Monitoring System Analysis for Evaluating a Building’s Envelope Energy Performance through Estimation of Its Heat Loss Coefficient

The present article investigates the question of building energy monitoring systems used for data collection to estimate the Heat Loss Coefficient (HLC) with existing methods, in order to determine the Thermal Envelope Performance (TEP) of a building. The data requirements of HLC estimation methods are related to commonly used methods for fault detection, calibration, and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests to estimate the HLC undertaken since 1978, qualitative and quantitative analyses of the Monitoring and Controlling System (MCS) specifications have been carried out. The results show that no Fault Detection and Diagnosis (FDD) methods have been implemented in the reviewed literature. Furthermore, it was not possible to identify a trend of technology type used in sensors, hardware, software, and communication protocols, because a high percentage of the reviewed experimental tests do not specify the model, technical characteristics, or selection criteria of the implemented MCSs. Although most actual Building Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply HLC estimation methods.This work was supported by: Spanish Economy and Competitiveness Ministry and European Regional Development Fund through the IMMOEN project: "Implementation of automated calibration and multiobjective optimization techniques applied to Building Energy Model simulations by means of monitored buildings". Project reference: ENE2015-65999-C2-2-R (MINECO/FEDER); European Commission through the A2PBEER project "Affordable and Adaptable Public Buildings through Energy Efficient Retrofitting". Grant agreement No.: 609060; Laboratory for the Quality Control of Buildings (LCCE) of the Basque Government; University of the Basque Country (UPV/EHU). Framework agreement: Euro-regional Campus of Excellence within the context of their respective excellence projects, Euskampus and IdEx Bordeaux. Funder reference: PIFBUR 16/26

Community-Driven Methods for Open and Reproducible Software Tools for Analyzing Datasets from Atom Probe Microscopy

Atom probe tomography, and related methods, probe the composition and the three-dimensional architecture of materials. The software tools which microscopists use, and how these tools are connected into workflows, make a substantial contribution to the accuracy and precision of such material characterization experiments. Typically, we adapt methods from other communities like mathematics, data science, computational geometry, artificial intelligence, or scientific computing. We also realize that improving on research data management is a challenge when it comes to align with the FAIR data stewardship principles. Faced with this global challenge, we are convinced it is useful to join forces. Here, we report the results and challenges with an inter-laboratory call for developing test cases for several types of atom probe microscopy software tools. The results support why defining detailed recipes of software workflows and sharing these recipes is necessary and rewarding: Open source tools and (meta)data exchange can help to make our day-to-day data processing tasks become more efficient, the training of new users and knowledge transfer become easier, and assist us with automated quantification of uncertainties to gain access to substantiated results

Multiple data set integration and GIS techniques used to investigate linear structural controls in the southern Powder River Basin, Wyoming

Lineaments in the Powder River Basin were mapped and categorized from Landsat TM imagery, a Digital Elevation Model (30 m), and Digital Line Graph (DLG) hydrology files. A GIS program was written using DLG information to eliminate those lineaments that were within 20° of roads and railroads. The DLG and DEM information was further used to stratify the topographic lineaments into slope-breaks, ridge-tops, or valleys. Rose diagrams created for each class of lineaments give important clues on the timing of their formation. The major northwest and northeast trends that these lineaments follow appear to correspond with those identified by previous works. Comparisons with subsurface data suggests that the concentration of lineaments is strongly coincident with gravity and magnetic highs and lows, possibly representing the surface expression of basement-rooted structure. Lastly, the location many reservoirs in the basin also coincide with higher concentration of lineaments over gravity and magnetic highs.;This project was partially funded by the Schumaker Fund, donated by Dr. and Mrs. Robert Schumaker. Also, supplemental funding was given by the American Society for Photogrammetry and Remote Sensing. I would like to thank all of those involved in supporting both of these funds. Their donations has made it possible for continuing research of GIS and geology

A Life Cycle Software Quality Model Using Bayesian Belief Networks

Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems