An optimized mean variance estimation method for uncertainty quantification of wind power forecasts

A statistical optimized technique for rapid development of reliable prediction intervals (PIs) is presented in this study. The mean-variance estimation (MVE) technique is employed here for quantification of uncertainties related with wind power predictions. In this method, two separate neural network models are used for estimation of wind power generation and its variance. A novel PI-based training algorithm is also presented to enhance the performance of the MVE method and improve the quality of PIs. For an in-depth analysis, comprehensive experiments are conducted with seasonal datasets taken from three geographically dispersed wind farms in Australia. Five confidence levels of PIs are between 50% and 90%. Obtained results show while both traditional and optimized PIs are hypothetically valid, the optimized PIs are much more informative than the traditional MVE PIs. The informativeness of these PIs paves the way for their application in trouble-free operation and smooth integration of wind farms into energy systems. © 2014 Elsevier Ltd. All rights reserved.

Insights into the evolution of a cryptic radiation of bats: dispersal and ecological radiation of Malagasy Miniopterus (Chiroptera: Miniopteridae)

The past decade has seen a proliferation of new species of Miniopterus bats (family Miniopteridae) recognized from Madagascar and the neighboring Comoros archipelago. The interspecific relationships of these taxa, their colonization history, and the evolution of this presumed adaptive radiation have not been sufficiently explored. Using the mitochondrial cytochrome-b gene, we present a phylogeny of the Malagasy members of this widespread Old World genus, based on 218 sequences, of which 82 are new and 136 derived from previous studies. Phylogenetic analyses recovered 18 clades, which divide into five primary lineages: (1) M. griveaudi; (2) M. mahafaliensis, M. sororculus and X3; (3) M. majori, M. gleni and M. griffithsi; (4) M. brachytragos; M. aelleniA, and M. aelleniB; and (5) M. manavi and M. petersoni recovered as sister species, which were in turn linked to a group comprising M. egeri and five genetically distinct populations referred to herein as P3, P4, P5, P6 and P7. Beast analysis indicated that the initial divergence within the Malagasy Miniopterus radiation took place 4.5 Myr; most species diverged between 4 and 2.5 Myr, and a secondary period was between 1.25 and 1 Myr. DNA K2P-distances between recognized taxa ranged from 12.9% to 2.5% and intraspecific variation was less than 1.8%. Of the 18 identified clades, Latin binomials are only associated with 11, which indicates much greater differentiation than currently recognized for Malagasy Miniopterus. These data are placed in a context of the dispersal history of this genus on the island and patterns of ecological diversity.

Blind estimation of MIMO relay channels

In this paper, we integrate two blind source separation (BSS) methods to estimate the individual channel state information (CSI) for the source-relay and relay-destination links of three-node two-hop multiple-input multiple-output (MIMO) relay systems. In particular, we propose a first-order Z-domain precoding technique for the blind estimation of the relay-destination channel matrix, while an algorithm based on the constant modulus and mutual information properties is developed to estimate the source-relay channel matrix. Compared with training-based MIMO relay channel estimation approaches, our algorithm has a better bandwidth efficiency as no bandwidth is wasted for sending the training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm. © 2014 IEEE.

Channel estimation for frequency-selective two-way MIMO relay systems

In this paper, we investigate the channel estimation problem for two-way multiple-input multiple-output (MIMO) relay communication systems in frequency-selective fading environments. We propose a superimposed channel training algorithm to estimate the individual channel state information (CSI) of the first-hop and second-hop links for two-way MIMO relay systems with frequency-selective fading channels. In this algorithm, a relay training sequence is superimposed on the received signals at the relay node to assist the estimation of the second-hop channel matrices. The optimal structure of the source and relay training sequences is derived to minimize the meansquared error (MSE) of channel estimation. We also derive the optimal power allocation between the source and relay training sequences. Numerical examples are shown to demonstrate the performance of the proposed algorithm.

Optical coherence tomography : age estimation of Calliphora vicina pupae in vivo?

Necrophagous blowfly pupae are valuable contributors to the estimation of post-mortem interval, should an accurate age estimate be obtained. At present, this is reliant on a combination of rearing and destructive methods conducted on preserved samples, including morphological observation and gene expression analyses. This study demonstrates the use of optical coherence tomography (OCT) as a tool for in vivo morphological observation and pupal age estimation. Using a Michelson OCT microscope, alive and preserved four and ten-day old Calliphora vicina pupae were scanned in different orientations. Two and three-dimensional images were created. Morphological characteristics such as the brain, mouthparts and legs were identifiable in both living and preserved samples, with distinct differences noted between the two ages. Absorption of light by the puparium results in a vertical resolution of 1-2 mm, preventing observation of deeper tissues. The use of contrast agents or a longer wavelength laser would improve the images obtainable. At present, the data suggests OCT provides a primary view of external and internal morphology, which can be used to distinguish younger and older pupae for further analysis of age and PMI estimation.

A general pose estimation algorithm in a multi-Kinect system

 Microsoft Kinect which has been primarily aimed at the computer gaming industry has been used in bio-kinematic research related implementations. A multi-Kinect system can be useful in exploiting spatial diversity to increase measurement accuracy. One of the main problems in deploying multi-Kinect systems is to estimate the pose, including the position and orientation of each Kinect. In this paper, a singular value decomposition (SVD) least-squares algorithm is extended to a more generic time-series based approach to solve this pose estimation problem utilising 3D positions of one or more joints in skeletons obtained from a multi-Kinect system. Additionally, computer simulations are performed to demonstrate the use and to evaluate the efficiency of the proposed algorithm. The former is further validated with a commercial Vicon system.

Estimation of accumulated lethality under pressure-assisted thermal processing

A study was conducted to develop an integrated process lethality model for pressure-assisted thermal processing (PATP) taking into consideration the lethal contribution of both pressure and heat on spore inactivation. Assuming that the momentary inactivation rate was dependent on the survival ratio and momentary pressure-thermal history, a differential equation was formulated and numerically solved using the Runge-Kutta method. Published data on combined pressure-heat inactivation of Bacillus amyloliquefaciens spores were used to obtain model kinetic parameters that considered both pressure and thermal effects. The model was experimentally validated under several process scenarios using a pilot-scale high-pressure food processor. Using first-order kinetics in the model resulted in the overestimation of log reduction compared to the experimental values. When the n th-order kinetics was used, the computed accumulated lethality and the log reduction values were found to be in reasonable agreement with the experimental data. Within the experimental conditions studied, spatial variation in process temperature resulted up to 3.5 log variation in survivors between the top and bottom of the carrier basket. The predicted log reduction of B. amyloliquefaciens spores in deionized water and carrot purée had satisfactory accuracy (1.07-1.12) and regression coefficients (0.83-0.92). The model was also able to predict log reductions obtained during a double-pulse treatment conducted using a pilot-scale high-pressure processor. The developed model can be a useful tool to examine the effect of combined pressure-thermal treatment on bacterial spore lethality and assess PATP microbial safety. © 2013 Springer Science+Business Media New York.

Indirect estimation of semiparametric binary choice models*

One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993), in which the authors propose a semiparametric estimator for use when the distribution of the error term is unknown. However, although theoretically appealing, the estimator has been found to be difficult to implement, and therefore not very attractive from an applied point of view. The current study offers an indirect inference-based solution to this problem. The new estimator is not only simple with good small-sample properties, but also consistent and asymptotically normal.

Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Reduced order functional observers with application to partial state estimation of linear systems with input delay

Reduced order multi-functional observer design for multi-input multi-utput (MIMO) linear time-invariant (LTI) systems with constant delayed inputs is studied. This research is useful in the input estimation of LTI systems with actuator delay, as well as system monitoring and fault detection of these systems. Two approaches for designing an asymptotically stable functional observer for the system are proposed: delay-dependent and delay-free. The delay-dependent observer is infinite-dimensional, while the delay-free structure is finite-dimensional. Moreover, since the delay-free observer does not require any information on the time delay, it is more practical in real applications. However, the delay-dependent observer contains less restrictive assumptions and covers more variety of systems. The proposed observer design schemes are novel, simple to implement, and have improved numerical features compared to some of the other available approaches to design (unknown-input) functional observers. In addition, the proposed observers usually possess lower order than ordinary Luenberger observers, and the design schemes do not need the observability or detectability requirements of the system. The necessary and sufficient conditions of the existence of an asymptoticobserver in each scenario are explored. The extensions of the proposed observers to systems with multiple delayed-inputs are also discussed. Several numerical examples and simulation results are employed to support our theories.

Drug use in Australian nightlife settings: estimation of prevalence and validity of self-report

AIMS: This study aimed to 1) estimate the prevalence of illicit drug use in night-time entertainment districts across five major cities in Australia; and 2) validate self-reported drug use using biochemical marker oral swabs. DESIGN: Street intercept surveys and oral drug swabs conducted over a seven-month period during 2011-2012. SETTING: The night-time entertainment districts of three metropolitan cities (Sydney, Melbourne and Perth) and two regional cities (Wollongong and Geelong) in Australia, between the hours of 10 pm and 5 am. PARTICIPANTS: 7,340 individuals agreed to participate in the survey (a 93% response rate). More than half (62%) of the sample was male, with a median age of 22 years (range 18-73). MEASUREMENTS: Patrons were approached in thoroughfares, and while entering and leaving licensed venues. Data collected included demographics and current session alcohol and other substance use. Drug swabs (n = 401) were performed with a sub-sample of participants. FINDINGS: Approximately 9% (95% CI, 7% to 12%) of participants self-reported consumption of illicit or non-prescribed pharmaceutical drugs prior to interview; of those, 81% identified psychostimulants as the drug used. One in five drug swabs returned a positive result, with psychostimulants the most commonly detected drugs (15%; 95% CI, 12%-19%). Kappa statistics indicate agreement between self-report of any illicit drug and a positive drug swab is in the slight range (κ = 0.12 (95% CI, .05 to .20) p = .000). CONCLUSIONS: Self-report findings suggest drug use in the nightlife in Australia is common, though still very much a minority past-time. Drug swabs indicate a higher prevalence of use (20%) than self-report (9%), which suggests that self-reported drug use may not be reliable in this context. This article is protected by copyright. All rights reserved.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.