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Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case study
Miha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, original scientific article

Abstract: Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
Keywords: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Published in DKUM: 25.03.2024; Views: 157; Downloads: 10
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PALANTIR : An NFV-Based Security-as-a-Service Approach for Automating Threat Mitigation
Maxime Compastié, Antonio López Martínez, Carolina Fernandez, Manuel Gil Pérez, Stylianos Tsarsitalidis, George Xylouris, Izidor Mlakar, Michail Alexandros Kourtis, Valentino Šafran, 2023, original scientific article

Abstract: Small and medium enterprises are significantly hampered by cyber-threats as they have inherently limited skills and financial capacities to anticipate, prevent, and handle security incidents. The EU-funded PALANTIR project aims at facilitating the outsourcing of the security supervision to external providers to relieve SMEs/MEs from this burden. However, good practices for the operation of SME/ME assets involve avoiding their exposure to external parties, which requires a tightly defined and timely enforced security policy when resources span across the cloud continuum and need interactions. This paper proposes an innovative architecture extending Network Function Virtualisation to externalise and automate threat mitigation and remediation in cloud, edge, and on-premises environments. Our contributions include an ontology for the decision-making process, a Fault-and-Breach-Management-based remediation policy model, a framework conducting remediation actions, and a set of deployment models adapted to the constraints of cloud, edge, and on-premises environment(s). Finally, we also detail an implementation prototype of the framework serving as evaluation material.
Keywords: Security-as-a-Service, security orchestration, policy-driven management, virtual network functions, finite state machines, constraints programming
Published in DKUM: 06.02.2024; Views: 213; Downloads: 5
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Optimal positioning of mobile cranes on construction sites using nonlinear programming with discontinuous derivatives
Matjaž Hozjan, Uroš Klanšek, 2023, original scientific article

Abstract: Mobile cranes represent conventional construction machinery that is indispensable for the erection of most prefabricated buildings, especially those containing heavy components. However, it is also common knowledge that the engagement of these machines has a significant influence on the environment, various social aspects of the construction process, and its economic benefits. Optimal positioning of the mobile crane on the construction site, primarily driven by the contractor’s interest to perform assembly operations with expensive machinery as effectively as possible, considerably reduces not only the costs of engaging such a machine but indirectly also its negative impacts on construction sustainability. This paper discusses an exact nonlinear model for the optimization task. The optimization model consists of a cost objective function that is subject to various duration and positioning constraints for the mobile crane, including bounds on its degrees of freedom of movement and stop positions. Because the model formulation includes discontinuous and non-smooth expressions, nonlinear programming with discontinuous derivatives (DNLP) was employed to ensure the optimal solution was reached. The model provides the mobile crane operator with exact key information that enables the complete and optimal assembly of the building structure under consideration. Additionally, the information gained on the optimal distribution of the mobile crane rental period to assembly operations allows for a detailed duration analysis of the entire process of building structure erection, which can be used for its further improvement. An application example is given in this study to demonstrate the advantages of the proposed approach.
Keywords: construction sustainability, mobile crane, nonlinear programming with discontinuous derivatives, optimization, positioning
Published in DKUM: 18.12.2023; Views: 310; Downloads: 8
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Response surface method-based optimization of outer rotor permanent magnet synchronous motor
Vahid Rafiee, Jawad Faiz, 2019, original scientific article

Abstract: The Finite Element Method (FEM) is a prominent analysis approach. Although it is applicable for simulation and optimization of electrical machines, FEM is a very time-consuming technique. One of the approaches to shorten the optimization runtime is the use of surrogate models instead of FEM. In this paper, the design and optimization of an outer rotor permanent magnet synchronous motor for a hybrid vehicle are investigated. Response surface methodology (RSM) with four input variables is integrated with a sequential quadratic programming algorithm for optimization. Before the optimization, the performance of the surrogate model in the prediction of untried points is validated. Finally, the optimal motor is simulated by FEM to verify the results of RSM-based optimization, and the outputs of both models are compared.
Keywords: response surface surrogate method, outer rotor permanent magnet synchronous motor, sequential quadratic programming optimization
Published in DKUM: 05.12.2023; Views: 411; Downloads: 3
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Generalised fuzzy linear programming
Janez Usenik, Maja Žulj, 2023, original scientific article

Abstract: Linear programming is one of the widely used methods for optimising business systems, which includes organisational, financial, logistic and control subsystems of energy systems in general. It is possible to express numerous real-world problems in a form of linear program and then solve by simplex method [1]. In the development of linear programming, we are facing a number of upgrades and generalisations, as well as replenishment. Particularly interesting in recent years is an option that decision variables and coefficients are fuzzy numbers. In this case we are dealing with fuzzy linear programming. If we also include in a fuzzy linear program a generalisation with respect to Wolfe’s modified simplex method [1], we obtain a generalised fuzzy linear program (GFLP). Usenik and Žulj introduced methods for solving those programs and proved the existence of the optimal solution in [2]. In the article, the simplex algorithm which enables the determining of an optimal solution for GFLP is described. There is a numerical example at the end of the article that illustrates the algorithm.
Keywords: linear programming, fuzzy linear programming, generalised linear programming, generalised fuzzy linear programmin
Published in DKUM: 11.10.2023; Views: 207; Downloads: 4
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Editorial: combined water and heat integration in the process industries
Elvis Ahmetović, Ignacio E. Grossmann, Zdravko Kravanja, François Marechal, Jiri Klemeš, Luciana E. Savulescu, Dong Hongguang, 2022, preface, editorial, afterword

Abstract: Water and energy are resources that are used in large quantities in different sectors (domestic, agricultural, and industrial). Based on data on global water and energy consumption in the world over the recent past, as well as forecasts for the coming years, a continuous trend of increasing water and energy consumption can be observed. ...
Keywords: water integration, heat integration, systematic methods, pinch analysis, mathematical programming, heat-integrated water networks, optimisation, process industry
Published in DKUM: 18.08.2023; Views: 321; Downloads: 22
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Using Artificial Intelligence in Software Development: a case analysis
Patricija Vrentuša, 2023, master's thesis

Abstract: In this work, we talk about artificial intelligence and its impact on software development. We define the AI concept, the software development process, and the various uses of AI in programming. We put a special focus on the ethical aspects of AI. In the empirical part, we analyzed the AI pair programming tool created by Microsoft called GitHub Copilot. We examine several usage examples, as well as the opinions of developers that have been using GitHub Copilot. We found that the AI pair programmer tool helps programmers increase productivity and automate mundane tasks to concentrate on more complex and interesting parts of software development. However, some programmers raise concerns about unethical concepts connected to the development of GitHub Copilot, implying violations of original code author copyrights. We discuss current licenses, other possible solutions, and the morality of building AI on open-sourced repositories. Moreover, many believe that the future will be increasingly intertwined with AI in all industries and that the only way to preserve (software development) jobs is to keep learning and being innovative and creative. Programmers say that almost certainly their job will not vanish because of AI advancement; only the scope of their work will change.
Keywords: AI, software development, ethics, AI pair programming, GitHub Copilot.
Published in DKUM: 21.02.2023; Views: 916; Downloads: 120
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Automatic compiler/interpreter generation from programs for domain-specific languages using semantic inference : doktorska disertacija
Željko Kovačević, 2022, doctoral dissertation

Abstract: Presented doctoral dissertation describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can sometimes also be accompanied by a set of negative samples. Successfully applying Grammar Inference can result only in identifying the correct syntax of a language. But, when valid syntactical structures are additionally constrained with context-sensitive information the Grammar Inference needs to be extended to the Semantic Inference. With the Semantic Inference a further step is realised, namely, towards inducing language semantics. In this doctoral dissertation it is shown that a complete compiler/interpreter for small Domain-Specific Languages (DSLs) can be generated automatically solely from given programs and their associated meanings using Semantic Inference. For the purpose of this research work the tool LISA.SI has been developed on the top of the compiler/interpreter generator tool LISA that uses Evolutionary Computations to explore and exploit the enormous search space that appears in Semantic Inference. A wide class of Attribute Grammars has been learned. Using Genetic Programming approach S-attributed and L-attributed have been inferred successfully, while inferring Absolutely Non-Circular Attribute Grammars (ANC-AG) with complex dependencies among attributes has been achieved by integrating a Memetic Algorithm (MA) into the LISA.SI tool.
Keywords: Grammatical Inference, Semantic Inference, Genetic Programming, Attribute Grammars, Memetic Algorithm, Domain-Specific Languages
Published in DKUM: 17.02.2022; Views: 1124; Downloads: 115
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Cloud Computing Deployment and Management : A Collection of Exercises and Tasks with Solutions
Aleš Zamuda, other educational material

Abstract: The subject Cloud Computing Deployment and Management is included in the second-cycle Bologna Study Programme Computer Science and Information Technologies as a full-time study unit. This document presents study material for computer exercises in this subject. After an introduction, then individual tasks with solutions to the computer exercises in the subsequent chapters are provided. This is followed by a list of archived files containing the computer format of the provided solutions.
Keywords: cloud computing, virtualization, parallel programming, distributed programming, virtual computing environments, high-performance computing
Published in DKUM: 11.09.2020; Views: 1297; Downloads: 61
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