Upstream Cutoff and Downstream Filters to Control of Seepage in Dams

The finite element method was used in this study to investigate cutoff walls and downstream filters to control seepage, the exit hydraulic gradient, and uplift forces for dams. Experimental data was used for validating the numerical modelling. The effective parameters are the length of filter and its distance downstream from the dam, the depth of the cutoff walls, the upstream dam head, and the thickness of alluvial foundation. The results show that by increasing filter length, the exit hydraulic gradient, uplift force, and seepage are reduced. The optimum relative length of the filter is L/H?=?0.028 which results in a decrease of about 65% in the exit hydraulic gradient, a 35% decrease in seepage and 10% reduction in the uplift force at the upstream foundation and a 60% decrease in the uplift force at the downstream foundation. Increase of cutoff wall depth reduces the exit hydraulic gradient, uplift force, and seepage. Using two cutoff walls both upstream and downstream of the dam decreases seepage, hydraulic gradient, and uplift force 132%, 450% and 11% respectively. However, using an upstream cutoff and downstream filter decreases seepage, hydraulic gradient, and uplift force by 180%, 490%, and 119% respectively. Thus, based on this study, recommendations for suitable combinations of upstream cutoff and downstream filter are provided.

     

Study of sub-word acoustical models for <Emphasis Type="Italic">Kannada</Emphasis> isolated word recognition system

The speech recognition system basically extracts the textual information present in the speech. In the present work, speaker independent isolated word recognition system for one of the south Indian language—Kannada has been developed. For European languages such as English, large amount of research has been carried out in the context of speech recognition. But, speech recognition in Indian languages such as Kannada reported significantly less amount of work and there are no standard speech corpus readily available. In the present study, speech database has been developed by recording the speech utterances of regional Kannada news corpus of different speakers. The speech recognition system has been implemented using the Hidden Markov Tool Kit. Two separate pronunciation dictionaries namely phone based and syllable based dictionaries are built in-order to design and evaluate the performances of phone-level and syllable-level sub-word acoustical models. Experiments have been carried out and results are analyzed by varying the number of Gaussian mixtures in each state of monophone Hidden Markov Model (HMM). Also, context dependent triphone HMM models have been built for the same Kannada speech corpus and the recognition accuracies are comparatively analyzed. Mel frequency cepstral coefficients along with their first and second derivative coefficients are used as feature vectors and are computed in acoustic front-end processing. The overall word recognition accuracy of 60.2 and 74.35 % respectively for monophone and triphone models have been obtained. The study shows a good improvement in the accuracy of isolated-word Kannada speech recognition system using triphone HMM models compared to that of monophone HMM models.     

The Term Vector Database: fast access to indexing terms for Web pages

We have built a database that provides term vector information for large numbers of pages (hundreds of millions). The basic operation of the database is to take URLs and return term vectors. Compared to computing vectors by downloading pages via HTTP, the Term Vector Database is several orders of magnitude faster, enabling a large class of applications that would be impractical without such a database. This paper describes the Term Vector Database in detail. It also reports on two applications built on top of the database. The first application is an optimization of connectivity-based topic distillation. The second application is a Web page classifier used to annotate results returned by a Web search engine.     

Compromise ranking approach for sustainable concept selection in an Indian modular switches manufacturing organization

The modern manufacturing organizations are forced to adopt the principles of sustainability to sustain in the competitive market scenario. Sustainability paradigm stipulates that manufacturing organizations develop eco-friendly products coupled with economic and social dimensions. Sustainability concepts include life cycle impact assessment, environmental conscious quality function deployment, environmental impact assessment, life cycle assessment, and theory of inventive problem solving. The concept selection in the context of sustainability is the typical multicriteria decision-making problem (MCDM). MCDM method can be used to find out the best alternative among the given alternatives. In our study, we have utilized the compromise ranking method namely VlseKriterijumska Optimizacija I Kompromisno Resenje. This method focuses on ranking and selecting from a set of conflicting criteria based on the measure of closeness to the ideal solution. The best concept has been selected for implementation in the organization as stated by Opricovic and Tzeng (Eur J Oper Res 156:445–455, 2004).     

An Efficient Active Contour Model Through Curvature Scale Space Filtering

Active contour models can be successfully used in multimedia database retrieval systems if they have good accuracy and high speed. The majority of existing active contour models do not lock on to interest objects very accurately and quickly especially in complex images. The behavior of the active contour is generally controlled by its internal and external energies. Internal energy is composed of two parts; the first part acts to shorten the active contour as it iterates towards the interest object, while the second part is the curvature of the active contour and forces smoothness of active contour during its movement towards interest object. In this paper, first a reformulated internal energy is proposed to improve the computation of curvature at point v _i by making use of the three points v _{i – 1}, v _i and v _{i + 1}. Second, an accurate and high speed active contour model, SAC is proposed based on reformulating internal energy by removing the curvature part and using Gaussian filtering with low scale of smoothing. The SAC model has only one parameter that affects the internal energy of active contour and as a result of using the Curvature Scale Space (CSS)¹ technique for smoothing, the SAC model is more independent of model parameter setting and the initial snake.     

Graph structure in the Web

The study of the Web as a graph is not only fascinating in its own right, but also yields valuable insight into Web algorithms for crawling, searching and community discovery, and the sociological phenomena which characterize its evolution. We report on experiments on local and global properties of the Web graph using two AltaVista crawls each with over 200 million pages and 1.5 billion links. Our study indicates that the macroscopic structure of the Web is considerably more intricate than suggested by earlier experiments on a smaller scale.     

Reducing CO2 emission from exhaust gases using molten carbonate fuel cells: a new approach

The aim of this study is to couple molten carbonate fuel cell (MCFC) stack with integrated gasification combined cycle fed by refinery residues, to remove CO₂ from gas turbine exhaust gases that have CO₂ emission rate of 14,200 ton/year. By applying multi-objective optimisation (MOO) using genetic algorithm, the optimal values of operating load and the corresponding values of objective functions are obtained. The MOO of the MCFC system regarding two scenarios is performed. The first scenario is minimisation of cost of electricity (COE) and CO₂ emission rate. Objective functions of the second scenario are the same as in the first scenario while CO₂ tax is taken into account. Results show that the second scenario has 29.5% lower average optimal COE and 2.5% lower average emission rate in comparison with the first scenario. A sensitivity analysis is also performed to study the effect of fuel price and CO₂ tax variations on optimal solutions.     

Seismic response of a 40‐storey buckling‐restrained braced frame designed for the Los Angeles region

This study utilized nonlinear response history analysis to compare the seismic demand on three variations of a 40‐storey buckling‐restrained braced frame designed for high seismic hazard in the Los Angeles region. The three designs were referred to as a ‘code‐based design’, based on the 2006 International Building Code, a ‘performance‐based design’, based on criteria published by the Los Angeles Tall Building Design Council (LATBSDC) and a ‘performance‐based design plus’, based on newly developed criteria from The Pacific Earthquake Engineering Research Center (PEER). The response history analysis utilized spectrum‐matched ground motions as well as simulated ground motions for the Puente Hills fault. The spectrum‐matched motions were selected from the Next Generation Attenuation of Ground Motions (NGA) database, which is largely composed of recorded motions and scaled to five hazard levels. The simulated ground motions were broadband signals generated from a moment magnitude (M_w) 7.15 scenario rupture of the Puente Hills fault for two near fault regions and exhibit long period energy content that significantly exceeds the uniform hazard spectrum. Structural performance was assessed in terms of exceedance of a safe inter‐storey drift ratio (IDR). It was seen that the simulated ground motions impose higher IDR demands on the structures than the spectrum‐matched NGA ground motions. Furthermore, the number of instances of exceedance of a safe IDR, considered for this study as IDR = 0.03, is substantially higher for the simulated ground motions, pointing to the importance of considering such motions in the collapse prevention of tall buildings on a site‐specific basis. Copyright © 2010 John Wiley & Sons, Ltd.