Filter design using radial basis function neural network and genetic algorithm for improved operational health monitoring

The problem of denoising damage indicator signals for improved operational health monitoring of systems is addressed by applying soft computing methods to design filters. Since measured data in operational settings is contaminated with noise and outliers, pattern recognition algorithms for fault detection and isolation can give false alarms. A direct approach to improving the fault detection and isolation is to remove noise and outliers from time series of measured data or damage indicators before performing fault detection and isolation. Many popular signal-processing approaches do not work well with damage indicator signals, which can contain sudden changes due to abrupt faults and non-Gaussian outliers. Signal-processing algorithms based on radial basis function (RBF) neural network and weighted recursive median (WRM) filters are explored for denoising simulated time series. The RBF neural network filter is developed using a K-means clustering algorithm and is much less computationally expensive to develop than feedforward neural networks trained using backpropagation. The nonlinear multimodal integer-programming problem of selecting optimal integer weights of the WRM filter is solved using genetic algorithm. Numerical results are obtained for helicopter rotor structural damage indicators based on simulated frequencies. Test signals consider low order polynomial growth of damage indicators with time to simulate gradual or incipient faults and step changes in the signal to simulate abrupt faults. Noise and outliers are added to the test signals. The WRM and RBF filters result in a noise reduction of 54 - 71 and 59 - 73% for the test signals considered in this study, respectively. Their performance is much better than the moving average FIR filter, which causes significant feature distortion and has poor outlier removal capabilities and shows the potential of soft computing methods for specific signal-processing applications. (C) 2005 Elsevier B. V. All rights reserved.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

Genetic algorithms: a survey

Genetic algorithms provide an alternative to traditional optimization techniques by using directed random searches to locate optimal solutions in complex landscapes. We introduce the art and science of genetic algorithms and survey current issues in GA theory and practice. We do not present a detailed study, instead, we offer a quick guide into the labyrinth of GA research. First, we draw the analogy between genetic algorithms and the search processes in nature. Then we describe the genetic algorithm that Holland introduced in 1975 and the workings of GAs. After a survey of techniques proposed as improvements to Holland's GA and of some radically different approaches, we survey the advances in GA theory related to modeling, dynamics, and deception

Clan-based evolutionary approach for solving control problems

This paper presents a dan-based evolutionary approach for solving control problems. Three selected control problems, viz. linear-quadratic, harvest, and push-cart problems, are solved using the proposed approach. Results are compared with those of the evolutionary programming (EP) approach. In most of the cases, the proposed approach is successful in obtaining (near) optimal solutions for these selected problems.

Genetic Algorithm for Damage Assessment

Genetic Algorithms (GAs) are recognized as an alternative class of computational model, which mimic natural evolution to solve problems in a wide domain including machine learning, music generation, genetic synthesis etc. In the present study Genetic Algorithm has been employed to obtain damage assessment of composite structural elements. It is considered that a state of damage can be modeled as reduction in stiffness. The task is to determine the magnitude and location of damage. In a composite plate that is discretized into a set of finite elements, if a jth element is damaged, the GA based technique will predict the reduction in Ex and Ey and the location j. The fact that the natural frequency decreases with decrease in stiffness is made use of in the method. The natural frequency of any two modes of the damaged plates for the assumed damage parameters is facilitated by the use of Eigen sensitivity analysis. The Eigen value sensitivities are the derivatives of the Eigen values with respect to certain design parameters. If ωiu is the natural frequency of the ith mode of the undamaged plate and ωid is that of the damaged plate, with δωi as the difference between the two, while δωk is a similar difference in the kth mode, R is defined as the ratio of the two. For a random selection of Ex,Ey and j, a ratio Ri is obtained. A proper combination of Ex,Ey and j which makes Ri−R=0 is obtained by Genetic Algorithm.

Evolutionary Algorithm for the Determination of Optimal Mode of Bioreactor Operation

Stirred tank bioreactors, employed in the production of a variety of biologically active chemicals, are often operated in batch, fed-batch, and continuous modes of operation. The optimal design of bioreactor is dependent on the kinetics of the biological process, as well as the performance criteria (yield, productivity, etc.) under consideration. In this paper, a general framework is proposed for addressing the two key issues related to the optimal design of a bioreactor, namely, (i) choice of the best operating mode and (ii) the corresponding flow rate trajectories. The optimal bioreactor design problem is formulated with initial conditions and inlet and outlet flow rate trajectories as decision variables to maximize more than one performance criteria (yield, productivity, etc.) as objective functions. A computational methodology based on genetic algorithm approach is developed to solve this challenging multiobjective optimization problem with multiple decision variables. The applicability of the algorithm is illustrated by solving two challenging problems from the bioreactor optimization literature.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses. © 2012 American Physical Society.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses.

The origins of the evolutionary signal used to predict protein-protein interactions

Background: The correlation of genetic distances between pairs of protein sequence alignments has been used to infer protein-protein interactions. It has been suggested that these correlations are based on the signal of co-evolution between interacting proteins. However, although mutations in different proteins associated with maintaining an interaction clearly occur (particularly in binding interfaces and neighbourhoods), many other factors contribute to correlated rates of sequence evolution. Proteins in the same genome are usually linked by shared evolutionary history and so it would be expected that there would be topological similarities in their phylogenetic trees, whether they are interacting or not. For this reason the underlying species tree is often corrected for. Moreover processes such as expression level, are known to effect evolutionary rates. However, it has been argued that the correlated rates of evolution used to predict protein interaction explicitly includes shared evolutionary history; here we test this hypothesis. Results: In order to identify the evolutionary mechanisms giving rise to the correlations between interaction proteins, we use phylogenetic methods to distinguish similarities in tree topologies from similarities in genetic distances. We use a range of datasets of interacting and non-interacting proteins from Saccharomyces cerevisiae. We find that the signal of correlated evolution between interacting proteins is predominantly a result of shared evolutionary rates, rather than similarities in tree topology, independent of evolutionary divergence. Conclusions: Since interacting proteins do not have tree topologies that are more similar than the control group of non-interacting proteins, it is likely that coevolution does not contribute much to, if any, of the observed correlations.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Fast and accurate quantification using Genetic Algorithm optimized H-1-C-13 refocused constant-time INEPT

Using Genetic Algorithm, a global optimization method inspired by nature's evolutionary process, we have improved the quantitative refocused constant-time INEPT experiment (Q-INEPT-CT) of Makela et al. (JMR 204 (2010) 124-130) with various optimization constraints. The improved `average polarization transfer' and `min-max difference' of new delay sets effectively reduces the experimental time by a factor of two (compared with Q-INEPT-CT, Makela et al.) without compromising on accuracy. We also discuss a quantitative spectral editing technique based on average polarization transfer. (C) 2013 Elsevier Inc. All rights reserved.

Differences in host species relationships and biogeographic influences produce contrasting patterns of prevalence, community composition and genetic structure in two genera of avian malaria parasites in southern Melanesia

1. Host-parasite interactions have the potential to influence broadscale ecological and evolutionary processes, levels of endemism, divergence patterns and distributions in host populations. Understanding the mechanisms involved requires identification of the factors that shape parasite distribution and prevalence. 2. A lack of comparative information on community-level host-parasite associations limits our understanding of the role of parasites in host population divergence processes. Avian malaria (haemosporidian) parasites in bird communities offer a tractable model system to examine the potential for pathogens to influence evolutionary processes in natural host populations. 3. Using cytochrome b variation, we characterized phylogenetic diversity and prevalence of two genera of avian haemosporidian parasites, Plasmodium and Haemoproteus, and analysed biogeographic patterns of lineages across islands and avian hosts, in southern Melanesian bird communities to identify factors that explain patterns of infection. 4. Plasmodium spp. displayed isolation-by-distance effects, a significant amount of genetic variation distributed among islands but insignificant amounts among host species and families, and strong local island effects with respect to prevalence. Haemoproteus spp. did not display isolation-by-distance patterns, showed marked structuring of genetic variation among avian host species and families, and significant host species prevalence patterns. 5. These differences suggest that Plasmodium spp. infection patterns were shaped by geography and the abiotic environment, whereas Haemoproteus spp. infection patterns were shaped predominantly by host associations. Heterogeneity in the complement and prevalence of parasite lineages infecting local bird communities likely exposes host species to a mosaic of spatially divergent disease selection pressures across their naturally fragmented distributions in southern Melanesia. Host associations for Haemoproteus spp. indicate a capacity for the formation of locally co-adapted host-parasite relationships, a feature that may limit intraspecific gene flow or range expansions of closely related host species.

De-DUFing the DUFs: Deciphering distant evolutionary relationships of Domains of Unknown Function using sensitive homology detection methods

Background: In the post-genomic era where sequences are being determined at a rapid rate, we are highly reliant on computational methods for their tentative biochemical characterization. The Pfam database currently contains 3,786 families corresponding to ``Domains of Unknown Function'' (DUF) or ``Uncharacterized Protein Family'' (UPF), of which 3,087 families have no reported three-dimensional structure, constituting almost one-fourth of the known protein families in search for both structure and function. Results: We applied a `computational structural genomics' approach using five state-of-the-art remote similarity detection methods to detect the relationship between uncharacterized DUFs and domain families of known structures. The association with a structural domain family could serve as a start point in elucidating the function of a DUF. Amongst these five methods, searches in SCOP-NrichD database have been applied for the first time. Predictions were classified into high, medium and low-confidence based on the consensus of results from various approaches and also annotated with enzyme and Gene ontology terms. 614 uncharacterized DUFs could be associated with a known structural domain, of which high confidence predictions, involving at least four methods, were made for 54 families. These structure-function relationships for the 614 DUF families can be accessed on-line at http://proline.biochem.iisc.ernet.in/RHD_DUFS/. For potential enzymes in this set, we assessed their compatibility with the associated fold and performed detailed structural and functional annotation by examining alignments and extent of conservation of functional residues. Detailed discussion is provided for interesting assignments for DUF3050, DUF1636, DUF1572, DUF2092 and DUF659. Conclusions: This study provides insights into the structure and potential function for nearly 20 % of the DUFs. Use of different computational approaches enables us to reliably recognize distant relationships, especially when they converge to a common assignment because the methods are often complementary. We observe that while pointers to the structural domain can offer the right clues to the function of a protein, recognition of its precise functional role is still `non-trivial' with many DUF domains conserving only some of the critical residues. It is not clear whether these are functional vestiges or instances involving alternate substrates and interacting partners. Reviewers: This article was reviewed by Drs Eugene Koonin, Frank Eisenhaber and Srikrishna Subramanian.