Recurrence Quantification Analysis of System Signals for Detecting Tool and Chatter in Turning

In this thesis, the applications of the recurrence quantification analysis in metal cutting operation in a lathe, with specific objective to detect tool wear and chatter, are presented.This study is based on the discovery that process dynamics in a lathe is low dimensional chaotic. It implies that the machine dynamics is controllable using principles of chaos theory. This understanding is to revolutionize the feature extraction methodologies used in condition monitoring systems as conventional linear methods or models are incapable of capturing the critical and strange behaviors associated with the metal cutting process.As sensor based approaches provide an automated and cost effective way to monitor and control, an efficient feature extraction methodology based on nonlinear time series analysis is much more demanding. The task here is more complex when the information has to be deduced solely from sensor signals since traditional methods do not address the issue of how to treat noise present in real-world processes and its non-stationarity. In an effort to get over these two issues to the maximum possible, this thesis adopts the recurrence quantification analysis methodology in the study since this feature extraction technique is found to be robust against noise and stationarity in the signals.The work consists of two different sets of experiments in a lathe; set-I and set-2. The experiment, set-I, study the influence of tool wear on the RQA variables whereas the set-2 is carried out to identify the sensitive RQA variables to machine tool chatter followed by its validation in actual cutting. To obtain the bounds of the spectrum of the significant RQA variable values, in set-i, a fresh tool and a worn tool are used for cutting. The first part of the set-2 experiments uses a stepped shaft in order to create chatter at a known location. And the second part uses a conical section having a uniform taper along the axis for creating chatter to onset at some distance from the smaller end by gradually increasing the depth of cut while keeping the spindle speed and feed rate constant.The study concludes by revealing the dependence of certain RQA variables; percent determinism, percent recurrence and entropy, to tool wear and chatter unambiguously. The performances of the results establish this methodology to be viable for detection of tool wear and chatter in metal cutting operation in a lathe. The key reason is that the dynamics of the system under study have been nonlinear and the recurrence quantification analysis can characterize them adequately.This work establishes that principles and practice of machining can be considerably benefited and advanced from using nonlinear dynamics and chaos theory.

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.

Analysis of System Signals Based on Cross Recurrence Method for System Dynamics Characterization

Natural systems are inherently non linear. Recurrent behaviours are typical of natural systems. Recurrence is a fundamental property of non linear dynamical systems which can be exploited to characterize the system behaviour effectively. Cross recurrence based analysis of sensor signals from non linear dynamical system is presented in this thesis. The mutual dependency among relatively independent components of a system is referred as coupling. The analysis is done for a mechanically coupled system specifically designed for conducting experiment. Further, cross recurrence method is extended to the actual machining process in a lathe to characterize the chatter during turning. The result is verified by permutation entropy method. Conventional linear methods or models are incapable of capturing the critical and strange behaviours associated with the dynamical process. Hence any effective feature extraction methodologies should invariably gather information thorough nonlinear time series analysis. The sensor signals from the dynamical system normally contain noise and non stationarity. In an effort to get over these two issues to the maximum possible extent, this work adopts the cross recurrence quantification analysis (CRQA) methodology since it is found to be robust against noise and stationarity in the signals. The study reveals that the CRQA is capable of characterizing even weak coupling among system signals. It also divulges the dependence of certain CRQA variables like percent determinism, percent recurrence and entropy to chatter unambiguously. The surrogate data test shows that the results obtained by CRQA are the true properties of the temporal evolution of the dynamics and contain a degree of deterministic structure. The results are verified using permutation entropy (PE) to detect the onset of chatter from the time series. The present study ascertains that this CRP based methodology is capable of recognizing the transition from regular cutting to the chatter cutting irrespective of the machining parameters or work piece material. The results establish this methodology to be feasible for detection of chatter in metal cutting operation in a lathe.

Magnetic field-induced cluster formation and variation of magneto-optical signals in zinc-substituted ferrofluids

Fine magnetic particles (sizeffi100A ˚ ) belonging to the series ZnxFe1 xFe2O4 were synthesized by cold co-precipitation methods and their structural properties were evaluated using X-ray diffraction. Magnetization studies have been carried out using vibrating sample magnetometry (VSM) showing near-zero loss loop characteristics. Ferrofluids were then prepared employing these fine magnetic powders using oleic acid as surfactant and kerosene as carrier liquid by modifying the usually reported synthesis technique in order to induce anisotropy and enhance the magneto-optical signals. Liquid thin films of these fluids were prepared and field-induced laser transmission through these films was studied. The transmitted light intensity decreases at the centre with applied magnetic field in a linear fashion when subjected to low magnetic fields and saturate at higher fields. This is in accordance with the saturation in cluster formation. The pattern exhibited by these films in the presence of different magnetic fields was observed with the help of a CCD camera and was recorded photographically

Quadratic Predictor based Differential Encoding and Decoding of Speech Signals

Modeling nonlinear systems using Volterra series is a century old method but practical realizations were hampered by inadequate hardware to handle the increased computational complexity stemming from its use. But interest is renewed recently, in designing and implementing filters which can model much of the polynomial nonlinearities inherent in practical systems. The key advantage in resorting to Volterra power series for this purpose is that nonlinear filters so designed can be made to work in parallel with the existing LTI systems, yielding improved performance. This paper describes the inclusion of a quadratic predictor (with nonlinearity order 2) with a linear predictor in an analog source coding system. Analog coding schemes generally ignore the source generation mechanisms but focuses on high fidelity reconstruction at the receiver. The widely used method of differential pnlse code modulation (DPCM) for speech transmission uses a linear predictor to estimate the next possible value of the input speech signal. But this linear system do not account for the inherent nonlinearities in speech signals arising out of multiple reflections in the vocal tract. So a quadratic predictor is designed and implemented in parallel with the linear predictor to yield improved mean square error performance. The augmented speech coder is tested on speech signals transmitted over an additive white gaussian noise (AWGN) channel.

A Hybrid Architecture for Recognising Speech Signals in Malayalam

Speech is the primary, most prominent and convenient means of communication in audible language. Through speech, people can express their thoughts, feelings or perceptions by the articulation of words. Human speech is a complex signal which is non stationary in nature. It consists of immensely rich information about the words spoken, accent, attitude of the speaker, expression, intention, sex, emotion as well as style. The main objective of Automatic Speech Recognition (ASR) is to identify whatever people speak by means of computer algorithms. This enables people to communicate with a computer in a natural spoken language. Automatic recognition of speech by machines has been one of the most exciting, significant and challenging areas of research in the field of signal processing over the past five to six decades. Despite the developments and intensive research done in this area, the performance of ASR is still lower than that of speech recognition by humans and is yet to achieve a completely reliable performance level. The main objective of this thesis is to develop an efficient speech recognition system for recognising speaker independent isolated words in Malayalam.

A STUDY ON POLITICAL CONSCIOUSNESS IN HINDI NOVELS SINCE 1960

HINDI

SOCIAL CONSCIOUSNESS IN THE POETRY oF MUKTHIBODH

HINDI

SOCIAL CONSCIOUSNESS IN POST-INDEPENDENT HINDI POETRY (I947-I970)

HINDI

Empathie in der Psychotherapie aus neuer Perspektive

Die traditionellen Empathiekonzepte (z. B. Rogers, Kohut), die in der Psychotherapie bis heute maßgebend sind, werden einer gründlichen Überprüfung unterzogen. Dabei ergeben sich drei wesentliche Kritikpunkte: (1) Empathische Vorgänge in der Psychotherapie werden als einseitige Prozesse verstanden; der Therapeut fühlt sich in die Klientin ein, nicht auch umgekehrt. (2) Empathie wird in Cartesianischer Tradition schwerpunktmäßig als kognitive Leistung gesehen; ein körperloser Geist vergegenwärtigt sich die mentalen Inhalte eines anderen. (3) Das traditionelle Empathieverständnis ist individualistisch: Therapeutin und Klient halten sich demnach scheinbar im luftleeren Raum auf. Es sieht so aus, als existiere kein Kontext, der sie umgibt. So einseitig, wie ihre Beziehung gedacht wird, so abgetrennt, wie ihr Körper von ihrem Geist zu sein scheint, so unverbunden sind sie scheinbar mit dem Rest der Welt. Aus diesen drei Kritikpunkten folgt die Notwendigkeit, den Empathiebegriff der Psychotherapie zu erweitern, d. h. (a) Empathie als gegenseitigen Prozess der Beteiligten zu begreifen, (b) ihre tiefe Verwurzelung in der Leiblichkeit des Menschen zu berücksichtigen und (c) ihre Einbettung in die Dynamiken einer gemeinsamen Situation im Rahmen eines kulturellen Kontextes einzubeziehen. Mit Rückgriff auf neuere Untersuchungsergebnisse aus der Entwicklungspsychologie (z. B. Emde, Hobson, Meltzoff, Stern, Trevarthen), der Sozial- und Emotionspsychologie (z. B. Chartrand, Ekman, Goleman, Hatfield, Holodynski), der sozialen Neurowissenschaften (z. B. Damasio, Gallese, Iacoboni, LeDoux, Rizzolatti), aber auch mit Hilfe der Erkenntnisse aus der klassischen (Husserl, Merleau- Ponty, Edith Stein) und der Neuen Phänomenologie (Schmitz) sowie aus symbolischem Interaktionismus (Mead) und aus der kulturhistorischen Schule (Vygotskij) werden diese drei bislang wenig beleuchteten Dimensionen der Empathie betrachtet. ad a) Die Gegenseitigkeit empathischer Vorgänge in der Psychotherapie wird anhand des entwicklungspsychologischen Konzepts des social referencing erläutert und untersucht: Kleinkinder, die in eine unbekannte bzw. unsichere Situation geraten (z. B. im Experiment mit der "visuellen Klippe"), orientieren sich an den nonverbalen Signalen ihrer Bezugspersonen, um diese Situation zu bewältigen. Dabei erfasst die Mutter die Situation des Kindes, versucht ihm ihre Stellungnahme zu seiner Situation zu übermitteln, und das Kind begreift die Reaktion der Mutter als Stellungnahme zu seiner Situation. ad b) Die Körperlichkeit bzw. Leiblichkeit der Einfühlung manifestiert sich in vielfältigen Formen, wie sie von der Psychologie, der Phänomenologie und den Neurowissenschaften erforscht werden. Das kulturübergreifende Erkennen des Gesichtsausdrucks von Basisemotionen ist hier ebenso zu nennen wie die Verhaltensweisen des motor mimicry, bei dem Menschen Körperhaltungen und – bewegungen ihrer Bezugspersonen unwillkürlich imitieren; des Weiteren das unmittelbare Verstehen von Gesten sowie die Phänomene der „Einleibung“, bei denen die körperliche Situation des Anderen (z. B. eines stürzenden Radfahrers, den man beobachtet) am eigenen Leib mitgefühlt wird; und außerdem die Entdeckung der „Spiegelneurone“ und anderer neuronaler Strukturen, durch die Wahrgenommenes direkt in analoge motorische Aktivität übersetzt wird. ad c) Intersubjektivitätstheoretische Überlegungen, Konzepte wie „dyadisch erweiterter Bewusstseinszustand“ (Tronick) und „gemeinsame Situation“ (Gurwitsch, Schmitz) verweisen auf die Bedeutung überindividueller, ‚emergenter’ Dimensionen, die für die Verständigung zwischen Menschen wichtig sind. Sie folgen gestaltpsychologischen Prinzipien („Das Ganze ist mehr und anders als die Summe seiner Teile.“), die mit Hilfe von Gadamers Begriff des „Spiels“ analysiert werden. Am Ende der Arbeit stehen die Definition eines neuen Empathiebegriffs, wie er sich aus den vorangegangenen Überlegungen ergibt, sowie eine These über die psychotherapeutische Wirkweise menschlicher Einfühlung, die durch weitere Forschungen zu überprüfen wäre.

Sparse Representations of Multiple Signals

We discuss the problem of finding sparse representations of a class of signals. We formalize the problem and prove it is NP-complete both in the case of a single signal and that of multiple ones. Next we develop a simple approximation method to the problem and we show experimental results using artificially generated signals. Furthermore,we use our approximation method to find sparse representations of classes of real signals, specifically of images of pedestrians. We discuss the relation between our formulation of the sparsity problem and the problem of finding representations of objects that are compact and appropriate for detection and classification.

Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision

In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions.

Modification of personality variables through application of a psycho-educational program of full consciousness (mindfulness) in university students

This study examines the effects in university students of a psycho-educational program in full awareness (mindfulness) on certain personality variables. A quasi-experimental (group comparison) design with pretest and postest measurements was employed in an experimental (n = 26) and a control group (n = 27). Barratt impulsiveness Scale (BiS- 11), Acceptance and Action Questionnaire (AAQ), Social Avoidance and Distress Scale (SAD), and the Profile of Mood States (POMS) were applied to experimental and control groups. The results show statistically significant changes in impulsivity variables, experiential avoidance, social avoidance, social anxiety, tension and fatigue when comparing the posttest mean scores of the groups.

The language of children : evolution and development of secondary consciousness and language.

Proporciona una introducción sobre como los niños adquieren y utilizan el lenguaje. Hace hincapié en la explicación psicológica del desarrollo del lenguaje, pero, sin olvidar, los aspectos cognitivo, biológico y social en el desarrollo de éste, y también, considera la influencia de la escolaridad y la experiencia social.

Reception of acoustic speech signals as a function of sensation levels by hearing-impaired children

This paper is a review of a study to determine the sensation level at which the best discrimination and recognition occurs for severely and profoundly deaf children and the effect of distortion (peak clipping) has on the child's ability to recognize speech.