Fault diagnosis using partial syndromes: a modified Hopfield neural network approach

This paper presents a modified Hopfield neural network (HNN) for solving the system-level fault diagnosis problem which aims at identifying the set of faulty nodes. This problem has been extensively studied in the last three decades. Nevertheless, identifying the set of all faulty nodes using only partial syndromes, i.e. when some of the testing or comparison outcomes are missing prior to initiating the diagnosis phase, remains an outstanding research issue. The new HNN-based diagnosis algorithm does not require any prior learning or knowledge about the system, nor about any faulty situation, hence providing a better generalisation performance. Results from a thorough simulation study demonstrate the effectiveness of the HNN-based fault diagnosis algorithm in terms of diagnosis correctness, diagnosis latency and diagnosis scalability, for randomly generated diagnosable systems of different sizes and under various fault scenarios. We have also conducted extensive simulations using partial syndromes. Simulations showed that the HNN-based diagnosis performed efficiently, i.e. diagnosis correctness was around 99% when at most half of the test or comparison outcomes are missing, making it a viable alternative to existing diagnosis algorithms.     

Effect of extraction methods on kinetic,chemical composition and antibacterial activities of Tunisian Thymus vulgaris. L. essential oil

The present study aims to compare two innovative extraction techniques: microwave-assisted hydrodistillation (MAHD) and solvent-free microwave extraction (SFME) through traditional extraction techniques: hydrodistillation (HD) and steam distillation (SD) for their efficiency in the extraction of the volatile compounds from Tunisian Thymus vulgaris leaves; the kinetic, yield, composition and antibacterial activities of the essential oil were assessed in vitro. Results show that the essential oils extracted by microwaves were quantitatively (yield) similar to those obtained through the conventional methods, but qualitatively, essential oils analysed by gas chromatography–mass spectrometry (GC–MS) presented 17, 11, 11 and 8 compounds obtained through SFME, MAHD, SD and HD, respectively, mostly consisting of carvacrol (89.24–41.17%), followed by γ-terpinene (11.37–1.37%) and para-cymene (27.95–2.05%). The essential oils were screened for antibacterial activity against 5 microorganisms. All essential oils obtained by studied extraction methods showed the same resistance against Gram (?) and Gram (+) bacteria. The SFME method gave the best results: rapid kinetic of extraction (30 min vs. 35 min for MAHD, 120 min for SD, and 180 min for HD), less energy saving and cleanest process.     

Osmotic Dehydration Kinetics of Pomegranate Seeds Using Date Juice as an Immersion Solution Base

Pomegranate seeds were osmodehydrated using date juice added with sucrose (final °Brix, 55) as immersion solution. The kinetics of osmotic dehydration showed that the most significant changes of mass transfer took place during the first 20 min of the process, regardless of date juice varieties. During this time, seed water loss and solid gain were estimated to be ∼39% and ∼6%, respectively. After 20 min of the process, the percentage of water loss and solid gain varied slightly and ranged on average close to ∼40% and ∼9%, respectively. During osmotic dehydration, there was a leaching of natural solutes from seeds into the solution, which is quantitatively not negligible, and might have an important impact on the sensorial and nutritional value of seeds and date juices. Both scanning electron microscopy and texture (compression) analysis revealed that osmotic dehydration process induced modifications of seed texture and cell structure. Sucrose was found to be the essential element which influences the texture of seed and the viscosity of date juice. Additionally, natural sugar present in date juice permits substituting 35% of the total quantity of sucrose added to the osmotic solution.     

Occurrence of Yersinia enterocolitica in milk and dairy products in Morocco.

A total of 227 samples of milk and dairy products were examined for the presence of Yersinia enterocolitica. Yersinia spp. were recovered from 11 of 30 raw milks (36.6%), one of 20 pasteurized milks (5%), 15 of 63 traditional fermented milks (23.8%), seven of 94 cheeses and one of 20 cream samples (5%). The overall incidence of Y. enterocolitica in milk and dairy products was 6.6%. The other Yersinia species were Y. intermedia, Y. kristensenii, Y. frederiksenii and Y. pseudotuberculosis. Y. enterocolitica was detected only in raw milk (30% of the samples), in traditional fermented milks (6.3%) and in raw milk-made cheese (4%). The majority of the Y. enterocolitica isolates were of biotype 1 (environmental strains). The Celfulodin-Irgasan-Novobiocin (CIN) Agar was found to be more efficient than the Mac Conkey Agar in the isolation of Yersinia organisms.     

Utilisation of attenuated total reflectance MIR and front-face fluorescence spectroscopies for the identification of Saint-Nectaire cheeses varying by manufacturing conditions

Twelve samples of cheeses (three types of Saint-Nectaire PDO cheeses and Savaron cheeses) differing by manufacturing and ripening conditions, from 12 different producers, were characterised by attenuated total reflectance mid-infrared (MIR) and front-face fluorescence spectroscopies, dynamic testing rheology and physico-chemical analysis. Fluorescence spectra (tryptophan residues, vitamin A and riboflavin) and MIR (3,000–2,800 (fat region), 1,700–1,500 (protein region) and 1,500–900 cm⁻¹ (fingerprint region)) spectra were recorded on cheese samples. The potential of the data tables was investigated for discriminating the four different groups of cheeses. The results of factorial discriminant analysis (FDA) performed on the fluorescence and mid-infrared spectra showed a good discrimination of the four cheese groups. Considering cross-validation results, the best classifications (100%) were achieved from mid-infrared and fluorescence spectra, while only 91.7 and 72.2% of correct classification were obtained by applying FDA to rheology and physico-chemical data, respectively. Spectroscopic techniques could provide useful fingerprints and allow the identification of investigated cheeses according to manufacturing conditions. Simple and rapid spectroscopic methods offer a promising approach to the authentication of cheeses.     

Prediction of the rheology parameters of ripened semi-hard cheeses using fluorescence spectra in the UV and visible ranges recorded at a young stage

Storage modulus (G′), loss modulus (G″), strain, tan (δ) and complex viscosity (η*) of 20 semi-hard cheeses were measured by dynamic oscillatory analysis after 2, 30 and 60 days of ripening. On the same cheeses and at the same ages, tryptophan and riboflavin fluorescence spectra were recorded. The aim was to predict the rheology parameters of ripened cheeses from spectra recorded on these cheeses at a young stage. Using partial least square, tryptophan fluorescence spectra recorded at 20 °C on 2-days-old cheeses predicted G′, G″, strain, tan (δ) and η* measured at 80 °C on the 60-days-old cheeses with correlation coefficients (R) of 0.98, 0.97, 0.98, 0.98 and 0.97, respectively. Riboflavin fluorescence spectra gave slightly lower correlation coefficients of 0.88, 0.88, 0.92, 0.87 and 0.88, respectively. Dependent only on visible light, the riboflavin fluorescence spectra potentially provide viable and economic prediction of the rheology of ripe cheese.     

An evolutionary algorithm for abductive reasoning

Abductive reasoning (or abduction) is the process of inferring hypotheses from observed data using a certain ‘knowledge’ encoded in the form of inference rules (or causal relations). Many important kinds of intellectual tasks, including medical diagnosis, fault diagnosis, scientific discovery, legal reasoning, and natural language understanding have been characterised as abduction. Unfortunately, abduction is 𝒩𝒫-hard. Genetic algorithms and biologically motivated computational paradigms inspired by the natural evolution turned out to be efficient in solving many hard problems while other existing approaches failed to solve in general. In this article, we present a genetic algorithm called HAKIM, for solving abduction problems. We encode an explanation in a chromosome-like structure, where every gene models a possible single hypothesis. Thereafter, we develop a fitness function that characterises the overall ‘quality’ of a chromosome representing an explanation; and then use standard genetic operators to compute a set of hypotheses that best explains the observed data. Simulation results on large-scale medical problems reveal the good performance of our model HAKIM.     

An Artificial Network Simulating Cause-to-Effect Reasoning: Cancellation Interactions and Numerical Studies

During the last few decades, a variety of models have been proposed to address causal reasoning (known also as abduction); most of these dealt with a probabilistic or a logical framework. Recently, a few models have been proposed within a neural framework. The investigation of neural approaches is mainly motivated by the computational burden of the causal reasoning task and by the satisfactory results given by neural networks in solving hard problems in general. A particular class of causal reasoning that raises several difficulties is the cancellation class. From an abstract point of view, cancellation occurs when two causes (hypotheses) cancel each other's explanation capabilities with respect to a given effect (observation). The present work is twofold. First, we extend an existing neural model to handle cancellation interactions. Second, we test the model on a large database and propose objective criteria to quantitatively evaluate the scenarios (explanations) produced. Simulation results show good performance and stability of the model. Received 17 November 1999 / Revised 12 May 2000 / Accepted in revised form 16 June 2000     

Size distribution of CdS nanocrystal-doped silica xerogels

Pure and porous silica xerogels doped with CdS nanocrystals have been prepared by a sol-gel process. In order to determine parameters convenient for non-linear optical properties, particle size distributions were obtained by two complementary techniques: transmission electron microscopy (conventional, CTEM, and high resolution, HRTEM) and small angle X-ray scattering (SAXS). Monolithic samples having CdO concentrations varying from 5 to 20 wt% have been studied. Details are given of an image analysis technique used to study the CTEM micrographs.     

Cutting parameter optimization in NC milling

The continuous demand for higher productivity and product quality asks for better optimizing of the machining process. In this case, numerical controlled (NC) milling is a processing technology massively applied in the metal manufacturing industry; it has received very important interest in this century because it has a very high productivity and high work piece surface quality. The main objective of this work is to evaluate the machining time of different cycles, in 2.5?D NC milling. The prediction of the optimal values of cutting speed was analyzed to minimize both time and cost of die production. Optimum and economical values of cutting speed give, respectively, minimum production time and minimum production cost. An experimental study is carried out to validate machining time calculation models developed in this work. The cutting parameters analyzed in this study are cutting speed, feed per tooth, and the radial cutting depth.