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A Glimpse of LabVIEW
Published in Jivan Shrikrishna Parab, Ingrid Anne Nazareth, Rajendra S. Gad, Gourish Naik, Learning by Doing with National Instruments Development Boards, 2020
Jivan Shrikrishna Parab, Ingrid Anne Nazareth, Rajendra S. Gad, Gourish Naik
A one dimensional array can be converted into a two dimensional array by right clicking on the index display of a particular array and then selecting Add Dimension. A multi dimensional array can be created by further right clicking on the index display on a particular array and then selecting Add Dimension. An array can be uninitialized if it is left blank or initialized if data is entered in it. In order to insert elements in a particular row or column, right click on the array and then select Data Operations and then Insert Row Before or Column Before on the front panel. In order to delete a particular row or column, right click on the array and then select Data Operations and then Delete Row or Delete Column on the front panel. The above are shown in Figure 1.10.
Network Analysis
Published in Michael W. Carter, Camille C. Price, Ghaith Rabadi, Operations Research, 2018
Michael W. Carter, Camille C. Price, Ghaith Rabadi
The second thing to notice is that we must always start at x11 and we must finish at xmn (for m warehouses and n customers). Moreover, at each step, the algorithm will delete one row or one column. In the last cell, the remaining demand in column n and the supply in row m must be identical. Because there are m rows and n columns, the solution will use exactly (m + n − 1) cells and therefore (m + n − 1) of the xij will have a positive value. In our example, we have 3 + 5 − 1 = 7 cells that are selected for a positive flow.
Transportation Problem
Published in N.V.S. Raju, Operations Research, 2019
Allocate maximum possible units to the cell whose cost is minimum in the row or column selected (corresponding to max. penalty). If the capacity or demand is exhausted, delete the row or column for next iteration. Prepare the next iteration table with revised capacities and demand.
Development and Evaluation of an Attendance Tracking System Using Smartphones with GPS and NFC
Published in Applied Artificial Intelligence, 2022
Te-Wei Chiang, Cheng-Ying Yang, Gwo-Jen Chiou, Frank Yeong-Sung Lin, Yi-Nan Lin, Victor R. L. Shen, Tony Tong-Ying Juang, Chia-Yang Lin
The first part, which is the most important one, is to develop GUI (Graphical User Interface). It refers to the users’ experiences in using the App. Once the GUI was developed, the users can easily find the information in the App, i.e. how to log in the system, how to get the course information, and how to guide the users to tag on the smartphones. The second part is to create the connection between the Android phone and the database system. In this part, the App is designed to allow smartphones to get data items and upload data items into the database system. For the connection, the App is required to use the SQLite. SQLite stores information in the online database system, e. g. students’ location and course number, etc. To connect the online database with SQLite to smartphones, this study has designed eight programs in PHP. With these programs the Android App can easily connect with the online database system, update the table, delete the table, and create the row of data items on the table. The third part is GPS development. Although one can easily get the geolocation on the Android smartphone, this paper establishes a function to calculate the distance between the phone and others. The fourth part is the NFC function, in which the App is designed to read the information via the staff ID cards and student ID cards. Also, students show their attendance based on the NFC tagging action.
Stochastic coordinate-exchange optimal designs with complex constraints
Published in Quality Engineering, 2019
An important question is which coordinate should be exchanged to improve the design in each iteration. Meyer and Nachtsheim (1995) suggested cyclically changing every coordinate of the k “worst” design points. For irregularly shaped design space, this method is not efficient, because not each one of the k × d coordinates can lead to a significant improvement worthy of the computation cost of obtaining the one-dimensional constraints. Also, the deterministic selection follows the greedy fashion, so the algorithm usually stops at a local optimal point. Instead, we judiciously choose the coordinate for exchange based on “delete functions”. They evaluate which row and column could lead to the most improvement. The row index is chosen by the row delete functions for and the column index is chosen by the column delete functions for . Then is to be replaced by another value found by optimizing the improvement of ψ or ψ itself. To make the algorithm stochastic, we sample the row and column indices with probability proportional to and , respectively.
From digital to universal manufacturing
Published in International Journal of Production Research, 2022
The extended topological sorting algorithmSet iteration number r = 1. The solution set S = {Empty}.Draw a horizontal line through empty row k of incidence matrix [ai,j] or draw a vertical line through empty column l of incidence matrix [ai,j].Draw a vertical line through column k of the incidence matrix (same column number k as the row number in Step 2) of the incidence matrix or draw a horizontal line through column l (same row number l as the column number in Step 2).If horizontal line k drawn first, include label k corresponding the cross-out row k and column k of the matrix at the beginning of the solution set S. Delete row k and column k from the matrix. If horizontal line l is drawn first, include label l corresponding to the cross-out column l and row l of the matrix at the end of the solution set S. Delete column l and row l from the matrix.If each row and column of the incidence matrix has been labelled, stop; otherwise set r = r + 1 and go to Step 2.The extended topological sorting algorithm is illustrated with the data in matrix (8) representing the three digital models of Figure 10.