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Model Reduction of Second-Order Index 1 Descriptor Systems
Published in Mohammad Monir Uddin, Computational Methods for Approximation of Large-Scale Dynamical Systems, 2019
For the second-order index 1 descriptor system in Exercise 11.1,second-order index 1 system construct the block matrices1 using MATLAB as follows n=500; %number of differential variables l=50; %number of algebraic variables io=1; %(number of input/output) den=0.01; I=speye (n); M11=.5*I+;spdiags (−0.2*ones (n, 1), 2, n, n)+; spdiags (−0.2* ones (n, 1), −2, n, n)+; spdiags (0.2* ones (n, 1), 4, n, n)+; spdiags (0.2* ones (n, 1), −4, n, n); K11=spdiags (5* ones (n, 1), 0, n, n)+; spdiags (−1*ones (n, 1), 2, n, n)+; spdiags (−1*ones (n, 1), −2, n, n)+; spdiags (2* ones (n, 1), 4, n, n)+; spdiags (2* ones (n, 1), −4, n, n); mu=0.005; nu = .1; D11=mu*M1+;nu*K11; K22=spdiags (5* ones (l, 1), 0, l, l)+; spdiags (−1*ones (l, 1), 2, l, l)+; spdiags (−1*ones (l, 1), −2, l, l)+; spdiags (2* ones (n, 1), 4, l, l)+; spdiags (2* ones (n, 1), −4, l, l); K12= sprand (n, l, den); H1=spdiags (ones (n, 1), 0, n, io); H2 = spdiags (ones (l, 1), 0, l, io);
IO systems
Published in Lyubomir T. Gruyitch, Control of Linear Systems, 2018
The Laplace transform IIO(s) $ {\text{I}}_{IO} (s) $ of the IO system input vector IIO(t) $ {\text{I}}_{IO} (t) $ reads: IIO(s)=D(s)U(s). $$ {\text{I}}_{{IO}} (s)~ = ~\left[ {\begin{array}{*{20}l} {{\text{D}}(s)} \hfill \\ {{\text{U}}(s)} \hfill \\ \end{array} } \right]. $$
IO systems
Published in Lyubomir T. Gruyitch, Control of Linear Systems, 2018
The Laplace transform IIO(s) of the IO system input vector IIO(t) $ {\mathbf{I}}_{IO} (t) $ reads: IIO(s)=D(s)U(s). $$ {\mathbf{I}}_{{IO}} (s)~ = \,\left[ {\begin{array}{*{20}l} {{\mathbf{D}}(s)} \hfill \\ {{\mathbf{U}}(s)} \hfill \\ \end{array} } \right]. $$
Utilizing Sneak Paths for Memristor Test Time Improvement
Published in IETE Journal of Research, 2023
The IO test vector set for a memristor crossbar array consists of the IO switch-vector settings for the rows (input) and columns (output) [5]. For a crossbar array of size m × n, the wordlines are the horizontal connections and the bitlines are the vertical connections. m is defined as the number of rows or wordlines, mopen as the number of wordlines open, and mclosed as the number of wordlines closed. A closed wordline means that the input voltage source is connected to that wordline and a open wordline means it’s not connected to a voltage source. A closed wordline is also called a selected wordline. Xi is the input switch state for the ith row, where “1” is closed and “0” is open. n is defined as the number of columns or bitlines, nopen as the number of bitlines open, and nclosed as the number of bitlines closed. A closed bitline means that the grounded current sensor on that column output is connected to that bitline and a open bitline means it’s not connected to a grounded output current sensor. A closed bitline is also called as selected bitline. Yj is the output switch state for the jth column, where “1” is closed and “0” is open. In summary, the I/O switch-vector of X1X2 … XmY1Y2 … Yn is defined as the input state of X1X2 … Xi … Xm combined with the output state of Y1Y2 … Yj … Yn [5].
Review of existing studies on maritime clusters
Published in Maritime Policy & Management, 2021
Many papers in the latest period applied input-output (IO) analysis. Morrissey and Cummins (2016), Salvador, Simões, and Guedes Soares (2016) and Pagano et al. (2016) studied intra-cluster linkages in the Irish maritime cluster, Portuguese Maritime Cluster and Panama’s maritime cluster, respectively. Morrissey and Cummins (2016) investigated four pillar sectors of the Irish maritime cluster, namely: Shipping, logistics and transport; marine energy; maritime safety and security; and yachting products and services. They found that these four pillars have low correlation with each other, but they share similar inputs and outputs. Salvador, Simões, and Guedes Soares (2016) also found that the Portuguese maritime cluster has weak intra-cluster linkages. Pagano et al. (2016) revealed the low correlation of sectors in Panama’s maritime cluster.
A review of input–output models on multisectoral modelling of transportation–economic linkages
Published in Transport Reviews, 2018
Critics of IO approach revolve around its underlying modelling assumptions. Principal among these is the assumption of no supply and production capability constraints in the conventional demand models. Many reviewed studies have focused on the demand side of the economy by arguing that exogenous change will affect final purchases in transportation (e.g. Park et al., 2011; Seetharaman et al., 2003) – a disturbance affects final demand and requires additional inputs through the rippling effect under the assumption that the supply is infinite and perfectly elastic without any constraints. A further issue is that IO implicitly assumes the supply reacts passively, and there is no input price changes in IO models. However, El-Hodiri and Nourzad (1988) argued under prefect competition prices changes are counterbalanced by input changes. Input price (labour, land and inputs) can go up, for example, if there is new significant new demand. Thus, the fixed price assumption can lead to inaccurate representations of the interaction of demand and supply.