A Group Recommender for Investment in Microgrid Renewable Energy Sources

Integration of renewable energy sources such as photovoltaic arrays and wind turbines into electric power microgrids can significantly reduce greenhouse gas (GHG) emissions. However, deciding on investment in microgrid renewable energy sources is a complex problem due to (1) the space of alternatives which is exponential in a number of components; (2) the complex interactions between old and new equipment in every time interval over an investment time horizon; (3) the multiple criteria that should be considered such as net present value, GHG emissions, and system reliability; and (4) dealing with a group of decision makers with diverse priorities. In this paper, we propose and report on the development of a Power Microgrid Operation and Investment Recommender (PMOIR) to guide a group of decision makers toward investment decisions on microgrid renewable energy sources. This is done under the assumption of optimal operational control over the investment time horizon. PMOIR uses a framework of extracting user preferences, estimating the group utility, optimizing and diversifying a small number of recommended alternatives, and voting. To support optimization, we mathematically model different power components and formalize the overall optimization problem, which is implemented using a mixed integer linear programming model. We also conduct an experimental study to demonstrating PMOIR feasibility, in terms of computational time, to be applied on microgrids involving 200 power components, over a five-year time horizon, with around 8 million binary variables

RICIS Software Engineering 90 Symposium: Aerospace Applications and Research Directions Proceedings Appendices

Papers presented at RICIS Software Engineering Symposium are compiled. The following subject areas are covered: flight critical software; management of real-time Ada; software reuse; megaprogramming software; Ada net; POSIX and Ada integration in the Space Station Freedom Program; and assessment of formal methods for trustworthy computer systems

Multiscale optimisation of dynamic properties for additively manufactured lattice structures

A framework for tailoring the dynamic properties of functionally graded lattice structures through the use of multiscale optimisation is presented in this thesis. The multiscale optimisation utilises a two scale approach to allow for complex lattice structures to be simulated in real time at a similar computational expense to traditional finite element problems. The micro and macro scales are linked by a surrogate model that predicts the homogenised material properties of the underlying lattice geometry based on the lattice design parameters. Optimisation constraints on the resonant frequencies and the Modal Assurance Criteria are implemented that can induce the structure to resonate at specific frequencies whilst simultaneously tracking and ensuring the correct mode shapes are maintained. This is where the novelty of the work lies, as dynamic properties have not previously been optimised for in a multiscale, functionally graded lattice structure. Multiscale methods offer numerous benefits and increased design freedom when generating optimal structures for dynamic environments. These benefits are showcased in a series of optimised cantilever structures. The results show a significant improvement in dynamic behavior when compared to the unoptimised case as well as when compared to a single scale topology optimised structure. The validation of the resonant properties for the lattice structures is performed through a series of mechanical tests on additive manufactured lattices. These tests address both the micro and the macro scale of the multiscale method. The homogeneous and surrogate model assumptions of the micro scale are investigated through both compression and tensile tests of uniform lattice samples. The resonant frequency predictions of the macro scale optimisation are verified through mechanical shaker testing and computed tomography scans of the lattice structure. Sources of discrepancy between the predicted and observed behavior are also investigated and explained.Open Acces

Data-Driven Modeling For Decision Support Systems And Treatment Management In Personalized Healthcare

Massive amount of electronic medical records (EMRs) accumulating from patients and populations motivates clinicians and data scientists to collaborate for the advanced analytics to create knowledge that is essential to address the extensive personalized insights needed for patients, clinicians, providers, scientists, and health policy makers. Learning from large and complicated data is using extensively in marketing and commercial enterprises to generate personalized recommendations. Recently the medical research community focuses to take the benefits of big data analytic approaches and moves to personalized (precision) medicine. So, it is a significant period in healthcare and medicine for transferring to a new paradigm. There is a noticeable opportunity to implement a learning health care system and data-driven healthcare to make better medical decisions, better personalized predictions; and more precise discovering of risk factors and their interactions. In this research we focus on data-driven approaches for personalized medicine. We propose a research framework which emphasizes on three main phases: 1) Predictive modeling, 2) Patient subgroup analysis and 3) Treatment recommendation. Our goal is to develop novel methods for each phase and apply them in real-world applications. In the fist phase, we develop a new predictive approach based on feature representation using deep feature learning and word embedding techniques. Our method uses different deep architectures (Stacked autoencoders, Deep belief network and Variational autoencoders) for feature representation in higher-level abstractions to obtain effective and more robust features from EMRs, and then build prediction models on the top of them. Our approach is particularly useful when the unlabeled data is abundant whereas labeled one is scarce. We investigate the performance of representation learning through a supervised approach. We perform our method on different small and large datasets. Finally we provide a comparative study and show that our predictive approach leads to better results in comparison with others. In the second phase, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables. Finally, in the third phase, we introduce a new survival analysis framework using deep learning and active learning with a novel sampling strategy. First, our approach provides better representation with lower dimensions from clinical features using labeled (time-to-event) and unlabeled (censored) instances and then actively trains the survival model by labeling the censored data using an oracle. As a clinical assistive tool, we propose a simple yet effective treatment recommendation approach based on our survival model. In the experimental study, we apply our approach on SEER-Medicare data related to prostate cancer among African-Americans and white patients. The results indicate that our approach outperforms significantly than baseline models

Collected software engineering papers, volume 9

This document is a collection of selected technical papers produced by participants in the Software Engineering Laboratory (SEL) from November 1990 through October 1991. The purpose of the document is to make available, in one reference, some results of SEL research that originally appeared in a number of different forums. This is the ninth such volume of technical papers produced by the SEL. Although these papers cover several topics related to software engineering, they do not encompass the entire scope of SEL activities and interests. For the convenience of this presentation, the eight papers contained here are grouped into three major categories: (1) software models studies; (2) software measurement studies; and (3) Ada technology studies. The first category presents studies on reuse models, including a software reuse model applied to maintenance and a model for an organization to support software reuse. The second category includes experimental research methods and software measurement techniques. The third category presents object-oriented approaches using Ada and object-oriented features proposed for Ada. The SEL is actively working to understand and improve the software development process at GSFC