Structural Decomposition Method Research Articles

Loosely coupled fusion of camera and inertial sensors for distributed error compensation in strapdown inertial navigation system

The problem of error estimation and compensation in strapdown inertial navigation system (SINS) is investigated in this paper. The error dynamic model is derived and employed for this purpose. Information gathered from a micro-electro-mechanical inertial measurement unit (MEMS IMU) is fused with camera information in a loosely coupled integration scenario. Although this integration is computationally efficient, it suffers from fault propagation in the navigation algorithm. To overcome this problem, it is proposed in this paper to estimate and compensate for the propagated faults in a timely manner to provide a short start-up and computation time. This goal is achieved through the design of decentralized error estimation and compensation algorithms running in parallel with the inertial navigation system. To this end, firstly, the sparse error system is decomposed to cascaded subsystems using a combination of structural and behavioural decomposition methods. Then, cascaded Kalman filters (KFs) and decentralized state feedback regulators are designed for error estimation and compensation, respectively. The experimental results based on data from the 3D MEMS IMU and camera system are provided to demonstrate the efficiency of the proposed method.

Structural decomposition of a polynomial transition function into a collection of linear automation links

We consider a method of structural decomposition for an object's polynomial transition function obtained from an output transition function into a set of structural circuits consisting of standard automation links. The choice of an "optimal" structural circuit is done with the minimal "entropy" criterion.

Materialization Optimizations for Feature Selection Workloads

There is an arms race in the data management industry to support statistical analytics. Feature selection, the process of selecting a feature set that will be used to build a statistical model, is widely regarded as the most critical step of statistical analytics. Thus, we argue that managing the feature selection process is a pressing data management challenge. We study this challenge by describing a feature selection language and a supporting prototype system that builds on top of current industrial R-integration layers. From our interactions with analysts, we learned that feature selection is an interactive human-in-the-loop process, which means that feature selection workloads are rife with reuse opportunities. Thus, we study how to materialize portions of this computation using not only classical database materialization optimizations but also methods that have not previously been used in database optimization, including structural decomposition methods (like QR factorization) and warmstart. These new methods have no analogue in traditional SQL systems, but they may be interesting for array and scientific database applications. On a diverse set of datasets and programs, we find that traditional database-style approaches that ignore these new opportunities are more than two orders of magnitude slower than an optimal plan in this new trade-off space across multiple R backends. Furthermore, we show that it is possible to build a simple cost-based optimizer to automatically select a near-optimal execution plan for feature selection.

Free-Form Interest Rate Term Structure Decomposition: A 2nd Order Optimization Problem

The paper discusses an interest rate term structure decomposition method that breaks from the conventional, in that it does not superimpose any model, form or structure on the decomposition output – hence, the term free-form. The premise is simple: if the model does not presuppose any structure beforehand, and if the structure underlying the input data is indeed that implemented and applied by conventional models, and if the model is not internally constrained by method itself, such that its model power can hardly be drawn into question, it should have no problem surfacing such structures. On the other hand, if the structure underlying the input data in fact does not necessarily follow a presupposed structure, the model should achieve greater modelling accuracy, and offer greater explanatory power. This in itself may also offer a better reflection on cases and scenarios where the modelling residual is significant, for instance, in the case of modelling (corporate) vanilla bonds.To realize such a model, a 2nd (higher) order or nested optimization problem – a series of optimizations pertaining to the same problem, and resulting from floating one or more constraints – is defined, and solved. The model is applied to a simulated term structure, as well as a market sample of government vanilla bonds, and the results are discussed.

Automatic feature extraction based structure decomposition method for multi-classification

For years, researchers in neural network (NN) area have been carried out much productive research in improving the generalization ability of NNs. In this paper, a novel neural network design algorithm is presented for solving multi-class problems, structure decomposition based on Skeletonization (SDBSkeletonization), which is to simplify NNs further. The proposed method decomposes a complex multi-class problem into a set of two-class problems, each of which can be regarded as an individual problem. After learning all these individual problems in parallel with Skeletonization algorithm, we then integrate these results to final decision. In addition, Skeletonization solves the classification problem based on automatic feature extraction. This perspective gives a broader range of application of our method. Our experimental results on Waveform and Handwritten Digits database demonstrate that SDBSkeletonization improves the overall classification performance.

Assessing driving factors of regional water use in production sectors using a structural decomposition method: a case study in Jiangsu Province, China

A key issue in water resources management in China is the adoption of a total volume control framework of water supply at a regional level corresponding to socioeconomic development. This requires more efficient water demand management in order to achieve a balance between water supply and demand. The purpose of this paper is to conduct a thorough analysis of the water use structure of production sectors in order to identify the major impact factors and thus provide insights for demand management policy design. Taking Jiangsu Province as a case study, we compile a series of extended input-output (I-O) tables at a constant price and establish an I-O structural decomposition model. The major factors leading to change in water use by production sectors (primary, secondary and tertiary) in the Jiangsu Province during five time periods from 1997–2010 are categorized into structure effect, water use efficiency effect, and demand effect for each of three levels: (1) aggregated sectors, (2) sub-aggregated sectors, and (3) individual sectors. Within the study period, the demand effect consistently leads to an increase in water utilization and the increase effect becomes weaker over time, while the other two factors consistently lead to a decrease in water utilization and their dampening effect becomes stronger.

CO2 emissions of China's food industry: an input–output approach

In a joint U.S.–China statement on climate change, the Chinese government declared to peak CO2 emissions around 2030. This demonstrates the government's determination to deal with carbon emission and climate change. Although the food industry is not a carbon emission-intensive industry, its large scale makes the emission reduction in the industry very important. Based on the input–output structural decomposition method, this paper calculates the CO2 emissions of China's food industry from 1991 to 2012, and decomposes the change in CO2 emissions of the industry during 1992–1997, 1997–2002, 2002–2007 and 2007–2010 into four main effects: emission factor, energy structure, energy intensity, and total output (including four sub-effects: intermediate use, domestic final demand, import substitution and export extension). The results show that changes in CO2 emissions in the food industry mainly depends on total output effect and energy intensity effect. Energy intensity effect is the most important factor reducing CO2 emissions, as it reduced cumulative 213 million tons (Mt) CO2 emissions of the industry. Among total output effect, the effects of intermediate use and domestic final demand are the two biggest contributors to carbon emissions. Finally, we provide some policy advices for constraining and reducing CO2 emissions of China's food industry. The advices include increasing R&D investment and the substitution of energy with other input factors to decrease energy intensity, increasing the added value of the food industry, optimizing the energy structure with more clean and low-carbon energy, and maintaining the prices of raw materials of the food industry (i.e. agricultural products) with taxes or subsidies.

A general equilibrium, ex-post evaluation of the EU–Chile Free Trade Agreement

This paper evaluates the impact on the Chilean economy of the EU–Chile Free Trade Agreement, in force since 2003, based on a computable general equilibrium (CGE) model. The evaluation method, inspired by structural decomposition methods, consists of double calibration of the model, to account for interactions between the agreement's impact and structural change in the Chilean economy. Trade flows are modeled at the detailed product level (six-digit level classification). The agreement is found to be slightly beneficial to Chile's economy on the whole, benefiting mainly unskilled labor, with gains concentrated in few sectors (fruits, wine, fisheries and seafood processing).

An event-based distributed diagnosis framework using structural model decomposition

Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.

DISASTER AND ECONOMIC STRUCTURAL CHANGE: CASE STUDY ON THE 1995 KOBE EARTHQUAKE

In 1995, the Kobe Earthquake occurred in the second largest economic region of Japan, and its economic damages were accounted around 10 trillion yen. A catastrophic event of this magnitude would have surely created some long-run effects to the regional economy as well as to the surrounding regions. Additionally, the recovery and reconstruction activities would have affected the economic structure of the region and interdependence between regions in a potentially different way from the original growth trend before the event. While these long-run economic effects may have become sizable, few studies have been conducted to empirically measure or evaluate such effects, due to the significant noises in economic data muddled with macroeconomic influences from the outside. This paper presents an empirical investigation of long-run economic effects of the Kobe Earthquake, using structural decomposition methods. The results indicate significant changes in economic structure of the Kobe economy, and the changes are quite different across sectors and among factors. An additional investigation using shift-share analysis yielded the regional-specific changes; the corresponding decomposed factors of structural analysis with shift-share results appear complicated, and changes in regional final demand were found to be most influential to the changes in output for many sectors.

Tree projections and structural decomposition methods: Minimality and game-theoretic characterization

Tree projections provide a mathematical framework that encompasses all the various (purely) structural decomposition methods that have been proposed in the literature to single out classes of nearly-acyclic (hyper)graphs, such as the tree decomposition method, which is the most powerful decomposition method on graphs, and the (generalized) hypertree decomposition method, which is its natural counterpart on arbitrary hypergraphs.The paper analyzes this framework, by focusing in particular on “minimal” tree projections, that is, on tree projections without useless redundancies. First, it is shown that minimal tree projections enjoy a number of properties that are usually required for normal form decompositions in various structural decomposition methods. In particular, they enjoy the same kind of connection properties as (minimal) tree decompositions of graphs, with the result being tight in the light of the negative answer that is provided to the open question about whether they enjoy a slightly stronger notion of connection property, defined to speed-up the computation of hypertree decompositions. Second, it is shown that tree projections admit a natural game-theoretic characterization in terms of the Robber and Captain game. In this game, as for the Robber and Cops game characterizing tree decompositions, the existence of winning strategies implies the existence of monotone ones. As a special case, the Robber and Captain game can be used to characterize the generalized hypertree decomposition method, where such a game-theoretic characterization was missing and asked for. Besides their theoretical interest, these results have immediate algorithmic applications both for the general setting and for structural decomposition methods that can be recast in terms of tree projections.

Cylinder Detection in Large-Scale Point Cloud of Pipeline Plant

The huge number of points scanned from pipeline plants make the plant reconstruction very difficult. Traditional cylinder detection methods cannot be applied directly due to the high computational complexity. In this paper, we explore the structural characteristics of point cloud in pipeline plants and define a structure feature. Based on the structure feature, we propose a hierarchical structure detection and decomposition method that reduces the difficult pipeline-plant reconstruction problem in IR³ into a set of simple circle detection problems in IR². Experiments with industrial applications are presented, which demonstrate the efficiency of the proposed structure detection method.

The impact of crude oil price on the tanker market

The demand for crude oil transport services is a derived demand, which is dependent on international trade of crude oil and crude oil carriers transport crude oil from producer regions to consumer regions. The purpose of this study is to investigate the relationship between fluctuations in oil prices and the freight market (hereinafter referred to as ‘the tanker market’) using a structural vector autoregressive (SVAR) model. Referring to Kilian and Park's method of structural decomposition of fluctuations in crude oil prices, crude oil price shocks are classified into crude oil supply shocks and crude oil non-supply shocks in this study. Also, the response of the tanker market to different shocks is examined using the impulse response analysis. The empirical results provide evidence that crude oil supply shocks have significant effects on the contemporaneous tanker market. However, the impact of crude oil non-supply shocks is insignificant. In addition, the accumulated responses of the tanker market to crude oil non-supply shocks and crude oil supply shocks are positive but they are far lower than other shocks to the tanker market. Empirical results from this research are consistent with the fact of high crude oil tanker freight rates but low profits for tanker operators.

A Study on Module Clustering and Evaluation of Large Gantry Machining Centers

Due to the various requirements of users and large volume of machine tools, most machine tool manufacturers are adopting the small-lot production mode, which largely prolongs the product development cycle. To raise the design efficiency and reduce the complexity, modular design is becoming widely used in serial production of machine tools, especially large gantry machining centers. In this paper, we introduced a function and structure decomposition method to construct the function and structure correlations between components and employed a clustering method based on atomic theory to solve the module clustering problems. The clustering algorithm is modified by integrating the function and structure matrices in order that it applies to modularization of large machine tools. Furthermore, an evaluation method based on the concept of entropy is proposed here to evaluate and optimal select module partition schemes. Finally, we took the large gantry machining center QLMT6300 as an example and compiled the program with Microsoft Visual C++ 6.0 to illustrate the validity of the methods proposed. The presented clustering and evaluation method can be easily applied to different machine tools for modular design, reducing the work amount of designers.

Trade and the environment: An application of the WIOD database

The new WIOD database allows for improved empirical analysis on a wide range of important environmental research questions. In this paper we demonstrate the scientific power of the WIOD database and analyze very urgent policy questions on the impacts of international trade and structural change on the environment. We apply recent econometric approaches to show the impact of international trade on the environment via its different channels as for instance to increase welfare and potentially affects environmental regulation as well as countries’ sector. This approach has become known as the econometric structural decomposition method. In addition to this guidelines by the literature, an econometric panel data approach is offered to shed some light on the impact of structural change and international trade on environmental pressure, where we especially address and solve several endogeneity issues that add further complexity to the analysis.

A Hierarchical Structure Decomposition Method of a Product Model in System Architecting

A Hierarchical Structure Decomposition Method of a Product Model in System Architecting

Trade and the Environment: An Application of the WIOD Database

The new WIOD database allows for improved empirical analysis on a wide range of important environmental research questions. In this paper we demonstrate the scientific power of the WIOD database and analyze very urgent policy questions on the impacts of international trade and structural change on the environment. We apply recent econometric approaches to show the impact of international trade on the environment via its different channels as for instance to increase welfare and potentially affects environmental regulation as well as countries’ sector. This approach has become known as the econometric structural decomposition method. In addition to this guidelines by the literature, an econometric panel data approach is offered to shed some light on the impact of structural change and international trade on environmental pressure, where we especially address and solve several endogeneity issues that add further complexity to the analysis.

Inactivation of the Heme Degrading Enzyme IsdI by an Active Site Substitution That Diminishes Heme Ruffling

IsdG and IsdI are paralogous heme degrading enzymes from the bacterium Staphylococcus aureus. Heme bound by these enzymes is extensively ruffled such that the meso-carbons at the sites of oxidation are distorted toward bound oxygen. In contrast, the canonical heme oxygenase family degrades heme that is bound with minimal distortion. Trp-66 is a conserved heme pocket residue in IsdI implicated in heme ruffling. IsdI variants with Trp-66 replaced with residues having less bulky aromatic and alkyl side chains were characterized with respect to catalytic activity, heme ruffling, and electrochemical properties. The heme degradation activity of the W66Y and W66F variants was approximately half that of the wild-type enzyme, whereas the W66L and W66A variants were inactive. A crystal structure and NMR spectroscopic analysis of the W66Y variant reveals that heme binds to this enzyme with less heme ruffling than observed for wild-type IsdI. The reduction potential of this variant (-96 ± 7 mV versus standard hydrogen electrode) is similar to that of wild-type IsdI (-89 ± 7 mV), so we attribute the diminished activity of this variant to the diminished heme ruffling observed for heme bound to this enzyme and conclude that Trp-66 is required for optimal catalytic activity.

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.

Calculation and decomposition of indirect carbon emissions from residential consumption in China based on the input–output model

Based on the input–output model and the comparable price input–output tables, the current paper investigates the indirect carbon emissions from residential consumption in China in 1992–2005, and examines the impacts on the emissions using the structural decomposition method. The results demonstrate that the rise of the residential consumption level played a dominant role in the growth of residential indirect emissions. The persistent decline of the carbon emission intensity of industrial sectors presented a significant negative effect on the emissions. The change in the intermediate demand of industrial sectors resulted in an overall positive effect, except in the initial years. The increase in population prompted the indirect emissions to a certain extent; however, population size is no longer the main reason for the growth of the emissions. The change in the consumption structure showed a weak positive effect, demonstrating the importance for China to control and slow down the increase in the emissions while in the process of optimizing the residential consumption structure. The results imply that the means for restructuring the economy and improving efficiency, rather than for lowering the consumption scale, should be adopted by China to achieve the targets of energy conservation and emission reduction.