Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

A Generalized Bayesian Approach for Localizing Static Natural Obstacles on Unpaved Roads

This paper presents an approach that implements sensor fusion and recursive Bayesian estimation (RBE) to improve a vehicle\u27s ability to perform obstacle detection and localization in unpaved road environments. The proposed approach utilizes RADAR, LiDAR and stereovision fully for sensor fusion to detect and localize static natural obstacles. Each sensor is characterized by a probabilistic sensor model which quantifies level of confidence (LOC) and probability of detection (POD) associatively. Deploying these sensor models enables the fusion of heterogeneous sensors without extensive formulations and with the incorporation of each sensor\u27s strengths. An Extended Kalman filter (EKF) is formulated and implemented for robust and computationally efficient RBE of obstacles\u27 locations while a sensor-equipped vehicle moves and observes them. Results with a test vehicle show the successful detection and localization of a static natural object on an unpaved road has demonstrated the effectiveness of the proposed approach

Operational characteristics of mixed-format multistage tests using the 3PL testlet response theory model

textMultistage tests (MSTs) have received renewed interest in recent years as an effective compromise between fixed-length linear tests and computerized adaptive test. Most MSTs studies scored the assessments based on item response theory (IRT) methods. Many assessments are currently being developed as mixed-format assessments that administer both standalone items and clusters of items associated with a common stimulus called testlets. By the nature of a testlet, a natural dependency occurs between the items within the testlet that violates the local independence of items. Local independence is a fundamental assumption of the IRT models. Using dichotomous IRT methods on a mixed-format testlet-based assessment knowingly violates local independence. By combining the score points within a testlet, researchers have successfully applied polytomous IRT models. However, the use of such models loses information by not using the unique response patterns provided by each item within a testlet. The three-parameter logistic testlet response theory (3PL-TRT) model is a measurement model developed to retain the uniqueness in response patterns of each item, while accounting for the local dependency exhibited by a testlet, or testlet effect. Because few studies have examined mixed-format MSTs administration under the 3PL-TRT model, the dissertation performed a simulation to investigate the administration of a mixed-format testlet based MSTs under the 3PL-TRT model. Simulee responses were generated based on the 3PL-TRT calibrated item parameters from a real large-scale passage based standardized assessment. The manipulated testing conditions considered four panel designs, two test lengths, three routing procedures, and three conditions of local item dependence. The study found functionally no bias across testing conditions. All conditions showed adequate measurement properties, but a few differences did occur between some of the testing conditions. The measurement precision was impacted by panel design, test length and the magnitude of local item dependence. The three-stage MSTs consistently illustrated slightly lower measurement precision than the two-stage MSTs. As expected, the longer test length conditions had better measurement precision than the shorter test length conditions. Conditions with the largest magnitude of local item dependency showed the worst measurement precision. The routing procedure had little impact on the measurement effectiveness.Educational Psycholog

Structural violence and maternal healthcare utilisation in sub-Saharan Africa: A Bayesian multilevel analysis

Considerable advances have been made in medical sociology and other population health- related subject areas to understand structural sources of disparities in health outcomes. We know that structural factors are the ‘fundamental causes’ of disease and illness. However, few studies focusing on structural conditions have considered maternal healthcare especially in sub-Saharan Africa. There is a dearth of knowledge regarding the effects of wider structural factors on maternal healthcare utilisation. Specifically, it is not well-known as to which dimension of the social structure is strongly associated with maternal healthcare and what specific combinations of factors influence adequate use of maternal healthcare in sub- Saharan Africa. This study was conceived to fill this gap in literature. The study focuses on community and country-level inequalities in gender relations, human rights violations and globalisation as the three dimensions of structural violence that are consequential to maternal healthcare in sub-Saharan Africa. I also consider individual level maternal characteristics that are associated with maternal healthcare utilisation. The analysis pools data of 245,955 respondents from the most recent Demographic and Health Surveys (DHS) and several other international datasets. I apply separate three-level Bayesian multilevel models on women who have had births five years prior to the recent DHS, nested in 17,000 communities, which are nested in 35 sub-Saharan African countries. On each aspect of structural violence, I estimate four models predicting the odds of having four or more antenatal care visits, institutional delivery and postnatal care based on a set of individual, community and country-level variables. Overall, the results indicate that inequalities in gender relations and disrespect for human rights are negatively associated with adequate use of maternal healthcare in sub-Saharan Africa. The relationship between globalisation and maternal healthcare is conditional on the specific dimension of globalisation. In comparison with other dimensions of globalisation, social globalisation is the most significant predictor of adequate maternal healthcare. These results help to underscore the importance of contextual factors in understanding women’s utilisation of maternal healthcare in sub-Saharan Africa

Sparse Bayesian information filters for localization and mapping

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 2008This thesis formulates an estimation framework for Simultaneous Localization and Mapping (SLAM) that addresses the problem of scalability in large environments. We describe an estimation-theoretic algorithm that achieves significant gains in computational efficiency while maintaining consistent estimates for the vehicle pose and the map of the environment. We specifically address the feature-based SLAM problem in which the robot represents the environment as a collection of landmarks. The thesis takes a Bayesian approach whereby we maintain a joint posterior over the vehicle pose and feature states, conditioned upon measurement data. We model the distribution as Gaussian and parametrize the posterior in the canonical form, in terms of the information (inverse covariance) matrix. When sparse, this representation is amenable to computationally efficient Bayesian SLAM filtering. However, while a large majority of the elements within the normalized information matrix are very small in magnitude, it is fully populated nonetheless. Recent feature-based SLAM filters achieve the scalability benefits of a sparse parametrization by explicitly pruning these weak links in an effort to enforce sparsity. We analyze one such algorithm, the Sparse Extended Information Filter (SEIF), which has laid much of the groundwork concerning the computational benefits of the sparse canonical form. The thesis performs a detailed analysis of the process by which the SEIF approximates the sparsity of the information matrix and reveals key insights into the consequences of different sparsification strategies. We demonstrate that the SEIF yields a sparse approximation to the posterior that is inconsistent, suffering from exaggerated confidence estimates. This overconfidence has detrimental effects on important aspects of the SLAM process and affects the higher level goal of producing accurate maps for subsequent localization and path planning. This thesis proposes an alternative scalable filter that maintains sparsity while preserving the consistency of the distribution. We leverage insights into the natural structure of the feature-based canonical parametrization and derive a method that actively maintains an exactly sparse posterior. Our algorithm exploits the structure of the parametrization to achieve gains in efficiency, with a computational cost that scales linearly with the size of the map. Unlike similar techniques that sacrifice consistency for improved scalability, our algorithm performs inference over a posterior that is conservative relative to the nominal Gaussian distribution. Consequently, we preserve the consistency of the pose and map estimates and avoid the effects of an overconfident posterior. We demonstrate our filter alongside the SEIF and the standard EKF both in simulation as well as on two real-world datasets. While we maintain the computational advantages of an exactly sparse representation, the results show convincingly that our method yields conservative estimates for the robot pose and map that are nearly identical to those of the original Gaussian distribution as produced by the EKF, but at much less computational expense. The thesis concludes with an extension of our SLAM filter to a complex underwater environment. We describe a systems-level framework for localization and mapping relative to a ship hull with an Autonomous Underwater Vehicle (AUV) equipped with a forward-looking sonar. The approach utilizes our filter to fuse measurements of vehicle attitude and motion from onboard sensors with data from sonar images of the hull. We employ the system to perform three-dimensional, 6-DOF SLAM on a ship hull

Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

A model-based approach to System of Systems risk management

The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies

Workshop proceedings: Information Systems for Space Astrophysics in the 21st Century, volume 1

The Astrophysical Information Systems Workshop was one of the three Integrated Technology Planning workshops. Its objectives were to develop an understanding of future mission requirements for information systems, the potential role of technology in meeting these requirements, and the areas in which NASA investment might have the greatest impact. Workshop participants were briefed on the astrophysical mission set with an emphasis on those missions that drive information systems technology, the existing NASA space-science operations infrastructure, and the ongoing and planned NASA information systems technology programs. Program plans and recommendations were prepared in five technical areas: Mission Planning and Operations; Space-Borne Data Processing; Space-to-Earth Communications; Science Data Systems; and Data Analysis, Integration, and Visualization