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Articles by G. Ramachandran
Total Records ( 2 ) for G. Ramachandran
  G. Ramachandran and K. Selvakumar
  Most of the existing unstructured Peer to Peer (P2P) supports the filename or keyword based limited search techniques. This study proposes a novel Semantic Oriented Adaptive Search (SOAS) strategy based on semantic content. It is a multi-layer architecture model which utilize a dynamic caching technique to achieve effective search on unstructured P2P network. The proposed scheme constructs as a two-tier P2P network with ultra peers of high connectivity based on a power law model. The novelty of the proposed scheme is that the query is processed through multi-tier summary indexing framework. The proposed approach extensively used Vector Space Model (VSM) and Latent Semantic Index (LSI) to derive local indices from summarized semantic vectors. Query searching concedes through a round of searches using derived indices from semantic data objects. If one search fails, the next round search is invoked sequentially in the order of local index, cache index, response index, global index and adaptive search among ultra peers. The proposed SOAS produces a high success rate and generates a minimal amount of network traffic for an effective content search. Dynamic Time to Live (TTL) based cache consistency is proposed where each ultra peer dynamically caches the responses of previously requested queries based on the query popularity rate and TTL. An implementation and large scale simulation are performed to evaluate the proposed approach. The experimental result proves that the proposed system performs better than the existing approach in terms of accuracy, response time, success rate and cache hit.
  Y Zhang , S Banerjee , R Yang , C Lungu and G. Ramachandran

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters.

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