Self and mutual ground impedances of cylindrical metal plates buried in homogeneous earth

This paper presents a numerical procedure for computation of self and mutual impedance of cylindrical metal plates buried in homogeneous earth. Procedure is based on an analytical expression for scalar potential distribution of an equipotential metal plate in homogeneous unbounded medium. The effect of the air–earth boundary condition is taken into account by the exact imaging method. The robustness and accuracy of the computation procedure is based on the combination of analytical integration and 1D and 2D Gaussian quadratures for solving integrals present in expressions for self and mutual ground impedances of metal plates. The attenuation and phase shift is taken into account approximately by introducing the attenuation‐phase shift factor. The numerical procedure developed for the computation of self and mutual ground impedances of cylindrical metal plates buried in homogeneous earth is efficient, numerically stable and generally applicable. Numerical model developed for the computation of self and mutual ground impedances of cylindrical metal plates buried in homogeneous earth represents a basis of a wider numerical model for computation of ground fault current distribution in which grounding grids are approximated by metal cylindrical plates. Copyright © 2014 John Wiley & Sons, Ltd.     

Exponential approximation of the Heidler function for the reproduction of lightning current waveshapes

In this paper the lightning current function proposed by Heidler is approximated by a linear combination of exponential functions in the time domain, which is afterward analytically transformed into the frequency domain. The unknown coefficients of the exponential approximation for the most frequently used values of the current steepness factor are computed using the least squares method. The developed exponential approximation is general with respect to the current steepness factor in the Heidler function. Approximation of the Heidler function with a linear combination of exponential functions enables the approximation of various Heidler function based lightning current functions. Heidler function in the frequency domain can be used for transient analysis of electromagnetic phenomena that include lightning protection, grounding grid analysis and electromagnetic compatibility problems.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Using TRIO for designing a CORBA‐based application

We report on our experience in the Esprit OpenDREAMS project that targets the domain of Supervision and Control Systems (SCS). During this project we studied how a CORBA‐based application can be designed starting from a typical SCS requirement document by integrating a formal approach with some CORBA concepts. We present a case study that shows how an existing object‐oriented methodology based on the formal specification language TRIO can be tailored towards supporting CORBA‐based applications. The application taken into account is in the field of the Energy Management Systems, namely a diagnostic system for the steam condenser of a thermoelectric power plant. The paper describes how to obtain the architectural design of a CORBA‐based application starting from the formal specification of its requirements expressed in TRIO by means of a sequence of transformation steps. At the end of such sequence of steps a complete structure of the application classes, with their mutual relations and their IDL interfaces, is built. Finally, the paper discusses how TRIO can be used to validate the architectural choices made with respect to the application critical requirements. Copyright © 2000 John Wiley & Sons, Ltd.     

User Interfaces for Complex Engineering Software: Application of Virtual Reality

IT systems for engineers are becoming more numerous and complex and will further proliferate in the future. It is infeasible to expect users to be trained to work with a plethora of systems, especially as many of them will only see occasional use. Therefore, in the near future, systems will need user interfaces that allow intuitive access and usage. This paper examines a new style of user interface based on virtual reality.     

理解LINQ-to-SQL

大多数Web应用程序都是以数据驱动的,并且以关系数据库为中心。然而,多年来架构师已经设计了通过对象来建模问题域的应用程序。对象和关系数据库之间的通信一直都不流畅和简单。建立这类通信时遇到的困难通常称为“对象/关系阻抗失配”（O/ RM）。需要一个持久层来保存对象和从关系表载入对象。这些年来,产生了许多设计样式,为从事这项任务的架构师提供指南。现在,开发人员可以自己编写整个持久层,或者借助像NHibernate或Genome等O/RM工具的帮助,进行设计。     

Operator precedence and the visibly pushdown property

Floyd?s operator precedence grammars and languages (FG, FL) are a classical subclass of deterministic context-free (DCF) grammars and languages. We prove that several recently introduced language families motivated by the needs of model checking and of specifying XML-like languages are proper subsets of FL. The main cases considered include visibly pushdown languages (VPL) and balanced languages (BALAN), which are characterized by restricted precedence relations. FL have all the closure properties available for regular languages and generally viewed as necessary for application to model checking: reversal, prefixing and suffixing, concatenation, Kleene star, and boolean operations. All but the last results are new, and some require complex proofs, due to the necessary changes of syntax structure. Thus FL are the largest known subfamily of DCF having the same closure properties as VPL. FG, unlike VPL grammars, which are intended for abstract syntax modelling, are structurally adequate to specify real programming languages.     

Anonymity preserving sequential pattern mining

The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.     

小议JavaScript库——Dojo、jQuery和PrototypeJS的比较

这是一篇小文章,讨论了JavaScript的应用库,JavaScript是最近两年飞速发展的技术领域,希望这篇文章能引发你的兴趣,帮助你进入这个领域。