Using artificial neural networks for identification of electrical losses in transformers during the manufacturing phase

The paper describes a novel neural model to estimate electrical losses in transformer during the manufacturing phase. The network acts as an identifier of structural features on electrical loss process, so that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through experimental data taking into account core losses, copper losses, resistance, current and temperature. The results obtained in the simulations have shown that the developed technique can be used as an alternative tool to make the analysis of electrical losses on distribution transformer more appropriate regarding to manufacturing process. Thus, this research has led to an improvement on the rational use of energy.

Structural health evaluation by optimization techinique and artificial neural network

This paper presents two different approaches to detect, locate, and characterize structural damage. Both techniques utilize electrical impedance in a first stage to locate the damaged area. In the second stage, to quantify the damage severity, one can use neural network, or optimization technique. The electrical impedance-based, which utilizes the electromechanical coupling property of piezoelectric materials, has shown engineering feasibility in a variety of practical field applications. Relying on high frequency structural excitations, this technique is very sensitive to minor structural changes in the near field of the piezoelectric sensors, and therefore, it is able to detect the damage in its early stage. Optimization approaches must be used for the case where a good condensed model is known, while neural network can be also used to estimate the nature of damage without prior knowledge of the model of the structure. The paper concludes with an experimental example in a welded cubic aluminum structure, in order to verify the performance of these two proposed methodologies.

Potentiometric determination of saccharin in commercial artificial sweeteners using a silver electrode

A simple, precise, rapid and low-cost potentiometric method for saccharin determination in commercial artificial sweeteners is proposed. Saccharin present in several samples of artificial sweeteners is potentiometrically titrated with silver nitrate solution using a silver wire as the indicator electrode, coupled to a titroprocessor. The best pH range was from 3.0 to 3.5 and the detection limit of sodium saccharin was 2.5 mg/ml. Substances normally found along with saccharin in several commercial artificial sweeteners such as maltodextrin, glucose, sucrose, fructose, aspartame, cyclamate, caffeine, sorbitol, lactose, nitrate, methyl- and n-propyl-p-hydroxybenzoate, benzoic, citric and ascorbic acids do not interfere even in significant amounts (e.g. 20 excess relative to saccharin). Chloride ion interferes when present in concentrations larger than 10 mg l(-1); this interference is eliminated with previous extraction of the sweetener from the aqueous medium with ethyl acetate. The results obtained by applying the proposed method compared very favorably with those given by the HPLC method recommended by the FDA. (C) 2003 Elsevier Ltd. All rights reserved.

Combining artificial intelligence and human problem solving: Proposing a new architecture for ITS's

This paper shows a comparative study between the Artificial Intelligence Problem Solving and the Human Problem Solving. The study is based on the solution by many ways of problems proposed via multiple-choice questions. General techniques used by humans to solve this kind of problems are grouped in blocks and each block is divided in steps. A new architecture for ITS - Intelligent Tutoring System is proposed to support experts' knowledge representation and novices' activities. Problems are represented by a text and feasible answers with particular meaning and form, to be rigorously analyzed by the solver to find the right one. Paths through a conceptual space of states represent each right solution.

Treatment of urinary stress incontinence by intravaginal electrical stimulation and pelvic floor physiotherapy

Treatment of urinary stress incontinence (USI) by intravaginal electrical stimulation (IES) and pelvic floor physiotherapy represents an alternative to other therapies. The purpose of this work was to evaluate the effectiveness of this treatment inpatients with urinary incontinence. From January 1998 to May 2000, 30 women (mean age 54 years) were studied. All patients had USI and 70% urge incontinence; average follow-up was 7 months. Selection criteria were based on clinical history, objective evaluation of perineal musculature by perineometry, and urodynamics. The treatment protocol consisted of three sessions of IES per week for 14 weeks using INNOVA equipment. Physiotherapy was initiated in the fifth week of IES. A significant decrease in the number of micturitions and urgency was observed after treatment (P<0.01). The pad test showed a reduction in urinary leakage from 13.9 to 5.9 g after treatment (P<0.01). Objective evaluation of perineal muscle strength showed a significant improvement in all patients after treatment (P<0.01). A positive correlation was observed between maximum flow rate (Q(max)) and all three variables: urethral pressure profile at rest and on straining (stop test), and abdominal leak-point pressure (ALPP). A positive correlation was also observed between ALPP and the stop test. Over 100 different surgical and conservative treatments have been tried to manage USI. The majority of these procedures reveal that despite progress already made in this area, there is no ideal treatment. Satisfactory results can be achieved with this method, especially with patients who are reluctant to undergo surgery because of personal or clinical problems.