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Analyzing and Optimizing Fixed-Point Operations on a MATLAB Simulink based Motion Control System
(2023)
This paper deals with the analysis and optimization of fixed-point operations in the context of motion control systems and the MathWorks MATLAB® Simulink® environment. It presents a procedure for estimating the maximum arithmetic error of fixed-point operations inside a modeled control loop, which can be used to verify compliance with a defined precision. Additionally, it shows the results of a runtime analysis for different numerical data types in combination with two fixed-point operations: addition and multiplication. In order to improve the fixed-point performance for MATLAB® Simulink® models, two optimized implementations are provided, which can improve the fixed-point operations by a factor up to 3, compared to the automatically generated operation implementations. The paper results are based on the MATLAB® R2022b release.
The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy, but also for increasing the overall efficiency. Among the different job scheduling problems, the Job Shop Scheduling Problem (JSSP) is addressed in this work. JSSP falls into the category of NP-hard Combinatorial Optimization Problem (COP), in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as First-In, First-Out, Largest Processing Time First and metaheuristics such as taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using Deep Reinforcement Learning (DRL) to solve COPs has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the Proximal Policy Optimization (PPO) algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated a new method called Order Swapping Mechanism (OSM) in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.
Within the framework of a research focus at the TH Rosenheim on prediction methods for sound and impact sound insulation in timber constructions, methods of mathematical statistics and artificial intelligence are applied to sound insulation. To estimate the potential of those methods, one-third octave band spectra of measured sound insulation of sand-lime brickwork have been analyzed first. On selected data sets for certain building constructions, physically based calculation approaches according to the \backslashDIN{} series of standards, are compared with purely statistical methods such as GAMLSS (Generalized Additive Models for Location, Scale and Shape Parameters). The parameters derived from these procedures can be used for prediction purposes. The interval estimators resulting from these methods are compared. In addition, methods to classify the separating construction based on measurements are discussed. Thereby, in situ, measurements are used in addition to laboratory measurements.