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Matrix Analysis
Published in Ramin S. Esfandiari, Bei Lu, Modeling and Analysis of Dynamic Systems, 2018
In this chapter, we discuss the fundamentals of matrix analysis, including matrix operations and properties, as well as matrix characteristics such as the rank, the determinant, and the inverse. Matrix analysis plays a particularly important role in the treatment of systems of algebraic and/or differential equations that are coupled, that is, when several unknown variables are involved in several equations in the system model. Also discussed in this chapter is the matrix eigenvalue problem, which plays a key role in the determination of a system’s natural frequencies, as well as the solution process for a system of differential equations. First, we focus on algebraic systems and then extend the ideas to systems of differential equations and the matrix eigenvalue problem.
Factors influencing the adoption of telemedicine health services during COVID-19 pandemic crisis: an integrative research model
Published in Enterprise Information Systems, 2021
Samar Rahi, Mubbsher Munawar Khan, Mahmoud Alghizzawi
For managerial implications, a post-hoc analysis, namely, Importance performance matrix analysis (IPMA) are incorporated in line with earlier studies (Samar; Rahi, Khan, and Alghizzawi 2020; Samar Rahi et al. 2019). The importance-performance matrix analysis estimates the importance and performance of the constructs by rescaling the construct scores range 0 for lowest to 100 for highest (Hair et al. 2016; Samar Rahi, Abd.Ghani, and Hafaz Ngah 2019). Results indicate that patient attitude had the highest importance to determine patient behaviour towards the adoption of telemedicine websites. Therefore, constructs like service quality, computer self-efficacy and performance expectancy had an intermediate level of importance to determine patient intention to adopt telemedicine health services. Information quality had the highest performance-index values therefore this may not be an important construct for managerial implication. The results of the importance-performance matrix analysis can be seen in Table 6.
Hardware-in-the Loop Testing of Power Transformer Differential Relay Using RTDS and DSP
Published in Electric Power Components and Systems, 2019
Senthil Kumar Murugan, Sishaj Pulikottil Simon, Kinattingal Sundareswaran, Srinivasa Rao Nayak Panugothu, Narayana Prasad Padhy
Similarly, the performance of EFTDR over various cases of internal fault, inrush and CT saturation cases are investigated. Generally, the DFTDR maloperate during certain cases of the inrush, CT saturation and concurrent events of the aforesaid cases as SHR falls below the set value. The accuracy and sensitivity of the method are evaluated through confusion matrix analysis [24]. The sensitivity shows the efficiency of the positive classification and specificity depicts the usefulness of the negative classification. Whereas, the accuracy demonstrates the overall effectiveness of the classification. Here, a comprehensive analysis of classifier’s performance is investigated with the correlation of true positive, true negative, false positive and false negative. The accuracy is defined as the ratio of the sum of true positive and true negative to the total number of cases. The sensitivity is defined as the ratio of true positive to the total number of cases and specificity is defined as the ratio of true negative to the total number of cases.
Integrated project team performance in early design stages – performance indicators influencing effectiveness in bridge design
Published in Architectural Engineering and Design Management, 2019
Daniel Ekström, Rasmus Rempling, Mario Plos
To examine how the collaboration between different disciplines works during the planning, design, and development of construction documents for new bridges in Sweden, the authors established the framework presented in Figure 3. The framework was developed by combining the theoretical references, previously described in Figure 1 (Josephson & Björkman, 2011) and Figure 2 (Rempling, Fall, & Lundgren, 2015), and was designed to evaluate prevailing attitudes and identify possible opportunities and obstacles for more integrated cooperation between different professions. The framework consists of a vertical and a horizontal dimension to form a matrix-analysis. From the matrix, it is possible to identify different areas of measurement in the crossings between the vertical and horizontal dimensions. To follow the framework questions were developed with inspiration based on a self-assessment questionnaire (Wheelan, 2016). This type of questionnaire is commonly used in team literature as an indicator to provide aid to identify how to develop a group of individuals into high-performance teams (HPT). The questions were sorted and organised into the three different levels of organisational hierarchy: organisation, project, and individual and finally to the driving forces for customer value: culture, structure, and competence, see Figure 3.