An improved technique for SAR internal wave signal detection

634-640One improved boundary process method for synthetic aperture radar (SAR) internal wave signal detection, which tries to reduce the errors during the extending process for empirical mode decomposition (EMD), has been introduced in this paper. This method is based on EMD and the criterion created by the theory that the max normalized deflection stands for the largest energy. The method was first applied both on several time series decompositions and on SAR nonlinear internal wave temporal series decompositions. The comparison of results showed that this method was more feasible and precise indicating that the method can detect the internal wave signals successfully from SAR image even with a lot noise

A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

Electrical load forecast is an important part of the power system energy management system. Reliable load forecast technique will help the electric utility to make unit commitment decisions, reduce spinning reserve capacity, and schedule device maintenance plan properly. Thus, besides being a key element in reducing the generation cost, power load forecast is an essential procedure in enhancing the reliability of the power systems. Generally speaking, power systems worldwide are using load forecast as an essential part of off-line network analysis. This is in order to determine the status of the system, and the necessity to implement corrective actions, such as load shedding, power purchases or using peaking units. Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and 168-hours ahead is a necessary daily task for power dispatch. Its accuracy will significantly affect the cost of generation and the reliability of the system. The majority of the single variable based techniques are using autoregressive-moving average (ARMA) model to solve the STLF problem. In this thesis, a new AR algorithm especially designed for long data records as a solution to STLF problem is proposed. The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. In order to verify the proposed algorithm as a solution to STLF problem, its performance is compared with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA (SARIMA). In addition to the parametric algorithms, the comparison is extended towards artificial neural networks (ANN). Three years data power demand record collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC, between the beginning of 2005 and the end of 2007 are used for the comparison. The results show the potential of the proposed algorithm as a reliable solution to STLF

Earth Resources: A continuing bibliography with indexes

This bibliography lists 475 reports, articles and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Earth resources: A continuing bibliography, issue 46

This bibliography lists 467 reports, articles and other documents introdcued into the NASA scientific and technical information system between April 1 and June 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental cultural resources geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Statistical Modelling and Variability of the Subtropical Front, New Zealand

Ocean fronts are narrow zones of intense dynamic activity that play an important role in global ocean-atmosphere interactions. Of particular significance is the circumglobal frontal system of the Southern Ocean where intermediate water masses are formed, heat, salt, nutrients and momentum are redistributed and carbon dioxide is absorbed. The northern limit of this frontal band is marked by the Subtropical Front, where subtropical gyre water convergences with colder subantarctic water. Owing to their highly variable nature, both in space and time, ocean fronts are notoriously difficult features to adequately sample using traditional in-situ techniques. We therefore propose a new and innovative statistical modelling approach to detecting and monitoring ocean fronts from AVHRR SST images. Weighted local likelihood is used to provide a nonparametric description of spatial variations in the position and strength of individual fronts within an image. Although we use the new algorithm on AVHRR data it is suitable for other satellite data or model output. The algorithm is used to study the spatial and temporal variability of a localized section of the Subtropical Front past New Zealand, known locally as the Southland Front. Twenty-one years (January 1985 to December 2005) of estimates of the front’s position, temperature and strength are examined using cross correlation and wavelet analysis to investigate the role that remote atmospheric and oceanic forcing relating to the El Nino-Southern Oscillation may play in interannual frontal variability. Cold (warm) anomalies are observed at the Southland Front three to four months after peak El Nino (La Nina) events. The gradient of the front changes one to two seasons in advance of extreme ENSO events suggesting that it may be used as a precursor to changes in the Southern Oscillation. There are strong seasonal dependencies to the correlation between ENSO indices and frontal characteristics. In addition, the frequency and phase relationships are inconsistent indicating that no one physical mechanism or mode of climate variability is responsible for the teleconnection

A NOVEL FORWARD BACKWARD LINEAR PREDICTION ALGORITHM FOR SHORT TERM POWER LOAD FORECAST

Electrical load forecast is an important part of the power system energy management system. Reliable load forecast technique will help the electric utility to make unit commitment decisions, reduce spinning reserve capacity, and schedule device maintenance plan properly. Thus, besides being a key element in reducing the generation cost, power load forecast is an essential procedure in enhancing the reliability of the power systems. Generally speaking, power systems worldwide are using load forecast as an essential part of off-line network analysis. This is in order to determine the status of the system, and the necessity to implement corrective actions, such as load shedding, power purchases or using peaking units. Short term load forecast (STLF), in terms of one-hour ahead, 24-hours ahead, and 168-hours ahead is a necessary daily task for power dispatch. Its accuracy will significantly affect the cost of generation and the reliability of the system. The majority of the single variable based techniques are using autoregressive-moving average (ARMA) model to solve the STLF problem. In this thesis, a new AR algorithm especially designed for long data records as a solution to STLF problem is proposed. The proposed AR-based algorithm divides long data record into short segments and searches for the AR coefficients that simultaneously model the data with the least means squared errors. In order to verify the proposed algorithm as a solution to STLF problem, its performance is compared with other AR-based algorithms, like Burg and the seasonal Box-Jenkins ARIMA (SARIMA). In addition to the parametric algorithms, the comparison is extended towards artificial neural networks (ANN). Three years data power demand record collected by NEMMCO in four Australian states, NSW, QLD, SA, and VIC, between the beginning of 2005 and the end of 2007 are used for the comparison. The results show the potential of the proposed algorithm as a reliable solution to STLF

Computational and Numerical Simulations

Computational and Numerical Simulations is an edited book including 20 chapters. Book handles the recent research devoted to numerical simulations of physical and engineering systems. It presents both new theories and their applications, showing bridge between theoretical investigations and possibility to apply them by engineers of different branches of science. Numerical simulations play a key role in both theoretical and application oriented research

Earth resources: A continuing bibliography (issue 26)

This bibliography lists 480 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1, 1980 and June 30, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Towards an Integrated Assessment of Sea-Level Observations Along the U.S. Atlantic Coast

Sea levels are rising globally due to anthropogenic climate change. However, local sea levels that impact coastal ecosystems often differ from the global trend, sometimes by a factor of two or more. Improved understanding of this regional variability provides insights into geophysical processes and has implications for coastal communities developing resilience to ongoing sea-level rise. This dissertation conducts an investigation of sea level and its contributing processes at multiple spatial scales. Focusing on primarily interannual time-scales and data-driven approaches, new data sources and technologies are utilized to reduce current uncertainties. First, sea-level trends are assessed over the global ocean and at coastlines using data from the recently launched ICESat-2 satellite. These trends agree well with independent measurements, while also filling observational gaps along undersampled coastlines and at high-latitudes. Next, the spatial focus is narrowed to the U.S. East Coast, which is experiencing exceptionally high rates of relative sea-level rise, largely due to land subsidence. By incorporating new state-of-the-art estimates of land-ice melt, an existing Bayesian hierarchical space-time model is expanded to assess the relative contributions of sea surface height and vertical land motion to 20th century relative-sea level change. Model results confirm previous findings that identified regional-scale geological processes as the primary driver of spatial variability in East Coast relative sea level. By rigorously quantifying uncertainties, constraints are placed on the current state of knowledge with clear directions for future research. Finally, small-scale vertical land motion in Hampton Roads, VA is investigated using the remote-sensing technology of Interferometric Synthetic Aperture Radar (InSAR). Two different data sources and processing strategies are implemented which independently reveal substantial rates of vertical land motion that vary over short spatial scales. The results highlight the importance of vertical land motion in exacerbating negative impacts of relative sea-level rise such as flooding and inundation. Overall, this study leverages new spaceborne sensors, an innovative statistical model, and state-of-the-art processing strategies to enhance our understanding of ongoing sea-level change

Earth resources: A continuing bibliography with indexes (issue 61)

This bibliography lists 606 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors, and economic analysis