996 resultados para Temporal Precision
Resumo:
The volcanic succession on Montserrat provides an opportunity to examine the magmatic evolution of island arc volcanism over a ∼2.5 Ma period, extending from the andesites of the Silver Hills center, to the currently active Soufrière Hills volcano (February 2010). Here we present high-precision double-spike Pb isotope data, combined with trace element and Sr-Nd isotope data throughout this period of Montserrat's volcanic evolution. We demonstrate that each volcanic center; South Soufrière Hills, Soufrière Hills, Centre Hills and Silver Hills, can be clearly discriminated using trace element and isotopic parameters. Variations in these parameters suggest there have been systematic and episodic changes in the subduction input. The SSH center, in particular, has a greater slab fluid signature, as indicated by low Ce/Pb, but less sediment addition than the other volcanic centers, which have higher Th/Ce. Pb isotope data from Montserrat fall along two trends, the Silver Hills, Centre Hills and Soufrière Hills lie on a general trend of the Lesser Antilles volcanics, whereas SSH volcanics define a separate trend. The Soufrière Hills and SSH volcanic centers were erupted at approximately the same time, but retain distinctive isotopic signatures, suggesting that the SSH magmas have a different source to the other volcanic centers. We hypothesize that this rapid magmatic source change is controlled by the regional transtensional regime, which allowed the SSH magma to be extracted from a shallower source. The Pb isotopes indicate an interplay between subduction derived components and a MORB-like mantle wedge influenced by a Galapagos plume-like source.
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Objective To identify the spatial and temporal clusters of Barmah Forest virus (BFV) disease in Queensland in Australia, using geographical information systems (GIS) and spatial scan statistic (SaTScan). Methods We obtained BFV disease cases, population and statistical local areas boundary data from Queensland Health and Australian Bureau of Statistics respectively during 1992-2008 for Queensland. A retrospective Poisson-based analysis using SaTScan software and method was conducted in order to identify both purely spatial and space-time BFV disease high-rate clusters. A spatial cluster size of a proportion of the population and a 200km circle radius and varying time windows from 1 month to 12 months were chosen (for the space-time analysis). Results The spatial scan statistic detected a most likely significant purely spatial cluster (including 23 SLAs) and a most likely significant space-time cluster (including 24 SLAs) in approximately the same location. Significant secondary clusters were also identified from both the analyses in several locations. Conclusions This study provides evidence of the existence of statistically significant BFV disease clusters in Queensland, Australia. The study also demonstrated the relevance and applicability of SaTScan in analysing on-going surveillance data to identify clusters to facilitate the development of effective BFV disease prevention and control strategies in Queensland, Australia.
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Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.
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Traditional analytic models for power system fault diagnosis are usually formulated as an unconstrained 0–1 integer programming problem. The key issue of the models is to seek the fault hypothesis that minimizes the discrepancy between the actual and the expected states of the concerned protective relays and circuit breakers. The temporal information of alarm messages has not been well utilized in these methods, and as a result, the diagnosis results may be not unique and hence indefinite, especially when complicated and multiple faults occur. In order to solve this problem, this paper presents a novel analytic model employing the temporal information of alarm messages along with the concept of related path. The temporal relationship among the actions of protective relays and circuit breakers, and the different protection configurations in a modern power system can be reasonably represented by the developed model, and therefore, the diagnosed results will be more definite under different circumstances of faults. Finally, an actual power system fault was served to verify the proposed method.
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Many substation applications require accurate time-stamping. The performance of systems such as Network Time Protocol (NTP), IRIG-B and one pulse per second (1-PPS) have been sufficient to date. However, new applications, including IEC 61850-9-2 process bus and phasor measurement, require accuracy of one microsecond or better. Furthermore, process bus applications are taking time synchronisation out into high voltage switchyards where cable lengths may have an impact on timing accuracy. IEEE Std 1588, Precision Time Protocol (PTP), is the means preferred by the smart grid standardisation roadmaps (from both the IEC and US National Institute of Standards and Technology) of achieving this higher level of performance, and integrates well into Ethernet based substation automation systems. Significant benefits of PTP include automatic path length compensation, support for redundant time sources and the cabling efficiency of a shared network. This paper benchmarks the performance of established IRIG-B and 1-PPS synchronisation methods over a range of path lengths representative of a transmission substation. The performance of PTP using the same distribution system is then evaluated and compared to the existing methods to determine if the performance justifies the additional complexity. Experimental results show that a PTP timing system maintains the synchronising performance of 1-PPS and IRIG-B timing systems, when using the same fibre optic cables, and further meets the needs of process buses in large substations.
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Crop simulation models have the potential to assess the risk associated with the selection of a specific N fertilizer rate, by integrating the effects of soil-crop interactions on crop growth under different pedo-climatic and management conditions. The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N2O emissions) of a spatially variable N fertilizer application in an irrigated maize field in Italy. The validated SALUS model was run with 5 nitrogen rates scenarios, 50, 100, 150, 200, and 250 kg N ha−1, with the latter being the N fertilization adopted by the farmer. The long-term (25 years) simulations were performed on two previously identified spatially and temporally stable zones, a high yielding and low yielding zone. The simulation results showed that N fertilizer rate can be reduced without affecting yield and net return. The marginal net return was on average higher for the high yield zone, with values ranging from 1550 to 2650 € ha−1 for the 200 N and 1485 to 2875 € ha−1 for the 250 N. N leaching varied between 16.4 and 19.3 kg N ha−1 for the 200 N and the 250 N in the high yield zone. In the low yield zone, the 250 N had a significantly higher N leaching. N2O emissions varied between 0.28 kg N2O ha−1 for the 50 kg N ha−1 rate to a maximum of 1.41 kg N2O ha−1 for the 250 kg N ha−1 rate.
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Recently, a stream of project management research has recognized the critical role of boundary objects in the organization of projects. In this paper, we investigate how one advanced scheduling tool, the Integrated Master Schedule (IMS), is used as a temporal boundary object at various stages of complex projects. The IMS is critical to megaprojects which typically span long periods of time and face a high degree of complexity and uncertainty. In this paper, we conceptualize projects of this type as complex adaptive systems (CAS). We report the findings of four case projects on how the IMS mapped interactions, interdependencies, constraints, and fractal patterns of these emerging projects, and how the process of IMS visualization enabled communication and negotiation of project realities. This paper highlights that this advanced timeline tool acts as a boundary object and elicits shared understanding of complex projects from their stakeholders.
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Background: Falciparum malaria is the most deadly among the four main types of human malaria. Although great success has been achieved since the launch of the National Malaria Control Programme in 1955, malaria remains a serious public health problem in China. This paper aimed to analyse the geographic distribution, demographic patterns and time trends of falciparum malaria in China. Methods: The annual numbers of falciparum malaria cases during 1992–2003 and the individual case reports of each clinical falciparum malaria during 2004–2005 were extracted from communicable disease information systems in China Center for Diseases Control and Prevention. The annual number of cases and the annual incidence were mapped by matching them to corresponding province- and county-level administrative units in a geographic information system. The distribution of falciparum malaria by age, gender and origin of infection was analysed. Time-series analysis was conducted to investigate the relationship between the falciparum malaria in the endemic provinces and the imported falciparum malaria in non-endemic provinces. Results: Falciparum malaria was endemic in two provinces of China during 2004–05. Imported malaria was reported in 26 non-endemic provinces. Annual incidence of falciparum malaria was mapped at county level in the two endemic provinces of China: Yunnan and Hainan. The sex ratio (male vs. female) for the number of cases in Yunnan was 1.6 in the children of 0–15 years and it reached 5.7 in the adults over 15 years of age. The number of malaria cases in Yunnan was positively correlated with the imported malaria of concurrent months in the non-endemic provinces. Conclusion: The endemic area of falciparum malaria in China has remained restricted to two provinces, Yunnan and Hainan. Stable transmission occurs in the bordering region of Yunnan and the hilly-forested south of Hainan. The age and gender distribution in the endemic area is characterized by the predominance of adult men cases. Imported falciparum malaria in the non-endemic area of China, affected mainly by the malaria transmission in Yunnan, has increased both spatially and temporally. Specific intervention measures targeted at the mobile population groups are warranted.
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Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒÓ2 = 7587, p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. A cluster analysis was used to detect the hot spots/clusters of BFV disease at a SLA level. Most likely spatial and space-time clusters are detected at the same locations across coastal Queensland (p<0.05). The study demonstrates heterogeneity of disease risk at a SLA level and reveals the spatial and temporal clustering of BFV disease in Queensland. Discriminant analysis was employed to establish a link between wetland classes, climate zones and BFV disease. This is because the importance of wetlands in the transmission of BFV disease remains unclear. The multivariable discriminant modelling analyses demonstrate that wetland types of saline 1, riverine and saline tidal influence were the most significant risk factors for BFV disease in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. The model accuracies were 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV disease risk varied with wetland class and climate zone. The study suggests that wetlands may act as potential breeding habitats for BFV vectors. Multivariable spatial regression models were applied to assess the impact of spatial climatic, socio-economic and tidal factors on the BFV disease in Queensland. Spatial regression models were developed to account for spatial effects. Spatial regression models generated superior estimates over a traditional regression model. In the spatial regression models, BFV disease incidence shows an inverse relationship with minimum temperature, low tide and distance to coast, and positive relationship with rainfall in coastal areas whereas in whole Queensland the disease shows an inverse relationship with minimum temperature and high tide and positive relationship with rainfall. This study determines the most significant spatial risk factors for BFV disease across Queensland. Empirical models were developed to forecast the future risk of BFV disease outbreaks in coastal Queensland using existing climatic, socio-economic and tidal conditions under climate change scenarios. Logistic regression models were developed using BFV disease outbreak data for the existing period (2000-2008). The most parsimonious model had high sensitivity, specificity and accuracy and this model was used to estimate and forecast BFV disease outbreaks for years 2025, 2050 and 2100 under climate change scenarios for Australia. Important contributions arising from this research are that: (i) it is innovative to identify high-risk coastal areas by creating buffers based on grid-centroid and the use of fine-grained spatial units, i.e., mesh blocks; (ii) a spatial regression method was used to account for spatial dependence and heterogeneity of data in the study area; (iii) it determined a range of potential spatial risk factors for BFV disease; and (iv) it predicted the future risk of BFV disease outbreaks under climate change scenarios in Queensland, Australia. In conclusion, the thesis demonstrates that the distribution of BFV disease exhibits a distinct spatial and temporal variation. Such variation is influenced by a range of spatial risk factors including climatic, demographic, socio-economic, ecological and tidal variables. The thesis demonstrates that spatial regression method can be applied to better understand the transmission dynamics of BFV disease and its risk factors. The research findings show that disease notification data can be integrated with multi-factorial risk factor data to develop build-up models and forecast future potential disease risks under climate change scenarios. This thesis may have implications in BFV disease control and prevention programs in Queensland.
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A significant issue encountered when fusing data received from multiple sensors is the accuracy of the timestamp associated with each piece of data. This is particularly important in applications such as Simultaneous Localisation and Mapping (SLAM) where vehicle velocity forms an important part of the mapping algorithms; on fastmoving vehicles, even millisecond inconsistencies in data timestamping can produce errors which need to be compensated for. The timestamping problem is compounded in a robot swarm environment due to the use of non-deterministic readily-available hardware (such as 802.11-based wireless) and inaccurate clock synchronisation protocols (such as Network Time Protocol (NTP)). As a result, the synchronisation of the clocks between robots can be out by tens-to-hundreds of milliseconds making correlation of data difficult and preventing the possibility of the units performing synchronised actions such as triggering cameras or intricate swarm manoeuvres. In this thesis, a complete data fusion unit is designed, implemented and tested. The unit, named BabelFuse, is able to accept sensor data from a number of low-speed communication buses (such as RS232, RS485 and CAN Bus) and also timestamp events that occur on General Purpose Input/Output (GPIO) pins referencing a submillisecondaccurate wirelessly-distributed "global" clock signal. In addition to its timestamping capabilities, it can also be used to trigger an attached camera at a predefined start time and frame rate. This functionality enables the creation of a wirelessly-synchronised distributed image acquisition system over a large geographic area; a real world application for this functionality is the creation of a platform to facilitate wirelessly-distributed 3D stereoscopic vision. A ‘best-practice’ design methodology is adopted within the project to ensure the final system operates according to its requirements. Initially, requirements are generated from which a high-level architecture is distilled. This architecture is then converted into a hardware specification and low-level design, which is then manufactured. The manufactured hardware is then verified to ensure it operates as designed and firmware and Linux Operating System (OS) drivers are written to provide the features and connectivity required of the system. Finally, integration testing is performed to ensure the unit functions as per its requirements. The BabelFuse System comprises of a single Grand Master unit which is responsible for maintaining the absolute value of the "global" clock. Slave nodes then determine their local clock o.set from that of the Grand Master via synchronisation events which occur multiple times per-second. The mechanism used for synchronising the clocks between the boards wirelessly makes use of specific hardware and a firmware protocol based on elements of the IEEE-1588 Precision Time Protocol (PTP). With the key requirement of the system being submillisecond-accurate clock synchronisation (as a basis for timestamping and camera triggering), automated testing is carried out to monitor the o.sets between each Slave and the Grand Master over time. A common strobe pulse is also sent to each unit for timestamping; the correlation between the timestamps of the di.erent units is used to validate the clock o.set results. Analysis of the automated test results show that the BabelFuse units are almost threemagnitudes more accurate than their requirement; clocks of the Slave and Grand Master units do not di.er by more than three microseconds over a running time of six hours and the mean clock o.set of Slaves to the Grand Master is less-than one microsecond. The common strobe pulse used to verify the clock o.set data yields a positive result with a maximum variation between units of less-than two microseconds and a mean value of less-than one microsecond. The camera triggering functionality is verified by connecting the trigger pulse output of each board to a four-channel digital oscilloscope and setting each unit to output a 100Hz periodic pulse with a common start time. The resulting waveform shows a maximum variation between the rising-edges of the pulses of approximately 39¥ìs, well below its target of 1ms.
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Automated airborne collision-detection systems are a key enabling technology for facilitat- ing the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine air- borne collision threats, but not so sensitive as to cause excessive false-alarms. Hence, an accurate characterisation of detection and false alarm sensitivity is essential for understand- ing performance trade-offs, and system designers can exploit this characterisation to help achieve a desired balance in system performance. In this paper we experimentally evaluate a sky-region, image based, aircraft collision detection system that is based on morphologi- cal and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter back- ground). A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540m in 3 flight test cases with no false alarm events in 14.14 hours of non-target data (under cloudy conditions, the system achieved detection ranges greater than 1170m in 4 flight test cases with no false alarm events in 6.63 hours of non-target data). Importantly, this paper is the first documented presentation of detection range versus false alarm curves generated from airborne target and non-target image data.
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Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.