909 resultados para On-line monitoring


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We report on a new technique to reconstruct the 3D dielectric function change in transparent dielectric materials and the application of the technique for on-line monitoring of refractive index modification in BK7 glass during direct femtosecond laser microfabrication. The complex optical field scattered from the modified region is measured using two-beam, single-shot interferogram and the distribution of the modified refractive index is reconstructed by numerically solving the inverse scattering problem in Born approximation. The optical configuration suggested is further development of digital holographic microscopy. It takes advantage of high spatial resolution and almost the same optical paths for both interfering beams, and allows ultrafast time resolution. © Springer Science+Business Media, LLC. 2011.

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The formation of groups acquired new features with the arrival of the internet. The links before straitened between family, work groups and close friends today reach long distances through online communities. These communities represent groups that have affinities and common interests, and use the community space to discuss these. Examples of these communities are those related to franchise Game of Thrones, a literary phenomenon that has expanded by various media, including the social, television, and communities. This report aims to present the work steps and the theoretical reflection, necessary for the achievement of a final product in file format, which aimed to measure the engagement and participation of GOT fans on the Internet, especially in two Facebook GOT communities during the fifth season of the series aired on HBO.

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Frequency domain spectroscopy (FDS) is being used to assess the insulation condition of oil–paper power transformers. However, it has to date only been implemented on de-energised transformers, which requires the transformers to be shut down for an extended period and may cause significant costs. To solve this issue, a newly improved monitoring method based on the FDS principle is proposed to implement the dielectric measurement on energised transformers. Moreover, a chirp waveform excitation and its novel processing method are introduced. Compared with the conventional FDS results, dielectric results from the energised insulation system have higher tanδ values because of the increased losses. To further understand the insulation ageing process, the effects of the geometric capacitance are removed from the measured imaginary admittance of the insulation system to enhance the ageing signature. The resulting imaginary admittance is then shown to correlate well with the central time constant in return voltage measurements results. The proposed methods address the issues on techniques used on energised transformers and provide a clue for on-line FDS diagnostic application.

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The purpose of this research is design considerations for environmental monitoring platforms for the detection of hazardous materials using System-on-a-Chip (SoC) design. Design considerations focus on improving key areas such as: (1) sampling methodology; (2) context awareness; and (3) sensor placement. These design considerations for environmental monitoring platforms using wireless sensor networks (WSN) is applied to the detection of methylmercury (MeHg) and environmental parameters affecting its formation (methylation) and deformation (demethylation). ^ The sampling methodology investigates a proof-of-concept for the monitoring of MeHg using three primary components: (1) chemical derivatization; (2) preconcentration using the purge-and-trap (P&T) method; and (3) sensing using Quartz Crystal Microbalance (QCM) sensors. This study focuses on the measurement of inorganic mercury (Hg) (e.g., Hg2+) and applies lessons learned to organic Hg (e.g., MeHg) detection. ^ Context awareness of a WSN and sampling strategies is enhanced by using spatial analysis techniques, namely geostatistical analysis (i.e., classical variography and ordinary point kriging), to help predict the phenomena of interest in unmonitored locations (i.e., locations without sensors). This aids in making more informed decisions on control of the WSN (e.g., communications strategy, power management, resource allocation, sampling rate and strategy, etc.). This methodology improves the precision of controllability by adding potentially significant information of unmonitored locations.^ There are two types of sensors that are investigated in this study for near-optimal placement in a WSN: (1) environmental (e.g., humidity, moisture, temperature, etc.) and (2) visual (e.g., camera) sensors. The near-optimal placement of environmental sensors is found utilizing a strategy which minimizes the variance of spatial analysis based on randomly chosen points representing the sensor locations. Spatial analysis is employed using geostatistical analysis and optimization occurs with Monte Carlo analysis. Visual sensor placement is accomplished for omnidirectional cameras operating in a WSN using an optimal placement metric (OPM) which is calculated for each grid point based on line-of-site (LOS) in a defined number of directions where known obstacles are taken into consideration. Optimal areas of camera placement are determined based on areas generating the largest OPMs. Statistical analysis is examined by using Monte Carlo analysis with varying number of obstacles and cameras in a defined space. ^

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The requirement to monitor the rapid pace of environmental change due to global warming and to human development is producing large volumes of data but placing much stress on the capacity of ecologists to store, analyse and visualise that data. To date, much of the data has been provided by low level sensors monitoring soil moisture, dissolved nutrients, light intensity, gas composition and the like. However, a significant part of an ecologist’s work is to obtain information about species diversity, distributions and relationships. This task typically requires the physical presence of an ecologist in the field, listening and watching for species of interest. It is an extremely difficult task to automate because of the higher order difficulties in bandwidth, data management and intelligent analysis if one wishes to emulate the highly trained eyes and ears of an ecologist. This paper is concerned with just one part of the bigger challenge of environmental monitoring – the acquisition and analysis of acoustic recordings of the environment. Our intention is to provide helpful tools to ecologists – tools that apply information technologies and computational technologies to all aspects of the acoustic environment. The on-line system which we are building in conjunction with ecologists offers an integrated approach to recording, data management and analysis. The ecologists we work with have different requirements and therefore we have adopted the toolbox approach, that is, we offer a number of different web services that can be concatenated according to need. In particular, one group of ecologists is concerned with identifying the presence or absence of species and their distributions in time and space. Another group, motivated by legislative requirements for measuring habitat condition, are interested in summary indices of environmental health. In both case, the key issues are scalability and automation.

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Aerosol mass spectrometers (AMS) are powerful tools in the analysis of the chemical composition of airborne particles, particularly organic aerosols which are gaining increasing attention. However, the advantages of AMS in providing on-line data can be outweighed by the difficulties involved in its use in field measurements at multiple sites. In contrast to the on-line measurement by AMS, a method which involves sample collection on filters followed by subsequent analysis by AMS could significantly broaden the scope of AMS application. We report the application of such an approach to field studies at multiple sites. An AMS was deployed at 5 urban schools to determine the sources of the organic aerosols at the schools directly. PM1 aerosols were also collected on filters at these and 20 other urban schools. The filters were extracted with water and the extract run through a nebulizer to generate the aerosols, which were analysed by an AMS. The mass spectra from the samples collected on filters at the 5 schools were found to have excellent correlations with those obtained directly by AMS, with r2 ranging from 0.89 to 0.98. Filter recoveries varied between the schools from 40 -115%, possibly indicating that this method provides qualitative rather than quantitative information. The stability of the organic aerosols on Teflon filters was demonstrated by analysing samples stored for up to two years. Application of the procedure to the remaining 20 schools showed that secondary organic aerosols were the main source of aerosols at the majority of the schools. Overall, this procedure provides accurate representation of the mass spectra of ambient organic aerosols and could facilitate rapid data acquisition at multiple sites where AMS could not be deployed for logistical reasons.

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This paper investigates the feasibility of an on-line damage detection capability for helicopter main rotor blades made of composite material. Damage modeled in the composite is matrix cracking. A box-beam with stiffness properties similar to a hingeless rotor blade is designed using genetic algorithm for the typical [+/-theta(m)/90(n)](s) family of composites. The effect of matrix cracks is included in an analytical model of composite box-beam. An aeroelastic analysis of the helicopter rotor based on finite elements in space and time is used to study the effects of matrix cracking in the rotor blade in forward flight. For global fault detection, rotating frequencies, tip bending and torsion response, and blade root loads are studied. It is observed that the effect of matrix cracking on lag bending and elastic twist deflection at the blade tip and blade root yawing moment is significant and these parameters can be monitored for online health monitoring. For implementation of local fault detection technique, the effect on axial and shear strain, for matrix cracks in the whole blade as well as matrix cracks occurring locally is studied. It is observed that using strain measurement along the blade it is possible to locate the matrix cracks as well as to predict density of matrix cracks. (C) 2004 Elsevier Ltd. All rights reserved.

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Thermoforming processes generally employ sheet temperature monitoring as the primary means of process control. In this paper the development of an alternative system that monitors plug force is described. Tests using a prototype device have shown that the force record over a forming cycle creates a unique map of the process operation. Key process features such as the sheet modulus, sheet sag and the timing of the process stages may be readily observed, and the effects of changes in all of the major processing parameters are easily distinguished. Continuous, cycle-to-cycle tests show that the output is consistent and repeatable over a longer time frame, providing the opportunity for development of an on-line process control system. Further testing of the system is proposed.

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Polymer extrusion is regarded as an energy-intensive production process, and the real-time monitoring of both energy consumption and melt quality has become necessary to meet new carbon regulations and survive in the highly competitive plastics market. The use of a power meter is a simple and easy way to monitor energy, but the cost can sometimes be high. On the other hand, viscosity is regarded as one of the key indicators of melt quality in the polymer extrusion process. Unfortunately, viscosity cannot be measured directly using current sensory technology. The employment of on-line, in-line or off-line rheometers is sometimes useful, but these instruments either involve signal delay or cause flow restrictions to the extrusion process, which is obviously not suitable for real-time monitoring and control in practice. In this paper, simple and accurate real-time energy monitoring methods are developed. This is achieved by looking inside the controller, and using control variables to calculate the power consumption. For viscosity monitoring, a ‘soft-sensor’ approach based on an RBF neural network model is developed. The model is obtained through a two-stage selection and differential evolution, enabling compact and accurate solutions for viscosity monitoring. The proposed monitoring methods were tested and validated on a Killion KTS-100 extruder, and the experimental results show high accuracy compared with traditional monitoring approaches.

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Polymer extrusion, in which a polymer is melted and conveyed to a mould or die, forms the basis of most polymer processing techniques. Extruders frequently run at non-optimised conditions and can account for 15–20% of overall process energy losses. In times of increasing energy efficiency such losses are a major concern for the industry. Product quality, which depends on the homogeneity and stability of the melt flow which in turn depends on melt temperature and screw speed, is also an issue of concern of processors. Gear pumps can be used to improve the stability of the production line, but the cost is usually high. Likewise it is possible to introduce energy meters but they also add to the capital cost of the machine. Advanced control incorporating soft sensing capabilities offers opportunities to this industry to improve both quality and energy efficiency. Due to strong correlations between the critical variables, such as the melt temperature and melt pressure, traditional decentralized PID (Proportional–Integral–Derivative) control is incapable of handling such processes if stricter product specifications are imposed or the material is changed from one batch to another. In this paper, new real-time energy monitoring methods have been introduced without the need to install power meters or develop data-driven models. The effects of process settings on energy efficiency and melt quality are then studied based on developed monitoring methods. Process variables include barrel heating temperature, water cooling temperature, and screw speed. Finally, a fuzzy logic controller is developed for a single screw extruder to achieve high melt quality. The resultant performance of the developed controller has shown it to be a satisfactory alternative to the expensive gear pump. Energy efficiency of the extruder can further be achieved by optimising the temperature settings. Experimental results from open-loop control and fuzzy control on a Killion 25 mm single screw extruder are presented to confirm the efficacy of the proposed approach.

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Bioresorbable polymers such as PLA have an important role to play in the development of temporary implantable medical devices with significant benefits over traditional therapies. However, development of new devices is hindered by high manufacturing costs associated with difficulties in processing the material. A major problem is the lack of insight on material degradation during processing. In this work, a method of quantifying degradation of PLA using IR spectroscopy coupled with computational chemistry and chemometric modeling is examined. It is shown that the method can predict the quantity of degradation products in solid-state samples with reasonably good accuracy, indicating the potential to adapt the method to developing an on-line sensor for monitoring PLA degradation in real-time during processing.

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years 8 months) and 24 older (M == 7 years 4 months) children. A Monitoring Process Model (MPM) was developed and tested in order to ascertain at which component process ofthe MPM age differences would emerge. The MPM had four components: (1) assessment; (2) evaluation; (3) planning; and (4) behavioural control. The MPM was assessed directly using a referential communication task in which the children were asked to make a series of five Lego buildings (a baseline condition and one building for each MPM component). Children listened to instructions from one experimenter while a second experimenter in the room (a confederate) intetjected varying levels ofverbal feedback in order to assist the children and control the component ofthe MPM. This design allowed us to determine at which "stage" ofprocessing children would most likely have difficulty monitoring themselves in this social-cognitive task. Developmental differences were obselVed for the evaluation, planning and behavioural control components suggesting that older children were able to be more successful with the more explicit metacomponents. Interestingly, however, there was no age difference in terms ofLego task success in the baseline condition suggesting that without the intelVention ofthe confederate younger children monitored the task about as well as older children. This pattern ofresults indicates that the younger children were disrupted by the feedback rather than helped. On the other hand, the older children were able to incorporate the feedback offered by the confederate into a plan ofaction. Another aim ofthis study was to assess similar processing components to those investigated by the MPM Lego task in a more naturalistic observation. Together the use ofthe Lego Task ( a social cognitive task) and the naturalistic social interaction allowed for the appraisal of cross-domain continuities and discontinuities in monitoring behaviours. In this vein, analyses were undertaken in order to ascertain whether or not successful performance in the MPM Lego Task would predict cross-domain competence in the more naturalistic social interchange. Indeed, success in the two latter components ofthe MPM (planning and behavioural control) was related to overall competence in the naturalistic task. However, this cross-domain prediction was not evident for all levels ofthe naturalistic interchange suggesting that the nature ofthe feedback a child receives is an important determinant ofresponse competency. Individual difference measures reflecting the children's general cognitive capacity (Working Memory and Digit Span) and verbal ability (vocabulary) were also taken in an effort to account for more variance in the prediction oftask success. However, these individual difference measures did not serve to enhance the prediction oftask performance in either the Lego Task or the naturalistic task. Similarly, parental responses to questionnaires pertaining to their child's temperament and social experience also failed to increase prediction oftask performance. On-line measures ofthe children's engagement, positive affect and anxiety also failed to predict competence ratings.

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Activity of the medial frontal cortex (MFC) has been implicated in attention regulation and performance monitoring. The MFC is thought to generate several event-related potential (ERPs) components, known as medial frontal negativities (MFNs), that are elicited when a behavioural response becomes difficult to control (e.g., following an error or shifting from a frequently executed response). The functional significance of MFNs has traditionally been interpreted in the context of the paradigm used to elicit a specific response, such as errors. In a series of studies, we consider the functional similarity of multiple MFC brain responses by designing novel performance monitoring tasks and exploiting advanced methods for electroencephalography (EEG) signal processing and robust estimation statistics for hypothesis testing. In study 1, we designed a response cueing task and used Independent Component Analysis (ICA) to show that the latent factors describing a MFN to stimuli that cued the potential need to inhibit a response on upcoming trials also accounted for medial frontal brain responses that occurred when individuals made a mistake or inhibited an incorrect response. It was also found that increases in theta occurred to each of these task events, and that the effects were evident at the group level and in single cases. In study 2, we replicated our method of classifying MFC activity to cues in our response task and showed again, using additional tasks, that error commission, response inhibition, and, to a lesser extent, the processing of performance feedback all elicited similar changes across MFNs and theta power. In the final study, we converted our response cueing paradigm into a saccade cueing task in order to examine the oscillatory dynamics of response preparation. We found that, compared to easy pro-saccades, successfully preparing a difficult anti-saccadic response was characterized by an increase in MFC theta and the suppression of posterior alpha power prior to executing the eye movement. These findings align with a large body of literature on performance monitoring and ERPs, and indicate that MFNs, along with their signature in theta power, reflects the general process of controlling attention and adapting behaviour without the need to induce error commission, the inhibition of responses, or the presentation of negative feedback.

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A Kalman filter algorithm has been applied to interpret the optical reflectance excursions during vacuum deposition of infrared coatings and multilayer thin-film filters. The application has been described in detail elsewhere and this paper now reports on-line experience for estimating deposition rate and thickness. The estimation proved sufficiently reliable to firstly 'navigate' regular manufacture (as controlled by a skilled operator) and to subsequently reproduce the skill without interpretation or intervention whilst maintaining exemplary product quality. Optical control by means of this Kalman filter application is therefore considered suitable as a basis for the automated manufacture of infrared coatings and multilayer thin-film filters.

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One of the most important objectives of cold metal forming research is to develop techniques that enable better manufacturing efficiencies. Within this monitoring of tooling condition is vital to providing high quality manufacturing. The objective of this research is to determine the signature derived from Acoustic Emission (AE) sensors, in order to establish the current condition of a machine tool, as applied to bolt-making. From here we aim to develop and implement an on-line condition monitoring tool for the cold forming process. A review of the literature has shown that much research into AE has been successfully applied in metal cutting operations; such as milling, drilling and turning, but little research has been done related to metal forming. This appears to be due to the complexity of obtaining consistent signals using Acoustic Emission systems, because the presence of noise in many forms. This paper will detail many of the AE signals acquired and analysed through our research. The extensive results indicate this form of condition monitoring is not suitable for metal forming in its current configuration. Further tests are proposed to enable such research to move forward, so a condition monitoring system can be established.