Low Storage Requirements Research Articles

New Hybrid Runge-Kutta Methods for Unsteady Reactive Flow Simulation: Applications

The applicability is examined of the new hybrid Runge-Kutta methods derived in the companion paper by specifically analyzing both nonstiff and stiff system of equations that represent multidimensional reactive flows. In a reactive flow simulation, the standard explicit calculation is prohibitively expensive because of the small time steps needed to address the stiffness of a governing differential system. The new hybrid Runge-Kutta schemes are suitable for this task because the time-step size is controlled by the Courant condition, whereas the stiffness is treated by an unconditionally stable method. This set of methods also meets the modern computing needs of high-order accuracy and low-storage requirement. A representative scheme is used to simulate a series of combustion problems that include model equations with stiff sources and multidimensional detonations with complex chemical kinetics

Artificial Intelligent Controller for Current Source Converter-Based Modular Active Power Filters

The control methodology of the active power-line filter is the key element for its successful performance in mitigating the line current harmonics. The control system processes the distorted line current signal and forces the converter to inject the proper compensating current. At the same time, it regulates the dc term, i.e., the dc current in the current source converter topology and the dc voltage in the voltage source converter topology. In this paper, a new intelligent control scheme for a modular single-phase active power filter based on the current source converter (CSC) topology is proposed. The intelligent controller utilizes two adaptive linear neurons (ADALINEs) to process the signals obtained from the power-line. The first ADALINE (the Current ADALINE) extracts the harmonic components of the distorted line current signal and the second ADALINE (the Voltage ADALINE) estimates the fundamental component of the line voltage signal. The outputs of the two ADALINEs are used to construct the modulating signals of a number of CSC modules, each dedicated to eliminate a specific harmonic. The proposed controller is also responsible for activating selected CSC filter module(s) to the electric grid. The automated activation of the corresponding filter module(s) is based on the decision-making rules in accordance with the current total harmonic distortion (THD/sub i/) and the harmonic factor (HF) levels set by the IEEE 519-1992 standard. A special 3-level PWM switching strategy is proposed for the filter modules which results in a 50% reduction in the overall switching losses compared with the 2-level method. The proposed controller adjusts the I/sub dc/ in each CSC module based on the present magnitude of the corresponding harmonic current which results in optimum dc-side current value and minimal converter losses. The high speed, accuracy, efficiency and flexibility offered by the proposed controller, combined with the fast response and low dc energy storage requirement of CSC topology, are the main advantages of the proposed active filter system. The analytical expectations are verified by digital simulation using EMTDC simulation package.

Storage requirement estimation for optimized design of data intensive applications

A novel storage requirement estimation methodology is presented for use in the early system design phases when the data transfer ordering is only partially fixed. At that stage, none of the existing estimation tools are adequate, as they either assume a fully specified execution order or ignore it completely. A prototype CAD tool has been developed that includes major parts of the storage requirement estimation and optimization methodology. Using representative application demonstrators, we show how our techniques and tool can effectively guide the designer to achieve a transformed specification with low storage requirement.

Block-Circulant Low-Density Parity-Check Codes for Optical Communication Systems

A novel family of structured low-density parity-check (LDPC) codes with block-circulant parity-check matrices that consist of permutation blocks is proposed. The codes from this family are based on new combinatorial objects termed cycle-invariant difference sets, and they have low storage requirements, fast encoding algorithms, and girth of at least six. Most importantly, they tend to outperform many other known structured LDPCs of comparable rate and length.

PITFALLS IN DISTRIBUTED NONBLOCKING CHECKPOINTING

Coordinated checkpointing has low stable storage requirements and simplifies the recovery process by reserving a set of consistent global checkpoints. Unfortunately, most algorithms that were proposed either incurred a high communication overhead or blocked all processes. Then, a coordinated algorithm was presented which was nonblocking and which forced only a subset of all processes to participate in a checkpointing event. This algorithm was shown to create inconsistencies in some situations and new algorithms to take consistent checkpoints were proposed. However, we found that these algorithms can still result in inconsistencies when typical behavior in a distributed environment is considered, such as multiple forced checkpoints and multiple concurrent checkpoint initiations. In this paper we identify the inconsistencies that can occur and present an efficient nonblocking algorithm that collects consistent global checkpoints and avoids some of the pitfalls in distributed nonblocking checkpointing.

Data dependency size estimation for use in memory optimization

A novel storage requirement estimation methodology is presented for use in the early system design phases when the data transfer ordering is only partly fixed. At that stage, none of the existing estimation tools are adequate, as they either assume a fully specified execution order or ignore it completely. This paper presents an algorithm for automated estimation of strict upper and lower bounds on the individual data dependency sizes in high-level application code given a partially fixed execution ordering. In the overall estimation technique, this is followed by a detection of the maximally combined size of simultaneously alive dependencies, resulting in the overall storage requirement of the application. Using representative application demonstrators, we show how our techniques can effectively guide the designer to achieve a transformed specification with low storage requirement.

Rapid omnidirectional vision acquisition using an intelligent linear scanning technique

Many computer vision applications can benefit from omnidirectional vision sensing, rather than depending solely on conventional cameras that have constrained fields of view. For example, mobile robots often require a full 360° view of their environment in order to perform navigational tasks such identifying landmarks, localizing within the environment, and determining free paths in which to move. There has been much research interest in omnidirectional vision in the past decade and many techniques have been developed. These techniques include: (i) catadioptric methods which can provide rapid image acquisition, but lack image resolution; and (ii) mosaicing and linear scanning techniques which have high image resolution but typically have slow image acquisition speed. In this paper, we introduce a novel linear scanning panoramic vision system that can acquire panoramic images quickly with little loss of image resolution. The system makes use of a fast line-scan camera, instead of a slower, conventional area-scan camera. In addition, a unique coarse-to-fine panoramic imaging technique has been developed that is based on smart sensing principles. Using the active vision paradigm, we control the motion of the rotating camera using feedback from the images. This results in high acquisition speeds and proportionally low storage requirements. Experimentation has been carried out, and results are given.

A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems

This paper describes a technique for constructing robust preconditioners for the CGLS method applied to the solution of large and sparse least squares problems. The algorithm computes an incomplete LDLT factorization of the normal equations matrix without the need to form the normal matrix itself. The preconditioner is reliable (pivot breakdowns cannot occur) and has low intermediate storage requirements. Numerical experiments illustrating the performance of the preconditioner are presented. A comparison with incomplete QR preconditioners is also included.

Using IDDs for Packet Filtering

Firewalls are one of the key technologies used to control the traffic going in and out of a network. A central feature of the firewall is the <em>packet filter</em>. In this paper, we propose a complete framework for packet classification. Through two applications we demonstrate that both performance and security can be improved.<br /> <br />We show that a traditional ordered rule set can always be expressed as a first-order logic formula on integer variables. Moreover, we emphasize that, with such specification, the packet filtering problem is known to be constant time. We propose to represent the first-order logic formula as <em> Interval Decision Diagrams</em>. This structure has several advantages. First, the algorithm for removing redundancy and unnecessary tests is very simple. Secondly, it allows us to handle integer variables which makes it efficient on a generic CPUs. And, finally, we introduce an extension of IDDs called <em>Multi-Terminal Interval Decision Diagrams</em> in order to deal with any number of policies.<br /> <br />In matter of efficiency, we evaluate the performance our framework through a prototype toolkit composed by a <em> compiler</em> and a <em>packet filter</em>. The results of the experiments shows that this method is efficient in terms of CPU usage and has a low storage requirements.<br /> <br />Finally, we outline a tool, called <em>Network Access Verifier</em>. This tool demonstrates how the IDD representation can be used for verifying access properties of a network. In total, potentially improving the security of a network.

The Multimodal Neighborhood Signature for Modeling Object Color Appearance and Applications in Object Recognition and Image Retrieval

We propose a general-purpose color-based object model called the Multimodal Neighborhood Signature (MNS) with applications in object recognition and image retrieval. Object modeling is example-based and can cope with many appearance variations due to the image formation/rendering process. The local nature of the color representation facilitates robustness to occlusion and clutter. Unlike other methods, neither segmentation nor edge detection is required and the area of homogeneously colored regions is not used. The algorithm is simple to implement and has low storage requirements. In the reported experiments, eight recognition and two other retrieval methods are reviewed and systematically compared with MNS. Results show good and fast performance under severe scale, viewpoint, occlusion, and background change using a single image for object modeling. Although spatial information was not used and its default internal parameters were used, MNS outperformed most compared methods.

Mono-Implicit Runge-Kutta-Nyström Methods with Application to Boundary Value Ordinary Differential Equations

The successful use of mono-implicit Runge—Kutta methods has been demonstrated by several researchers who have employed these methods in software packages for the numerical solution of boundary value ordinary differential equations. However, these methods are only applicable to first order systems of equations while many boundary value systems involve higher order equations. While it is straightforward to convert such systems to first order, several advantages, including substantial gains in efficiency, higher continuity of the approximate solution, and lower storage requirements, are realized when the equations can be treated in their original higher order form. In this paper, we consider generalizations of mono-implicit Runge—Kutta methods, called mono-implicit Runge—Kutta—Nystrom methods, suitable for systems of second order ordinary differential equations having the general form, y″(t) = f(t,y(t),y′(t)), and derive optimal symmetric methods of orders two, four, and six. We also introduce continuous mono-implicit Runge—Kutta—Nystrom methods which allow us to provide continuous solution and derivative approximations. Numerical results are included to demonstrate the effectiveness of these methods; savings of 65% are attained in some instances.

Dynamic Model and Control of a Hybrid Electric Vehicle

This paper describes the Low Storage Requirement (LSR) Hybrid Electric Vehicle configuration and discusses the advantages of this type of hybrid vehicle. A description of the LSR operating strategy and dynamic vehicle model are presented including simulation results.

Solution of the quantum fluid dynamical equations with radial basis function interpolation

The paper proposes a numerical technique within the Lagrangian description for propagating the quantum fluid dynamical (QFD) equations in terms of the Madelung field variables R and S, which are connected to the wave function via the transformation psi = exp(R + iS)/[symbol: see text]. The technique rests on the QFD equations depending only on the form, not the magnitude, of the probability density rho = magnitude of psi 2 and on the structure of R = [symbol: see text]/2 ln rho generally being simpler and smoother than rho. The spatially smooth functions R and S are especially suitable for multivariate radial basis function interpolation to enable the implementation of a robust numerical scheme. Examples of two-dimensional model systems show that the method rivals, in both efficiency and accuracy, the split-operator and Chebychev expansion methods. The results on a three-dimensional model system indicates that the present method is superior to the existing ones, especially, for its low storage requirement and its uniform accuracy. The advantage of the new algorithm is expected to increase for higher dimensional systems to provide a practical computational tool.

Electric drive subsystem for a low-storage requirement hybrid electric vehicle

Integrating an electric machine drive system into the powertrain of a hybrid electric vehicle (HEV) represents a challenging exercise in packaging complex electromechanical and power electronic subsystems. The Ford combined alternator starter (FCAS) and its attendant power and control electronics are physically partitioned because power electronics has not yet evolved to the stage in which fully packaged drives can be realized. A similar situation exists for the control and sensor subsystems necessary for a fully functional high-performance drive. Hardware partitioning requires that more attention be given to installation issues and to mitigating system interactions. The FCAS system consists of an integrated starter/alternator (S/A), an S/A module (SAM), and a vehicle electrical infrastructure that can support the power and energy levels demanded. Our field experience with the FCAS system is presented along with test results obtained from vehicle operation.

Arithmetic coding revisited

Over the last decade, arithmetic coding has emerged as an important compression tool. It is now the method of choice for adaptive coding on myltisymbol alphabets because of its speed, low storage requirements, and effectiveness of compression. This article describes a new implementation of arithmetic coding that incorporates several improvements over a widely used earlier version by Witten, Neal, and Cleary, which has become a de facto standard. These improvements include fewer multiplicative operations, greatly extended range of alphabet sizes and symbol probabilities, and the use of low-precision arithmetic, permitting implementation by fast shift/add operations. We also describe a modular structure that separates the coding, modeling, and probability estimation components of a compression system. To motivate the improved coder, we consider the needs of a word-based text compression program. We report a range of experimental results using this and other models. Complete source code is available.

Speaker verification method and apparatus using mixture decomposition discrimination

A new speaker verification method, termed Mixture Decomposition Discrimination, (MDD) and a new apparatus for using MDD are presented. MDD takes mixture component score information from a speaker independent recognizer and transmits this information while it is still decomposed as a mixture of component scores that indicate the response of the states of the HMM before this information is combined into a single speaker independent recognizer parameter. MDD can be very effective in improving the performance of existing verification methods based on speaker dependent HMMs with cohort normalization because the errors of the two speaker verification methods are very uncorrelated statistically. Experimental results have shown that when MDD is incorporated into a system that also uses speaker dependent HMMs, the resulting hybrid system has its average equal error rate reduced by 46% compared to cohort normalized speaker independent HMM. MDD is used with a speaker dependent linear discriminator which has relatively low computational and storage requirements. Thus, the increased performance of a hybrid MDD/CNHMM system may be achieved with minimal increase in computational and data storage assets.

Krylov methods for solving models with forward-looking variables

Abstract The simulation of large macroeconometric models containing forward-looking variables can become impractical when using exact Newton methods. The difficulties generally arise from the use of direct methods for the solution of the linear system in the Newton step. In such cases, nonstationary iterative methods, also called Krylov methods, provide an interesting alternative. In this paper we apply such methods to simulate a real world econometric model. Our numerical experiments confirm the interesting features of these techniques: low computational complexity and storage requirements. We also discuss a block preconditioner suitable for the particular class of models solved.

Kernel orthonormalization in radial basis function neural networks

This paper deals with optimization of the computations involved in training radial basis function (RBF) neural networks. The main contribution of the reported work is the method for network weights calculation, in which the key idea is to transform the RBF kernels into an orthonormal set of functions (using the standard Gram-Schmidt orthogonalization). This significantly reduces the computing time if the RBF training scheme, which relies on adding one kernel hidden node at a time to improve network performance, is adopted. Another property of the method is that, after the RBF network weights are computed, the original network structure can be restored back. An additional strength of the method is the possibility to decompose the proposed computing task into a number of parallel subtasks so gaining further savings on computing time. Also, the proposed weight calculation technique has low storage requirements. These features make the method very attractive for hardware implementation. The paper presents a detailed derivation of the proposed network weights calculation procedure and demonstrates its validity for RBF network training on a number of data classification and function approximation problems.

Fast spatial estimation

Spatial estimators usually provide lower prediction errors than their aspatial counterparts. However, most of the standard techniques require a large number of operations. Fortunately, for a given observation only a relatively small number of nearby observations typically exhibit correlated errors. This means that most of the elements of the n by n spatial matrices are zero. The use of sparse matrix techniques can dramatically lower storage requirements and reduce execution times. In addition, adopting a first differencing model allows the use of GLS which avoids the necessity of evaluating an n by n determinant. This also greatly reduces computational costs.

Low-Dissipation and Low-Dispersion Runge–Kutta Schemes for Computational Acoustics

In this paper, we investigate accurate and efficient time advancing methods for computational acoustics, where nondissipative and nondispersive properties are of critical importance. Our analysis pertains to the application of Runge–Kutta methods to high-order finite difference discretization. In many CFD applications, multistage Runge–Kutta schemes have often been favored for their low storage requirements and relatively large stability limits. For computing acoustic waves, however, the stability consideration alone is not sufficient, since the Runge–Kutta schemes entail both dissipation and dispersion errors. The time step is now limited by the tolerable dissipation and dispersion errors in the computation. In the present paper, it is shown that if the traditional Runge–Kutta schemes are used for time advancing in acoustic problems, time steps greatly smaller than those allowed by the stability limit are necessary. Low-dissipation and low-dispersion Runge–Kutta (LDDRK) schemes are proposed, based on an optimization that minimizes the dissipation and dispersion errors for wave propagation. Optimizations of both single-step and two-step alternating schemes are considered. The proposed LDDRK schemes are remarkably more efficient than the classical Runge–Kutta schemes for acoustic computations. Moreover, low storage implementations of the optimized schemes are discussed. Special issues of implementing numerical boundary conditions in the LDDRK schemes are also addressed.