Detection And Classification Of Buried Radioactive Materials

This dissertation develops new approaches for detection and classification of buried radioactive materials. Different spectral transformation methods are proposed to effectively suppress noise and to better distinguish signal features in the transformed space. The contributions of this dissertation are detailed as follows. 1) Propose an unsupervised method for buried radioactive material detection. In the experiments, the original Reed-Xiaoli (RX) algorithm performs similarly as the gross count (GC) method; however, the constrained energy minimization (CEM) method performs better if using feature vectors selected from the RX output. Thus, an unsupervised method is developed by combining the RX and CEM methods, which can efficiently suppress the background noise when applied to the dimensionality-reduced data from principle component analysis (PCA). 2) Propose an approach for buried target detection and classification, which applies spectral transformation followed by noisejusted PCA (NAPCA). To meet the requirement of practical survey mapping, we focus on the circumstance when sensor dwell time is very short. The results show that spectral transformation can alleviate the effects from spectral noisy variation and background clutters, while NAPCA, a better choice than PCA, can extract key features for the following detection and classification. 3) Propose a particle swarm optimization (PSO)-based system to automatically determine the optimal partition for spectral transformation. Two PSOs are incorporated in the system with the outer one being responsible for selecting the optimal number of bins and the inner one for optimal bin-widths. The experimental results demonstrate that using variable bin-widths is better than a fixed bin-width, and PSO can provide better results than the traditional Powell’s method. 4) Develop parallel implementation schemes for the PSO-based spectral partition algorithm. Both cluster and graphics processing units (GPU) implementation are designed. The computational burden of serial version has been greatly reduced. The experimental results also show that GPU algorithm has similar speedup as cluster-based algorithm

Towards a real-time fully-coherent all-sky search for gravitational waves from compact binary coalescences using particle swarm optimization

While a fully-coherent all-sky search is known to be optimal for detecting gravitational wave signals from compact binary coalescences, its high computational cost has limited current searches to less sensitive coincidence-based schemes. Following up on previous work that has demonstrated the effectiveness of particle swarm optimization (PSO) in reducing the computational cost of this search, we present an implementation that achieves near real-time computational speed. This is achieved by combining the search efficiency of PSO with a significantly revised and optimized numerical implementation of the underlying mathematical formalism along with additional multithreaded parallelization layers in a distributed computing framework. For a network of four second-generation detectors with 60 min data from each, the runtime of the implementation presented here ranges between ≈1.4 to ≈0.5 times the data duration for network signal-to-noise ratios (SNRs) of ≳10 and ≳12, respectively. The reduced runtimes are obtained with small to negligible losses in detection sensitivity: for a false alarm rate of ≃1 event per year in Gaussian stationary noise, the loss in detection probability is ≤5% and ≤2% for SNRs of 10 and 12, respectively. Using the fast implementation, we are able to quantify frequentist errors in parameter estimation for signals in the double neutron star mass range using a large number of simulated data realizations. A clear dependence of parameter estimation errors and detection sensitivity on the condition number of the network antenna pattern matrix is revealed. Combined with previous work, this paper securely establishes the effectiveness of PSO-based fully-coherent all-sky search across the entire binary inspiral mass range that is relevant to ground-based detectors

Evolutionary approaches to signal decomposition in an application service management system

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers

Techno-Economic modelling of hybrid renewable mini-grids for rural electrification planning in Sub-Saharan Africa

Access to clean, modern energy services is a necessity for sustainable development. The UN Sustainable Development Goals and SE4ALL program commit to the provision of universal access to modern energy services by 2030. However, the latest available figures estimate that 1.1 billion people are living without access to electricity, with over 55% living in Sub-Saharan Africa. Furthermore, 85% live in rural areas, often with challenging terrain, low income and population density; or in countries with severe underinvestment in electricity infrastructure making grid extension unrealistic. Recently, improvements in technology, cost efficiency and new business models have made mini-grids which combine multiple energy technologies in hybrid systems one of the most promising alternatives for electrification off the grid. The International Energy Agency has estimated that up to 350,000 new mini-grids will be required to reach universal access goals by 2030. Given the intermittent and location-dependent nature of renewable energy sources, the evolving costs and performance characteristics of individual technologies, and the characteristics of interacting technologies, detailed system simulation and demand modelling is required to determine the cost optimal combinations of technologies for each-and-every potential mini-grid site. Adding to this are the practical details on the ground such as community electricity demand profiles and distances to the grid or fuel sources, as well asthe social and political contexts,such as unknown energy demand uptake or technology acceptance, national electricity system expansion plans and subsidies or taxes, among others. These can all have significant impacts in deciding the applicability of a mini-grid within that context. The scope of the research and modelling framework presented focuses primarily on meeting the specific energy needs in the sub-Saharan African context. Thus, in being transparent, utilizing freely available software and data as well as aiming to be reproducible, scalable and customizable; the model aims to be fully flexible, staying relevant to other unique contexts and useful in answering unknown future research questions. The techno-economic model implementation presented in this paper simulates hourly mini-grid operation using meteorological data, demand profiles, technology capabilities, and costing data to determine the optimal component sizing of hybrid mini-grids appropriate for rural electrification. The results demonstrate the location, renewable resource, technology cost and performance dependencies on system sizing. The model is applied for the investigation of 15 hypothetical mini-grids sites in different regions of South Africa to validate and demonstrate the model’s capabilities. The effect of technology hybridization and future technology cost reductions on the expected cost of energy and the optimal technology configurations are demonstrated. The modelling results also showed that the combination of hydrogen fuel cell and electrolysers was not an economical energy storage with present day technology costs and performance. Thereafter, the model was used to determine an approximate fuel cell and electrolyser cost target curve up to the year 2030. Ultimately, any research efforts through the application of the model, building on the presented framework, are intended to bridge the science-policy boundary and give credible insight for energy and electrification policies, as well as identifying high impact focus areas for ongoing further research