7 datasets for Advanced Engineering Informatics
Data for: Fostering the transfer of empirical engineering knowledge under technological paradigm shift: an experimental study in conceptual design
Contributors: Xinyu Li
Data for: A Co-Occurrence-based Design Structure Matrix Support with Three-Way-based Learning for Engineering Change Management in Smart Product-Service Systems
Contributors: Pai Zheng, Chun-Hsien Chen, Suiyue Shang
... This research data (.zip file), as the supplementary materials of the original article, contain the python programs, MATLAB code, engineering change records, and processed data all along the proposed systematic approach consequently. For confidentiality and readability purposes, raw data has been filtered and simplified into a pre-defined information table with only numbers (i.e. number of change records, condition label, and change of the model) presented. It is hoped that the research work together with this elaborate research data can provide insightful knowledge of data-driven engineering change management to other scholars and manufacturers.
Data for: Collaborative engineering decision-making for building information channels and improving Web visibility of product manufacturers
Contributors: Sylvain Sagot
... Table4full, Table5full, Figure10
Data for: Community detection in national-scale high voltage transmission networks using genetic algorithms
Contributors: Raúl Baños, Consolación Gil, Alfredo Alcayde garcia, Francisco G. Montoya, Manuel Guerrero
... This directory contains the data set of the benchmarks and graphical results. A separated folder is used for each benchmark, including: 1) Structure of the graph (nodes and edges) that is used by the algorithms. 2) Graphical results that can be visualized using Gephi (free Gephi software can be download from: https://gephi.org/users/download/)
Contributors: J. J. McArthur, Aijun An, Ricky Fok
... The hierarchical prediction algorithm developed in Python and discussed in this paper.
Contributors: J. J. McArthur, Nima Shahbazi
... The R scripts (with cropped outputs for RF and FIA approaches) providing both the algorithm details as well as insight on the actual dataset features. Note that due to Canadian Privacy laws (FIPPA), we are not permitted to upload the full dataset as employee names are present.
Data for: Automatic classification of fine grained soils using CPT measurements and Artificial Neural Networks
Contributors: Cormac Reale, Danijela Jurić Kaćunić, Lovorka Librić, Ken Gavin
... Normalised CPT results and corresponding laboratory results for 6 test sites in Northern Croatia.