Sequential tripping strategy for a transmission network back-upprotection expert system

This paper describes a sequential tripping strategy used in a wide area back-up protection expert system (BPES) to combat situations in which protection relays have maloperated or information is missing. The BPES is an innovative back-up protection scheme designed to prevent the occurrence of widespread blackouts. The BPES evaluates the certainty that transmission lines are likely to be affected by the fault and uses a sequential tripping strategy to isolate the fault if a firm decision is not available due to maloperated relays and/or missing information. The mode of analysis and the sequential tripping strategy ensures that the BPES will clear a fault at minimum risk to the network. An example is included to demonstrate how the certainty factor based sequential tripping strategy is employed by the BPES to clear a fault which occurred on the South Western part of the UK National Grid System     

Generalisations of Heyman's and Foulkes' theorems using dual formulation

Existing duality principles in structural optimisation are briefly reviewed and then they are extended to structures with segment-wise constant cross-sections. All theories are discussed in the particular context of optimal plastic beam design with symmetric convex specific cost functions and are confirmed by independent calculations on illustrative examples. It is shown that the optimal solution is always associated with a displacement field in which the mean absolute curvature value for each segment equals the subgradient of the specific cost function, with respect to the maximum absolute moment value for that segment. Moreover, the dual problem consists of the maximisation of the difference of two terms: the first one is the integral of the product of load and deflection (external work), and the second is the sum of products of segment lengths and the mean complementary cost values (taken with respect to the mean absolute curvature for that segment). Finally, some tentative proposals for a class of non-convex optimisation problems are presented. For special cases, the proposed general statements reduce to theorems by Heyman, Foulkes and Hemp.     

Resonant neutron filters in elemental analysis and radiography

Translated from Atomnaya Énergiya, Vol. 67, No. 4, pp. 271–274, October, 1989.     

Multifunctional alkylphenol additive

Translated from Khimiya i Tekhnologiya Topliv i Masel, No. 2, pp. 14–15, February, 1989.     

Computing hysteresis points of nonlinear equations depending on two parameters

A direct method is described for computing a hysteresis point (double turning point) corresponding to a cusp point of a system ofn nonlinear equations inn variables depending on two parameters. By addition of two equations a minimally extended system ofn+2 nonlinear equations is constructed for which the hysteresis point is an isolated solution. An efficient implementation of Newton's method is presented not requiring evaluations of second derivatives of the original problem. Two numerical examples show the efficiency of theQ-quadratically convergent method.     

Shallow Linear Action Graphs and their Embeddings

Action calculi, which generalise process calculi such as Petri nets, π-calculusand ambient calculus, have been presented in terms of action graphs. We here offer linear action graphs as a primitive basis for action calculi. This paper presents the category of embeddings of undirected linear action graphs without nesting, using a novel form of graphical reasoning which simplifies some otherwise complex manipulations in regular algebra. The results are adapted in a few lines to directed graphs. This work is part of a long-term search for a uniform behavioural theory for process calculi. Received October 2000 / Accepted in revised form April 2001     

Mechanisms of Active Fuzzy Expertise

Consideration was given to the decision making procedures based on the fuzzy messages of experts whose preferences on the set of collective decisions can also be fuzzy.     

Intelligent identification and control for autonomous guided vehicles using adaptive fuzzy-based algorithms

Fuzzy logic was first suggested as the mechanism by which humans drive cars. This paper addresses the use of fuzzy logic and algorithms towards the intelligent autonomous motion control of land vehicles. To cope with vehicle complexities, internal parametric changes, and with unpredictable environmental effects, the controllers that are presented, whilst heuristic in nature, are self-organizing or self-learning in that they generate automatically by observation an experiential rule base that models the vehicle, and via an appropriate performance index an optimal control rule base that is robust to large parametric changes. The methodology presented is applicable to any complex process which is too difficult to model or control using conventional methods, or which has relied on the experience of a human operator. An overview of fuzzy logic and static fuzzy logic control (akin to expert systems) is provided, together with illustrative examples.