Comparison of 2nd generation LiDAR wind measurement technique with CFD numerical modelling

With the rapid increase in both on and offshore wind turbine deployment there is a requirement for a better understanding of the flow field in which such devices are deployed. Greater understanding of the flow field is necessary for optimisation of turbine control, turbine design, and machine interaction as well as maximise operation and performance. Advanced measurement tools can characterise the flow regime by either acoustic or laser pulses to measure the line of sight velocity of airborne particles. Such technology facilitates the acquisition of detailed and precise measurements of wind speed and direction remote from the device location; some solutions can even provide detail of the flow structure of the wind in the measurement field. In the current study an analysis of the methodology, relevance and potential of a 2nd generation LiDAR is presented along with results of a deployment at an onshore wind farm. The results demonstrate the potential of the LiDAR to capture details of wind farm flow and structures, along with the potential to corroborate numerical techniques with the measured data. Advances in Computational Fluid Dynamics (CFD) approaches coupled with the availability of significant computational resources makes it possible to conduct a valid comparative assessment. This paper presents the details of this comparative assessment and makes a judgement on the accuracy of the approach. The results show that remote sensing devices offer a useful and accurate capability for wind vector analysis and flow visualisation, along with the flexibility to organise bespoke measurement campaigns. The study also presents methodologies by which such devices can be used as validation tools for CFD

Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance

The Wasserstein distance between two probability measures on a metric space is a measure of closeness with applications in statistics, probability, and machine learning. In this work, we consider the fundamental question of how quickly the empirical measure obtained from $n$ independent samples from $\mu$ approaches $\mu$ in the Wasserstein distance of any order. We prove sharp asymptotic and finite-sample results for this rate of convergence for general measures on general compact metric spaces. Our finite-sample results show the existence of multi-scale behavior, where measures can exhibit radically different rates of convergence as $n$ grows

Implementing log-add algorithm in hardware

A hardware implementation of the log-add algorithm, being a simple method of computing ln(A + B) given ln(A) and ln(B), as used in speech recognition, is presented. It is shown that it can be efficiently implemented in hardware using a small look-up table plus some additional arithmetic logic, with no significant loss of accuracy over direct calculation

A casein kinase 1 prevents expulsion of the oocyte meiotic spindle into a polar body by regulating cortical contractility.

During female meiosis, haploid eggs are generated from diploid oocytes. This reduction in chromosome number occurs through two highly asymmetric cell divisions, resulting in one large egg and two small polar bodies. Unlike mitosis, where an actomyosin contractile ring forms between the sets of segregating chromosomes, the meiotic contractile ring forms on the cortex adjacent to one spindle pole, then ingresses down the length of the spindle to position itself at the exact midpoint between the two sets of segregating chromosomes. Depletion of casein kinase 1 gamma (CSNK-1) in Caenorhabditis elegans led to the formation of large polar bodies that contain all maternal DNA, because the contractile ring ingressed past the spindle midpoint. Depletion of CSNK-1 also resulted in the formation of deep membrane invaginations during meiosis, suggesting an effect on cortical myosin. Both myosin and anillin assemble into dynamic rho-dependent cortical patches that rapidly disassemble in wild-type embryos. CSNK-1 was required for disassembly of both myosin patches and anillin patches. Disassembly of anillin patches was myosin independent, suggesting that CSNK-1 prevents expulsion of the entire meiotic spindle into a polar body by negatively regulating the rho pathway rather than through direct inhibition of myosin

Dissecting trade: firms, industries, and export destinations

We examine entry across 113 national markets in 16 different industries using a comprehensive data set of French manufacturing firms. The data are unique in indicating how much each firm exports to each destination. Looking across all manufacturers: (1) Firms differ substantially in export participation, with most selling only at home; (2) The number of firms selling to multiple markets falls off with the number of destinations with an elasticity of ?2.5; (3) Decomposing French exports to each destination into the size of the market and French share, variation in market share translates nearly completely into firm entry while about 60 percent of the variation in market size is reflected in firm entry. Looking within each of 16 industries we find little variation in these patterns. We propose that any successful model of trade and market structure must confront these facts.Exports ; International trade

Dissecting Trade: Firms, Industries, and Export Destinations

We examine entry across 113 national markets in 16 different industries using a comprehensive data set of French manufacturing firms. The data are unique in indicating how much each firm exports to each destination. Looking across all manufacturers: (1) Firms differ substantially in export participation, with most selling only at home; (2) The number of firms selling to multiple markets falls off with the number of destinations with an elasticity of -2.5; (3) Decomposing French exports to each destination into the size of the market and French share, variation in market share translates nearly completely into firm entry while about 60 percent of the variation in market size is reflected in firm entry. Looking within each of 16 industries we find little variation in these patterns. We propose that any successful model of trade and market structure must confront these facts.

Speech Recognition on an FPGA Using Discrete and Continuous Hidden Markov Models

Speech recognition is a computationally demanding task, particularly the stage which uses Viterbi decoding for converting pre-processed speech data into words or sub-word units. Any device that can reduce the load on, for example, a PC’s processor, is advantageous. Hence we present FPGA implementations of the decoder based alternately on discrete and continuous hidden Markov models (HMMs) representing monophones, and demonstrate that the discrete version can process speech nearly 5,000 times real time, using just 12% of the slices of a Xilinx Virtex XCV1000, but with a lower recognition rate than the continuous implementation, which is 75 times faster than real time, and occupies 45% of the same device

Implementing a simple continuous speech recognition system on an FPGA

Speech recognition is a computationally demanding task, particularly the stage which uses Viterbi decoding for converting pre-processed speech data into words or sub-word units. We present an FPGA implementations of the decoder based on continuous hidden Markov models (HMMs) representing monophones, and demonstrate that it can process speech 75 times real time, using 45% of the slices of a Xilinx Virtex XCV100