complex air flow in the naturally ventilated double skin façade cavity. In this paper the modelling of the DSF using a thermal simulation program, BSim, is discussed. The simulations are based on the measured weather boundary conditions...
Deep Convolutional Neural Networks for Classifying Body Constitution Based on Face Image.
Huan, Er-Yang; Wen, Gui-Hua; Zhang, Shi-Jun; Li, Dan-Yang; Hu, Yang; Chang, Tian-Yuan; Wang, Qing; Huang, Bing-Lin
2017-01-01
Body constitution classification is the basis and core content of traditional Chinese medicine constitution research. It is to extract the relevant laws from the complex constitution phenomenon and finally build the constitution classification system. Traditional identification methods have the disadvantages of inefficiency and low accuracy, for instance, questionnaires. This paper proposed a body constitution recognition algorithm based on deep convolutional neural network, which can classify individual constitution types according to face images. The proposed model first uses the convolutional neural network to extract the features of face image and then combines the extracted features with the color features. Finally, the fusion features are input to the Softmax classifier to get the classification result. Different comparison experiments show that the algorithm proposed in this paper can achieve the accuracy of 65.29% about the constitution classification. And its performance was accepted by Chinese medicine practitioners.
A Novel Image Tag Completion Method Based on Convolutional Neural Transformation
Geng, Yanyan; Zhang, Guohui; Li, Weizhi; Gu, Yi; Liang, Ru-Ze; Liang, Gaoyuan; Wang, Jingbin; Wu, Yanbin; Patil, Nitin; Wang, Jing-Yan
2017-01-01
In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.
A Novel Image Tag Completion Method Based on Convolutional Neural Transformation
Geng, Yanyan
2017-10-24
In the problems of image retrieval and annotation, complete textual tag lists of images play critical roles. However, in real-world applications, the image tags are usually incomplete, thus it is important to learn the complete tags for images. In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN). The method estimates the complete tags from the convolutional filtering outputs of images based on a linear predictor. The CNN parameters, linear predictor, and the complete tags are learned jointly by our method. We build a minimization problem to encourage the consistency between the complete tags and the available incomplete tags, reduce the estimation error, and reduce the model complexity. An iterative algorithm is developed to solve the minimization problem. Experiments over benchmark image data sets show its effectiveness.
REAL-TIME VIDEO SCALING BASED ON CONVOLUTION NEURAL NETWORK ARCHITECTURE
Directory of Open Access Journals (Sweden)
S Safinaz
2017-08-01
Full Text Available In recent years, video super resolution techniques becomes mandatory requirements to get high resolution videos. Many super resolution techniques researched but still video super resolution or scaling is a vital challenge. In this paper, we have presented a real-time video scaling based on convolution neural network architecture to eliminate the blurriness in the images and video frames and to provide better reconstruction quality while scaling of large datasets from lower resolution frames to high resolution frames. We compare our outcomes with multiple exiting algorithms. Our extensive results of proposed technique RemCNN (Reconstruction error minimization Convolution Neural Network shows that our model outperforms the existing technologies such as bicubic, bilinear, MCResNet and provide better reconstructed motioning images and video frames. The experimental results shows that our average PSNR result is 47.80474 considering upscale-2, 41.70209 for upscale-3 and 36.24503 for upscale-4 for Myanmar dataset which is very high in contrast to other existing techniques. This results proves our proposed model real-time video scaling based on convolution neural network architecture’s high efficiency and better performance.
Min, Xu; Zeng, Wanwen; Chen, Ning; Chen, Ting; Jiang, Rui
2017-07-15
Experimental techniques for measuring chromatin accessibility are expensive and time consuming, appealing for the development of computational approaches to predict open chromatin regions from DNA sequences. Along this direction, existing methods fall into two classes: one based on handcrafted k -mer features and the other based on convolutional neural networks. Although both categories have shown good performance in specific applications thus far, there still lacks a comprehensive framework to integrate useful k -mer co-occurrence information with recent advances in deep learning. We fill this gap by addressing the problem of chromatin accessibility prediction with a convolutional Long Short-Term Memory (LSTM) network with k -mer embedding. We first split DNA sequences into k -mers and pre-train k -mer embedding vectors based on the co-occurrence matrix of k -mers by using an unsupervised representation learning approach. We then construct a supervised deep learning architecture comprised of an embedding layer, three convolutional layers and a Bidirectional LSTM (BLSTM) layer for feature learning and classification. We demonstrate that our method gains high-quality fixed-length features from variable-length sequences and consistently outperforms baseline methods. We show that k -mer embedding can effectively enhance model performance by exploring different embedding strategies. We also prove the efficacy of both the convolution and the BLSTM layers by comparing two variations of the network architecture. We confirm the robustness of our model to hyper-parameters by performing sensitivity analysis. We hope our method can eventually reinforce our understanding of employing deep learning in genomic studies and shed light on research regarding mechanisms of chromatin accessibility. The source code can be downloaded from https://github.com/minxueric/ismb2017_lstm . tingchen@tsinghua.edu.cn or ruijiang@tsinghua.edu.cn. Supplementary materials are available at
International Nuclear Information System (INIS)
Jackson, V.L.
2011-01-01
The primary purpose of the tank mixing and sampling demonstration program is to mitigate the technical risks associated with the ability of the Hanford tank farm delivery and celtification systems to measure and deliver a uniformly mixed high-level waste (HLW) feed to the Waste Treatment and Immobilization Plant (WTP) Uniform feed to the WTP is a requirement of 24590-WTP-ICD-MG-01-019, ICD-19 - Interface Control Document for Waste Feed, although the exact definition of uniform is evolving in this context. Computational Fluid Dynamics (CFD) modeling has been used to assist in evaluating scaleup issues, study operational parameters, and predict mixing performance at full-scale.
DEFF Research Database (Denmark)
Kaulio, Matti; Thorén, Kent; Rohrbeck, René
2017-01-01
We leverage the business model innovation and ambidexterity literature to investigate a contradictory case, the Swedish-Finnish Telecom operator TeliaSonera. Despite being challenged by three major disruptions, the company not only still exists but also enjoys remarkably good financial performance....... Building on extant archival data and interviews, we carefully identify and map 26 organizational responses during 1992–2016. We find that the firm has overcome three critical phases by experimenting and pioneering with portfolios of business models and/or technological innovations. We describe...... this behaviour as double ambidexterity. We use an in-depth case study to conceptualize double ambidexterity and discuss its impact on the business's survival and enduring success....
Metaheuristic Algorithms for Convolution Neural Network.
Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni
2016-01-01
A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).
Metaheuristic Algorithms for Convolution Neural Network
Directory of Open Access Journals (Sweden)
L. M. Rasdi Rere
2016-01-01
Full Text Available A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN, a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent.
Microaneurysm detection using fully convolutional neural networks.
Chudzik, Piotr; Majumdar, Somshubra; Calivá, Francesco; Al-Diri, Bashir; Hunter, Andrew
2018-05-01
Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three. Furthermore, to the best of the authors' knowledge, this is the first paper that shows how to successfully transfer knowledge between datasets in the microaneurysm detection domain. The proposed method was evaluated using three publicly available and widely used datasets: E-Ophtha, DIARETDB1, and ROC. It achieved better results than state-of-the-art methods using the FROC metric. The proposed algorithm accomplished highest sensitivities for low false positive rates, which is particularly important for screening purposes. Performance, simplicity, and robustness of the proposed method demonstrates its suitability for diabetic retinopathy screening applications. Copyright © 2018 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
ARIF A. EBRAHEEM AL-QASSAR
2008-12-01
Full Text Available The design of the re-entry space vehicles and high-speed aircrafts requires special attention to the nonlinear thermoelastic and aerodynamic instabilities of their structural components. The thermal effects are important since temperature environment influences significantly the static and dynamic behaviors of flight structures in supersonic/hypersonic regimes. To contribute to the understanding of dynamic behavior of these “hot” structures, a double-wedge lifting surface with combined freeplay and cubic stiffening structural nonlinearities in both plunging and pitching degrees-of-freedom operating in supersonic/hypersonic flight speed regimes has been analyzed. A third order Piston Theory Aerodynamics is used to evaluate the applied nonlinear unsteady aerodynamic loads. The loss of torsional stiffness that may be incurred by lifting surfaces subjected to axial stresses induced by aerodynamic heating is also considered. The aerodynamic heating effect is estimated based on the adiabatic wall temperature due to high speed airstreams. Modelling issues as well as simulation results have been presented and pertinent conclusions outlined. It is highlighted that a serious loss of torsional stiffness may induce the dynamic instability of the lifting surfaces. The influence of various parameters such as flight condition, thickness ratio, freeplays and pitching stiffness nonlinearity are also discussed.
Directory of Open Access Journals (Sweden)
Jaume Freire-González
2018-02-01
Full Text Available An environmental fiscal reform (EFR represents a transition of a taxation system toward one based in environmental taxation, rather than on taxation of capital, labor, or consumption. It differs from an environmental tax reform (ETR in that an EFR also includes a reform of subsidies which counteract environmental policy. This research details different ways in which an EFR is not only possible but also a good option that provides economic and environmental benefits. We have developed a detailed dynamic CGE model examining 101 industries and commodities in Spain, with an energy and an environmental extension comprising 31 pollutant emissions, in order to simulate the economic and environmental effects of an EFR. The reform focuses on 39 industries related to the energy, water, transport and waste sectors. We simulate an increase in taxes and a reduction on subsidies for these industries and at the same time we use new revenues to reduce labor, capital and consumption taxes. All revenue recycling options provide both economic and environmental benefits, suggesting that the “double dividend” hypothesis can be achieved. After three to four years after implementing an EFR, GDP is higher than the base case, hydrocarbons consumption declines and all analyzed pollutants show a reduction.
A double-superconducting axial bearing system for an energy storage flywheel model
Deng, Z.; Lin, Q.; Ma, G.; Zheng, J.; Zhang, Y.; Wang, S.; Wang, J.
2008-02-01
The bulk high temperature superconductors (HTSCs) with unique flux-pinning property have been applied to fabricate two superconducting axial bearings for an energy storage flywheel model. The two superconducting axial bearings are respectively fixed at two ends of the vertical rotational shaft, whose stator is composed of seven melt-textured YBa2Cu3O7-x (YBCO) bulks with diameter of 30 mm, height of 18 mm and rotor is made of three cylindrical axial-magnetized NdFeB permanent magnets (PM) by superposition with diameter of 63 mm, height of 27 mm. The experimental results show the total levitation and lateral force produced by the two superconducting bearings are enough to levitate and stabilize the 2.4 kg rotational shaft. When the two YBCO stators were both field cooled to the liquid nitrogen temperature at respective axial distances above or below the PM rotor, the shaft could be automatically levitated between the two stators without any contact. In the case of a driving motor, it can be stably rotated along the central axis besides the resonance frequency. This double-superconducting axial bearing system can be used to demonstrate the flux-pinning property of bulk HTSC for stable levitation and suspension and the principle of superconducting flywheel energy storage system to visitors.
The entanglement between two isolated atoms in the double mode–mode competition model
International Nuclear Information System (INIS)
Qin, Wu; Mao-Fa, Fang; Yao-Hua, Hu; Jian-Wu, Cai
2009-01-01
Extending the double Jaynes–Cummings model to a more complicated case where the mode–mode competition is considered, we investigate the entanglement character of two isolated atoms by means of concurrence, and discuss the dependence of atom–atom entanglement on the different initial state and the relative coupling strength between the atom and the corresponding cavity field. The results show that the amplitude and the period of the atom–atom entanglement evolution can be controlled by the choice of initial state and relative coupling strength, respectively. We find that the phenomenon of entanglement sudden death (ESD) is sensitive to the initial conditions. The length of the time interval for zero entanglement depends not only on the initial degree of entanglement between two atoms but also on the relative coupling strength of atom–field interaction. The ESD effect can be weakened by enhancing the mode–mode competition between the three- and single-photon processes. (classical areas of phenomenology)
Chen, Yongchao; Zhu, Youzhi; Zhang, Yu; Zhang, Zixuan; Lian, Juan; Luo, Fucheng; Deng, Xuefei; Wong, Kelvin K L
2016-02-06
Double injection of blood into cisterna magna using a rabbit model results in cerebral vasospasm. An unacceptably high mortality rate tends to limit the application of model. Ultrasound guided puncture can provide real-time imaging guidance for operation. The aim of this paper is to establish a safe and effective rabbit model of cerebral vasospasm after subarachnoid hemorrhage with the assistance of ultrasound medical imaging. A total of 160 New Zealand white rabbits were randomly divided into four groups of 40 each: (1) manual control group, (2) manual model group, (3) ultrasound guided control group, and (4) ultrasound guided model group. The subarachnoid hemorrhage was intentionally caused by double injection of blood into their cisterna magna. Then, basilar artery diameters were measured using magnetic resonance angiography before modeling and 5 days after modeling. The depth of needle entering into cisterna magna was determined during the process of ultrasound guided puncture. The mortality rates in manual control group and model group were 15 and 23 %, respectively. No rabbits were sacrificed in those two ultrasound guided groups. We found that the mortality rate in ultrasound guided groups decreased significantly compared to manual groups. Compared with diameters before modeling, the basilar artery diameters after modeling were significantly lower in manual and ultrasound guided model groups. The vasospasm aggravated and the proportion of severe vasospasms was greater in ultrasound guided model group than that of manual group. In manual model group, no vasospasm was found in 8 % of rabbits. The ultrasound guided double injection of blood into cisterna magna is a safe and effective rabbit model for treatment of cerebral vasospasm.
Some new contributions to neutrinoless double β-decay in an SU(2)xU(1) model
International Nuclear Information System (INIS)
Escobar, C.O.
1982-11-01
An SU(2) x U(1) model having both Dirac and Majorana mass terms for the neutrinos, with an extended Higgs sector without natural flavor conservation is considered. Under these conditions, it is shown that for a certain range of the mass parameters of the model, some new contributions become important for the neutrinoless double β-decay (ββ)oν. (Author) [pt
Real-time hybrid simulation using the convolution integral method
International Nuclear Information System (INIS)
Kim, Sung Jig; Christenson, Richard E; Wojtkiewicz, Steven F; Johnson, Erik A
2011-01-01
This paper proposes a real-time hybrid simulation method that will allow complex systems to be tested within the hybrid test framework by employing the convolution integral (CI) method. The proposed CI method is potentially transformative for real-time hybrid simulation. The CI method can allow real-time hybrid simulation to be conducted regardless of the size and complexity of the numerical model and for numerical stability to be ensured in the presence of high frequency responses in the simulation. This paper presents the general theory behind the proposed CI method and provides experimental verification of the proposed method by comparing the CI method to the current integration time-stepping (ITS) method. Real-time hybrid simulation is conducted in the Advanced Hazard Mitigation Laboratory at the University of Connecticut. A seismically excited two-story shear frame building with a magneto-rheological (MR) fluid damper is selected as the test structure to experimentally validate the proposed method. The building structure is numerically modeled and simulated, while the MR damper is physically tested. Real-time hybrid simulation using the proposed CI method is shown to provide accurate results
Bilinear Convolutional Neural Networks for Fine-grained Visual Recognition.
Lin, Tsung-Yu; RoyChowdhury, Aruni; Maji, Subhransu
2017-07-04
We present a simple and effective architecture for fine-grained recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 84.5% and 91.3% per-image accuracy on the Caltech-UCSD birds [66], NABirds [63], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU. We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture. Finally, we present visualizations of these models on various datasets using top activations of neural units and gradient-based inversion techniques. The source code for the complete system is available at http://vis-www.cs.umass.edu/bcnn.
Image reconstruction in computerized tomography using the convolution method
International Nuclear Information System (INIS)
Oliveira Rebelo, A.M. de.
1984-03-01
In the present work an algoritin was derived, using the analytical convolution method (filtered back-projection) for two-dimensional or three-dimensional image reconstruction in computerized tomography applied to non-destructive testing and to the medical use. This mathematical model is based on the analytical Fourier transform method for image reconstruction. This model consists of a discontinuous system formed by an NxN array of cells (pixels). The attenuation in the object under study of a colimated gamma ray beam has been determined for various positions and incidence angles (projections) in terms of the interaction of the beam with the intercepted pixels. The contribution of each pixel to beam attenuation was determined using the weight function W ij which was used for simulated tests. Simulated tests using standard objects with attenuation coefficients in the range of 0,2 to 0,7 cm -1 were carried out using cell arrays of up to 25x25. One application was carried out in the medical area simulating image reconstruction of an arm phantom with attenuation coefficients in the range of 0,2 to 0,5 cm -1 using cell arrays of 41x41. The simulated results show that, in objects with a great number of interfaces and great variations of attenuation coefficients at these interfaces, a good reconstruction is obtained with the number of projections equal to the reconstruction matrix dimension. A good reconstruction is otherwise obtained with fewer projections. (author) [pt
Reconstruction of Micropattern Detector Signals using Convolutional Neural Networks
Flekova, L.; Schott, M.
2017-10-01
Micropattern gaseous detector (MPGD) technologies, such as GEMs or MicroMegas, are particularly suitable for precision tracking and triggering in high rate environments. Given their relatively low production costs, MPGDs are an exemplary candidate for the next generation of particle detectors. Having acknowledged these advantages, both the ATLAS and CMS collaborations at the LHC are exploiting these new technologies for their detector upgrade programs in the coming years. When MPGDs are utilized for triggering purposes, the measured signals need to be precisely reconstructed within less than 200 ns, which can be achieved by the usage of FPGAs. In this work, we present a novel approach to identify reconstructed signals, their timing and the corresponding spatial position on the detector. In particular, we study the effect of noise and dead readout strips on the reconstruction performance. Our approach leverages the potential of convolutional neural network (CNNs), which have recently manifested an outstanding performance in a range of modeling tasks. The proposed neural network architecture of our CNN is designed simply enough, so that it can be modeled directly by an FPGA and thus provide precise information on reconstructed signals already in trigger level.
Convolutional neural network with transfer learning for rice type classification
Patel, Vaibhav Amit; Joshi, Manjunath V.
2018-04-01
Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.
Transfer Learning with Convolutional Neural Networks for SAR Ship Recognition
Zhang, Di; Liu, Jia; Heng, Wang; Ren, Kaijun; Song, Junqiang
2018-03-01
Ship recognition is the backbone of marine surveillance systems. Recent deep learning methods, e.g. Convolutional Neural Networks (CNNs), have shown high performance for optical images. Learning CNNs, however, requires a number of annotated samples to estimate numerous model parameters, which prevents its application to Synthetic Aperture Radar (SAR) images due to the limited annotated training samples. Transfer learning has been a promising technique for applications with limited data. To this end, a novel SAR ship recognition method based on CNNs with transfer learning has been developed. In this work, we firstly start with a CNNs model that has been trained in advance on Moving and Stationary Target Acquisition and Recognition (MSTAR) database. Next, based on the knowledge gained from this image recognition task, we fine-tune the CNNs on a new task to recognize three types of ships in the OpenSARShip database. The experimental results show that our proposed approach can obviously increase the recognition rate comparing with the result of merely applying CNNs. In addition, compared to existing methods, the proposed method proves to be very competitive and can learn discriminative features directly from training data instead of requiring pre-specification or pre-selection manually.
Analytical Solutions of a Model for Brownian Motion in the Double Well Potential
International Nuclear Information System (INIS)
Liu Ai-Jie; Zheng Lian-Cun; Zhang Xin-Xin; Ma Lian-Xi
2015-01-01
In this paper, the analytical solutions of Schrödinger equation for Brownian motion in a double well potential are acquired by the homotopy analysis method and the Adomian decomposition method. Double well potential for Brownian motion is always used to obtain the solutions of Fokker—Planck equation known as the Klein—Kramers equation, which is suitable for separation and additive Hamiltonians. In essence, we could study the random motion of Brownian particles by solving Schrödinger equation. The analytical results obtained from the two different methods agree with each other well. The double well potential is affected by two parameters, which are analyzed and discussed in details with the aid of graphical illustrations. According to the final results, the shapes of the double well potential have significant influence on the probability density function. (general)
A finite element model for independent wire rope core with double ...
Indian Academy of Sciences (India)
1Istanbul Technical University, Institute of Informatics, Computational Science and ... a little work has been done using the double helical geometry and real ... Finite element approach has been embedded in wire rope analysis since 1999.
Double folding model of nucleus-nucleus potential: formulae, iteration method and computer code
International Nuclear Information System (INIS)
Luk'yanov, K.V.
2008-01-01
Method of construction of the nucleus-nucleus double folding potential is described. Iteration procedure for the corresponding integral equation is presented. Computer code and numerical results are presented
Fast pencil beam dose calculation for proton therapy using a double-Gaussian beam model
Directory of Open Access Journals (Sweden)
Joakim eda Silva
2015-12-01
Full Text Available The highly conformal dose distributions produced by scanned proton pencil beams are more sensitive to motion and anatomical changes than those produced by conventional radiotherapy. The ability to calculate the dose in real time as it is being delivered would enable, for example, online dose monitoring, and is therefore highly desirable. We have previously described an implementation of a pencil beam algorithm running on graphics processing units (GPUs intended specifically for online dose calculation. Here we present an extension to the dose calculation engine employing a double-Gaussian beam model to better account for the low-dose halo. To the best of our knowledge, it is the first such pencil beam algorithm for proton therapy running on a GPU. We employ two different parametrizations for the halo dose, one describing the distribution of secondary particles from nuclear interactions found in the literature and one relying on directly fitting the model to Monte Carlo simulations of pencil beams in water. Despite the large width of the halo contribution, we show how in either case the second Gaussian can be included whilst prolonging the calculation of the investigated plans by no more than 16%, or the calculation of the most time-consuming energy layers by about 25%. Further, the calculation time is relatively unaffected by the parametrization used, which suggests that these results should hold also for different systems. Finally, since the implementation is based on an algorithm employed by a commercial treatment planning system, it is expected that with adequate tuning, it should be able to reproduce the halo dose from a general beam line with sufficient accuracy.
Callari, C.; Federico, F.
2000-04-01
Laboratory consolidation of structured clayey soils is analysed in this paper. The research is carried out by two different methods. The first one treats the soil as an isotropic homogeneous equivalent Double Porosity (DP) medium. The second method rests on the extensive application of the Finite Element Method (FEM) to combinations of different soils, composing 2D or fully 3D ordered structured media that schematically discretize the complex material. Two reference problems, representing typical situations of 1D laboratory consolidation of structured soils, are considered. For each problem, solution is obtained through integration of the equations governing the consolidation of the DP medium as well as via FEM applied to the ordered schemes composed of different materials. The presence of conventional experimental devices to ensure the drainage of the sample is taken into account through appropriate boundary conditions. Comparison of FEM results with theoretical results clearly points out the ability of the DP model to represent consolidation processes of structurally complex soils. Limits of applicability of the DP model may arise when the rate of fluid exchange between the two porous systems is represented through oversimplified relations. Results of computations, obtained having assigned reasonable values to the meso-structural and to the experimental apparatus parameters, point out that a partially efficient drainage apparatus strongly influences the distribution along the sample and the time evolution of the interstitial water pressure acting in both systems of pores. Data of consolidation tests in a Rowe's cell on samples of artificially fissured clays reported in the literature are compared with the analytical and numerical results showing a significant agreement.
Krieter, Detlef H; Lange, Florian; Lemke, Horst-Dieter; Bonn, Florian; Wanner, Christoph
2018-04-01
Technical problems during clinical lipid apheresis interfere with fractionator performance. Therefore, a large animal model was established to characterize a new plasma fractionation membrane. Four sheep were randomized, controlled, and crossover subjected to double ofiltration lipoprotein apheresis with three specimens of FractioPES R having slightly different HDL sieving coefficients (S K ) (FPESa, 0.30, FPESb, 0.26, and FPESc, 0.22) versus a control fractionator (EVAL). S K and reduction ratios were determined for LDL, HDL, fibrinogen, IgG, and albumin. Compared to EVAL (0.42 ± 0.04 to 0.74 ± 0.08) and FPESa (0.36 ± 0.06 to 0.64 ± 0.04), S K for HDL were lower (P < 0.05) with FPESc (0.30 ± 0.04 to 0.49 ± 0.10). Fibrinogen S K were higher (P < 0.05) with EVAL (0.02 ± 0.01 to 0.40 ± 0.08) compared to FPESb (0.05 ± 0.02 to 0.26 ± 0.34) and FPESc (0.01 ± 0.01 to 0.21 ± 0.16). No further differences were determined. The animal model distinguished between minor differences in fractionation membrane permeability, demonstrating equivalent sieving of FPESa and EVAL and slightly inferior permeability of FPESb and FPESc. © 2018 International Society for Apheresis, Japanese Society for Apheresis, and Japanese Society for Dialysis Therapy.
Probing new physics models of neutrinoless double beta decay with SuperNEMO
Energy Technology Data Exchange (ETDEWEB)
Arnold, R. [CNRS/IN2P3, IPHC, Universite de Strasbourg, Strasbourg (France); Augier, C.; Bongrand, M.; Garrido, X.; Jullian, S.; Sarazin, X.; Simard, L. [CNRS/IN2P3, LAL, Universite Paris-Sud 11, Orsay (France); Baker, J.; Caffrey, A.J.; Horkley, J.J.; Riddle, C.L. [INL, Idaho Falls, ID (United States); Barabash, A.S.; Konovalov, S.I.; Umatov, V.I.; Vanyushin, I.A. [Institute of Theoretical and Experimental Physics, Moscow (Russian Federation); Basharina-Freshville, A.; Evans, J.J.; Flack, R.; Holin, A.; Kauer, M.; Richards, B.; Saakyan, R.; Thomas, J.; Vasiliev, V.; Waters, D. [University College London, London (United Kingdom); Brudanin, V.; Egorov, V.; Kochetov, O.; Nemchenok, I.; Timkin, V.; Tretyak, V.; Vasiliev, R. [Joint Institute for Nuclear Research, Dubna (Russian Federation); Cebrian, S.; Dafni, T.; Irastorza, I.G.; Gomez, H.; Iguaz, F.J.; Luzon, G.; Rodriguez, A. [University of Zaragoza, Zaragoza (Spain); Chapon, A.; Durand, D.; Guillon, B.; Mauger, F. [Universite de Caen, LPC Caen, ENSICAEN, Caen (France); Chauveau, E.; Hubert, P.; Hugon, C.; Lutter, G.; Marquet, C.; Nachab, A.; Nguyen, C.H.; Perrot, F.; Piquemal, F.; Ricol, J.S. [UMR 5797, Universite de Bordeaux, Centre d' Etudes Nucleaires de Bordeaux Gradignan, Gradignan (France); UMR 5797, CNRS/IN2P3, Centre d' Etudes Nucleaires de Bordeaux Gradignan, Gradignan (France); Deppisch, F.F.; Jackson, C.M.; Nasteva, I.; Soeldner-Rembold, S. [Univ. of Manchester (United Kingdom); Diaz, J.; Monrabal, F.; Serra, L.; Yahlali, N. [CSIC - Univ. de Valencia, IFIC (Spain); Fushima, K.I. [Tokushima Univ., Tokushima (Japan); Holy, K.; Povinec, P.P.; Simkovic, F. [Comenius Univ., FMFI, Bratislava (Slovakia); Ishihara, N. [KEK, Tsukuba, Ibaraki (Japan); Kovalenko, V. [CNRS/IN2P3, IPHC, Univ. de Strasbourg (France); Joint Inst. for Nuclear Research, Dubna (Russian Federation); Lamhamdi, T. [USMBA, Fes (Morocco); Lang, K.; Pahlka, R.B. [Univ. of Texas, Austin, TX (United States)] (and others)
2010-12-15
The possibility to probe new physics scenarios of light Majorana neutrino exchange and right-handed currents at the planned next generation neutrinoless double {beta} decay experiment SuperNEMO is discussed. Its ability to study different isotopes and track the outgoing electrons provides the means to discriminate different underlying mechanisms for the neutrinoless double {beta} decay by measuring the decay half-life and the electron angular and energy distributions. (orig.)
Directory of Open Access Journals (Sweden)
Qingshan Liu
2017-12-01
Full Text Available This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM network to automatically learn the spectral-spatial features from hyperspectral images (HSIs. In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN, a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center. The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.
Forecasting short-term data center network traffic load with convolutional neural networks
Ordozgoiti, Bruno; Gómez-Canaval, Sandra
2018-01-01
Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution. PMID:29408936
Forecasting short-term data center network traffic load with convolutional neural networks.
Mozo, Alberto; Ordozgoiti, Bruno; Gómez-Canaval, Sandra
2018-01-01
Efficient resource management in data centers is of central importance to content service providers as 90 percent of the network traffic is expected to go through them in the coming years. In this context we propose the use of convolutional neural networks (CNNs) to forecast short-term changes in the amount of traffic crossing a data center network. This value is an indicator of virtual machine activity and can be utilized to shape the data center infrastructure accordingly. The behaviour of network traffic at the seconds scale is highly chaotic and therefore traditional time-series-analysis approaches such as ARIMA fail to obtain accurate forecasts. We show that our convolutional neural network approach can exploit the non-linear regularities of network traffic, providing significant improvements with respect to the mean absolute and standard deviation of the data, and outperforming ARIMA by an increasingly significant margin as the forecasting granularity is above the 16-second resolution. In order to increase the accuracy of the forecasting model, we exploit the architecture of the CNNs using multiresolution input distributed among separate channels of the first convolutional layer. We validate our approach with an extensive set of experiments using a data set collected at the core network of an Internet Service Provider over a period of 5 months, totalling 70 days of traffic at the one-second resolution.
Li, Siqi; Jiang, Huiyan; Pang, Wenbo
2017-05-01
Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.
A pre-trained convolutional neural network based method for thyroid nodule diagnosis.
Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing
2017-01-01
In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.
Design and Implementation of Convolutional Encoder and Viterbi Decoder Using FPGA.
Directory of Open Access Journals (Sweden)
Riham Ali Zbaid
2018-01-01
Full Text Available Keeping the fineness of data is the most significant thing in communication.There are many factors that affect the accuracy of the data when it is transmitted over the communication channel such as noise etc. to overcome these effects are encoding channels encryption.In this paper is used for one type of channel coding is convolutional codes. Convolution encoding is a Forward Error Correction (FEC method used in incessant one-way and real time communication links .It can offer a great development in the error bit rates so that small, low energy, and devices cheap transmission when used in applications such as satellites. In this paper highlight the design, simulation and implementation of convolution encoder and Viterbi decoder by using MATLAB- program (2011. SIMULINK HDL coder is used to convert MATLAB-SIMULINK models to VHDL using plates Altera Cyclone II code DE2-70. Simulation and evaluation of the implementation of the results coincided with the results of the design show the coinciding with the designed results.
Directory of Open Access Journals (Sweden)
ZHENG Zhuo
2018-05-01
Full Text Available High resolution remote sensing imagery scene classification is important for automatic complex scene recognition, which is the key technology for military and disaster relief, etc. In this paper, we propose a novel joint multi-scale convolution neural network (JMCNN method using a limited amount of image data for high resolution remote sensing imagery scene classification. Different from traditional convolutional neural network, the proposed JMCNN is an end-to-end training model with joint enhanced high-level feature representation, which includes multi-channel feature extractor, joint multi-scale feature fusion and Softmax classifier. Multi-channel and scale convolutional extractors are used to extract scene middle features, firstly. Then, in order to achieve enhanced high-level feature representation in a limit dataset, joint multi-scale feature fusion is proposed to combine multi-channel and scale features using two feature fusions. Finally, enhanced high-level feature representation can be used for classification by Softmax. Experiments were conducted using two limit public UCM and SIRI datasets. Compared to state-of-the-art methods, the JMCNN achieved improved performance and great robustness with average accuracies of 89.3% and 88.3% on the two datasets.
Identification of double-yolked duck egg using computer vision.
Directory of Open Access Journals (Sweden)
Long Ma
Full Text Available The double-yolked (DY egg is quite popular in some Asian countries because it is considered as a sign of good luck, however, the double yolk is one of the reasons why these eggs fail to hatch. The usage of automatic methods for identifying DY eggs can increase the efficiency in the poultry industry by decreasing egg loss during incubation or improving sale proceeds. In this study, two methods for DY duck egg identification were developed by using computer vision technology. Transmittance images of DY and single-yolked (SY duck eggs were acquired by a CCD camera to identify them according to their shape features. The Fisher's linear discriminant (FLD model equipped with a set of normalized Fourier descriptors (NFDs extracted from the acquired images and the convolutional neural network (CNN model using primary preprocessed images were built to recognize duck egg yolk types. The classification accuracies of the FLD model for SY and DY eggs were 100% and 93.2% respectively, while the classification accuracies of the CNN model for SY and DY eggs were 98% and 98.8% respectively. The CNN-based algorithm took about 0.12 s to recognize one sample image, which was slightly faster than the FLD-based (about 0.20 s. Finally, this work compared two classification methods and provided the better method for DY egg identification.
Krasilnikov, P M
2014-01-01
The potential energy cross-section profile along a hydrogen bond may contain two minima in certain conditions; it is so-called a double well potential. The H-bond double well potential is essential for proton transfer along this hydrogen bond. We have considered the two-dimensional model of such double well potential in harmonic approximation, and we have also investigated the proton tunneling in it. In real environments thermal motion of atoms or conformational changes may cause reorientation and relative shift of molecule fragment forming the hydrogen bond and, as a result, the hydrogen bond isdeformed. This deformation is liable to change the double well potential form and, hence, the probability of the proton tunneling is changed too. As it is shown the characteristic time of proton tunneling is essentially increased by even small relative shift of heavy atoms forming the H-bond and also rotational displacement of covalent bond generated by one of heavy atoms and the proton (hydrogen atom). However, it is also shown, at the certain geometry of the H-bond deformation the opposite effect occurred, i.e., the characteristic time is not increased and even decreased. Notice that such its behavior arises from two-dimensionality of potential wells; this and other properties of our model are discussed in detail.
Results on neutrinoless double beta decay search in GERDA. Background modeling and limit setting
Energy Technology Data Exchange (ETDEWEB)
Becerici Schmidt, Neslihan
2014-07-22
The search for the neutrinoless double beta decay (0νββ) process is primarily motivated by its potential of revealing the possible Majorana nature of the neutrino, in which the neutrino is identical to its antiparticle. It has also the potential to yield information on the intrinsic properties of neutrinos, if the underlying mechanism is the exchange of a light Majorana neutrino. The Gerda experiment is searching for 0νββ decay of {sup 76}Ge by operating high purity germanium (HPGe) detectors enriched in the isotope {sup 76}Ge (∝ 87%), directly in ultra-pure liquid argon (LAr). The first phase of physics data taking (Phase I) was completed in 2013 and has yielded 21.6 kg.yr of data. A background index of B∼10{sup -2} cts/(keV.kg.yr) at Q{sub ββ}=2039 keV has been achieved. A comprehensive background model of the Phase I energy spectrum is presented as the major topic of this dissertation. Decomposition of the background energy spectrum into the individual contributions from different processes provides many interesting physics results. The specific activity of {sup 39}Ar has been determined. The obtained result, A=(1.15±0.11) Bq/kg, is in good agreement with the values reported in literature. The contribution from {sup 42}K decays in LAr to the background spectrum has yielded a {sup 42}K({sup 42}Ar) specific activity of A=(106.2{sub -19.2}{sup +12.7}) μBq/kg, for which only upper limits exist in literature. The analysis of high energy events induced by α decays in the {sup 226}Ra chain indicated a total {sup 226}Ra activity of (3.0±0.9) μBq and a total initial {sup 210}Po activity of (0.18±0.01) mBq on the p{sup +} surfaces of the enriched semi-coaxial HPGe detectors. The half life of the two-neutrino double beta (2νββ) decay of {sup 76}Ge has been determined as T{sub 1/2}{sup 2ν}=(1.926±0.094).10{sup 21} yr, which is in good agreement with the result that was obtained with lower exposure and has been published by the Gerda collaboration
Results on neutrinoless double beta decay search in GERDA. Background modeling and limit setting
International Nuclear Information System (INIS)
Becerici Schmidt, Neslihan
2014-01-01
The search for the neutrinoless double beta decay (0νββ) process is primarily motivated by its potential of revealing the possible Majorana nature of the neutrino, in which the neutrino is identical to its antiparticle. It has also the potential to yield information on the intrinsic properties of neutrinos, if the underlying mechanism is the exchange of a light Majorana neutrino. The Gerda experiment is searching for 0νββ decay of 76 Ge by operating high purity germanium (HPGe) detectors enriched in the isotope 76 Ge (∝ 87%), directly in ultra-pure liquid argon (LAr). The first phase of physics data taking (Phase I) was completed in 2013 and has yielded 21.6 kg.yr of data. A background index of B∼10 -2 cts/(keV.kg.yr) at Q ββ =2039 keV has been achieved. A comprehensive background model of the Phase I energy spectrum is presented as the major topic of this dissertation. Decomposition of the background energy spectrum into the individual contributions from different processes provides many interesting physics results. The specific activity of 39 Ar has been determined. The obtained result, A=(1.15±0.11) Bq/kg, is in good agreement with the values reported in literature. The contribution from 42 K decays in LAr to the background spectrum has yielded a 42 K( 42 Ar) specific activity of A=(106.2 -19.2 +12.7 ) μBq/kg, for which only upper limits exist in literature. The analysis of high energy events induced by α decays in the 226 Ra chain indicated a total 226 Ra activity of (3.0±0.9) μBq and a total initial 210 Po activity of (0.18±0.01) mBq on the p + surfaces of the enriched semi-coaxial HPGe detectors. The half life of the two-neutrino double beta (2νββ) decay of 76 Ge has been determined as T 1/2 2ν =(1.926±0.094).10 21 yr, which is in good agreement with the result that was obtained with lower exposure and has been published by the Gerda collaboration. According to the model, the background in Q ββ ±5 keV window is resulting from close
DEFF Research Database (Denmark)
Liu, Mingzhe; Wittchen, Kim Bjarne; Heiselberg, Per
2012-01-01
The project aims at developing simplified calculation methods for the different features that influence energy demand and indoor environment behind “intelligent” glazed façades. This paper describes how to set up simplified model to calculate the thermal and solar properties (U and g value......) together with comfort performance (internal surface temperature of the glazing) of a double glazing unit. Double glazing unit is defined as 1D model with nodes representing different layers of material. Several models with different number of nodes and position of these are compared and verified in order...... to find a simplified method which can calculate the performance as accurately as possible. The calculated performance in terms of internal surface temperature is verified with experimental data collected in a full-scale façade element test facility at Aalborg University (DK). The advantage...
Niu, Xuming; Sun, Zhigang; Song, Yingdong
2017-11-01
In this thesis, a double-scale model for 3 Dimension-4 directional(3D-4d) braided C/SiC composites(CMCs) has been proposed to investigate mechanical properties of it. The double-scale model involves micro-scale which takes fiber/matrix/porosity in fibers tows into consideration and the unit cell scale which considers the 3D-4d braiding structure. Basing on the Micro-optical photographs of composite, we can build a parameterized finite element model that reflects structure of 3D-4d braided composites. The mechanical properties of fiber tows in transverse direction are studied by combining the crack band theory for matrix cracking and cohesive zone model for interface debonding. Transverse tensile process of 3D-4d CMCs can be simulated by introducing mechanical properties of fiber tows into finite element of 3D-4d braided CMCs. Quasi-static tensile tests of 3D-4d braided CMCs have been performed with PWS-100 test system. The predicted tensile stress-strain curve by the double scale model finds good agreement with the experimental results.
The k-ε-fP model applied to double wind turbine wakes using different actuator disk force methods
DEFF Research Database (Denmark)
Laan, van der, Paul Maarten; Sørensen, Niels N.; Réthoré, Pierre-Elouan
2015-01-01
The newly developed k-ε-fP eddy viscosity model is applied to double wind turbine wake configurations in a neutral atmospheric boundary layer, using a Reynolds-Averaged Navier–Stokes solver. The wind turbines are represented by actuator disks. A proposed variable actuator disk force method...... two methods overpredict it. The results of the k-ε-fP eddy viscosity model are also compared with the original k-ε eddy viscosity model and large-eddy simulations. Compared to the large-eddy simulations-predicted velocity and power deficits, the k-ε-fP is superior to the original k-ε model...
Liu, Yang; Gao, Bo; Gong, Min; Shi, Ruiying
2017-06-01
The influence of a GaN layer as a sub-quantum well for an AlGaN/GaN/AlGaN double barrier resonant tunneling diode (RTD) on device performance has been investigated by means of numerical simulation. The introduction of the GaN layer as the sub-quantum well turns the dominant transport mechanism of RTD from the 3D-2D model to the 2D-2D model and increases the energy difference between tunneling energy levels. It can also lower the effective height of the emitter barrier. Consequently, the peak current and peak-to-valley current difference of RTD have been increased. The optimal GaN sub-quantum well parameters are found through analyzing the electrical performance, energy band, and transmission coefficient of RTD with different widths and depths of the GaN sub-quantum well. The most pronounced electrical parameters, a peak current density of 5800 KA/cm2, a peak-to-valley current difference of 1.466 A, and a peak-to-valley current ratio of 6.35, could be achieved by designing RTD with the active region structure of GaN/Al0.2Ga0.8 N/GaN/Al0.2Ga0.8 N (3 nm/1.5 nm/1.5 nm/1.5 nm).
A quantum algorithm for Viterbi decoding of classical convolutional codes
Grice, Jon R.; Meyer, David A.
2015-07-01
We present a quantum Viterbi algorithm (QVA) with better than classical performance under certain conditions. In this paper, the proposed algorithm is applied to decoding classical convolutional codes, for instance, large constraint length and short decode frames . Other applications of the classical Viterbi algorithm where is large (e.g., speech processing) could experience significant speedup with the QVA. The QVA exploits the fact that the decoding trellis is similar to the butterfly diagram of the fast Fourier transform, with its corresponding fast quantum algorithm. The tensor-product structure of the butterfly diagram corresponds to a quantum superposition that we show can be efficiently prepared. The quantum speedup is possible because the performance of the QVA depends on the fanout (number of possible transitions from any given state in the hidden Markov model) which is in general much less than . The QVA constructs a superposition of states which correspond to all legal paths through the decoding lattice, with phase as a function of the probability of the path being taken given received data. A specialized amplitude amplification procedure is applied one or more times to recover a superposition where the most probable path has a high probability of being measured.
On the Relationship between Visual Attributes and Convolutional Networks
Castillo, Victor; Ghanem, Bernard; Niebles, Juan Carlos
2015-01-01
One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.
On the Relationship between Visual Attributes and Convolutional Networks
Castillo, Victor
2015-06-02
One of the cornerstone principles of deep models is their abstraction capacity, i.e. their ability to learn abstract concepts from ‘simpler’ ones. Through extensive experiments, we characterize the nature of the relationship between abstract concepts (specifically objects in images) learned by popular and high performing convolutional networks (conv-nets) and established mid-level representations used in computer vision (specifically semantic visual attributes). We focus on attributes due to their impact on several applications, such as object description, retrieval and mining, and active (and zero-shot) learning. Among the findings we uncover, we show empirical evidence of the existence of Attribute Centric Nodes (ACNs) within a conv-net, which is trained to recognize objects (not attributes) in images. These special conv-net nodes (1) collectively encode information pertinent to visual attribute representation and discrimination, (2) are unevenly and sparsely distribution across all layers of the conv-net, and (3) play an important role in conv-net based object recognition.
An improved multi-domain convolution tracking algorithm
Sun, Xin; Wang, Haiying; Zeng, Yingsen
2018-04-01
Along with the wide application of the Deep Learning in the field of Computer vision, Deep learning has become a mainstream direction in the field of object tracking. The tracking algorithm in this paper is based on the improved multidomain convolution neural network, and the VOT video set is pre-trained on the network by multi-domain training strategy. In the process of online tracking, the network evaluates candidate targets sampled from vicinity of the prediction target in the previous with Gaussian distribution, and the candidate target with the highest score is recognized as the prediction target of this frame. The Bounding Box Regression model is introduced to make the prediction target closer to the ground-truths target box of the test set. Grouping-update strategy is involved to extract and select useful update samples in each frame, which can effectively prevent over fitting. And adapt to changes in both target and environment. To improve the speed of the algorithm while maintaining the performance, the number of candidate target succeed in adjusting dynamically with the help of Self-adaption parameter Strategy. Finally, the algorithm is tested by OTB set, compared with other high-performance tracking algorithms, and the plot of success rate and the accuracy are drawn. which illustrates outstanding performance of the tracking algorithm in this paper.
A Hierarchical Convolutional Neural Network for vesicle fusion event classification.
Li, Haohan; Mao, Yunxiang; Yin, Zhaozheng; Xu, Yingke
2017-09-01
Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events. Then, a Gaussian Mixture Model (GMM) is applied on each image patch of the patch sequence with outliers rejected for robust Gaussian fitting. By utilizing the high-level time-series intensity change features introduced by GMM and the visual appearance features embedded in some key moments of the fusion process, the proposed HCNN architecture is able to classify each candidate patch sequence into three classes: full fusion event, partial fusion event and non-fusion event. Finally, we validate the performance of our method on 9 challenging datasets that have been annotated by cell biologists, and our method achieves better performances when comparing with three previous methods. Copyright © 2017 Elsevier Ltd. All rights reserved.
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Martin Längkvist
2016-04-01
Full Text Available The availability of high-resolution remote sensing (HRRS data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN can be applied to multispectral orthoimagery and a digital surface model (DSM of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water, and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.
Pneumothorax detection in chest radiographs using convolutional neural networks
Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit
2018-02-01
This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.
Vision-Based Fall Detection with Convolutional Neural Networks
Directory of Open Access Journals (Sweden)
Adrián Núñez-Marcos
2017-01-01
Full Text Available One of the biggest challenges in modern societies is the improvement of healthy aging and the support to older persons in their daily activities. In particular, given its social and economic impact, the automatic detection of falls has attracted considerable attention in the computer vision and pattern recognition communities. Although the approaches based on wearable sensors have provided high detection rates, some of the potential users are reluctant to wear them and thus their use is not yet normalized. As a consequence, alternative approaches such as vision-based methods have emerged. We firmly believe that the irruption of the Smart Environments and the Internet of Things paradigms, together with the increasing number of cameras in our daily environment, forms an optimal context for vision-based systems. Consequently, here we propose a vision-based solution using Convolutional Neural Networks to decide if a sequence of frames contains a person falling. To model the video motion and make the system scenario independent, we use optical flow images as input to the networks followed by a novel three-step training phase. Furthermore, our method is evaluated in three public datasets achieving the state-of-the-art results in all three of them.
Innervation of the renal proximal convoluted tubule of the rat
International Nuclear Information System (INIS)
Barajas, L.; Powers, K.
1989-01-01
Experimental data suggest the proximal tubule as a major site of neurogenic influence on tubular function. The functional and anatomical axial heterogeneity of the proximal tubule prompted this study of the distribution of innervation sites along the early, mid, and late proximal convoluted tubule (PCT) of the rat. Serial section autoradiograms, with tritiated norepinephrine serving as a marker for monoaminergic nerves, were used in this study. Freehand clay models and graphic reconstructions of proximal tubules permitted a rough estimation of the location of the innervation sites along the PCT. In the subcapsular nephrons, the early PCT (first third) was devoid of innervation sites with most of the innervation occurring in the mid (middle third) and in the late (last third) PCT. Innervation sites were found in the early PCT in nephrons located deeper in the cortex. In juxtamedullary nephrons, innervation sites could be observed on the PCT as it left the glomerulus. This gradient of PCT innervation can be explained by the different tubulovascular relationships of nephrons at different levels of the cortex. The absence of innervation sites in the early PCT of subcapsular nephrons suggests that any influence of the renal nerves on the early PCT might be due to an effect of neurotransmitter released from renal nerves reaching the early PCT via the interstitium and/or capillaries
Classification of breast cancer cytological specimen using convolutional neural network
Żejmo, Michał; Kowal, Marek; Korbicz, Józef; Monczak, Roman
2017-01-01
The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.
Very Deep Convolutional Neural Networks for Morphologic Classification of Erythrocytes.
Durant, Thomas J S; Olson, Eben M; Schulz, Wade L; Torres, Richard
2017-12-01
Morphologic profiling of the erythrocyte population is a widely used and clinically valuable diagnostic modality, but one that relies on a slow manual process associated with significant labor cost and limited reproducibility. Automated profiling of erythrocytes from digital images by capable machine learning approaches would augment the throughput and value of morphologic analysis. To this end, we sought to evaluate the performance of leading implementation strategies for convolutional neural networks (CNNs) when applied to classification of erythrocytes based on morphology. Erythrocytes were manually classified into 1 of 10 classes using a custom-developed Web application. Using recent literature to guide architectural considerations for neural network design, we implemented a "very deep" CNN, consisting of >150 layers, with dense shortcut connections. The final database comprised 3737 labeled cells. Ensemble model predictions on unseen data demonstrated a harmonic mean of recall and precision metrics of 92.70% and 89.39%, respectively. Of the 748 cells in the test set, 23 misclassification errors were made, with a correct classification frequency of 90.60%, represented as a harmonic mean across the 10 morphologic classes. These findings indicate that erythrocyte morphology profiles could be measured with a high degree of accuracy with "very deep" CNNs. Further, these data support future efforts to expand classes and optimize practical performance in a clinical environment as a prelude to full implementation as a clinical tool. © 2017 American Association for Clinical Chemistry.
Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.
Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo
2016-01-11
Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.
Classifying magnetic resonance image modalities with convolutional neural networks
Remedios, Samuel; Pham, Dzung L.; Butman, John A.; Roy, Snehashis
2018-02-01
Magnetic Resonance (MR) imaging allows the acquisition of images with different contrast properties depending on the acquisition protocol and the magnetic properties of tissues. Many MR brain image processing techniques, such as tissue segmentation, require multiple MR contrasts as inputs, and each contrast is treated differently. Thus it is advantageous to automate the identification of image contrasts for various purposes, such as facilitating image processing pipelines, and managing and maintaining large databases via content-based image retrieval (CBIR). Most automated CBIR techniques focus on a two-step process: extracting features from data and classifying the image based on these features. We present a novel 3D deep convolutional neural network (CNN)- based method for MR image contrast classification. The proposed CNN automatically identifies the MR contrast of an input brain image volume. Specifically, we explored three classification problems: (1) identify T1-weighted (T1-w), T2-weighted (T2-w), and fluid-attenuated inversion recovery (FLAIR) contrasts, (2) identify pre vs postcontrast T1, (3) identify pre vs post-contrast FLAIR. A total of 3418 image volumes acquired from multiple sites and multiple scanners were used. To evaluate each task, the proposed model was trained on 2137 images and tested on the remaining 1281 images. Results showed that image volumes were correctly classified with 97.57% accuracy.
Manufacturing Hydraulic Components for the Primary Double Entry S-Pump Model
Directory of Open Access Journals (Sweden)
S. Iu. Kuptsov
2015-01-01
Full Text Available The article describes a new design of the primary pump to run in powerful units (more than 1 GW of power plants. The new construction has some advantages such as compactness, theoretical lack of radial and axial forces, and high efficiency in a wide range of flow. The abovementioned advantages can be possible owing to applying an innovative shape of the pump flow path. An impeller with the guide vanes forms the three-row single stage in the each row axial double entry blade system. The inlet and outlet parts have a shape of the involute that can ensure (according to calculated data the efficiency and stability in a wide range of flow because of a lack of the spiral parts. The results of numerical calculations of the pump working flow theoretically confirm that demanding parameters of the pump (H=286 m; Q=1,15 m3 /s can be obtained with competitive efficiency. To verify the proposed advantages of the construction, there was decision made to conduct the real physical experiment. For this purpose the small model of a real pump was designed with parameters H=14 m, Q=13 l/s. Construction of the pump model has a cartridge conception. In addition, there is a possibility for quick replacement of the some parts of the blade system in case of operational development of the pump. In order to obtain hydraulic characteristics of the pump to say nothing of the electromotor the torque gauge coupling is used. Numerical calculations for the pump model were also performed which confirm the operability. For manufacturing of the blade system the new perspective technology is applied. The main hydraulic components (impellers and guide vanes are made of ABS plastic by using 3D-printer. According to this technology parts are made layer by layer by means of welded plastic filament. Using this method the satisfactory tolerance (approximately ±0,3 mm of the parts was obtained. At that moment, it is possible to create the parts with the maximum size no higher than 150 mm
Double coupling: modeling subjectivity and asymmetric organization in social-ecological systems
Directory of Open Access Journals (Sweden)
David Manuel-Navarrete
2015-09-01
Full Text Available Social-ecological organization is a multidimensional phenomenon that combines material and symbolic processes. However, the coupling between social and ecological subsystem is often conceptualized as purely material, thus reducing the symbolic dimension to its behavioral and actionable expressions. In this paper I conceptualize social-ecological systems as doubly coupled. On the one hand, material expressions of socio-cultural processes affect and are affected by ecological dynamics. On the other hand, coupled social-ecological material dynamics are concurrently coupled with subjective dynamics via coding, decoding, personal experience, and human agency. This second coupling operates across two organizationally heterogeneous dimensions: material and symbolic. Although resilience thinking builds on the recognition of organizational asymmetry between living and nonliving systems, it has overlooked the equivalent asymmetry between ecological and socio-cultural subsystems. Three guiding concepts are proposed to formalize double coupling. The first one, social-ecological asymmetry, expands on past seminal work on ecological self-organization to incorporate reflexivity and subjectivity in social-ecological modeling. Organizational asymmetry is based in the distinction between social rules, which are symbolically produced and changed through human agents' reflexivity and purpose, and biophysical rules, which are determined by functional relations between ecological components. The second guiding concept, conscious power, brings to the fore human agents' distinctive capacity to produce our own subjective identity and the consequences of this capacity for social-ecological organization. The third concept, congruence between subjective and objective dynamics, redefines sustainability as contingent on congruent relations between material and symbolic processes. Social-ecological theories and analyses based on these three guiding concepts would support the
Modelling of a Double-Track Railway Contact System Electric Field Intensity
Belinsky, Stanislav; Khanzhina, Olga; Sidorov, Alexander
2017-12-01
Working conditions of personnel that serves contact system (CS) are affected by factors including health and safety, security and working hours (danger of rolling stock accidents, danger of electric shock strokes, work at height, severity and tension of work, increased noise level, etc.) Low frequency electromagnetic fields as part of both electric and magnetic fields are among of the most dangerous and harmful factors. These factors can affect not only the working personnel, but also a lot of people, who do not work with the contact system itself, but could be influenced by electromagnetic field as the result of their professional activity. People, who use public transport or live not far from the electrified lines, are endangered by these factors as well. There are results of the theoretical researches in which low frequency electric fields of railway contact system were designed with the use of mathematical and computer modelling. Significant features of electric field distribution near double-track railway in presence or absence of human body were established. The studies showed the dependence of low frequency electric field parameters on the distance to the track axis, height, and presence or absence of human body. The obtained data were compared with permissible standards established in the Russian Federation and other countries with advanced electrified railway system. Evaluation of low frequency electric fields harmful effect on personnel is the main result of this work. It is also established, that location of personnel, voltage and current level, amount of tracks and other factors influence electric fields of contact systems.
Wenger, F A; Szucsik, E; Hoinoiu, B F; Ionac, M; Walz, M K; Schmid, K W; Reis, H
2013-12-01
A high incidence of anastomotic leakage (37.5%) is reported after low anterior rectal resection (LAR) and circular double-stapled anastomosis without protective ileostoma. Since the pathomechanism of anastomosis leakage is still unclear, a suitable animal model would be most desirable. The objective was to assess the incidence of clinically apparent and inapparent leakage after LAR in pigs (n = 20). Endpoints were radiological, clinical, macroscopic, and histologic proof of anastomotic leakage on the 9th postoperative day. Integrity of anastomosis was assessed by double-contrast barium examination on 9th postoperative day. Animals were sacrificed and anastomoses were resected for histopathological investigation. In case of earlier clinical apparent anastomotic leakage, radiologic double-contrast barium was performed immediately. LAR with a circular double-stapled anastomosis without protective ileostoma was performed in 20 pigs (m:f = 8:12). Length of resection was 10-20 cm, anastomosis was performed 7 cm ab ano. Five animals (25%) developed clinical apparent anastomotic leakage (no appetite, fever, inactivity, tachypnea, discomfort, pain) between the 6th (n = 1) and 9th (n = 4) postoperative day, proven by double-contrast barium radiographs. Additionally in 1 animal clinical inapparent anastomotic insufficiency was observed radiologically. Total rate of leakage was 30% (n = 6). These results were confirmed by leucocytosis, low potassium levels, in two cases high ALT and AST and local peritonitis in all cases. Including one additional case of clinical inapparent leakage, total rate of anastomotic leakage was 30% (6/20). Thus we managed to establish a new experimental model of anastomotic leakage after low rectal resection comparable to the human situation.
Double photoionization of H2: An experimental test of electronic-correlation models in molecules
International Nuclear Information System (INIS)
Dujardin, G.; Besnard, M.J.; Hellner, L.; Malinovitch, Y.
1987-01-01
The double-photoionization cross sections of molecular hydrogen (H 2 ) and molecular deuterium (D 2 ) were measured by using the photoion-photoion coincidence method for photon energies ranging from the threshold energy around 50 eV up to, respectively, 140 and 98 eV. The comparison with the recent ab initio calculations of Le Rouzo [J. Phys. B 19, L677 (1986)] indicates that an important part of the double-photoionization process is accounted for by a rigorous description of the electron-electron interaction in the initial state. As a by-product of this work, it was also concluded that double photoionization of hydrogen can be considered as a vertical process and that Franck-Condon approximations are quite valid to calculate the kinetic energy of the resulting H + +H + fragments
Dynamical models to explain observations with SPHERE in planetary systems with double debris belts
Lazzoni, C.; Desidera, S.; Marzari, F.; Boccaletti, A.; Langlois, M.; Mesa, D.; Gratton, R.; Kral, Q.; Pawellek, N.; Olofsson, J.; Bonnefoy, M.; Chauvin, G.; Lagrange, A. M.; Vigan, A.; Sissa, E.; Antichi, J.; Avenhaus, H.; Baruffolo, A.; Baudino, J. L.; Bazzon, A.; Beuzit, J. L.; Biller, B.; Bonavita, M.; Brandner, W.; Bruno, P.; Buenzli, E.; Cantalloube, F.; Cascone, E.; Cheetham, A.; Claudi, R. U.; Cudel, M.; Daemgen, S.; De Caprio, V.; Delorme, P.; Fantinel, D.; Farisato, G.; Feldt, M.; Galicher, R.; Ginski, C.; Girard, J.; Giro, E.; Janson, M.; Hagelberg, J.; Henning, T.; Incorvaia, S.; Kasper, M.; Kopytova, T.; LeCoroller, H.; Lessio, L.; Ligi, R.; Maire, A. L.; Ménard, F.; Meyer, M.; Milli, J.; Mouillet, D.; Peretti, S.; Perrot, C.; Rouan, D.; Samland, M.; Salasnich, B.; Salter, G.; Schmidt, T.; Scuderi, S.; Sezestre, E.; Turatto, M.; Udry, S.; Wildi, F.; Zurlo, A.
2018-03-01
circular or eccentric orbit. We then consider multi-planetary systems: two and three equal-mass planets on circular orbits and two equal-mass planets on eccentric orbits in a packed configuration. As a final step, we compare each couple of values (Mp, ap), derived from the dynamical analysis of single and multiple planetary models, with the detection limits obtained with SPHERE. Results: For one single planet on a circular orbit we obtain conclusive results that allow us to exclude such a hypothesis since in most cases this configuration requires massive planets which should have been detected by our observations. Unsatisfactory is also the case of one single planet on an eccentric orbit for which we obtained high masses and/or eccentricities which are still at odds with observations. Introducing multi planetary architectures is encouraging because for the case of three packed equal-mass planets on circular orbits we obtain quite low masses for the perturbing planets which would remain undetected by our SPHERE observations. The case of two equal-mass planets on eccentric orbits is also of interest since it suggests the possible presence of planets with masses lower than the detection limits and with moderate eccentricity. Our results show that the apparent lack of planets in gaps between double belts could be explained by the presence of a system of two or more planets possibly of low mass and on eccentric orbits whose sizes are below the present detection limits. Based on observations collected at Paranal Observatory, ESO (Chile) Program ID: 095.C-0298, 096.C-0241, 097.C-0865, and 198.C-0209.
Low-complexity object detection with deep convolutional neural network for embedded systems
Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong
2017-09-01
We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.
Invariant moments based convolutional neural networks for image analysis
Directory of Open Access Journals (Sweden)
Vijayalakshmi G.V. Mahesh
2017-01-01
Full Text Available The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.
Single image super-resolution based on convolutional neural networks
Zou, Lamei; Luo, Ming; Yang, Weidong; Li, Peng; Jin, Liujia
2018-03-01
We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5×5, 3×3 and 1×1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.
Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang
2017-10-01
This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.
Jaubert, Jean-Noël; Privat, Romain
2014-01-01
The double-tangent construction of coexisting phases is an elegant approach to visualize all the multiphase binary systems that satisfy the equality of chemical potentials and to select the stable state. In this paper, we show how to perform the double-tangent construction of coexisting phases for binary systems modeled with the gamma-phi…
Ledreux, Aurélie; Boger, Heather A; Hinson, Vanessa K; Cantwell, Kelsey; Granholm, Ann-Charlotte
2016-01-15
The anti-Parkinsonian drug rasagiline is a selective, irreversible inhibitor of monoamine oxidase and is used in the treatment of Parkinson׳s disease (PD). Its postulated neuroprotective effects may be attributed to MAO inhibition, or to its propargylamine moiety. The major metabolite of rasagiline, aminoindan, has shown promising neuroprotective properties in vitro but there is a paucity of studies investigating in vivo effects of this compound. Therefore, we examined neuroprotective effects of rasagiline and its metabolite aminoindan in a double lesion model of PD. Male Fisher 344 rats received i.p. injections of the noradrenergic neurotoxin DSP-4 and intra-striatal stereotaxic microinjections of the dopamine neurotoxin 6-OHDA. Saline, rasagiline or aminoindan (3mg/kg/day s.c.) were delivered via Alzet minipumps for 4 weeks. Rats were then tested for spontaneous locomotion and a novel object recognition task. Following behavioral testing, brain tissue was processed for ELISA measurements of growth factors and immunohistochemistry. Double-lesioned rats treated with rasagiline or aminoindan had reduced behavioral deficits, both in motor and cognitive tasks compared to saline-treated double-lesioned rats. BDNF levels were significantly increased in the hippocampus and striatum of the rasagiline- and aminoindan-lesioned groups compared to the saline-treated lesioned group. Double-lesioned rats treated with rasagiline or aminoindan exhibited a sparing in the mitochondrial marker Hsp60, suggesting mitochondrial involvement in neuroprotection. Tyrosine hydroxylase (TH) immunohistochemistry revealed a sparing of TH-immunoreactive terminals in double-lesioned rats treated with rasagiline or aminoindan in the striatum, hippocampus, and substantia nigra. These data provide evidence of neuroprotection by aminoindan and rasagiline via their ability to enhance BDNF levels. Published by Elsevier B.V.
Analytical and Numerical Modeling of Tsunami Wave Propagation for double layer state in Bore
Yuvaraj, V.; Rajasekaran, S.; Nagarajan, D.
2018-04-01
Tsunami wave enters into the river bore in the landslide. Tsunami wave propagation are described in two-layer states. The velocity and amplitude of the tsunami wave propagation are calculated using the double layer. The numerical and analytical solutions are given for the nonlinear equation of motion of the wave propagation in a bore.
Numerical Modeling of Pump Absorption in Coiled and Twisted Double-Clad Fibers
Czech Academy of Sciences Publication Activity Database
Koška, Pavel; Peterka, Pavel; Doya, V.
2016-01-01
Roč. 22, č. 2 (2016), s. 4401508 ISSN 1077-260X R&D Projects: GA ČR GA14-35256S; GA MŠk(CZ) LD15122 Institutional support: RVO:67985882 Keywords : double-clad optical fibers * beam propagation method * fiber amplifiers Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 3.971, year: 2016
Three-dimensional classical-ensemble modeling of non-sequential double ionization
International Nuclear Information System (INIS)
Haan, S.L.; Breen, L.; Tannor, D.; Panfili, R.; Ho, Phay J.; Eberly, J.H.
2005-01-01
Full text: We have been using 1d ensembles of classical two-electron atoms to simulate helium atoms that are exposed to pulses of intense laser radiation. In this talk we discuss the challenges in setting up a 3d classical ensemble that can mimic the quantum ground state of helium. We then report studies in which each one of 500,000 two-electron trajectories is followed in 3d through a ten-cycle (25 fs) 780 nm laser pulse. We examine double-ionization yield for various intensities, finding the familiar knee structure. We consider the momentum spread of outcoming electrons in directions both parallel and perpendicular to the direction of laser polarization, and find results that are consistent with experiment. We examine individual trajectories and recollision processes that lead to double ionization, considering the best phases of the laser cycle for recollision events and looking at the possible time delay between recollision and emergence. We consider also the number of recollision events, and find that multiple recollisions are common in the classical ensemble. We investigate which collisional processes lead to various final electron momenta. We conclude with comments regarding the ability of classical mechanics to describe non-sequential double ionization, and a quick summary of similarities and differences between 1d and 3d classical double ionization using energy-trajectory comparisons. Refs. 3 (author)
International Nuclear Information System (INIS)
Goertz, M.; Hansen, J.V.; Larsen, M.
1999-01-01
In this paper we develop a new CGE model of the Danish economy with the acronym ECOSMEC (Economic COuncil Simulation Model with Energy markets and Carbon taxation). The model is a hybrid of two existing static models developed by respectively the Secretariat of the Danish Economic Council and by the MobiDK model project in the Ministry of Business and Industry. Distinct features of the ECOSMEC models are a rather disaggregated modelling of energy demand and supply, introduction of various market structures in the energy sector, and a consistent specification of different household types. The simulations presented in the paper have the following implications: Firstly, a uniform CO 2 tax of approximately 300 DKK per ton could reduce emissions by 20 per cent in a scenario with perfect competition in the energy sector. Secondly, a double dividend (reduced emissions and increased welfare) could be gained by using the CO 2 tax revenue for reducing distorting income taxes. However, the double dividend result depends decisively on the applied elasticity of substitution between consumption and leisure. Thirdly, assuming different market structures in the energy sector influences the uniform CO 2 tax needed to reach a given emission target. Fourthly, the empirical aguments for differentiated CO 2 taxes motivated by imperfect energy markets are weak. Fifthly, the Danish economy could benefit from a deregulation of the electricity and district heating sector with respect to welfare and economic activity. This result holds also if CO 2 emissions are kept constant. (au)
Spacings and pair correlations for finite Bernoulli convolutions
International Nuclear Information System (INIS)
Benjamini, Itai; Solomyak, Boris
2009-01-01
We consider finite Bernoulli convolutions with a parameter 1/2 N . These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure ν λ , as N → ∞. Numerical evidence suggests that for a generic λ, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic λ the behaviour is totally different
A New Reverberator Based on Variable Sparsity Convolution
DEFF Research Database (Denmark)
Holm-Rasmussen, Bo; Lehtonen, Heidi-Maria; Välimäki, Vesa
2013-01-01
FIR filter coefficients are selected from a velvet noise sequence, which consists of ones, minus ones, and zeros only. In this application, it is sufficient perceptually to use very sparse velvet noise sequences having only about 0.1 to 0.2% non-zero elements, with increasing sparsity along...... the impulse response. The algorithm yields a parametric approximation of the late part of the impulse response, which is more than 100 times more efficient computationally than the direct convolution. The computational load of the proposed algorithm is comparable to that of FFT-based partitioned convolution...
Convolutional cylinder-type block-circulant cycle codes
Directory of Open Access Journals (Sweden)
Mohammad Gholami
2013-06-01
Full Text Available In this paper, we consider a class of column-weight two quasi-cyclic low-density paritycheck codes in which the girth can be large enough, as an arbitrary multiple of 8. Then we devote a convolutional form to these codes, such that their generator matrix can be obtained by elementary row and column operations on the parity-check matrix. Finally, we show that the free distance of the convolutional codes is equal to the minimum distance of their block counterparts.
Weed Growth Stage Estimator Using Deep Convolutional Neural Networks
DEFF Research Database (Denmark)
Teimouri, Nima; Dyrmann, Mads; Nielsen, Per Rydahl
2018-01-01
conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516...... in estimating the number of leaves and 96% accuracy when accepting a deviation of two leaves. These results show that this new method of using deep convolutional neural networks has a relatively high ability to estimate early growth stages across a wide variety of weed species....
Deep Convolutional Neural Networks: Structure, Feature Extraction and Training
Directory of Open Access Journals (Sweden)
Namatēvs Ivars
2017-12-01
Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.
Very deep recurrent convolutional neural network for object recognition
Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri
2017-03-01
In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.
Spectral interpolation - Zero fill or convolution. [image processing
Forman, M. L.
1977-01-01
Zero fill, or augmentation by zeros, is a method used in conjunction with fast Fourier transforms to obtain spectral spacing at intervals closer than obtainable from the original input data set. In the present paper, an interpolation technique (interpolation by repetitive convolution) is proposed which yields values accurate enough for plotting purposes and which lie within the limits of calibration accuracies. The technique is shown to operate faster than zero fill, since fewer operations are required. The major advantages of interpolation by repetitive convolution are that efficient use of memory is possible (thus avoiding the difficulties encountered in decimation in time FFTs) and that is is easy to implement.
Desai, S. D.; Yuan, D. -N.
2006-01-01
A computationally efficient approach to reducing omission errors in ocean tide potential models is derived and evaluated using data from the Gravity Recovery and Climate Experiment (GRACE) mission. Ocean tide height models are usually explicitly available at a few frequencies, and a smooth unit response is assumed to infer the response across the tidal spectrum. The convolution formalism of Munk and Cartwright (1966) models this response function with a Fourier series. This allows the total ocean tide height, and therefore the total ocean tide potential, to be modeled as a weighted sum of past, present, and future values of the tide-generating potential. Previous applications of the convolution formalism have usually been limited to tide height models, but we extend it to ocean tide potential models. We use luni-solar ephemerides to derive the required tide-generating potential so that the complete spectrum of the ocean tide potential is efficiently represented. In contrast, the traditionally adopted harmonic model of the ocean tide potential requires the explicit sum of the contributions from individual tidal frequencies. It is therefore subject to omission errors from neglected frequencies and is computationally more intensive. Intersatellite range rate data from the GRACE mission are used to compare convolution and harmonic models of the ocean tide potential. The monthly range rate residual variance is smaller by 4-5%, and the daily residual variance is smaller by as much as 15% when using the convolution model than when using a harmonic model that is defined by twice the number of parameters.
Directory of Open Access Journals (Sweden)
Huiru Zhao
2016-09-01
Full Text Available An important goal of China’s electric power system reform is to create a double-side day-ahead wholesale electricity market in the future, where the suppliers (represented by GenCOs and demanders (represented by DisCOs compete simultaneously with each other in one market. Therefore, modeling and simulating the dynamic bidding process and the equilibrium in the double-side day-ahead electricity market scientifically is not only important to some developed countries, but also to China to provide a bidding decision-making tool to help GenCOs and DisCOs obtain more profits in market competition. Meanwhile, it can also provide an economic analysis tool to help government officials design the proper market mechanisms and policies. The traditional dynamic game model and table-based reinforcement learning algorithm have already been employed in the day-ahead electricity market modeling. However, those models are based on some assumptions, such as taking the probability distribution function of market clearing price (MCP and each rival’s bidding strategy as common knowledge (in dynamic game market models, and assuming the discrete state and action sets of every agent (in table-based reinforcement learning market models, which are no longer applicable in a realistic situation. In this paper, a modified reinforcement learning method, called gradient descent continuous Actor-Critic (GDCAC algorithm was employed in the double-side day-ahead electricity market modeling and simulation. This algorithm can not only get rid of the abovementioned unrealistic assumptions, but also cope with the Markov decision-making process with continuous state and action sets just like the real electricity market. Meanwhile, the time complexity of our proposed model is only O(n. The simulation result of employing the proposed model in the double-side day-ahead electricity market shows the superiority of our approach in terms of participant’s profit or social welfare
Civitarese, O.; Suhonen, J.; Zuber, K.
2015-07-01
The minimal extension of the standard model of electroweak interactions allows for massive neutrinos, a massive right-handed boson WR, and a left-right mixing angle ζ. While an estimate of the light (electron) neutrino can be extracted from the non-observation of the neutrinoless double beta decay, the limits on the mixing angle and the mass of the righthanded (RH) boson may be extracted from a combined analysis of the double beta decay measurements (GERDA, EXO-200 and KamLAND-Zen collaborations) and ATLAS data on the two-jets two-leptons signals following the excitation of a virtual RH boson mediated by a heavy-mass neutrino. In this work we shall compare results of both types of experiments, and show that the estimates are not in tension.
Efficient forward propagation of time-sequences in convolutional neural networks using Deep Shifting
K.L. Groenland (Koen); S.M. Bohte (Sander)
2016-01-01
textabstractWhen a Convolutional Neural Network is used for on-the-fly evaluation of continuously updating time-sequences, many redundant convolution operations are performed. We propose the method of Deep Shifting, which remembers previously calculated results of convolution operations in order
Zhang, Jin-Yu; Meng, Xiang-Bing; Xu, Wei; Zhang, Wei; Zhang, Yong
2014-01-01
This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method. PMID:24696649
Directory of Open Access Journals (Sweden)
Jin-Yu Zhang
2014-01-01
Full Text Available This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method.
International Nuclear Information System (INIS)
Leung, P.; Wong, A.Y.; Quon, B.H.
1981-01-01
Experiments on both stationary and propagating double layers and a related analytical model are described. Stationary double layers were produced in a multiple plasma device, in which an electron drift current was present. An investigation of the plasma parameters for the stable double layer condition is described. The particle distribution in the stable double layer establishes a potential profile, which creates electron and ion beams that excite plasma instabilities. The measured characteristics of the instabilities are consistent with the existence of the double layer. Propagating double layers are formed when the initial electron drift current is large. Ths slopes of the transition region increase as they propagate. A physical model for the formation of a double layer in the experimental device is described. This model explains the formation of the low potential region on the basis of the space charge. This space charge is created by the electron drift current. The model also accounts for the role of ions in double layer formation and explains the formation of moving double layers. (Auth.)
Energy Technology Data Exchange (ETDEWEB)
Arora, N D
1987-05-01
A simple and accurate semi-empirical model for the threshold voltage of a small geometry double implanted enhancement type MOSFET, especially useful in a circuit simulation program like SPICE, has been developed. The effect of short channel length and narrow width on the threshold voltage has been taken into account through a geometrical approximation, which involves parameters whose values can be determined from the curve fitting experimental data. A model for the temperature dependence of the threshold voltage for the implanted devices has also been presented. The temperature coefficient of the threshold voltage was found to change with decreasing channel length and width. Experimental results from various device sizes, both short and narrow, show very good agreement with the model. The model has been implemented in SPICE as part of the complete dc model.
International Nuclear Information System (INIS)
Marc, Olivier; Praene, Jean-Philippe; Bastide, Alain; Lucas, Franck
2011-01-01
Solar cooling applied to buildings is without a doubt an interesting alternative for reducing energy consumption in traditional mechanical steam compression air conditioning systems. The study of these systems should have a closely purely fundamental approach including the development of numerical models in order to predict the overall installation performance. The final objective is to estimate cooling capacity, power consumption, and overall installation performance with relation to outside factors (solar irradiation, outside temperature...). The first stage in this work consists of estimating the primary energy produced by the solar collector field. The estimation of this primary energy is crucial to ensure the evaluation of the cooling capacity and therefore the cooling distribution and thermal comfort in the building. Indeed, the absorption chiller performance is directly related to its heat source. This study presents dynamic models for double glazing solar collectors and compares the results of the simulation with experimental results taken from our test bench (two collectors). In the second part, we present an extensive collector field model (36 collectors) from our solar cooling installation at The University Institute of Technology in St Pierre, Reunion Island as well as our stratified tank storage model. A comparison of the simulation results with real scale solar experimental data taken from our installation enables validation of the double glazing solar collector and stratified tank dynamic models.
The Research of Indoor Positioning Based on Double-peak Gaussian Model
Directory of Open Access Journals (Sweden)
Lina Chen
2014-04-01
Full Text Available Location fingerprinting using Wi-Fi signals has been very popular and is a well accepted indoor positioning method. The key issue of the fingerprinting approach is generating the fingerprint radio map. Limited by the practical workload, only a few samples of the received signal strength are collected at each reference point. Unfortunately, fewer samples cannot accurately represent the actual distribution of the signal strength from each access point. This study finds most Wi- Fi signals have two peaks. According to the new finding, a double-peak Gaussian arithmetic is proposed to generate a fingerprint radio map. This approach requires little time to receive WiFi signals and it easy to estimate the parameters of the double-peak Gaussian function. Compared to the Gaussian function and histogram method to generate a fingerprint radio map, this method better approximates the occurrence signal distribution. This paper also compared the positioning accuracy using K-Nearest Neighbour theory for three radio maps, the test results show that the positioning distance error utilizing the double-peak Gaussian function is better than the other two methods.
Directory of Open Access Journals (Sweden)
Chunyong Ma
2018-01-01
Full Text Available Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.
Discrete singular convolution for the generalized variable-coefficient ...
African Journals Online (AJOL)
Numerical solutions of the generalized variable-coefficient Korteweg-de Vries equation are obtained using a discrete singular convolution and a fourth order singly diagonally implicit Runge-Kutta method for space and time discretisation, respectively. The theoretical convergence of the proposed method is rigorously ...
Face recognition: a convolutional neural-network approach.
Lawrence, S; Giles, C L; Tsoi, A C; Back, A D
1997-01-01
We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.
Training Convolutional Neural Networks for Translational Invariance on SAR ATR
DEFF Research Database (Denmark)
Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten
2016-01-01
In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...
An Interactive Graphics Program for Assistance in Learning Convolution.
Frederick, Dean K.; Waag, Gary L.
1980-01-01
A program has been written for the interactive computer graphics facility at Rensselaer Polytechnic Institute that is designed to assist the user in learning the mathematical technique of convolving two functions. Because convolution can be represented graphically by a sequence of steps involving folding, shifting, multiplying, and integration, it…