Permutation Entropy Based Analysis of Complex Signals for Characterising Change in System Dynamics

Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.

Effect of Improving Energy Efficiency on System Reliability

In the present scenario of energy demand overtaking energy supply top priority is given for energy conservation programs and policies. Most of the process plants are operated on continuous basis and consumes large quantities of energy. Efficient management of process system can lead to energy savings, improved process efficiency, lesser operating and maintenance cost, and greater environmental safety. Reliability and maintainability of the system are usually considered at the design stage and is dependent on the system configuration. However, with the growing need for energy conservation, most of the existing process systems are either modified or are in a state of modification with a view for improving energy efficiency. Often these modifications result in a change in system configuration there by affecting the system reliability. It is important that system modifications for improving energy efficiency should not be at the cost of reliability. Any new proposal for improving the energy efficiency of the process or equipments should prove itself to be economically feasible for gaining acceptance for implementation. In order to arrive at the economic feasibility of the new proposal, the general trend is to compare the benefits that can be derived over the lifetime as well as the operating and maintenance costs with the investment to be made. Quite often it happens that the reliability aspects (or loss due to unavailability) are not taken into consideration. Plant availability is a critical factor for the economic performance evaluation of any process plant.The focus of the present work is to study the effect of system modification for improving energy efficiency on system reliability. A generalized model for the valuation of process system incorporating reliability is developed, which is used as a tool for the analysis. It can provide an awareness of the potential performance improvements of the process system and can be used to arrive at the change in process system value resulting from system modification. The model also arrives at the pay back of the modified system by taking reliability aspects also into consideration. It is also used to study the effect of various operating parameters on system value. The concept of breakeven availability is introduced and an algorithm for allocation of component reliabilities of the modified process system based on the breakeven system availability is also developed. The model was applied to various industrial situations.

Density modes in spherical 4He shells

We compute the density-fluctuation spectrum of spherical 4HeN shells adsorbed on the outer surface of Cn fullerenes. The excitation spectrum is obtained within the random-phase approximation, with particle-hole elementary excitations and effective interaction extracted from a density-functional description of the shell structure. The presence of one or two solid helium layers adjacent to the adsorbing fullerene is phenomenologically accounted for. We illustrate our results for a selection of numbers of adsorbed atoms on C20, C60, and C120. The hydrodynamical model that has proven successful to describe helium excitations in the bulk and in restricted geometries permits to perform a rather exhaustive analysis of various fluid spherical systems, namely, spheres, cavities, free bubbles, and bound shells of variable size.

An adaptive cluster based routing scheme for mobile wireless sensor networks

Clustering schemes improve energy efficiency of wireless sensor networks. The inclusion of mobility as a new criterion for the cluster creation and maintenance adds new challenges for these clustering schemes. Cluster formation and cluster head selection is done on a stochastic basis for most of the algorithms. In this paper we introduce a cluster formation and routing algorithm based on a mobility factor. The proposed algorithm is compared with LEACH-M protocol based on metrics viz. number of cluster head transitions, average residual energy, number of alive nodes and number of messages lost

Entwicklung und Anwendung einer Kraftwerks- und Speichereinsatzoptimierung für die Untersuchung von Energieversorgungsszenarien mit hohem Anteil erneuerbarer Energien in Deutschland

In dieser Arbeit wurde ein gemischt-ganzzahliges lineares Einsatzoptimierungsmodell für Kraftwerke und Speicher aufgebaut und für die Untersuchung der Energieversorgung Deutschlands im Jahre 2050 gemäß den Leitstudie-Szenarien 2050 A und 2050 C ([Nitsch und Andere, 2012]) verwendet, in denen erneuerbare Energien einen Anteil von über 85 % an der Stromerzeugung haben und die Wind- und Solarenergie starke Schwankungen der durch steuerbare Kraftwerke und Speicher zu deckenden residualen Stromnachfrage (Residuallast) verursachen. In Szenario 2050 A sind 67 TWh Wasserstoff, die elektrolytisch aus erneuerbarem Strom zu erzeugen sind, für den Verkehr vorgesehen. In Szenario 2050 C ist kein Wasserstoff für den Verkehr vorgesehen und die effizientere Elektromobilität hat einen Anteil von 100% am Individualverkehr. Daher wird weniger erneuerbarer Strom zur Erreichung desselben erneuerbaren Anteils im Verkehrssektor benötigt. Da desweiteren Elektrofahrzeuge Lastmanagementpotentiale bieten, weisen die Residuallasten der Szenarien eine unterschiedliche zeitliche Charakteristik und Jahressumme auf. Der Schwerpunkt der Betrachtung lag auf der Ermittlung der Auslastung und Fahrweise des in den Szenarien unterstellten ’Kraftwerks’-parks bestehend aus Kraftwerken zur reinen Stromerzeugung, Kraft-Wärme-Kopplungskraftwerken, die mit Wärmespeichern, elektrischen Heizstäben und Gas-Backupkesseln ausgestattet sind, Stromspeichern und Wärmepumpen, die durch Wärmespeicher zum Lastmanagment eingesetzt werden können. Der Fahrplan dieser Komponenten wurde auf minimale variable Gesamtkosten der Strom- und Wärmeerzeugung über einen Planungshorizont von jeweils vier Tagen hin optimiert. Das Optimierungsproblem wurde mit dem linearen Branch-and-Cut-Solver der software CPLEX gelöst. Mittels sogenannter rollierender Planung wurde durch Zusammensetzen der Planungsergebnisse für überlappende Planungsperioden der Kraftwerks- und Speichereinsatz für die kompletten Szenariojahre erhalten. Es wurde gezeigt, dass der KWK-Anteil an der Wärmelastdeckung gering ist. Dies wurde begründet durch die zeitliche Struktur der Stromresiduallast, die wärmeseitige Dimensionierung der Anlagen und die Tatsache, dass nur eine kurzfristige Speicherung von Wärme vorgesehen war. Die wärmeseitige Dimensionierung der KWK stellte eine Begrenzung des Deckungsanteils dar, da im Winter bei hoher Stromresiduallast nur wenig freie Leistung zur Beladung der Speicher zur Verfügung stand. In den Berechnungen für das Szenario 2050 A und C lag der mittlere Deckungsanteil der KWK an der Wärmenachfrage von ca. 100 TWh_th bei 40 bzw. 60 %, obwohl die Auslegung der KWK einen theoretischen Anteil von über 97 % an der Wärmelastdeckung erlaubt hätte, gäbe es die Beschränkungen durch die Stromseite nicht. Desweiteren wurde die CO2-Vermeidungswirkung der KWK-Wärmespeicher und des Lastmanagements mit Wärmepumpen untersucht. In Szenario 2050 A ergab sich keine signifikante CO2-Vermeidungswirkung der KWK-Wärmespeicher, in Szenario 2050 C hingegen ergab sich eine geringe aber signifikante CO2-Einsparung in Höhe von 1,6 % der Gesamtemissionen der Stromerzeugung und KWK-gebundenen Wärmeversorgung. Das Lastmanagement mit Wärmepumpen vermied Emissionen von 110 Tausend Tonnen CO2 (0,4 % der Gesamtemissionen) in Szenario A und 213 Tausend Tonnen in Szenario C (0,8 % der Gesamtemissionen). Es wurden darüber hinaus Betrachtungen zur Konkurrenz zwischen solarthermischer Nahwärme und KWK bei Einspeisung in dieselben Wärmenetze vorgenommen. Eine weitere Einschränkung der KWK-Erzeugung durch den Einspeisevorrang der Solarthermie wurde festgestellt. Ferner wurde eine untere Grenze von 6,5 bzw. 8,8 TWh_th für die in den Szenarien mindestens benötigte Wasserstoff-Speicherkapazität ermittelt. Die Ergebnisse dieser Arbeit legen nahe, das technisch-ökonomische Potential von Langzeitwärmespeichern für eine bessere Integration von KWK ins System zu ermitteln bzw. generell nach geeigneteren Wärmesektorszenarien zu suchen, da deutlich wurde, dass für die öffentliche Wärmeversorgung die KWK in Kombination mit Kurzzeitwärmespeicherung, Gaskesseln und elektrischen Heizern keine sehr effektive CO2 -Reduktion in den Szenarien erreicht. Es sollte dabei z.B. untersucht werden, ob ein multivalentes System aus KWK, Wärmespeichern und Wärmepumpen eine ökonomisch darstellbare Alternative sein könnte und im Anschluss eine Betrachtung der optimalen Anteile von KWK, Wärmepumpen und Solarthermie im Wärmemarkt vorgenommen werden.

Model-Based Matching by Linear Combinations of Prototypes

We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.

Quantifying rock fabrics: a test of autocorrelation of the spatial distribution of cristals

A novel test of spatial independence of the distribution of crystals or phases in rocks based on compositional statistics is introduced. It improves and generalizes the common joins-count statistics known from map analysis in geographic information systems. Assigning phases independently to objects in RD is modelled by a single-trial multinomial random function Z(x), where the probabilities of phases add to one and are explicitly modelled as compositions in the K-part simplex SK. Thus, apparent inconsistencies of the tests based on the conventional joins{count statistics and their possibly contradictory interpretations are avoided. In practical applications we assume that the probabilities of phases do not depend on the location but are identical everywhere in the domain of de nition. Thus, the model involves the sum of r independent identical multinomial distributed 1-trial random variables which is an r-trial multinomial distributed random variable. The probabilities of the distribution of the r counts can be considered as a composition in the Q-part simplex SQ. They span the so called Hardy-Weinberg manifold H that is proved to be a K-1-affine subspace of SQ. This is a generalisation of the well-known Hardy-Weinberg law of genetics. If the assignment of phases accounts for some kind of spatial dependence, then the r-trial probabilities do not remain on H. This suggests the use of the Aitchison distance between observed probabilities to H to test dependence. Moreover, when there is a spatial uctuation of the multinomial probabilities, the observed r-trial probabilities move on H. This shift can be used as to check for these uctuations. A practical procedure and an algorithm to perform the test have been developed. Some cases applied to simulated and real data are presented. Key words: Spatial distribution of crystals in rocks, spatial distribution of phases, joins-count statistics, multinomial distribution, Hardy-Weinberg law, Hardy-Weinberg manifold, Aitchison geometry

Image Segmentation Using Information Bottleneck Method

In image processing, segmentation algorithms constitute one of the main focuses of research. In this paper, new image segmentation algorithms based on a hard version of the information bottleneck method are presented. The objective of this method is to extract a compact representation of a variable, considered the input, with minimal loss of mutual information with respect to another variable, considered the output. First, we introduce a split-and-merge algorithm based on the definition of an information channel between a set of regions (input) of the image and the intensity histogram bins (output). From this channel, the maximization of the mutual information gain is used to optimize the image partitioning. Then, the merging process of the regions obtained in the previous phase is carried out by minimizing the loss of mutual information. From the inversion of the above channel, we also present a new histogram clustering algorithm based on the minimization of the mutual information loss, where now the input variable represents the histogram bins and the output is given by the set of regions obtained from the above split-and-merge algorithm. Finally, we introduce two new clustering algorithms which show how the information bottleneck method can be applied to the registration channel obtained when two multimodal images are correctly aligned. Different experiments on 2-D and 3-D images show the behavior of the proposed algorithms

Kernel density construction using orthogonal forward regression

An automatic algorithm is derived for constructing kernel density estimates based on a regression approach that directly optimizes generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. Local regularization is incorporated into the density construction process to further enforce sparsity. Examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample Parzen window density estimate.

Minimum stable convergence criteria for stochastic diffusion search

An analysis of Stochastic Diffusion Search (SDS), a novel and efficient optimisation and search algorithm, is presented, resulting in a derivation of the minimum acceptable match resulting in a stable convergence within a noisy search space. The applicability of SDS can therefore be assessed for a given problem.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

The dichotomous structure of the warm conveyor belt

The warm conveyor belt (WCB) of an extratropical cyclone generally splits into two branches. One branch (WCB1) turns anticyclonically into the downstream upper-level tropospheric ridge, while the second branch (WCB2) wraps cyclonically around the cyclone centre. Here, the WCB split in a typical North Atlantic cold-season cyclone is analysed using two numerical models: the Met Office Unified Model and the COSMO model. The WCB flow is defined using off-line trajectory analysis. The two models represent the WCB split consistently. The split occurs early in the evolution of the WCB with WCB1 experiencing maximum ascent at lower latitudes and with higher moisture content than WCB2. WCB1 ascends abruptly along the cold front where the resolved ascent rates are greatest and there is also line convection. In contrast, WCB2 remains at lower levels for longer before undergoing saturated large-scale ascent over the system's warm front. The greater moisture in WCB1 inflow results in greater net potential temperature change from latent heat release, which determines the final isentropic level of each branch. WCB1 also exhibits lower outflow potential vorticity values than WCB2. Complementary diagnostics in the two models are utilised to study the influence of individual diabatic processes on the WCB. Total diabatic heating rates along the WCB branches are comparable in the two models with microphysical processes in the large-scale cloud schemes being the major contributor to this heating. However, the different convective parameterisation schemes used by the models cause significantly different contributions to the total heating. These results have implications for studies on the influence of the WCB outflow in Rossby wave evolution and breaking. Key aspects are the net potential temperature change and the isentropic level of the outflow which together will influence the relative mass going into each WCB branch and the associated negative PV anomalies at the tropopause-level flow.

Prediction of Staphylococcus aureus antimicrobial resistance by whole-genome sequencing

Whole-genome sequencing (WGS) could potentially provide a single platform for extracting all the information required to predict an organism’s phenotype. However, its ability to provide accurate predictions has not yet been demonstrated in large independent studies of specific organisms. In this study, we aimed to develop a genotypic prediction method for antimicrobial susceptibilities. The whole genomes of 501 unrelated Staphylococcus aureus isolates were sequenced, and the assembled genomes were interrogated using BLASTn for a panel of known resistance determinants (chromosomal mutations and genes carried on plasmids). Results were compared with phenotypic susceptibility testing for 12 commonly used antimicrobial agents (penicillin, methicillin, erythromycin, clindamycin, tetracycline, ciprofloxacin, vancomycin, trimethoprim, gentamicin, fusidic acid, rifampin, and mupirocin) performed by the routine clinical laboratory. We investigated discrepancies by repeat susceptibility testing and manual inspection of the sequences and used this information to optimize the resistance determinant panel and BLASTn algorithm. We then tested performance of the optimized tool in an independent validation set of 491 unrelated isolates, with phenotypic results obtained in duplicate by automated broth dilution (BD Phoenix) and disc diffusion. In the validation set, the overall sensitivity and specificity of the genomic prediction method were 0.97 (95% confidence interval [95% CI], 0.95 to 0.98) and 0.99 (95% CI, 0.99 to 1), respectively, compared to standard susceptibility testing methods. The very major error rate was 0.5%, and the major error rate was 0.7%. WGS was as sensitive and specific as routine antimicrobial susceptibility testing methods. WGS is a promising alternative to culture methods for resistance prediction in S. aureus and ultimately other major bacterial pathogens.

Discovery of a mammalian splice variant of myostatin that stimulates myogenesis

Myostatin plays a fundamental role in regulating the size of skeletal muscles. To date, only a single myostatin gene and no splice variants have been identified in mammals. Here we describe the splicing of a cryptic intron that removes the coding sequence for the receptor binding moiety of sheep myostatin. The deduced polypeptide sequence of the myostatin splice variant (MSV) contains a 256 amino acid N-terminal domain, which is common to myostatin, and a unique C-terminus of 65 amino acids. Western immunoblotting demonstrated that MSV mRNA is translated into protein, which is present in skeletal muscles. To determine the biological role of MSV, we developed an MSV over-expressing C2C12 myoblast line and showed that it proliferated faster than that of the control line in association with an increased abundance of the CDK2/Cyclin E complex in the nucleus. Recombinant protein made for the novel C-terminus of MSV also stimulated myoblast proliferation and bound to myostatin with high affinity as determined by surface plasmon resonance assay. Therefore, we postulated that MSV functions as a binding protein and antagonist of myostatin. Consistent with our postulate, myostatin protein was co-immunoprecipitated from skeletal muscle extracts with an MSV-specific antibody. MSV over-expression in C2C12 myoblasts blocked myostatin-induced Smad2/3-dependent signaling, thereby confirming that MSV antagonizes the canonical myostatin pathway. Furthermore, MSV over expression increased the abundance of MyoD, Myogenin and MRF4 proteins (P,0.05), which indicates that MSV stimulates myogenesis through the induction of myogenic regulatory factors. To help elucidate a possible role in vivo, we observed that MSV protein was more abundant during early post-natal muscle development, while myostatin remained unchanged, which suggests that MSV may promote the growth of skeletal muscles. We conclude that MSV represents a unique example of intra-genic regulation in which a splice variant directly antagonizes the biological activity of the canonical gene product.