Multivariable Repetitive-predictive Controllers using Frequency Decomposition

Repetitive control is a methodology for the tracking of a periodic reference signal. This paper develops a new approach to repetitive control systems design using receding horizon control with frequency decomposition of the reference signal. Moreover, design and implementation issues for this form of repetitive predictive control are investigated from the perspectives of controller complexity and the effects of measurement noise. The analysis is supported by a simulation study on a multi-input multi-output robot arm where the model has been constructed from measured frequency response data, and experimental results from application to an industrial AC motor

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model

Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships

CVM studies on the atomic ordering in complex perovskite alloys

The atomic ordering in complex perovskite alloys is investigated by the cluster variation method (CVM). For the 1/3\{111\}-type ordered structure, the order-disorder phase transition is the first order, and the order parameter of the 1:2 complex perovskite reaches its maximum near x=0.25. For the 1/2\{111\}-type ordered structure, the ordering transition is the second order. Phase diagrams for both ordered structures are obtained. The order-disorder line obeys the linear law.Comment: 10 pages, 6 figure

Assessing the UK policies for broadband adoption

Broadband technology has been introduced to the business community and the public as a rapid way of exploiting the Internet. The benefits of its use (fast reliable connections, and always on) have been widely realised and broadband diffusion is one of the items at the top of the agenda for technology related polices of governments worldwide. In this paper an examination of the impact of the UK government’s polices upon broadband adoption is undertaken. Based on institutional theory a consideration of the manipulation of supply push and demand pull forces in the diffusion of broadband is offered. Using primary and secondary data sources, an analysis of the specific institutional actions related to IT diffusion as pursued by the UK government in the case of broadband is provided. Bringing the time dimension into consideration it is revealed that the UK government has shifted its attention from supply push-only strategies to more interventional ones where the demand pull forces are also mobilised. It is believed that this research will assist in the extraction of the “success factors” in government intervention that support the diffusion of technology with a view to render favourable results if applied to other national settings

Charge transfer electrostatic model of compositional order in perovskite alloys

We introduce an electrostatic model including charge transfer, which is shown to account for the observed B-site ordering in Pb-based perovskite alloys. The model allows charge transfer between A-sites and is a generalization of Bellaiche and Vanderbilt's purely electrostatic model. The large covalency of Pb^{2+} compared to Ba^{2+} is modeled by an environment dependent effective A-site charge. Monte Carlo simulations of this model successfully reproduce the long range compositional order of both Pb-based and Ba-based complex A(BB^{'}B^{''})O_3 perovskite alloys. The models are also extended to study systems with A-site and B-site doping, such as (Na_{1/2}La_{1/2})(Mg_{1/3}Nb_{2/3})O_3, (Ba_{1-x}La_{x})(Mg_{(1+x)/3}Nb_{(2-x)/3})O_3 and (Pb_{1-x}La_{x})(Mg_{(1+x)/3}Ta_{(2-x)/3})O_3. General trends are reproduced by purely electrostatic interactions, and charge transfer effects indicate that local structural relaxations can tip the balance between different B-site orderings in Pb based materials.Comment: 15 pages, 6 figure

Evaluation of stem rot in 339 Bornean tree species: implications of size, taxonomy, and soil-related variation for aboveground biomass estimates

Fungal decay of heart wood creates hollows and areas of reduced wood density within the stems of living trees known as stem rot. Although stem rot is acknowledged as a source of error in forest aboveground biomass (AGB) estimates, there are few data sets available to evaluate the controls over stem rot infection and severity in tropical forests. Using legacy and recent data from 3180 drilled, felled, and cored stems in mixed dipterocarp forests in Sarawak, Malaysian Borneo, we quantified the frequency and severity of stem rot in a total of 339 tree species, and related variation in stem rot with tree size, wood density, taxonomy, and species’ soil association, as well as edaphic conditions. Predicted stem rot frequency for a 50 cm tree was 53% of felled, 39% of drilled, and 28% of cored stems, demonstrating differences among methods in rot detection ability. The percent stem volume infected by rot, or stem rot severity, ranged widely among trees with stem rot infection (0.1–82.8 %) and averaged 9% across all trees felled. Tree taxonomy explained the greatest proportion of variance in both stem rot frequency and severity among the predictors evaluated in our models. Stem rot frequency, but not severity, increased sharply with tree diameter, ranging from 13% in trees 10–30 cm DBH to 54%in stems ≥ 50 cm DBH across all data sets. The frequency of stem rot increased significantly in soils with low pH and cation concentrations in topsoil, and stem rot was more common in tree species associated with dystrophic sandy soils than with nutrient-rich clays. When scaled to forest stands, the maximum percent of stem biomass lost to stem rot varied significantly with soil properties, and we estimate that stem rot reduces total forest AGB estimates by up to 7% relative to what would be predicted assuming all stems are composed strictly of intact wood. This study demonstrates not only that stem rot is likely to be a significant source of error in forest AGB estimation, but also that it strongly covaries with tree size, taxonomy, habitat association, and soil resources, underscoring the need to account for tree community composition and edaphic variation in estimating carbon storage in tropical forests

Site-Selective Spectroscopy And Crystal-Field Analysis For Nd3+ In Strontium Fluorovanadate

Site‐selective spectroscopy reveals that Nd3+ ions occupy more than 40 different crystal‐field environments in Sr5(VO4)3F. Preferential energy transfer to the site responsible for 1 μm lasing occurs but becomes less complete with increasing temperature. The 4I and 4F3/2 Stark levels of the lasing site have been determined and an analysis of the crystal field performed. From the crystal‐field fitting parameters Bkq, a calculated energy‐level spectrum is determined up to 17 500 cm−1 with a rms deviation from the available experimental levels of 6 cm−1

Electrostatic model of atomic ordering in complex perovskite alloys

We present a simple ionic model which successfully reproduces the various types of compositional long-range order observed in a large class of complex insulating perovskite alloys. The model assumes that the driving mechanism responsible for the ordering is simply the electrostatic interaction between the different ionic species. A possible new explanation for the anomalous long-range order observed in some Pb relaxor alloys, involving the proposed existence of a small amount of Pb^4+ on the B sublattice, is suggested by an analysis of the model.Comment: 4 pages, two-column style with 1 postscript figure embedded. Uses REVTEX and epsf macros. Also available at http://www.physics.rutgers.edu/~dhv/preprints/index.html#lb_orde

A Hybrid Time-Scaling Transformation for Time-Delay Optimal Control Problems

In this paper, we consider a class of nonlinear time-delay optimal control problems with canonical equality and inequality constraints. We propose a new computational approach, which combines the control parameterization technique with a hybrid time-scaling strategy, for solving this class of optimal control problems. The proposed approach involves approximating the control variables by piecewise constant functions, whose heights and switching times are decision variables to be optimized. Then, the resulting problem with varying switching times is transformed, via a new hybrid time-scaling strategy, into an equivalent problem with fixed switching times, which is much preferred for numerical computation. Our new time-scaling strategy is hybrid in the sense that it is related to two coupled time-delay systems—one defined on the original time scale, in which the switching times are variable, the other defined on the new time scale, in which the switching times are fixed. This is different from the conventional time-scaling transformation widely used in the literature, which is not applicable to systems with time-delays. To demonstrate the effectiveness of the proposed approach, we solve four numerical examples. The results show that the costs obtained by our new approach are lower, when compared with those obtained by existing optimal control methods