The use of current transformers for noise detection due to sparking in insulators strings in high voltage transmission lines

In a general way, in an electric power utility the current transformers (CT) are used to measurement and protection of transmission lines (TL) 1 The Power Line Carriers systems (PLC) are used for communication between electrical substations and transmission line protection. However, with the increasing use of optical fiber to communication (due mainly to its high data transmission rate and low signal-noise relation) this application loses potentiality. Therefore, other functions must be defined to equipments that are still in using, one of them is detecting faults (short-circuits) and transmission lines insulator strings damages 2. The purpose of this paper is to verify the possibility of using the path to the ground offered by the CTs instead of capacitive couplings / capacitive potential transformers to detect damaged insulators, since the current transformers are always present in all transmission lines (TL's) bays. To this a comparison between this new proposal and the PLC previous proposed system 2 is shown, evaluating the economical and technical points of view. ©2010 IEEE.

Optimization of quantum states for signaling across an arbitrary qubit noise channel with minimum-error detection

International audience

A data mining approach to improve the automated quality of data

This thesis describes the development of a robust and novel prototype to address the data quality problems that relate to the dimension of outlier data. It thoroughly investigates the associated problems with regards to detecting, assessing and determining the severity of the problem of outlier data; and proposes granule-mining based alternative techniques to significantly improve the effectiveness of mining and assessing outlier data.

Quantum Electromechanics with Two Tone Drive

In the field of mechanics, it is a long standing goal to measure quantum behavior in ever larger and more massive objects. It may now seem like an obvious conclusion, but until recently it was not clear whether a macroscopic mechanical resonator -- built up from nearly 10¹³ atoms -- could be fully described as an ideal quantum harmonic oscillator. With recent advances in the fields of opto- and electro-mechanics, such systems offer a unique advantage in probing the quantum noise properties of macroscopic electrical and mechanical devices, properties that ultimately stem from Heisenberg's uncertainty relations. Given the rapid progress in device capabilities, landmark results of quantum optics are now being extended into the regime of macroscopic mechanics.

The purpose of this dissertation is to describe three experiments -- motional sideband asymmetry, back-action evasion (BAE) detection, and mechanical squeezing -- that are directly related to the topic of measuring quantum noise with mechanical detection. These measurements all share three pertinent features: they explore quantum noise properties in a macroscopic electromechanical device driven by a minimum of two microwave drive tones, hence the title of this work: "Quantum electromechanics with two tone drive".

In the following, we will first introduce a quantum input-output framework that we use to model the electromechanical interaction and capture subtleties related to interpreting different microwave noise detection techniques. Next, we will discuss the fabrication and measurement details that we use to cool and probe these devices with coherent and incoherent microwave drive signals. Having developed our tools for signal modeling and detection, we explore the three-wave mixing interaction between the microwave and mechanical modes, whereby mechanical motion generates motional sidebands corresponding to up-down frequency conversions of microwave photons. Because of quantum vacuum noise, the rates of these processes are expected to be unequal. We will discuss the measurement and interpretation of this asymmetric motional noise in a electromechanical device cooled near the ground state of motion.

Next, we consider an overlapped two tone pump configuration that produces a time-modulated electromechanical interaction. By careful control of this drive field, we report a quantum non-demolition (QND) measurement of a single motional quadrature. Incorporating a second pair of drive tones, we directly measure the measurement back-action associated with both classical and quantum noise of the microwave cavity. Lastly, we slightly modify our drive scheme to generate quantum squeezing in a macroscopic mechanical resonator. Here, we will focus on data analysis techniques that we use to estimate the quadrature occupations. We incorporate Bayesian spectrum fitting and parameter estimation that serve as powerful tools for incorporating many known sources of measurement and fit error that are unavoidable in such work.

Sistema de adquisición de datos para una aplicación de detección del ruido de reversa en tiempo real

Entre todas las fuentes de ruido, la activación de la propulsión en reversa de un avión después de aterrizar es conocida por las autoridades del aeropuerto como una causa importante de impacto acústico, molestias y quejas en las proximidades vecinas de los aeropuertos. Por ello, muchos de los aeropuertos de todo el mundo han establecido restricciones en el uso de la reversa, especialmente en las horas de la noche. Una forma de reducir el impacto acústico en las actividades aeroportuarias es implementar herramientas eficaces para la detección de ruido en reversa en los aeropuertos. Para este proyecto de fin de carrera, aplicando la metodología TREND (Thrust Reverser Noise Detection), se pretende desarrollar un sistema software capaz de determinar que una aeronave que aterrice en la pista active el frenado en reversa en tiempo real. Para el diseño de la aplicación se plantea un modelo software, que se compone de dos módulos:  El módulo de adquisición de señales acústicas, simula un sistema de captación por señales de audio. Éste módulo obtiene muestra de señales estéreo de ficheros de audio de formato “.WAV” o del sistema de captación, para acondicionar las muestras de audio y enviarlas al siguiente módulo. El sistema de captación (array de micrófonos), se encuentra situado en una localización cercana a la pista de aterrizaje.  El módulo de procesado busca los eventos de detección aplicando la metodología TREND con las muestras acústicas que recibe del módulo de adquisición. La metodología TREND describe la búsqueda de dos eventos sonoros llamados evento 1 (EV1) y evento 2 (EV2); el primero de ellos, es el evento que se activa cuando una aeronave aterriza discriminando otros eventos sonoros como despegues de aviones y otros sonidos de fondo, mientras que el segundo, se producirá después del evento 1, sólo cuando la aeronave utilice la reversa para frenar. Para determinar la detección del evento 1, es necesario discriminar las señales ajenas al aterrizaje aplicando un filtrado en la señal capturada, después, se aplicará un detector de umbral del nivel de presión sonora y por último, se determina la procedencia de la fuente de sonido con respecto al sistema de captación. En el caso de la detección del evento 2, está basada en la implementación de umbrales en la evolución temporal del nivel de potencia acústica aplicando el modelo de propagación inversa, con ayuda del cálculo de la estimación de la distancia en cada instante de tiempo mientras el avión recorre la pista de aterrizaje. Con cada aterrizaje detectado se realiza una grabación que se archiva en una carpeta específica y todos los datos adquiridos, son registrados por la aplicación software en un fichero de texto. ABSTRACT. Among all noise sources, the activation of reverse thrust to slow the aircraft after landing is considered as an important cause of noise pollution by the airport authorities, as well as complaints and annoyance in the airport´s nearby locations. Therefore, many airports around the globe have restricted the use of reverse thrust, especially during the evening hours. One way to reduce noise impact on airport activities is the implementation of effective tools that deal with reverse noise detection. This Final Project aims to the development of a software system capable of detecting if an aircraft landing on the runway activates reverse thrust on real time, using the TREND (Thrust Reverser Noise Detection) methodology. To design this application, a two modules model is proposed: • The acoustic signals obtainment module, which simulates an audio waves based catchment system. This module obtains stereo signal samples from “.WAV” audio files or the catchment system in order to prepare these audio samples and send them to the next module. The catchment system (a microphone array) is located on a place near the landing runway. • The processing module, which looks for detection events among the acoustic samples received from the other module, using the TREND methodology. The TREND methodology describes the search of two sounds events named event 1 (EV1) and event 2 (EV2). The first is the event activated by a landing plane, discriminating other sound events such as background noises or taking off planes; the second one will occur after event one only when the aircraft uses reverse to slow down. To determine event 1 detection, signals outside the landing must be discriminated using a filter on the catched signal. A pressure level´s threshold detector will be used on the signal afterwards. Finally, the origin of the sound source is determined regarding the catchment system. The detection of event 2 is based on threshold implementations in the temporal evolution of the acoustic power´s level by using the inverse propagation model and calculating the distance estimation at each time step while the plane goes on the landing runway. A recording is made every time a landing is detected, which is stored in a folder. All acquired data are registered by the software application on a text file.

Advanced data acquisition system implementation for the ITER Neutron Diagnostic use case using EPICS and FlexRIO technology on a PXIe platform

In the framework of the ITER Control Breakdown Structure (CBS), Plant System Instrumentation & Control (I&C) defines the hardware and software required to control one or more plant systems [1]. For diagnostics, most of the complex Plant System I&C are to be delivered by ITER Domestic Agencies (DAs). As an example for the DAs, ITER Organization (IO) has developed several use cases for diagnostics Plant System I&C that fully comply with guidelines presented in the Plant Control Design Handbook (PCDH) [2]. One such use case is for neutron diagnostics, specifically the Fission Chamber (FC), which is responsible for delivering time-resolved measurements of neutron source strength and fusion power to aid in assessing the functional performance of ITER [3]. ITER will deploy four Fission Chamber units, each consisting of three individual FC detectors. Two of these detectors contain Uranium 235 for Neutron detection, while a third "dummy" detector will provide gamma and noise detection. The neutron flux from each MFC is measured by the three methods: . Counting Mode: measures the number of individual pulses and their location in the record. Pulse parameters (threshold and width) are user configurable. . Campbelling Mode (Mean Square Voltage): measures the RMS deviation in signal amplitude from its average value. .Current Mode: integrates the signal amplitude over the measurement period

A comparison of open-source segmentation architectures for dealing with imperfect data from the media in speech synthesis

Traditional Text-To-Speech (TTS) systems have been developed using especially-designed non-expressive scripted recordings. In order to develop a new generation of expressive TTS systems in the Simple4All project, real recordings from the media should be used for training new voices with a whole new range of speaking styles. However, for processing this more spontaneous material, the new systems must be able to deal with imperfect data (multi-speaker recordings, background and foreground music and noise), filtering out low-quality audio segments and creating mono-speaker clusters. In this paper we compare several architectures for combining speaker diarization and music and noise detection which improve the precision and overall quality of the segmentation.

Sistema de seguridad basado en la clasificación de eventos sonoros

La teoría de reconocimiento y clasificación de patrones y el aprendizaje automático son actualmente áreas de conocimiento en constante desarrollo y con aplicaciones prácticas en múltiples ámbitos de la industria. El propósito de este Proyecto de Fin de Grado es el estudio de las mismas así como la implementación de un sistema software que dé solución a un problema de clasificación de ruido impulsivo, concretamente mediante el desarrollo de un sistema de seguridad basado en la clasificación de eventos sonoros en tiempo real. La solución será integral, comprendiendo todas las fases del proceso, desde la captación de sonido hasta el etiquetado de los eventos registrados, pasando por el procesado digital de señal y la extracción de características. Para su desarrollo se han diferenciado dos partes fundamentales; una primera que comprende la interfaz de usuario y el procesado de la señal de audio donde se desarrollan las labores de monitorización y detección de ruido impulsivo y otra segunda centrada únicamente en la clasificación de los eventos sonoros detectados, definiendo una arquitectura de doble clasificador donde se determina si los eventos detectados son falsas alarmas o amenazas, etiquetándolos como de un tipo concreto en este segundo caso. Los resultados han sido satisfactorios, mostrando una fiabilidad global en el proceso de entorno al 90% a pesar de algunas limitaciones a la hora de construir la base de datos de archivos de audio, lo que prueba que un dispositivo de seguridad basado en el análisis de ruido ambiente podría incluirse en un sistema integral de alarma doméstico aumentando la protección del hogar. ABSTRACT. Pattern classification and machine learning are currently expertise areas under continuous development and also with extensive applications in many business sectors. The aim of this Final Degree Project is to study them as well as the implementation of software to carry on impulsive noise classification tasks, particularly through the development of a security system based on sound events classification. The solution will go over all process stages, from capturing sound to the labelling of the events recorded, without forgetting digital signal processing and feature extraction, everything in real time. In the development of the Project a distinction has been made between two main parts. The first one comprises the user’s interface and the audio signal processing module, where monitoring and impulsive noise detection tasks take place. The second one is focussed in sound events classification tasks, defining a double classifier architecture where it is determined whether detected events are false alarms or threats, labelling them from a concrete category in the latter case. The obtained results have been satisfactory, with an overall reliability of 90% despite some limitations when building the audio files database. This proves that a safety device based on the analysis of environmental noise could be included in a full alarm system increasing home protection standards.

Contrast matching and discrimation in human vision

The aim of this work was to investigate human contrast perception at various contrast levels ranging from detection threshold to suprathreshold levels by using psychophysical techniques. The work consists of two major parts. The first part deals with contrast matching, and the second part deals with contrast discrimination. Contrast matching technique was used to determine when the perceived contrasts of different stimuli were equal. The effects of spatial frequency, stimulus area, image complexity and chromatic contrast on contrast detection thresholds and matches were studied. These factors influenced detection thresholds and perceived contrast at low contrast levels. However, at suprathreshold contrast levels perceived contrast became directly proportional to the physical contrast of the stimulus and almost independent of factors affecting detection thresholds. Contrast discrimination was studied by measuring contrast increment thresholds which indicate the smallest detectable contrast difference. The effects of stimulus area, external spatial image noise and retinal illuminance were studied. The above factors affected contrast detection thresholds and increment thresholds measured at low contrast levels. At high contrast levels, contrast increment thresholds became very similar so that the effect of these factors decreased. Human contrast perception was modelled by regarding the visual system as a simple image processing system. A visual signal is first low-pass filtered by the ocular optics. This is followed by spatial high-pass filtering by the neural visual pathways, and addition of internal neural noise. Detection is mediated by a local matched filter which is a weighted replica of the stimulus whose sampling efficiency decreases with increasing stimulus area and complexity. According to the model, the signals to be compared in a contrast matching task are first transferred through the early image processing stages mentioned above. Then they are filtered by a restoring transfer function which compensates for the low-level filtering and limited spatial integration at high contrast levels. Perceived contrasts of the stimuli are equal when the restored responses to the stimuli are equal. According to the model, the signals to be discriminated in a contrast discrimination task first go through the early image processing stages, after which signal dependent noise is added to the matched filter responses. The decision made by the human brain is based on the comparison between the responses of the matched filters to the stimuli, and the accuracy of the decision is limited by pre- and post-filter noises. The model for human contrast perception could accurately describe the results of contrast matching and discrimination in various conditions.

The QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection algorithms

The QUT-NOISE-TIMIT corpus consists of 600 hours of noisy speech sequences designed to enable a thorough evaluation of voice activity detection (VAD) algorithms across a wide variety of common background noise scenarios. In order to construct the final mixed-speech database, a collection of over 10 hours of background noise was conducted across 10 unique locations covering 5 common noise scenarios, to create the QUT-NOISE corpus. This background noise corpus was then mixed with speech events chosen from the TIMIT clean speech corpus over a wide variety of noise lengths, signal-to-noise ratios (SNRs) and active speech proportions to form the mixed-speech QUT-NOISE-TIMIT corpus. The evaluation of five baseline VAD systems on the QUT-NOISE-TIMIT corpus is conducted to validate the data and show that the variety of noise available will allow for better evaluation of VAD systems than existing approaches in the literature.

Noise robust voice activity detection using normal probability testing and time-domain histogram analysis

This paper presents a method of voice activity detection (VAD) suitable for high noise scenarios, based on the fusion of two complementary systems. The first system uses a proposed non-Gaussianity score (NGS) feature based on normal probability testing. The second system employs a histogram distance score (HDS) feature that detects changes in the signal through conducting a template-based similarity measure between adjacent frames. The decision outputs by the two systems are then merged using an open-by-reconstruction fusion stage. Accuracy of the proposed method was compared to several baseline VAD methods on a database created using real recordings of a variety of high-noise environments.

Noise robust voice activity detection using features extracted from the time-domain autocorrelation function

This paper presents a method of voice activity detection (VAD) for high noise scenarios, using a noise robust voiced speech detection feature. The developed method is based on the fusion of two systems. The first system utilises the maximum peak of the normalised time-domain autocorrelation function (MaxPeak). The second zone system uses a novel combination of cross-correlation and zero-crossing rate of the normalised autocorrelation to approximate a measure of signal pitch and periodicity (CrossCorr) that is hypothesised to be noise robust. The score outputs by the two systems are then merged using weighted sum fusion to create the proposed autocorrelation zero-crossing rate (AZR) VAD. Accuracy of AZR was compared to state of the art and standardised VAD methods and was shown to outperform the best performing system with an average relative improvement of 24.8% in half-total error rate (HTER) on the QUT-NOISE-TIMIT database created using real recordings from high-noise environments.

Fault detection of slow speed rolling element bearing with noise removal techniques

Rolling Element Bearings (REBs) are vital components in rotating machineries for providing rotating motion. In slow speed rotating machines, bearings are normally subjected to heavy static loads and a catastrophic failure can cause enormous disruption to production and human safety. Due to its low operating speed the impact energy generated by the rotating elements on the defective components is not sufficient to produce a detectable vibration response. This is further aggravated by the inability of general measuring instruments to detect and process the weak signals at the initiation of the defect accurately. Furthermore, the weak signals are often corrupted by background noise. This is a serious problem faced by maintenance engineers today and the inability to detect an incipient failure of the machine can significantly increases the risk of functional failure and costly downtime. This paper presents the application of noise removal techniques for enhancing the detection capability for slow speed REB condition monitoring. Blind deconvolution (BD) and adaptive line enhancer (ALE) are compared to evaluate their performance in enhancing the source signal with consequential removal of background noise. In the experimental study, incipient defects were seeded on a number of roller bearings and the signals were acquired using acoustic emission (AE) sensor. Kurtosis and modified peak ratio (mPR) were used to determine the detectability of signal corrupted by noise.