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System Design of Data Fusion and the Relevant Performance Evaluation Metrics
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
In the past decades, many objective analysis methods have been proposed to evaluate the quality of fused outputs, mainly through mathematical modeling approaches. Such methods are also referred to as quantitative analysis, as opposed to qualitative analysis. Generally, performance evaluation via quantitative analysis is used to assess the spectral and/or spatial similarities between the fused image and the reference images (if applicable) based on several predefined statistical indicators. These indicators are in turn referred to as performance evaluation metrics (Jagalingam and Hegde, 2015). In the following subsections, several widely-used performance evaluation metrics are discussed mathematically. In general, these metrics and indicators can be divided into two distinct categories: without reference image and with reference image, by considering whether or not a reference image is required in calculating these metrics.
Construction Management
Published in Abdul Razzak Rumane, Handbook of Construction Management, 2016
Quantitative analysis is a process to quantify the probability of risk and its impact based on numerical estimation. The following tools and techniques are used for quantitative analysis: Event tree analysisProbability analysisSensitivity analysisSimulation techniques (Monte Carlo simulation)
Overview of Risk Management
Published in Abdul Razzak Rumane, Risk Management Applications Used to Sustain Quality in Projects, 2023
Quantitative analysis is a process to quantify the probability of risk and its impact based on numerical estimation. The quantitative risk analysis process aims to analyze numerically the probability of each risk and its consequences on project objectives, as well as the extent of overall project risk.
Assessing Performance and Risk in Complex Supply Chains and Tying Performance Measures to Strategic Concepts
Published in Supply Chain Forum: An International Journal, 2022
L. Douglas Smith, Anthony Vatterott, Wesley Boyce
Tang and Musa (2011) reviewed SCRM literature from 1995 to 2008 using a citation/co-citation approach with the Web of Science as the data source. They examined topical focus in three periods (1995–1999, 2000–2004, 2005–2008) and grouped risk as pertaining to material flow, information flow, and financial performance. The dominant focus shifted from traditional operational and financial performance in the first period; to relationships among supply-chain participants in the middle period; to global (international) issues in the third period. Over all, they found that most papers focused on material-flow risks, and fewest focused on information-flow risks. The authors listed specific strategies and related quantitative analysis that had been posed in published works. Only 25% of the articles discussed quantitative analysis for risk management. They suggest that researchers devote more attention to quantifying risk, issues related to information flows, and particular risks associated with international business activity (including currency risk, outsourcing risk, political risk, etc.).
Mobile Application User Experience Checklist: A Tool to Assess Attention to Core UX Principles
Published in International Journal of Human–Computer Interaction, 2021
Brianna Richardson, Marsha Campbell-Yeo, Michael Smit
Intra-correlation coefficients (ICC) were calculated from MAUX-C-beta scores to assess reliability and internal consistency. Quantitative analysis was completed using IBM SPSS Statistics version 25.0. Qualitative content analysis was used to analyze any participant narratives (i.e. participant reflections and comments from MAUX-C) (Neuendorf, 2002). Content analysis was deemed the most appropriate method of analysis to allow for qualitative data to be reported as summary statistics when appropriate (Gbrich, 2007). As the purpose of the study was preliminary validation of MAUX-C-beta, any comments about the app itself (rather than MAUX-C-beta) were not analyzed beyond a numerical count of comments per participant for each app. This count of app-specific data provided insight on the utility of the MAUX-C-beta comment section.
Interdependency of construction safety hazards from a network perspective: a mechanical installation case
Published in International Journal of Occupational Safety and Ergonomics, 2020
Pin-Chao Liao, Zhonghua Guo, Tao Wang, Jing Wen, Chung-Han Tsai
Numerous methods have been proposed for risk assessment of construction safety. The three main types of risk evaluation methods are qualitative, quantitative and combined qualitative–quantitative analyses [17,18]. Qualitative risk analysis relies on the perspective and experience of experts. However, because accidents occur in different patterns (abruptly or slowly), the perceived degree of risk can also differ [9,10], which increases the probability of assessment distortion [19]. Quantitative analysis is based on actual observed data and is more objective. However, the high cost of collecting data, a lack of prior knowledge as a foundation and probable conflicts between the assessment results and experience restrict its application. Combined qualitative–quantitative analysis synthesizes prior knowledge and statistics to set the foundation for management decisions.