Site characterization model using least-square support vector machine and relevance vector machine based on corrected SPT data (N-c)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N,), N values have been corrected (Ne) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three-dimensional site characterization model, the function N-c=N-c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N, value, is to be approximated in which N, value at any half-space point in Bangalore can be determined. The first algorithm uses least-square support vector machine (LSSVM), which is related to aridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel-based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright (C) 2009 John Wiley & Sons,Ltd.

Why Do Russians Support Censorship of the Media?

This article analyses support for censorship in Russia as part of the democratization process. Censorship has been an important part of Russian history and it was strengthened during the Soviet era. After the collapse of the Soviet system formal censorship was banned even though the reality has been different. Therefore it is not strange that many Russians would like to limit the freedom of the media and to censor certain topics. The views of Russians on censorship have been studied on the basis of a survey carried out in 2007. According to the results, three different dimensions of censorship were found. These dimensions include moral censorship, political censorship, and censorship of religious materials. Support for these dimensions varies on the basis of socio-demographic characteristics and media use. The article concludes that many Russians reject new phenomena, while support for the censorship of political criticism is not as high, but political censorship seems to enjoy more support among elites than among the common people.

Governance theory as a framework for empirical research : a case study on local environmental policy-making in Helsinki, Finland

Governance has been one of the most popular buzzwords in recent political science. As with any term shared by numerous fields of research, as well as everyday language, governance is encumbered by a jungle of definitions and applications. This work elaborates on the concept of network governance. Network governance refers to complex policy-making situations, where a variety of public and private actors collaborate in order to produce and define policy. Governance is processes of autonomous, self-organizing networks of organizations exchanging information and deliberating. Network governance is a theoretical concept that corresponds to an empirical phenomenon. Often, this phenomenon is used to descirbe a historical development: governance is often used to describe changes in political processes of Western societies since the 1980s. In this work, empirical governance networks are used as an organizing framework, and the concepts of autonomy, self-organization and network structure are developed as tools for empirical analysis of any complex decision-making process. This work develops this framework and explores the governance networks in the case of environmental policy-making in the City of Helsinki, Finland. The crafting of a local ecological sustainability programme required support and knowledge from all sectors of administration, a number of entrepreneurs and companies and the inhabitants of Helsinki. The policy process relied explicitly on networking, with public and private actors collaborating to design policy instruments. Communication between individual organizations led to the development of network structures and patterns. This research analyses these patterns and their effects on policy choice, by applying the methods of social network analysis. A variety of social network analysis methods are used to uncover different features of the networked process. Links between individual network positions, network subgroup structures and macro-level network patterns are compared to the types of organizations involved and final policy instruments chosen. By using governance concepts to depict a policy process, the work aims to assess whether they contribute to models of policy-making. The conclusion is that the governance literature sheds light on events that would otherwise go unnoticed, or whose conceptualization would remain atheoretical. The framework of network governance should be in the toolkit of the policy analyst.

Boost Customer Loyalty with Online Support. The Case of Mobile Telecoms Providers.

The paper explores the effect of customer satisfaction with online supporting services on loyalty to providers of an offline core service. Supporting services are provided to customers before, during, or after the purchase of a tangible or intangible core product, and have the purpose of enhancing or facilitating the use of this product. The internet has the potential to dominate all other marketing channels when it comes to the interactive and personalised communication that is considered quintessential for supporting services. Our study shows that the quality of online supporting services powerfully affects satisfaction with the provider and customer loyalty through its effect on online value and enjoyment. Managerial implications are provided.

PEDOT-PSSA as an alternative support for Pt electrodes in PEFCs

Poly (3,4-ethylenedioxythiophene) (PEDOT) and poly (styrene sulphonic acid) (PSSA) supported platinum (Pt) electrodes for application in polymer electrolyte fuel cells (PEFCs) are reported. PEDOT-PSSA support helps Pt particles to be uniformly distributed on to the electrodes, and facilitates mixed electronic and ionic (H+-ion) conduction within the catalyst, ameliorating Pt utilization. The inherent proton conductivity of PEDOT-PSSA composite also helps reducing Nation content in PEFC electrodes. During prolonged operation of PEFCs, Pt electrodes supported onto PEDOT-PSSA composite exhibit lower corrosion in relation to Pt electrodes supported onto commercially available Vulcan XC-72R carbon. Physical properties of PEDOT-PSSA composite have been characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and transmission electron microscopy. PEFCs with PEDOT-PSSA-supported Pt catalyst electrodes offer a peak power-density of 810 mW cm(-2) at a load current-density of 1800 mA cm(-2) with Nation content as low as 5 wt.% in the catalyst layer. Accordingly, the present study provides a novel alternative support for platinized PEFC electrodes.

PEDOT-PSSA as an alternative support for Pt electrodes in PEFCs

Poly (3,4-ethylenedioxythiophene) (PEDOT) and poly (styrene sulphonic acid) (PSSA) supported platinum (Pt) electrodes for application in polymer electrolyte fuel cells (PEFCs) are reported. PEDOT-PSSA support helps Pt particles to be uniformly distributed on to the electrodes, and facilitates mixed electronic and ionic (H+-ion) conduction within the catalyst, ameliorating Pt utilization. The inherent proton conductivity of PEDOT-PSSA composite also helps reducing Nation content in PEFC electrodes. During prolonged operation of PEFCs, Pt electrodes supported onto PEDOT-PSSA composite exhibit lower corrosion in relation to Pt electrodes supported onto commercially available Vulcan XC-72R carbon. Physical properties of PEDOT-PSSA composite have been characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and transmission electron microscopy. PEFCs with PEDOT-PSSA-supported Pt catalyst electrodes offer a peak power-density of 810 mW cm(-2) at a load current-density of 1800 mA cm(-2) with Nation content as low as 5 wt.% in the catalyst layer. Accordingly, the present study provides a novel alternative support for platinized PEFC electrodes

Decision support requirements for intelligent operation of southern grid in India

Control centers (CC) play a very important role in power system operation. An overall view of the system with information about all existing resources and needs is implemented through SCADA (Supervisory control and data acquisition system) and an EMS (energy management system). As advanced technologies have made their way into the utility environment, the operators are flooded with huge amount of data. The last decade has seen extensive applications of AI techniques, knowledge-based systems, Artificial Neural Networks in this area. This paper focuses on the need for development of an intelligent decision support system to assist the operator in making proper decisions. The requirements for realization of such a system are recognized for the effective operation and energy management of the southern grid in India The application of Petri nets leading to decision support system has been illustrated considering 24 bus system that is a part of southern grid.

An intelligent approach using support vector machines for monitoring and identification of faults on transmission systems

Power system disturbances are often caused by faults on transmission lines. When faults occur in a power system, the protective relays detect the fault and initiate tripping of appropriate circuit breakers, which isolate the affected part from the rest of the power system. Generally Extra High Voltage (EHV) transmission substations in power systems are connected with multiple transmission lines to neighboring substations. In some cases mal-operation of relays can happen under varying operating conditions, because of inappropriate coordination of relay settings. Due to these actions the power system margins for contingencies are decreasing. Hence, power system protective relaying reliability becomes increasingly important. In this paper an approach is presented using Support Vector Machine (SVM) as an intelligent tool for identifying the faulted line that is emanating from a substation and finding the distance from the substation. Results on 24-bus equivalent EHV system, part of Indian southern grid, are presented for illustration purpose. This approach is particularly important to avoid mal-operation of relays following a disturbance in the neighboring line connected to the same substation and assuring secure operation of the power systems.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.

Scalable non-linear Support Vector Machine using hierarchical clustering

This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data sets by using a selective sampling strategy for the training set. It employs a scalable hierarchical clustering algorithm to construct cluster indexing structures of the training data in the kernel induced feature space. These are then used for selective sampling of the training data for SVM to impart scalability to the training process. Empirical studies made on real world data sets show that the proposed strategy performs well on large data sets.

Multiple support excitations of open-plane frames by a filtered white noise and soil-structure interaction

Seismic structural design is essentially the estimation of structural response to a forced motion, which may be deterministic or stochastic, imposed on the ground. The assumption that the same ground motion acts at every point of the base of the structure (or at every support) is not always justifiable; particularly in case of very large structures when considerable spatial variability in ground motion can exist over significant distances example long span bridges. This variability is partly due to the delay in arrival of the excitation at different supports (which is called the wave passage effect) and due to heterogeneity in the ground medium which results in incoherency and local effects. The current study examines the influence of the wave passage effect (in terms of delay in arrival of horizontal ground excitation at different supports and neglecting transmission through the structure) on the response of a few open-plane frame building structures with soil-structure interaction. The ground acceleration has been modeled by a suitably filtered white noise. As a special case, the ground excitation at different supports has also been treated as statistically independent to model the extreme case of incoherence due to local effects and due to modifications to the ground motion resulting from wave reflections and refractions in heterogeneous soil media. The results indicate that, even for relatively short spanned building frames, wave passage effect can be significant. In the absence of soil-structure interaction, it can significantly increase the root mean square (rms) value of the shear in extreme end columns for the stiffer frames but has negligible effect on the flexible frames when total displacements are considered. It is seen that pseudo-static displacements increasingly contribute to the rms value of column shear as the time delay increases both for the stiffer and for the more flexible frames. When soil-structure interaction is considered, wave passage effect (in terms of total displacements) is significant only for low soil shear modulus, G. values (where soil-structure interaction significantly lowers the fundamental frequency) and for stiff frames. The contribution of pseudo-static displacement to these rms values is found to decrease with increase in G. In general, wave passage effect for most interactive frames is insignificant compared to the attenuating effect a decrease in G, has on the response of the interactive structure to uniform support excitation. When the excitations at different supports are statistically independent, it is seen that for both the stiff and flexible frames, the rms value of the column shear in extreme end columns is several times larger (more for the stiffer frames) than the value corresponding to uniform base excitation with the pseudo-static displacements contributing over 99% of the rms value of column shear. Soil-structure interaction has an attenuating effect on the rms value of the column shear, the effect decreasing with increase in G,. Here too, the pseudo-static displacements contribute very largely to the column shear. The influence of the wave passage effect on the response of three 2-bay frames with and without soil-structure interaction to a recorded horizontal accelerogram is also examined. (C) 2010 Elsevier Ltd. All rights reserved.

Support theorems on R-n and non-compact symmetric spaces

We consider convolution equations of the type f * T = g, where f, g is an element of L-P (R-n) and T is a compactly supported distribution. Under natural assumptions on the zero set of the Fourier transform of T, we show that f is compactly supported, provided g is. Similar results are proved for non-compact symmetric spaces as well. (C) 2010 Elsevier Inc. All rights reserved.