Data Decomposition Method Research Articles

An efficient tree structure for indexing feature vectors

This paper addresses the problem of feature indexing in feature vector space. A linked-node m-ary tree (LM-tree) structure is presented to quickly produce the queries for an approximate and exact nearest neighbour search. Three main contributions are made, which can be attributed to the proposed LM-tree. First, a new polar-space-based method of data decomposition is presented to construct the LM-tree. Second, a novel pruning rule is proposed to efficiently narrow down the search space. Finally, a bandwidth search method is introduced to explore the nodes of the LM-tree. Extensive experiments were performed to study the behaviour of the proposed indexing algorithm. These experimental results showed that the proposed algorithm provides a significant improvement in the search performance for the tasks of both exact and approximate nearest neighbour (ENN/ANN) searches.

Towards Scalable Parallelism in Monte Carlo Particle Transport Codes Using Remote Memory Access

One forthcoming challenge in the area of high-performance computing is having the ability to run large-scale problems while coping with less memory per compute node. In this work, we investigate a novel data decomposition method that would allow Monte Carlo transport calculations to be performed on systems with limited memory per compute node. In this method, each compute node remotely retrieves a small set of geometry and cross-section data as needed and remotely accumulates local tallies when crossing the boundary of the local spatial domain. Initial results demonstrate that while the method does allow large problems to be run in a memory-limited environment, achieving scalability may be difficult due to inefficiencies in the current implementation of RMA operations.

Topological solution of missing attribute values problem in incomplete information tables

In this paper, we present a new method of data decomposition to avoid the necessity of reasoning from data with missing attribute values. We define firstly a general binary relation on the original incomplete dataset. This binary relation generates data subsets without missing values. These data subsets are used to generate a topological base relation which decomposes datasets. We investigate a new approach to find the missing values in incomplete datasets. New pre-topological approximations are initiated and some of their properties are proved. Also, pre-topological measures are defined and studied. Finally, the reducts and the core of incomplete information system are determined.

On Parallel Numerical Algorithms for Simulating Industrial Filtration Problems

AbstractThe performance of oil filters used in the automotive industry can be significantly improved, especially when computer simulation is an essential component of the design process. In this paper, we consider parallel numerical algorithms for solving mathematical models describing the process of filtration, filtering solid particles out of liquid oil. The Navier — Stokes — Brinkmann system of equations is used to describe the laminar flow of incompressible isothermal oil. The space discretization in the complicated filter geometry is based on the finite-volume method. Special care is taken for an accurate approximation of the velocity and pressure on the interface between the fluid and the porous media. The time discretization used here is a proper modification of the fractional time step discretization (cf. Chorin scheme) of the Navier- Stokes equations, where the Brinkmann term is considered in both the prediction and the correction substeps. A data decomposition method is used to develop a parallel algorithm, where the domain is distributed among the processors by using a structured reference grid. The MPI library is used to implement the data communication part of the algorithm. A theoretical model is proposed for the estimation of the complexity of the given parallel algorithm and a scalability analysis is done on the basis of this model. The results of the computational experiments are presented, and the accuracy and efficiency of the parallel algorithm is tested on real industrial geometries.

A novel data decomposition and information translation method from CAD system to virtual assembly application

Virtual assembly (VA) is a key technology for virtual manufacturing systems. So far, CAD systems are still the main modeling tools for the VA system. There isn’t a standard data exchange criterion to transfer the data directly from CAD systems to VA applications, consequently an original data decomposition and information translation method (DDITM) was timely proposed to achieve the decomposition and translation. The information of the assembly bodies in the CAD system was divided into geometry information, topology information, and assembly information, etc., which were transferred to the VA application separately. The geometry information including the surface information was translated by the data translation interface (DTI) developed, the topology information was translated by a five-hierarchy topology structure (FHTS) constructed, and the assembly information was translated with database technology. A systematic architecture was formed with the interaction between the geometry information and the topology information, and the assembly information and the topology. Finally an experimental VA system was set up to verify the DDITM, and an assembly simulation was implemented to verify the assembly information further, which proves the translated information is precise and sufficient.

MARKET REPORT: Consumer Water & Air Purification Systems

The goal of sustainable development can be attained by the efficient management of renewable energy resources. Wind energy is attracting attention worldwide due to its renewable and sustainable nature. Accurate wind speed prediction is essential for the stable functioning of wind turbines to generate wind power. However, the flexible and intermittent nature of wind speed makes accurate wind speed forecasting a challenging task. The proposed wind speed forecasting framework combines the features of various data decomposition techniques and Bidirectional Long Short Term Memory (BiDLSTM) networks. Presently, Data Decomposition models such as the Wavelet Transform are extensively employed for wind speed forecasting to improve the accuracy of the forecasting models. Hence, in this paper, various data decomposition techniques that can denoise the signal are investigated and applied to partition the input time series into several high and low-frequency signals. The data decomposition methods, namely, Wavelet Transform, Empirical Model Decomposition, Ensemble Empirical Mode Decomposition, and Empirical Wavelet Transform, have been applied to denoise the dataset. The low and high-frequency sub-series are forecasted separately using Bidirectional LSTM networks, and the forecasting outcomes of low and high-frequency signals are aggregated to get the final forecasting results. The empirical results establish that the proposed EWT- based hybrid model outperforms other decomposition-based models in accuracy and stability. The performance of the EWT-BiDLSTM model is further compared with Bidirectional LSTM networks with skip connections. The experimental results substantiate that the proposed decomposition-based hybrid deep BiDLSTM models with skip connections exhibit better prediction accuracy than other models.

Data Adaptive Linear Decomposition Transform

Cadzow, J. A., and Yammen, S., Data Adaptive Linear Decomposition Transform, Digital Signal Processing 12 (2002) 494–523 In this paper a novel method for decomposing one-dimensional data sequences is developed. It is of the wavelet variety with important distinctions. A standard wavelet transform entails down-sampling the responses of two fixed structured filters to the sequence being decomposed to produce the half length detail sequence and a coarse sequence. The new transform entails processing down-sampled data by an optimum interpolation filter which seeks to approximate the odd indexed sequence elements by a linear combination of neighboring even indexed elements. The resulting interpolation error sequence of half length plays the role of the wavelet detail sequence. Unlike a traditional wavelet transform, the interpolation filter employed is adapted to the data being analyzed. This data dependency typically produces improved performance relative to traditional wavelet transforms. This enhanced performance is demonstrated on a number of standard test signals. Furthermore, this new transform is computationally efficient due to the inherent parallel structure of the data decomposition method.

Nonlinear multiresolution techniques with applications to scientific visualization in a haptic environment

This paper develops nonlinear multiresolution techniques for scientific visualization utilizing haptic methods. The visualization of data is critical to many areas of scientific pursuit. Scientific visualization is generally accomplished through computer graphic techniques. Recent advances in haptic technologies allow visual techniques to be augmented with haptic methods. The kinesthetic feedback provided through haptic techniques provides a second modality for visualization and allows for active exploration. Moreover, haptic methods can be utilized by individuals with visual impairments. Haptic representations of large data sets, however, can be confusing to a user, especially if a visual representation is not available or cannot be used. This paper develops a multiresolution data decomposition method based on the affine median filter. This results in a hybrid structure that can be tuned to yield a decomposition that varies from a linear wavelet decomposition to that produced by the median filter. The performance of this hybrid structure is analyzed utilizing deterministic signals and statistically in the frequency domain. This analysis and qualitative and quantitative implementation results show that the affine median decomposition has advantages over previously proposed methods. In addition to multiresolution decomposition development, analysis, and results, haptic implementation methods are presented.

TACTICAL ROUTE PLANNING: NEW ALGORITHMS FOR DECOMPOSING THE MAP

This paper defines a new approach and investigates a fundamental problem in route planners. This capability is important for robotic vehicles(Martian Rovers, etc.) and for planning off-road military maneuvers. The emphasis throughout this paper will be on the design and analysis and hieiaichical implementation of our route planner. This work was motivated by anticipation of the need to search a grid of a trillion points for optimum routes. This cannot be done simply by scaling upward from the algorithms used to search a grid of 10,000 points. Algorithms sufficient for the small grid are totally inadequate for the large grid. Soon, the challenge will be to compute off-road routes more than 100 km long and with a one or two-meter grid. Previous efforts are reviewed and the data structures, decomposition methods and search algorithms are analyzed and limitations are discussed. A detailed discussion of a hieraichical implementation is provided and the experimental results are analyzed.

The Use of Specially Structured Models for Obtaining Bounds in the Quadratic Assignment Problem

In this paper we study a procedure for finding bounds for the quadratic assignment problem. This procedure may be used as a sub-routine in hybrid procedures for solving this problem. The approach is based upon a data decomposition method, linking the actual data to the data of a special class of assignment problems for which bounds are computationally tractable.