Drag and inertia coefficients for horizontally submerged rectangular cylinders in waves and currents

The results of an experimental investigation carried out to measure combined wave and current loads on horizontally submerged square and rectangular cylinders are reported in this paper. The wave and current induced forces on a section of the cylinders with breadth-depth (aspect) ratios equal to 1, 0.5, and 0.75 are measured in a wave tank. The maximum value of Keulegan-Carpenter (KC) number obtained in waves alone is about 5 and Reynolds (Re) number ranged from 6.3976103 to 1.186105. The drag (CD) and inertia (CM) coefficients for each cylinder are evaluated using measured sectional wave forces and particle kinematics calculated from linear wave theory. The values of CD and CM obtained for waves alone have already been reported (Venugopal, V., Varyani, K. S., and Barltrop, N. D. P. Wave force coefficients for horizontally submerged rectangular cylinders. Ocean Engineering, 2006, 33, 11-12, 1669-1704) and the coefficients derived in combined waves and currents are presented here. The results indicate that both drag and inertia coefficients are strongly affected by the presenceof the current and show different trends for different cylinders. The values of the vertical component inertia coefficients (CMY) in waves and currents are generally smaller than the inertia coefficients obtained in waves alone, irrespective of the current's magnitude and direction. The results also illustrate the effect of a cylinder's aspect ratio on force coefficients. This study will be useful in the design of offshore structures whose columns and caissons are rectangular sections

DRSP : Dimension Reduction For Similarity Matching And Pruning Of Time Series Data Streams

Similarity matching and join of time series data streams has gained a lot of relevance in today's world that has large streaming data. This process finds wide scale application in the areas of location tracking, sensor networks, object positioning and monitoring to name a few. However, as the size of the data stream increases, the cost involved to retain all the data in order to aid the process of similarity matching also increases. We develop a novel framework to addresses the following objectives. Firstly, Dimension reduction is performed in the preprocessing stage, where large stream data is segmented and reduced into a compact representation such that it retains all the crucial information by a technique called Multi-level Segment Means (MSM). This reduces the space complexity associated with the storage of large time-series data streams. Secondly, it incorporates effective Similarity Matching technique to analyze if the new data objects are symmetric to the existing data stream. And finally, the Pruning Technique that filters out the pseudo data object pairs and join only the relevant pairs. The computational cost for MSM is O(l*ni) and the cost for pruning is O(DRF*wsize*d), where DRF is the Dimension Reduction Factor. We have performed exhaustive experimental trials to show that the proposed framework is both efficient and competent in comparison with earlier works.Comment: 20 pages,8 figures, 6 Table

Feature Extraction and Duplicate Detection for Text Mining: A Survey

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user

Two-Hop Routing with Traffic-Differentiation for QoS Guarantee in Wireless Sensor Networks

This paper proposes a Traffic-Differentiated Two-Hop Routing protocol for Quality of Service (QoS) in Wireless Sensor Networks (WSNs). It targets WSN applications having different types of data traffic with several priorities. The protocol achieves to increase Packet Reception Ratio (PRR) and reduce end-to-end delay while considering multi-queue priority policy, two-hop neighborhood information, link reliability and power efficiency. The protocol is modular and utilizes effective methods for estimating the link metrics. Numerical results show that the proposed protocol is a feasible solution to addresses QoS service differenti- ation for traffic with different priorities.Comment: 13 page