Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction

<p>A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. Forward selection methods are popular fast subset selection approaches. However, they may only produce suboptimal models and can be trapped into a local minimum. More recently, a two-stage fast recursive algorithm (TSFRA) combining forward selection and backward model refinement has been proposed to improve the compactness and generalization performance of the model. This paper proposes unified two-stage orthogonal least squares methods instead of the fast recursive-based methods. In contrast to the TSFRA, this paper derives a new simplified relationship between the forward and the backward stages to avoid repetitive computations using the inherent orthogonal properties of the least squares methods. Furthermore, a new term exchanging scheme for backward model refinement is introduced to reduce computational demand. Finally, given the error reduction ratio criterion, effective and efficient forward and backward subset selection procedures are proposed. Extensive examples are presented to demonstrate the improved model compactness constructed by the proposed technique in comparison with some popular methods.</p>

Kernel based approaches to local nonlinear non-parametric variable selection

In this paper, we consider the variable selection problem for a nonlinear non-parametric system. Two approaches are proposed, one top-down approach and one bottom-up approach. The top-down algorithm selects a variable by detecting if the corresponding partial derivative is zero or not at the point of interest. The algorithm is shown to have not only the parameter but also the set convergence. This is critical because the variable selection problem is binary, a variable is either selected or not selected. The bottom-up approach is based on the forward/backward stepwise selection which is designed to work if the data length is limited. Both approaches determine the most important variables locally and allow the unknown non-parametric nonlinear system to have different local dimensions at different points of interest. Further, two potential applications along with numerical simulations are provided to illustrate the usefulness of the proposed algorithms.

Accurate image capturing control of bottle caps based on iterative learning control and Kalman filtering

This paper investigates camera control for capturing bottle cap target images in the fault-detection system of an industrial production line. The main purpose is to identify the targeted bottle caps accurately in real time from the images. This is achieved by combining iterative learning control and Kalman filtering to reduce the effect of various disturbances introduced into the detection system. A mathematical model, together with a physical simulation platform is established based on the actual production requirements, and the convergence properties of the model are analyzed. It is shown that the proposed method enables accurate real-time control of the camera, and further, the gain range of the learning rule is also obtained. The numerical simulation and experimental results confirm that the proposed method can not only reduce the effect of repeatable disturbances but also non-repeatable ones.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

Chloride transport and the resulting corrosion of steel bars in alkali activated slag concretes

As the relative performance of alkali activated slag (AAS) concretes in comparison to Portland cement (PC) counterparts for chloride transport and resulting corrosion of steel bars is not clear, an investigation was carried out and the results are reported in this paper. The effect of alkali concentration and modulus of sodium silicate solution used in AAS was studied. Chloride transport and corrosion properties were assessed with the help of electrical resistivity, non-steady state chloride diffusivity, onset of corrosion, rate of corrosion and pore solution chemistry. It was found that: (i) although chloride content at surface was higher for the AAS concretes, they had lower chloride diffusivity than PC concrete; (ii) pore structure, ionic exchange and interaction effect of hydrates strongly influenced the chloride transport in the AAS concretes; (iii) steel corrosion resistance of the AAS concretes was comparable to that of PC concrete under intermittent chloride ponding regime, with the exception of 6% Na2O and Ms of 1.5; (iv) the corrosion behaviour of the AAS concretes was significantly influenced by ionic exchange, carbonation and sulphide concentration; (v) the increase of alkali concentration of the activator generally increased the resistance of AAS concretes to chloride transport and reduced its resulting corrosion, and a value of 1.5 was found to be an optimum modulus for the activator for improving the chloride transport and the corrosion resistance.

Treatment of Unexplained Chronic Cough: CHEST Guideline and Expert Panel Report

<p>Background: Unexplained chronic cough (UCC) causes significant quality of life impairment. There is a need to identify effective assessment and treatment approaches for UCC.</p><p>Methods: This systematic review of randomized controlled clinical trials asked: What is the efficacy of treatment compared to usual care on cough severity, cough frequency, and cough-related quality of life in patients with unexplained chronic cough (UCC)? Studies of adults and adolescents &gt;12 years with a chronic cough of &gt;8 weeks duration that was unexplained after systematic investigation and treatment were included and assessed for relevance and quality. Based upon the systematic review, guideline suggestions were developed and voted upon using CHEST organization methodology.</p><p>Results: 11 RCTs and 5 systematic reviews were included. The 11 RCTs reported data on 570 participants with chronic cough who received a variety of interventions. Study quality was high in 10 RCTs. The studies used a variety of descriptors and assessments to identify unexplained chronic cough. While gabapentin and morphine showed positive effects on cough-related quality of life, only gabapentin was supported as a treatment recommendation. Studies of inhaled corticosteroids (ICS) suffered from intervention fidelity bias, and when this was addressed, ICS were not found to be effective for UCC. Esomeprazole was not effective for UCC without features of gastroesophageal acid reflux. Studies addressing non-acid gastroesophageal reflux were not identified. A multimodality speech pathology intervention improved cough severity.</p><p>Conclusions: The evidence supporting the diagnosis and management of UCC is limited. UCC requires further study to establish agreed terminology and the optimal methods of investigation using established criteria for intervention fidelity. Speech pathology based cough suppression is suggested as a treatment option for UCC. This guideline presents suggestions for diagnosis and treatment based on the best available evidence and identifies gaps in our knowledge and areas for future research.</p>

Repeatability and reliability of new air and water permeability tests for assessing the durability of high-performance concretes

<p>This paper reports on the accuracy of new test methods developed to measure the air and water permeability of high-performance concretes (HPCs). Five representative HPC and one normal concrete (NC) mixtures were tested to estimate both repeatability and reliability of the proposed methods. Repeatability acceptance was adjudged using values of signal-noise ratio (SNR) and discrimination ratio (DR), and reliability was investigated by comparing against standard laboratory-based test methods (i.e., the RILEM gas permeability test and BS EN water penetration test). With SNR and DR values satisfying recommended criteria, it was concluded that test repeatability error has no significant influence on results. In addition, the research confirmed strong positive relationships between the proposed test methods and existing standard permeability assessment techniques. Based on these findings, the proposed test methods show strong potential to become recognized as international methods for determining the permeability of HPCs.</p>

Shape stabilised phase change materials based on a high melt viscosity HDPE and paraffin waxes

Shape stabilised phase change materials (SSPCMs) based on a high density poly(ethylene)(hv-HDPE) with high (H-PW, Tm = 56–58 °C) and low (L-PW, Tm = 18–23 °C) melting point paraffin waxes were readily prepared using twin-screw extrusion. The thermo-physical properties of these materials were assessed using a combination of techniques and their suitability for latent heat thermal energy storage (LHTES) assessed. The melt processing temperature (160 °C) of the HDPE used was well below the onset of thermal decomposition of H-PW (220 °C), but above that for L-PW (130 °C), although the decomposition process extended over a range of 120 °C and the residence time of L-PW in the extruder was &lt;30 s. The SSPCMs prepared had latent heats up to 89 J/g and the enthalpy values for H-PW in the respective blends decreased with increasing H-PW loading, as a consequence of co-crystallisation of H-PW and hv-HDPE. Static and dynamic mechanical analysis confirmed both waxes have a plasticisation effect on this HDPE. Irrespective of the mode of deformation (tension, flexural, compression) modulus and stress decreased with increased wax loading in the blend, but the H-PW blends were mechanically superior to those with L-PW.

An investigation of vision problems and the vision care system in Rural China

This paper examines the prevalence of vision problems and the accessibility to and quality of vision care in rural China. We obtained data from 4 sources: 1) the National Rural Vision Care Survey; 2) the Private Optometrists Survey; 3) the County Hospital Eye Care Survey; and 4) the Rural School Vision Care Survey. The data from each of the surveys were collected by the authors during 2012. Thirty-three percent of the rural population surveyed self-reported vision problems. Twenty-two percent of subjects surveyed had ever had a vision exam. Among those who self-reported having vision problems, 34% did not wear eyeglasses. Fifty-four percent of those with vision problems who had eyeglasses did not have a vision exam prior to receiving glasses. However, having a vision exam did not always guarantee access to quality vision care. Four channels of vision care service were assessed. The school vision examination program did not increase the usage rate of eyeglasses. Each county-hospital was staffed with three eye-doctors having one year of education beyond high school, serving more than 400,000 residents. Private optometrists often had low levels of education and professional certification. In conclusion, our findings shows that the vision care system in rural China is inadequate and ineffective in meeting the needs of the rural population sampled.