Molecular Dynamics Investigation of Structure and Transport in the K2O-2SiO2 System Using a Partial Charge Based Model Potential

Potassium disilicate glass and melt have been investigated by using a new partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygenrich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

Molecular dynamics Investigation of structure and transport in the K2O-2SiO2 system using a partial charge based model potential

Potassium disilicate glass and melt have been investigated by using anew partial charge based potential model in which nonbridging oxygens are differentiated from bridging oxygens by their charges. The model reproduces the structural data pertaining to the coordination polyhedra around potassium and the various bond angle distributions excellently. The dynamics of the glass has been studied by using space and time correlation functions. It is found that K ions migrate by a diffusive mechanism in the melt and by hops below the glass transition temperature. They are also found to migrate largely through nonbridging oxygen-rich sites in the silicate matrix, thus providing support to the predictions of the modified random network model.

Examining the effects of species richness on community stability: an assembly model approach

We build dynamic models of community assembly by starting with one species in our model ecosystem and adding colonists. We find that the number of species present first increases, then fluctuates about some level. We ask: how large are these fluctuations and how can we characterize them statistically? As in Robert May's work, communities with weaker interspecific interactions permit a greater number of species to coexist on average. We find that as this average increases, however, the relative variation in the number of species and return times to mean community levels decreases. In addition, the relative frequency of large extinction events to small extinction events decreases as mean community size increases. While the model reproduces several of May's results, it also provides theoretical support for Charles Elton's idea that diverse communities such as those found in the tropics should be less variable than depauperate communities such as those found in arctic or agricultural settings.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Fault-Diagnostic System using Support Vector Machines for Power Transmission Systems

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

Voltage and Temperature-Aware SSTA Using Neural Network Delay Model

With the emergence of voltage scaling as one of the most powerful power reduction techniques, it has been important to support voltage scalable statistical static timing analysis (SSTA) in deep submicrometer process nodes. In this paper, we propose a single delay model of logic gate using neural network which comprehensively captures process, voltage, and temperature variation along with input slew and output load. The number of simulation programs with integrated circuit emphasis (SPICE) required to create this model over a large voltage and temperature range is found to be modest and 4x less than that required for a conventional table-based approach with comparable accuracy. We show how the model can be used to derive sensitivities required for linear SSTA for an arbitrary voltage and temperature. Our experimentation on ISCAS 85 benchmarks across a voltage range of 0.9-1.1V shows that the average error in mean delay is less than 1.08% and average error in standard deviation is less than 2.85%. The errors in predicting the 99% and 1% probability point are 1.31% and 1%, respectively, with respect to SPICE. The two potential applications of voltage-aware SSTA have been presented, i.e., one for improving the accuracy of timing analysis by considering instance-specific voltage drops in power grids and the other for determining optimum supply voltage for target yield for dynamic voltage scaling applications.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

A model for growth and engulfment of gas microporosity during aluminum alloy solidification process

A new coupled approach is presented for modeling the hydrogen bubble evolution and engulfment during an aluminum alloy solidification process in a micro-scale domain. An explicit enthalpy scheme is used to model the solidification process which is coupled with a level-set method for tracking the hydrogen bubble evolution. The volume averaging techniques are used to model mass, momentum, energy and species conservation equations in the chosen micro-scale domain. The interaction between the solid, liquid and gas interfaces in the system have been studied. Using an order-of-magnitude study on growth rates of bubble and solid interfaces, a criterion is developed to predict bubble elongation which can occur during the engulfment phase. Using this model, we provide further evidence in support of a conceptual thought experiment reported in literature, with regard to estimation of final pore shape as a function of typical casting cooling rates. The results from the proposed model are qualitatively compared with in situ experimental observations reported in literature. The ability of the model to predict growth and movement of a hydrogen bubble and its subsequent engulfment by a solidifying front has been demonstrated for varying average cooling rates encountered in typical sand, permanent mold, and various casting processes. (C) 2012 Elsevier B.V. All rights reserved.

Classification of Vehicles using Magnetic Dipole Model

This paper presents an efficient approach to the modeling and classification of vehicles using the magnetic signature of the vehicle. A database was created using the magnetic signature collected over a wide range of vehicles(cars). A vehicle is modeled as an array of magnetic dipoles. The strength of the magnetic dipole and the separation between the magnetic dipoles varies for different vehicles and is dependent on the metallic composition and configuration of the vehicle. Based on the magnetic dipole data model, we present a novel method to extract a feature vector from the magnetic signature. In the classification of vehicles, a linear support vector machine configuration is used to classify the vehicles based on the obtained feature vectors.

Support vector machine based high speed protection relay for EHV/UHV transmission line

This paper presents a fast and accurate relaying technique for a long 765kv UHV transmission line based on support vector machine. For a long EHV/UHV transmission line with large distributed capacitance, a traditional distance relay which uses a lumped parameter model of the transmission line can cause malfunction of the relay. With a frequency of 1kHz, 1/4th cycle of instantaneous values of currents and voltages of all phases at the relying end are fed to Support Vector Machine(SVM). The SVM detects fault type accurately using 3 milliseconds of post-fault data and reduces the fault clearing time which improves the system stability and power transfer capability. The performance of relaying scheme has been checked with a typical 765kV Indian transmission System which is simulated using the Electromagnetic Transients Program(EMTP) developed by authors in which the distributed parameter line model is used. More than 15,000 different short circuit fault cases are simulated by varying fault location, fault impedance, fault incidence angle and fault type to train the SVM for high speed accurate relaying. Simulation studies have shown that the proposed relay provides fast and accurate protection irrespective of fault location, fault impedance, incidence time of fault and fault type. And also the proposed scheme can be used as augmentation for the existing relaying, particularly for Zone-2, Zone-3 protection.

Classification of Vehicles Using Magnetic Field Angle Model

This paper presents an efficient approach to the modeling and classification of vehicles using the magnetic signature of the vehicle. A database was created using the magnetic signature collected over a wide range of vehicles(cars). A sensor dependent approach called as Magnetic Field Angle Model is proposed for modeling the obtained magnetic signature. Based on the data model, we present a novel method to extract the feature vector from the magnetic signature. In the classification of vehicles, a linear support vector machine configuration is used to classify the vehicles based on the obtained feature vectors.

Dynamic state estimation for identifying earthquake support motions in instrumented structures

The problem of identification of multi-component and (or) spatially varying earthquake support motions based on measured responses in instrumented structures is considered. The governing equations of motion are cast in the state space form and a time domain solution to the input identification problem is developed based on the Kalman and particle filtering methods. The method allows for noise in measured responses, imperfections in mathematical model for the structure, and possible nonlinear behavior of the structure. The unknown support motions are treated as hypothetical additional system states and a prior model for these motions are taken to be given in terms of white noise processes. For linear systems, the solution is developed within the Kalman filtering framework while, for nonlinear systems, the Monte Carlo simulation based particle filtering tools are employed. In the latter case, the question of controlling sampling variance based on the idea of Rao-Blackwellization is also explored. Illustrative examples include identification of multi-component and spatially varying support motions in linear/nonlinear structures.

Design and construction of geocell foundation to support the embankment on settled red mud

This paper presents the case history of the construction of a 3 m high embankment on the geocell foundation over the soft settled red mud. Red mud is a waste product from the Bayer process of Aluminum industry. Geotechnical problems of the site, the design of the geocell foundation based on experimental investigation and the construction sequences of the geocell foundations in the field are discussed in the paper. Based on the experimental studies, an analytical model was also developed to estimate the load carrying capacity of the soft clay bed reinforced with geocell and combination of geocell and geogrid. The results of the experimental and analytical studies revealed that the use of combination of geocell and the geogrid is always beneficial than using the geocell alone. Hence, the combination of geocell and geogrid was recommended to stabilize the embankment base. The reported embankment is located in Lanjigharh (Orissa) in India. Construction of the embankment on the geocell foundation has already been completed. The constructed embankmenthas already sustained two monsoon rains without any cracks and seepage. (C) 2013 Elsevier Ltd. All rights reserved.

Multi-site downscaling of maximum and minimum daily temperature using support vector machine

Climate change impact assessment studies involve downscaling large-scale atmospheric predictor variables (LSAPVs) simulated by general circulation models (GCMs) to site-scale meteorological variables. This article presents a least-square support vector machine (LS-SVM)-based methodology for multi-site downscaling of maximum and minimum daily temperature series. The methodology involves (1) delineation of sites in the study area into clusters based on correlation structure of predictands, (2) downscaling LSAPVs to monthly time series of predictands at a representative site identified in each of the clusters, (3) translation of the downscaled information in each cluster from the representative site to that at other sites using LS-SVM inter-site regression relationships, and (4) disaggregation of the information at each site from monthly to daily time scale using k-nearest neighbour disaggregation methodology. Effectiveness of the methodology is demonstrated by application to data pertaining to four sites in the catchment of Beas river basin, India. Simulations of Canadian coupled global climate model (CGCM3.1/T63) for four IPCC SRES scenarios namely A1B, A2, B1 and COMMIT were downscaled to future projections of the predictands in the study area. Comparison of results with those based on recently proposed multivariate multiple linear regression (MMLR) based downscaling method and multi-site multivariate statistical downscaling (MMSD) method indicate that the proposed method is promising and it can be considered as a feasible choice in statistical downscaling studies. The performance of the method in downscaling daily minimum temperature was found to be better when compared with that in downscaling daily maximum temperature. Results indicate an increase in annual average maximum and minimum temperatures at all the sites for A1B, A2 and B1 scenarios. The projected increment is high for A2 scenario, and it is followed by that for A1B, B1 and COMMIT scenarios. Projections, in general, indicated an increase in mean monthly maximum and minimum temperatures during January to February and October to December.