78,429 research outputs found
Health and safety: Preliminary comparative assessment of the Satellite Power System (SPS) and other energy alternatives
Data readily available from the literature were used to make an initial comparison of the health and safety risks of a fission power system with fuel reprocessing; a combined-cycle coal power system with a low-Btu gasifier and open-cycle gas turbine; a central-station, terrestrial, solar photovoltaic power system; the satellite power system; and a first-generation fusion system. The assessment approach consists of the identification of health and safety issues in each phase of the energy cycle from raw material extraction through electrical generation, waste disposal, and system deactivation; quantitative or qualitative evaluation of impact severity; and the rating of each issue with regard to known or potential impact level and level of uncertainty
Sound mining in the North : a guide to environmental regulation and best practices supporting social sustainability
Julkaistu versi
Probabilistic latent semantic analysis as a potential method for integrating spatial data concepts
In this paper we explore the use of Probabilistic Latent Semantic Analysis (PLSA) as a method for quantifying semantic differences between land cover classes. The results are promising, revealing âhiddenâ or not easily discernible data concepts. PLSA provides a âbottom upâ approach to interoperability problems for users in the face of âtop downâ solutions provided by formal ontologies. We note the potential for a meta-problem of how to interpret the concepts and the need for further research to reconcile the top-down and bottom-up approaches
Report of activities of the advanced coal extraction systems definition project, 1979 - 1980
During this period effort was devoted to: formulation of system performance goals in the areas of production cost, miner safety, miner health, environmental impact, and coal conservation, survey and in depth assessment of promising technology, and characterization of potential resource targets. Primary system performance goals are to achieve a return on incremental investment of 150% of the value required for a low risk capital improvement project and to reduce deaths and disability injuries per million man-hour by 50%. Although these performance goals were developed to be immediately applicable to the Central Appalachian coal resources, they were also designed to be readily adaptable to other coals by appending a geological description of the new resource. The work done on technology assessment was concerned with the performance of the slurry haulage system
On the role of pre and post-processing in environmental data mining
The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed
Survey on the use of computational optimisation in UK engineering companies
The aim of this work is to capture current practices in the use of computational optimisation in UK engineering companies and identify the current challenges and future needs of the companies. To achieve this aim, a survey was conducted from June 2013 to August 2013 with 17 experts and practitioners from power, aerospace and automotive Original Equipment Manufacturers (OEMs), steel manufacturing sector, small- and medium-sized design, manufacturing and consultancy companies, and optimisation software vendors. By focusing on practitioners in industry, this work complements current surveys in optimisation that have mainly focused on published literature. This survey was carried out using a questionnaire administered through face-to-face interviews lasting around 2 h with each participant. The questionnaire covered 5 main topics: (i) state of optimisation in industry, (ii) optimisation problems, (iii) modelling techniques, (iv) optimisation techniques, and (v) challenges faced and future research areas. This survey identified the following challenges that the participant companies are facing in solving optimisation problems: large number of objectives and variables, availability of computing resources, data management and data mining for optimisation workflow, over-constrained problems, too many algorithms with limited help in selection, and cultural issues including training and mindset. The key areas for future research suggested by the participant companies are as follows: handling large number of variables, objectives and constraints particularly when solution robustness is important, reducing the number of iterations and evaluations, helping the users in algorithm selection and business case for optimisation, sharing data between different disciplines for multi-disciplinary optimisation, and supporting the users in model development and post-processing through design space visualisation and data mining
CASP-DM: Context Aware Standard Process for Data Mining
We propose an extension of the Cross Industry Standard Process for Data
Mining (CRISPDM) which addresses specific challenges of machine learning and
data mining for context and model reuse handling. This new general
context-aware process model is mapped with CRISP-DM reference model proposing
some new or enhanced outputs
Smart Asset Management for Electric Utilities: Big Data and Future
This paper discusses about future challenges in terms of big data and new
technologies. Utilities have been collecting data in large amounts but they are
hardly utilized because they are huge in amount and also there is uncertainty
associated with it. Condition monitoring of assets collects large amounts of
data during daily operations. The question arises "How to extract information
from large chunk of data?" The concept of "rich data and poor information" is
being challenged by big data analytics with advent of machine learning
techniques. Along with technological advancements like Internet of Things
(IoT), big data analytics will play an important role for electric utilities.
In this paper, challenges are answered by pathways and guidelines to make the
current asset management practices smarter for the future.Comment: 13 pages, 3 figures, Proceedings of 12th World Congress on
Engineering Asset Management (WCEAM) 201
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