Apparatus and method for heat-run test on high-power PWM converters with low energy expenditure

Before installation, a voltage source converter is usually subjected to heat-run test to verify its thermal design and performance under load. For heat-run test, the converter needs to be operated at rated voltage and rated current for a substantial length of time. Hence, such tests consume huge amount of energy in case of high-power converters. Also, the capacities of the source and loads available in the research and development (R&D) centre or the production facility could be inadequate to conduct such tests. This paper proposes a method to conduct heat-run tests on high-power, pulse width modulated (PWM) converters with low energy consumption. The experimental set-up consists of the converter under test and another converter (of similar or higher rating), both connected in parallel on the ac side and open on the dc side. Vector-control or synchronous reference frame control is employed to control the converters such that one draws certain amount of reactive power and the other supplies the same; only the system losses are drawn from the mains. The performance of the controller is validated through simulation and experiments. Experimental results, pertaining to heat-run tests on a high-power PWM converter, are presented at power levels of 25 kVA to 150 kVA.

Generative Maximum Entropy Learning for Multiclass Classification

Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative in nature. In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature. To tackle the curse of dimensionality of large data sets, we employ conditional independence assumption (Naive Bayes) and we perform feature selection simultaneously, by enforcing a `maximum discrimination' between estimated class conditional densities. For two class problems, in the proposed method, we use Jeffreys (J) divergence to discriminate the class conditional densities. To extend our method to the multi-class case, we propose a completely new approach by considering a multi-distribution divergence: we replace Jeffreys divergence by Jensen-Shannon (JS) divergence to discriminate conditional densities of multiple classes. In order to reduce computational complexity, we employ a modified Jensen-Shannon divergence (JS(GM)), based on AM-GM inequality. We show that the resulting divergence is a natural generalization of Jeffreys divergence to a multiple distributions case. As far as the theoretical justifications are concerned we show that when one intends to select the best features in a generative maximum entropy approach, maximum discrimination using J-divergence emerges naturally in binary classification. Performance and comparative study of the proposed algorithms have been demonstrated on large dimensional text and gene expression datasets that show our methods scale up very well with large dimensional datasets.

Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8-50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mgha(-1)) at spatial scales ranging from 5 to 250m (0.025-6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20-400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial ``dilution'' bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Common-Mode Injection PWM for Parallel Converters

The ac-side terminal voltages of parallel-connected converters are different if the line reactive drops of the individual converters are different. This could result either from differences in per-phase inductances or from differences in the line currents of the converters. In such cases, the modulating signals are different for the converters. Hence, the common-mode (CM) voltages for the converters, injected by conventional space vector pulsewidth modulation (CSVPWM) to increase dc-bus utilization, are different. Consequently, significant low-frequency zero-sequence circulating currents result. This paper proposes a new modulation method for parallel-connected converters with unequal terminal voltages. This method does not cause low-frequency zero-sequence circulating currents and is comparable with CSVPWM in terms of dc-bus utilization and device power loss. Experimental results are presented at a power level of 150 kVA from a circulating-power test setup, where the differences in converter terminal voltages are quite significant.

Laboratory determination of coefficient of consolidation from pore water pressure measurement

In the recent past, many studies have been carried out on the determination of coefficient of consolidation (c(v)) from the time (t)-deformation (d) data obtained from conventional consolidation tests. Several researchers have also proposed different curve fitting procedures for determining cv from the t-d data. It is anticipated that the cv values obtained from the t-d data may be influenced by initial and secondary compressions. Nevertheless, the pore water pressure data measured during the consolidation process will be independent of initial and secondary compressions. In this study, the conventional Asaoka (1978) method is extended to evaluate cv and end-of-primary (EOP) consolidation from the pore water pressure data measured from laboratory experiments. Laboratory experiments were carried out on the modified one-dimensional consolidation apparatus on different remoulded clay samples measuring pore water pressure during the consolidation process. The cv and EOP computed from the proposed approach have been compared with the results of the t-d data and found to be in good agreement.

FIVDL: Fingerprint Image Verification using Dictionary Learning

Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.

The environmental and economic sustainability of potential bioethanol from willow in the UK.

Life cycle assessment has been used to investigate the environmental and economic sustainability of a potential operation in the UK in which bioethanol is produced from the hydrolysis and subsequent fermentation of coppice willow. If the willow were grown on idle arable land in the UK, or, indeed, in Eastern Europe and imported as wood chips into the UK, it was found that savings of greenhouse gas emissions of 70-90%, when compared to fossil-derived gasoline on an energy basis, would be possible. The process would be energetically self-sufficient, as the co-products, e.g. lignin and unfermented sugars, could be used to produce the process heat and electricity, with surplus electricity being exported to the National Grid. Despite the environmental benefits, the economic viability is doubtful at present. However, the cost of production could be reduced significantly if the willow were altered by breeding to improve its suitability for hydrolysis and fermentation.