Contextual reliability discounting in welding process diagnostic based on DSmT

The monitoring and diagnosing of the welding process is an important task in assuring the quality of welded joints. Observation of such a dynamic process is often performed by measuring various signals of different modality. The main problem is how to use several of the possible signals simultaneously. Many techniques that can deal with this kind of task were elaborated and introduced in practical application. One of these is decision fusion, with a group of methods based on belief function theories: Dempster–Shafer and Dezert–Smarandache (DSmT). In this paper, extended fusion scheme for assessment of welding process condition, with contextual discounting of evidence sources reliability in DSmT framework, is presented. Discounting operation was used to distinguish the objective ability of each evidence source, to give a correct assessment of process condition. To validate this method, several series of welded joints representing different process conditions that result in various joint quality were made. On the basis of performed research, it was proven that contextual reliability discounting leads to increase of the condition recognition accuracy.     

Stochastic dominance-based rough set model for ordinal classification

In order to discover interesting patterns and dependencies in data, an approach based on rough set theory can be used. In particular, dominance-based rough set approach (DRSA) has been introduced to deal with the problem of ordinal classification with monotonicity constraints (also referred to as multicriteria classification in decision analysis). However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive. In this paper, we introduce a probabilistic model for ordinal classification problems with monotonicity constraints. Then, we generalize the notion of lower approximations to the stochastic case. We estimate the probabilities with the maximum likelihood method which leads to the isotonic regression problem for a two-class (binary) case. The approach is easily generalized to a multi-class case. Finally, we show the equivalence of the variable consistency rough sets to the specific empirical risk-minimizing decision rule in the statistical decision theory.     

Unsupervised statistical segmentation of nonstationary images using triplet Markov fields

Recent developments in statistical theory and associated computational techniques have opened new avenues for image modeling as well as for image segmentation techniques. Thus, a host of models have been proposed and the ones which have probably received considerable attention are the hidden Markov fields (HMF) models. This is due to their simplicity of handling and their potential for providing improved image quality. Although these models provide satisfying results in the stationary case, they can fail in the nonstationary one. In this paper, we tackle the problem of modeling a nonstationary hidden random field and its effect on the unsupervised statistical image segmentation. We propose an original approach, based on the recent triplet Markov field (TMF) model, which enables one to deal with nonstationary class fields. Moreover, the noise can be correlated and possibly non-Gaussian. An original parameter estimation method which uses the Pearson system to find the natures of the noise margins, which can vary with the class, is also proposed and used to perform unsupervised segmentation of such images. Experiments indicate that the new model and related processing algorithm can improve the results obtained with the classical ones.     

Effect of green manure on the incidence of cyanogenic Pseudomonas strains in hop garden soils

Incidence of cyanogenic Pseudomonas strains in hop garden soils in relation to the kind of fertilization was studied. Incidence differed with respect to the fertilization treatment and the age of the plantation. Amendment of soil with rye and with white mustard as green manures limited the number of cyanogenic Pseudomonas strains relative to farmyard manures and NPK fertilization. Among all fertilization treatments, cyanogenic Pseudomonas spp. strains had lowest populations in soils amended with white mustard.     

Unsupervised image segmentation using triplet Markov fields

Hidden Markov fields (HMF) models are widely applied to various problems arising in image processing. In these models, the hidden process of interest X is a Markov field and must be estimated from its observable noisy version Y. The success of HMF is mainly due to the fact that the conditional probability distribution of the hidden process with respect to the observed one remains Markovian, which facilitates different processing strategies such as Bayesian restoration. HMF have been recently generalized to “pairwise” Markov fields (PMF), which offer similar processing advantages and superior modeling capabilities. In PMF one directly assumes the Markovianity of the pair (X, Y). Afterwards, “triplet” Markov fields (TMF), in which the distribution of the pair (X, Y) is the marginal distribution of a Markov field (X, U, Y), where U is an auxiliary process, have been proposed and still allow restoration processing. The aim of this paper is to propose a new parameter estimation method adapted to TMF, and to study the corresponding unsupervised image segmentation methods. The latter are validated via experiments and real image processing.     

Measurement of radio-frequency temporal phase modulation using spectral interferometry

We present an optical method to measure radio-frequency electro-optic phase modulation profiles by employing spectrum-to-time mapping realized by highly chirped optical pulses. We directly characterize temporal phase modulation profiles of up to 12.5 GHz bandwidth, with temporal resolution comparable to high-end electronic oscilloscopes. The presented optical set-up is a valuable tool for direct characterization of complex temporal electro-optic phase modulation profiles, which is indispensable for practical realization of deterministic spectral-temporal reshaping of quantum light pulses     

Stuttering-Insensitive Automata for On-the-fly Detection of Livelock Properties

The research examines liveness and progress properties of concurrent systems and their on-the-fly verification. An alternative formalism to Büchi automata, called testing automata, is developed. The basic idea of testing automata is to observe changes in the values of state propositions instead of the values. Therefore, the testing automata are able to accept only stuttering-insensitive languages. Testing automata can accept the same stuttering-insensitive languages as (state-labelled) Büchi automata, and they have at most the same number of states. They are also more often deterministic. Moreover, on-the-fly verification using testing automata can often (but not always) use an algorithm performing only one search in the state space, whereas on-the-fly verification with Büchi automata requires two searches. Experimental results illustrating the benefits of testing automata are presented.     

Strength and structure of AlMg3 alloy after ECAP and post-ECAP processing

Equal-channel angular pressing (ECAP) is a promising method for the processing of metals with an ultrafine-grain (UFG) structure of a few hundred nanometers in size. In this paper, the influence of solution treatment and artificial aging conditions, combined with a severe plastic deformation process on the AlMg₃ aluminum alloy, was studied. Samples were processed up to six passes with the application of processing route Bc, in which the sample after each separate pass is rotated clockwise by 90 degrees. Optical microscopy and electron backscattered diffraction were used to investigate the microstructure evolution at particular stages of the investigation. To estimate the effect of the process parameters on the change of the strength of the alloy, tensile properties and hardness measurements were determined.