This paper presents the data preprocessing algorithm for real time digital simulation of large power systems based on energy management system data. Recently, the power systems have become complicated due to increase ...
详细信息
ISBN:
(纸本)9781538645369
This paper presents the data preprocessing algorithm for real time digital simulation of large power systems based on energy management system data. Recently, the power systems have become complicated due to increase of distributed power, including renewable energy and High Voltage Direct Current & Flexible AC Transmission System. In order to overcome the technological limitations, there are more and more cases of analyzing real time operation characteristics through Real-Time Digital Simulator. However, because the Real-Time Digital Simulator has a load unit limit per rack, it is difficult to simulate large scale power systems. When analyzing large scale power systems, we often use data obtained from actual power system operation data through Energy Management System. In this paper, we propose a solution to solve various errors and reduce the load unit limit for real time digital simulation by processing Energy Management System data.
Challenges to contextual filtering techniques include difficulties in estimating orientation field in poor quality images and subsequently failure to extract ridges reliably in large regions of low quality. This study...
详细信息
Challenges to contextual filtering techniques include difficulties in estimating orientation field in poor quality images and subsequently failure to extract ridges reliably in large regions of low quality. This study proposes a statistical-based and dynamic fingerprint preprocessing technique for adaptive contrast enhancement and binarisation of fair and poor qualities plain and rolled fingerprints with large regions of low quality, prior to orientation field estimation. The algorithm effectively enhances smudged and faded ridges uniformly in recoverable regions, based on values of statistical variables computed locally in each region. The preprocessing algorithm employs a locally adaptive thresholding approach resulting in enhanced binarised images. The performance of the proposed algorithm was determined by carrying out biometric verification evaluation using a popular commercial biometric matching software, on databases of fingerprints in their original forms, as well as same fingerprints enhanced with the proposed algorithm. Experiments show that fingerprints are uniformly enhanced and binarised;and smudged or faded ridges in recoverable regions made visible. Fingerprint verification evaluation on preprocessed fingerprints resulted in lower error rates in 12 databases. These results show that the proposed algorithm significantly improves recognition.
作者：
Luppe, M.EESC USP
Lab Microsistemas Dept Engn Eletr & Comp Sao Carlos SP Brazil
The fractal dimension (FD) is an important feature used for classification and shape recognition. The best method to obtain the FD is the Bouligand-Minkowski method. However, this method is computationally exhaustive ...
详细信息
The fractal dimension (FD) is an important feature used for classification and shape recognition. The best method to obtain the FD is the Bouligand-Minkowski method. However, this method is computationally exhaustive because of the use of the distance transform. Presented is a modification in the method and a proposed architecture, suitable for field programmable gate array implementation that enables calculating the FD in a simple and efficient way.
Background: Affymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change bet...
详细信息
Background: Affymetrix GeneChip microarrays are popular platforms for expression profiling in two types of studies: detection of differential expression computed by p-values of t-test and estimation of fold change between analyzed groups. There are many different preprocessing algorithms for summarizing Affymetrix data. The main goal of these methods is to remove effects of non-specific hybridization, and to optimally combine information from multiple probes annotated to the same transcript. The methods are benchmarked by comparison with reference methods, such as quantitative reverse-transcription PCR (qRT-PCR). Results: We present a comprehensive analysis of agreement between Affymetrix GeneChip and qRT-PCR results. We analyzed the influence of filtering by fraction Present calls introduced by J. N. McClintick and H. J. Edenberg (2006) and 2 mapping procedures: updated probe sets definitions proposed by Dai et al. (2005) and our "naive mapping" method. Because of evolution of genome sequence annotations since the time when microarrays were designed, we also studied the effect of the annotation release date. These comparisons were prepared for 6 popular preprocessing algorithms (MAS5, PLIER, RMA, GC-RMA, MBEI, and MBEImm) in the 2 above-mentioned types of studies. We used data sets from 6 independent biological experiments. As a measure of reproducibility of microarray and qRT-PCR values, we used linear and rank correlation coefficients. Conclusions: We show that filtering by fraction Present calls increased correlations for all 6 preprocessing algorithms. We observed the difference in performance of PM-MM and PM-only methods: using MM probes increased correlations in fold change studies, but PM-only methods proved to perform better in detection of differential expression. We recommend using GC-RMA for detection of differential expression and PLIER for estimation of fold change. The use of the more recent annotation improves the results in both types of studies, en
Background: To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a w...
详细信息
Background: To identify differentially expressed genes (DEGs) from microarray data, users of the Affymetrix GeneChip system need to select both a preprocessing algorithm to obtain expression-level measurements and a way of ranking genes to obtain the most plausible candidates. We recently recommended suitable combinations of a preprocessing algorithm and gene ranking method that can be used to identify DEGs with a higher level of sensitivity and specificity. However, in addition to these recommendations, researchers also want to know which combinations enhance reproducibility. Results: We compared eight conventional methods for ranking genes: weighted average difference (WAD), average difference (AD), fold change (FC), rank products (RP), moderated t statistic (modT), significance analysis of microarrays (samT), shrinkage t statistic (shrinkT), and intensity-based moderated t statistic (ibmT) with six preprocessing algorithms (PLIER, VSN, FARMS, multi-mgMOS (mmgMOS), MBEI, and GCRMA). A total of 36 real experimental datasets was evaluated on the basis of the area under the receiver operating characteristic curve (AUC) as a measure for both sensitivity and specificity. We found that the RP method performed well for VSN-, FARMS-, MBEI-, and GCRMA- preprocessed data, and the WAD method performed well for mmgMOS-preprocessed data. Our analysis of the MicroArray Quality Control (MAQC) project's datasets showed that the FC-based gene ranking methods (WAD, AD, FC, and RP) had a higher level of reproducibility: The percentages of overlapping genes (POGs) across different sites for the FC-based methods were higher overall than those for the t-statistic-based methods (modT, samT, shrinkT, and ibmT). In particular, POG values for WAD were the highest overall among the FC-based methods irrespective of the choice of preprocessing algorithm. Conclusion: Our results demonstrate that to increase sensitivity, specificity, and reproducibility in microarray analyses, we need to select
In underwater imaging, Planar Synthetic Aperture Sonar (P-SAS) technique has been validated on both simulated and tank data. It showed a significant improvement of 3D representation of bottom and sub-bottom. In this p...
详细信息
ISBN:
(纸本)9781424443451
In underwater imaging, Planar Synthetic Aperture Sonar (P-SAS) technique has been validated on both simulated and tank data. It showed a significant improvement of 3D representation of bottom and sub-bottom. In this paper we present its application to real data acquired during sea experiments on a dump site in the Baltic sea. Data were acquired during a European project "SITAR" (Seafloor Imaging and Toxicity: Assessment of Risks caused by buried waste). The transmitter was a Topographic Parametric Sonar (TOPAS) fixed on a ROV which position was monitored. Two central frequencies were explored, 10 kHz and 20 kHz. As P-SAS algorithm was designed for data obtained on a regular planar grid important modification were required to handle real sea data and "realistic" navigation conditions (irregular grid): a "rearrangement" algorithm was designed for preprocessing actual data and correct trajectory disturbances (in 2D). This algorithm is the re-projection of data positions to a new (virtual) regularly grid. The algorithm was validated on tank experimental data prior to application to sea data. P-SAS processed data will be presented. A strata representation technique was used for analyzing the seafloor and the sub-bottom and for locating buried objects on the scanned dump site.
As the Internet advances and with the great development of E-Commerce,companies involving in online shopping and services are eager for ways to find out what their customers *** now with the help of data mining,they c...
详细信息
As the Internet advances and with the great development of E-Commerce,companies involving in online shopping and services are eager for ways to find out what their customers *** now with the help of data mining,they can have a much more active role in promoting their online shops, merchandise and services compares to the conventional method of waiting for customer to look at their *** paper discusses about the new development of E-Commerce that differs a lot from the old conventional *** also goes in depth of a new architecture of data mining system based on E-commerce web *** conventional"single data-mining system"system is replaced by the new"distributed data mining system".The new distributed data mining system is a system that provides mining *** elaboration of the system,the E-Commerce Server request services in the network,Data Mining Server provides concurrent mining services for serial E-Commerce Servers (Serial E-Commerce Companies).Data Mining Server will summarize all the data in different E-Commerce Server for mining *** will produce more comprehensive and informative knowledge than conventional methods.A new effective preprocessing algorithm of distributed data mining for E-Commerce is provided in this *** to the architecture given in this paper,a distributed data-mining algorithm for finding association rules is provided.
暂无评论