Model Decomposition Technique Research Articles

Gradient‐based iterative parameter estimation for bilinear‐in‐parameter systems using the model decomposition technique

The parameter estimation issues of a block-oriented non-linear system that is bilinear in the parameters are studied, i.e. the bilinear-in-parameter system. Using the model decomposition technique, the bilinear-in-parameter model is decomposed into two fictitious submodels: one containing the unknown parameters in the non-linear block and the other containing the unknown parameters in the linear dynamic one and the noise model. Then a gradient-based iterative algorithm is proposed to estimate all the unknown parameters by formulating and minimising two criterion functions. The stochastic gradient algorithms are provided for comparison. The simulation results indicate that the proposed iterative algorithm can give higher parameter estimation accuracy than the stochastic gradient algorithms.

Agricultural Output And Macroeconomic Policy: Examining The Delicate Linkage in Nigeria

The Nigeria's agricultural sector performance has remained dismal, characterized by years of inertia and unpredictability in production and marketed volume, even though it is one of the leading sector of the economy contributing about 75% to its non-oil exports. Agricultural economists have shown that there exists a macroeconomic policy–agricultural sector linkage. This study assesses the short-run and long-run linkages of macroeconomic policies with real agricultural output in Nigeria (1980:Q1 - 2014:Q4) by a Vector Error Correction Model (VECM) and Variance Decomposition techniques. It adopts the Augmented Dickey Fuller (ADF) test for stationarity of each variable, and the Dickey-Fuller Generalised Least Square (DF-GLS) and Phillips-Perron (PP) tests for selected variable(s). However, since the ADF test is sensitive to the choice of lag order, the Akaike Information Criteria (AIC) is used first to determine lag lengths for the unit root test. The Granger-causality test is employed to investigate feedbacks while also garnering crucial insights from variance decomposition. The macroeconomic variables used are the nominal money supply, interest rate, inflation rate and real exchange rate as endogenous variables. The normalised co-integration result of the VEC model showed that in the long-run, inflation and money supply are the two macroeconomic variables that are statistically significant in explaining variation in real agricultural output, with inflation having the most impact on real agricultural output while the real interest rate is not a significant determinant of real agricultural output. The Toda and Yamamoto Granger non-causality also lends credence to the estimates of the other techniques and shows that the inflation rate Granger-causes real agricultural output in Nigeria, implying that the past values of inflation could help in predicting the current value of real agricultural output. The variance decomposition test revealed that the inflation rate is the highest contributor to the forecast error variance of real agricultural output both in the short and long-run. Going by the insignificance of interest rate, the study recommends the need for policy makers to administer considerate rate of interests while also keeping the price level stable in order to raise effective demand and investment and consequently boost real agricultural output in Nigeria.

Updating finite element model using frequency domain decomposition method and bees algorithm

The following study deals with the updating the finite element model of structures using the operational modal analysis. The updating process uses an evolutionary optimization algorithm, namely bees algorithm which applies instinctive behavior of honeybees for finding food sources. To determine the uncertain updated parameters such as geometry and material properties of the structure, local and global sensitivity analyses have been performed. The sum of the squared errors between the natural frequencies obtained from operational modal analysis and the finite element method is used to define the objective function. The experimental natural frequencies are determined by frequency domain decomposition technique which is considered as an efficient operational modal analysis method. To verify the accuracy of the proposed algorithm, it is implemented on a three-story structure to update its finite element model. Moreover, to study the efficiency of bees algorithm, its results are compared with those particle swarm optimization and Nelder and Mead methods. The results show that this algorithm leads more accurate results with faster convergence. In addition, modal assurance criterion is calculated for updated finite element model and frequency domain decomposition technique. Moreover, finding the best locations of acceleration and shaker mounting in order to accurate experiments are explained.

Decentralized adaptive neural network sliding mode position/force control of constrained reconfigurable manipulators

A decentralized adaptive neural network sliding mode position/force control scheme is proposed for constrained reconfigurable manipulators. Different from the decentralized control strategy in multi-manipulator cooperation, the proposed decentralized position/force control scheme can be applied to series constrained reconfigurable manipulators. By multiplying each row of Jacobian matrix in the dynamics by contact force vector, the converted joint torque is obtained. Furthermore, using desired information of other joints instead of their actual values, the dynamics can be represented as a set of interconnected subsystems by model decomposition technique. An adaptive neural network controller is introduced to approximate the unknown dynamics of subsystem. The interconnection and the whole error term are removed by employing an adaptive sliding mode term. And then, the Lyapunov stability theory guarantees the stability of the closed-loop system. Finally, two reconfigurable manipulators with different configurations are employed to show the effectiveness of the proposed decentralized position/force control scheme.

Dynamic Performance Analysis of Uplink Transmission in Cluster-Based Heterogeneous Vehicular Networks

As an emerging technology, vehicular networks have evolved into an indispensable part of intelligent transport systems (ITS). Thus, this paper focuses on the performance analysis of the cluster-based heterogeneous vehicular networks (C-HetVNETs) with dynamic data arrival rate and different vehicle densities. Different from the previous studies, which mainly focus on network capacity in just one wireless-technology-supported vehicular network, such as dedicated short-range communication (DSRC) or Third-Generation Partnership Project (3GPP) long-term evolution (LTE), we focus on the different performance metrics of uplink transmission in C-HetVNET. First, we develop a framework of C-HetVNET by employing the cluster mechanism. Then, the performance analysis models of intracluster and intercluster communications are designed based on a Markov queuing model. Due to the explosion of state space, model decomposition and iteration techniques are proposed to handle this problem. Based on the developed analytical model, the performance metrics of different scheduling schemes, in terms of throughput, delay, dropping probability, and queue length, are studied and compared. The performances of C-HetVNETs under different vehicle densities and data arrival rates are obtained via analysis and simulations, which give a useful guideline for network configuration of C-HetVNETs. The proposed analytical model can be also used for heterogeneous or two-tier network analysis and validation of network simulators under various traffic conditions. In addition, we investigate the performances of LTE networks used for vehicular communications for the purpose of performance comparison.

Finding a maximum k-club using the k-clique formulation and canonical hypercube cuts

Detecting low-diameter clusters is an important graph-based data mining technique used in social network analysis, bioinformatics and text-mining. Low pairwise distances within a cluster can facilitate fast communication or good reachability between vertices in the cluster. Formally, a subset of vertices that induce a subgraph of diameter at most k is called a k-club. For low values of the parameter k, this model offers a graph-theoretic relaxation of the clique model that formalizes the notion of a low-diameter cluster. Using a combination of graph decomposition and model decomposition techniques, we demonstrate how the fundamental optimization problem of finding a maximum size k-club can be solved optimally on large-scale benchmark instances that are available in the public domain. Our approach circumvents the use of complicated formulations of the maximum k-club problem in favor of a simple relaxation based on necessary conditions, combined with canonical hypercube cuts introduced by Balas and Jeroslow.

Energy efficiency of China's industry sector: An adjusted network DEA (data envelopment analysis)-based decomposition analysis

The process of energy conservation and emission reduction in China requires the specific and accurate evaluation of the energy efficiency of the industry sector because this sector accounts for 70 percent of China's total energy consumption. Previous studies have used a “black box” DEA (data envelopment analysis) model to obtain the energy efficiency without considering the inner structure of the industry sector. However, differences in the properties of energy utilization (final consumption or intermediate conversion) in different industry departments may lead to bias in energy efficiency measures under such “black box” evaluation structures. Using the network DEA model and efficiency decomposition technique, this study proposes an adjusted energy efficiency evaluation model that can characterize the inner structure and associated energy utilization properties of the industry sector so as to avoid evaluation bias. By separating the energy-producing department and energy-consuming department, this adjusted evaluation model was then applied to evaluate the energy efficiency of China's provincial industry sector.

Computer-aided modelling template: Concept and application

Modelling is an important enabling technology in modern chemical engineering applications. A template-based approach is presented in this work to facilitate the construction and documentation of the models and enable their maintenance for reuse in a wider application range. Based on a model decomposition technique which identifies generic steps and workflow involved, the computer-aided template concept has been developed. This concept is implemented as a software tool, which provides a user-friendly interface for following the workflow steps and guidance through the steps providing additional information and comments on model construction, storage and future use/reuse. The application of the tool is highlighted with a multi-scale modelling case study involving a catalytic membrane fixed bed reactor and a two-phase system for oxidation of unsaturated acid with hydrogen peroxide. Both case studies reflect different aspects of template creation and use with respect to model development.

A generic model decomposition technique and its application to the Eclipse modeling framework

Model-driven software development aims at easing the process of software development by using models as primary artifacts. Although less complex than the real systems, they are based on models tend to be complex nevertheless, thus making the task of handling them non-trivial in many cases. In this paper, we propose a generic model decomposition technique to facilitate model management by decomposing complex models into smaller sub-models that conform to the same metamodel as the original model. The technique is based upon a formal foundation that consists of a formal capturing of the concepts of models, metamodels, and model conformance; a formal constraint language based on EssentialOCL; and a set of formally proved properties of the technique. We organize the decomposed sub-models in a mathematical structure as a lattice, and design a linear-time algorithm for constructing this decomposition. The generic model decomposition technique is applied to the Eclipse modeling framework, and the result is used to build a solution to a specific model comprehension problem of Ecore models based upon model pruning. We report two case studies of the model comprehension method: one in BPMN and the other in fUML.

Using structural decomposition methods to design gray-box models for fault diagnosis of complex industrial systems: a beet sugar factory case study

Reliable and timely fault detection and isolation are necessary tasks to guarantee continuous performance in complex industrial systems, avoiding failure propagation in the system and helping to minimize downtime. Model-based diagnosis fulfils those requirements, and has the additional advantage of using reusable models. However, reusing existing complex non-linear models for diagnosis in large industrial systems is not straightforward. Most of the times the models have been created for other purposes different from diagnosis, and many times the required analytical redundancy is small. In this work we propose to use Possible Conflicts, which is a model decomposition technique, to provide the structure (equations, inputs, outputs, and state variables) of minimal models able to perform fault detection and isolation. Such structural information can be used to design a gray box model by means of state space neural networks. We demonstrate the feasibility of the approach in an evaporator for a beet sugar factory using real data.

Improving Multiple Fault Diagnosability using Possible Conflicts

Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.

Modal Decomposition Solution of Lamb Wave Scattering at Step-Like Discontinuity

Modal decomposition method is utilized to investigate Lamb wave scattering at step-like discontinuity. The structure is regard as a special delamination structure where one or two delamination layers are missing and only one delamination layer extended to infinite. Model decomposition technique is then combined with the boundary condition equations to get the scattering solution of the whole discontinuity region. The efficacy of the method is illustrated by calculation examples where the scattering of the second symmetric mode at step-discontinuity with different thickness are evaluated and Energy flux conservation is confirmed.

Parallel simulation of a large-scale aerospace system in a multicomputer environment

This work describes the application of newly developed parallel processing techniques that exploit the irregularly-structured functional parallelism present within a test-bed aerospace system (i.e., a Space Shuttle main rocket engine simulation) utilizing a reduced complexity multicomputing architecture with an arbitrary topology. The methodologies are applicable to real time parallel simulation of large-scale dynamic systems and include deterministic model optimization and decomposition techniques, automated translation mechanisms, and a newly developed task allocation heuristic. The effectiveness of these techniques are illustrated with performance being measured empirically for a wide range of topological configurations using a partially reconfigurable network of Transputers.