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Computer Programming
Published in Quamrul H. Mazumder, Introduction to Engineering, 2018
Instead of focusing on the syntax of a specific programming language, this introduction will employ pseudocode to demonstrate the basic concepts of computer programming. The word pseudo means false, and, thus, pseudocode means false code. Pseudocode is an informal way of expressing the details of a computer program or algorithm and can be easily read. Pseudocode is typically used in the planning phase of programming, and allows the programmer to develop a step-by-step algorithm that focuses on the logic involved with the task instead of language-specific syntax. When the time comes to write the code in the appropriate programming language, it should be a simple task of translating each line of pseudocode into the proper syntax.
Scientific Writing
Published in Phillip A. Laplante, Technical Writing, 2018
Getting back to the thesis, another feature is the representation of an algorithm in a programming language, in this case, pseudocode. Pseudocode is a generic name for any code syntax that resembles a programming language but is not intended to be compiled and executed. The idea is that if the reader understands C, Fortran, Java, or some other modern programming language, then they would get the general idea of the algorithm by inspecting the pseudocode. Here is the introductory text and the accompanying pseudocode:
Allen-Bradley RSLogix software and ladder-diagram programming
Published in Raymond F. Gardner, Introduction to Plant Automation and Controls, 2020
Pseudocode is a loose informal representation of the logic, control sequence, or control code, but it lacks the syntax precision needed by the programming language. Pseudocode is useful for describing the process in plain English and identifying the what-if conditions that affect the proper behavior of the system. Using the sample flow chart and control diagrams in Figure 13.29 and Figure 13.30, the pseudocode might look like the following: Press START: Enable ControlsSense tank level using level switches LSHH indicates excessively high levelLSH indicates the normal high-level extentsLSL indicates the normal low-level extentsLSLL indicates excessively low levelConditional Statements—IF-THEN-ELSE, WHILE, etc. IF: LSL is TRUE (low limit reached) then start the pumpWHILE: Pump running is TRUE AND LSH is FALSE (level is between limits) THEN keep the pump running until upper limit is reachedIF: LSH is TRUE (upper level is reached) THEN stop the pump AND close the valveWHILE: Pump running is FALSE AND LSH is FALSE (level is between limits) THEN keep the pump off until the lower limit is reachedIF: LSLL is TRUE (lowest level is reached) THEN actuate the alarm AND stop the pump AND close the valves (to avoid running without adequate differential)WHEN: LSLL becomes FALSE, PERMIT the control to function properlyIF: LSHH is TRUE, THEN Actuate the alarm AND stop the pump AND close the valves (to void flooding the tank)Press STOP: Stop the pump AND close the valves AND disable the controls
Solving multi-objective Modified Distributed Parallel Machine and Assembly Scheduling Problem (MDPMASP) with eligibility constraints using metaheuristics
Published in Production & Manufacturing Research, 2022
Ikhlasul Amallynda, Budi Santosa
A model that is built must be credible and representative. It is used to ensure the reproducibility of measurement results in real conditions. In software testing, there are two terms: verification and validation. Verification is the process of determining whether algorithms and programs run as intended by repeatedly running the program. The verification ensures that the conceptual model (pseudocode and assumptions) is translated correctly into the programming language (Law & Kelton, 2000). Validation is the process of determining whether the stated model accurately represents the system (Hoover & Perry, 1989). Validation is the process of determining whether a conceptual model (as opposed to a computer program) accurately represents the modeled system (Law & Kelton, 2000).
A quantized approach for occupancy grids for autonomous vehicles: Q-Trees
Published in Advanced Robotics, 2018
The pseudocode in Algorithm 1 explains the assignment procedure for obstacles. This method assigns the landmarks in the ROI to the left or right child through the graph tree branches. Every node has the value of the sampling rate of its own. These landmarks are stored in an associative array to list the occluded nodes and which landmarks are assigned to a specific node. The time complexity of this method takes steps in all cases (best, worst and the average). Variable m represents the depth of the farthest leaves in the branch. Therefore, the ultimate worst scenario takes and this term is defined as so, the worst case for searching the dependent node of an obstacle takes
Optimal scheduling of vehicle-to-Grid power exchange using particle swarm optimization technique
Published in International Journal of Computers and Applications, 2022
In this paper, an optimal scheduling scheme for V2G operation is proposed with the objective of minimizing the load variations in the power grid around a specified target load. There are various challenges related to the effective implementation of V2G power exchange, such as the availability of EVs for V2G operation, ever-changing SOC of EV batteries, etc. The proposed optimization algorithm responds to these challenges by taking into account the stochastic nature of EV-grid connectivity and constantly updating the parameters at regular intervals. A thorough explanation of the algorithm is done using a flowchart and pseudocode. The algorithm is validated by conducting various case studies for various scenarios of target load, and average initial SOC. The results of all scenarios are analysed and it is observed that the proposed algorithm can effectively and significantly flatten the load curve while complying with all the constraints. This can considerably improve the efficiency and performance of the power grid. It is also concluded that the achievement of the desired outcome is considerably affected by the selection of the target load and initial SOC of EVs participating in the V2G power exchange. Hence, these parameters have to be considered to achieve the best performance from the algorithm. The effect of the proposed load-leveling method on the price fluctuation of electricity is elaborated. The advantages of the proposed algorithm in terms of the execution time were further validated by means of time complexity analysis and comparing it with algorithms from previous literature on the same subject. For future development in this field, the compatibility of the V2G operation with the integration of renewable energy sources can be investigated considering the stochastic nature of these sources.