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1.
A new transformation technique for reducing information entropy : a case study on greyscale raster images
Borut Žalik, Damjan Strnad, David Podgorelec, Ivana Kolingerová, Luka Lukač, Niko Lukač, Simon Kolmanič, Krista Rizman Žalik, Štefan Kohek, 2023, izvirni znanstveni članek

Opis: This paper proposes a new string transformation technique called Move with Interleaving (MwI). Four possible ways of rearranging 2D raster images into 1D sequences of values are applied, including scan-line, left-right, strip-based, and Hilbert arrangements. Experiments on 32 benchmark greyscale raster images of various resolutions demonstrated that the proposed transformation reduces information entropy to a similar extent as the combination of the Burrows–Wheeler transform followed by the Move-To-Front or the Inversion Frequencies. The proposed transformation MwI yields the best result among all the considered transformations when the Hilbert arrangement is applied.
Ključne besede: computer science, algorithm, string transformation, information entropy, Hilbert space filling curve
Objavljeno v DKUM: 22.05.2024; Ogledov: 21; Prenosov: 0
.pdf Celotno besedilo (26,44 MB)
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Sustainable design of circular reinforced concrete column sections via multi-objective optimization
Primož Jelušič, Tomaž Žula, 2023, izvirni znanstveni članek

Opis: An optimization model for reinforced concrete circular columns based on the Eurocodes is presented. With the developed optimization model, which takes into account the exact distribution of the steel reinforcement, which is not the case when designing with conventional column design charts, an optimal design for the reinforced concrete cross section is determined. The optimization model uses discrete variables, which makes the results more suitable for actual construction practice and fully exploits the structural capacity of the structure. A parametric study of the applied axial load and bending moment was performed for material cost and CO2 emissions. The results based on a single objective function show that the optimal design of the reinforced concrete column cross section obtained for the material cost objective function contains a larger cross-sectional area of concrete and a smaller area of steel compared with the optimization results when CO2 emissions are determined as the objective function. However, the optimal solution in the case where the material cost was assigned as the objective function has much more reserve in axial load capacity than in the optimal design where CO2 was chosen as the objective function. In addition, the multi-objective optimization was performed to find a set of solutions that provide the best trade-offs between the material cost and CO2 emission objectives.
Ključne besede: reinforced concrete columns, circular cross section, costs, CO2 emissions, multi-objective optimization, genetic algorithm
Objavljeno v DKUM: 15.04.2024; Ogledov: 173; Prenosov: 198
.pdf Celotno besedilo (4,56 MB)
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4.
Optimization of chaboche material parameters with a genetic algorithm
Nejc Dvoršek, Iztok Stopeinig, Simon Klančnik, 2023, izvirni znanstveni članek

Ključne besede: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Objavljeno v DKUM: 04.04.2024; Ogledov: 72; Prenosov: 6
.pdf Celotno besedilo (3,62 MB)
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5.
Comparison and optimization of algorithms for simultaneous localization and mapping on a mobile robot : master's thesis
Matic Rašl, 2023, magistrsko delo

Opis: In this thesis, we compare and evaluate different SLAM solutions for a low-cost mobile robot. We present a simulator for the robot and use it to gather simulated data. Using this data, we then optimize the SLAM algorithms using an evolutionary algorithm. The optimized solutions are then validated and compared to default SLAM configurations. Up to 83 % reduction of error is achieved on validation data with multiple SLAM algorithms with improvements also visible on the real-world data.
Ključne besede: SLAM, optimization, mobile robot, evolutionary algorithm, simulation.
Objavljeno v DKUM: 05.10.2023; Ogledov: 305; Prenosov: 13
.pdf Celotno besedilo (2,64 MB)

6.
Indoor positioning system based on bluetooth low energy technology and a nature-inspired optimization algorithm
Primož Bencak, Darko Hercog, Tone Lerher, 2022, izvirni znanstveni članek

Opis: Warehousing is one of the most important activities in the supply chain, enabling competitive advantage. Effective management of warehousing processes is, therefore, crucial for achieving minimal costs, maximum efficiency, and overall customer satisfaction. Warehouse Management Systems (WMS) are the first steps towards organizing these processes; however, due to the human factor involved, information on products, vehicles and workers may be missing, corrupt, or misleading. In this paper, a cost-effective Indoor Positioning System (IPS) based on Bluetooth Low Energy (BLE) technology is presented for use in Intralogistics that works automatically, and therefore minimizes the possibility of acquiring incorrect data. The proposed IPS solution is intended to be used for supervising order-picker movements, movement of packages between workstations, and tracking other mobile devices in a manually operated warehouse. Only data that are accurate, reliable and represent the actual state of the system, are useful for detailed material flow analysis and optimization in Intralogistics. Using the developed solution, IPS technology is leveraged to enhance the manually operated warehouse operational efficiency in Intralogistics. Due to the hardware independence, the developed software solution can be used with virtually any BLE supported beacons and receivers. The results of IPS testing in laboratory/office settings show that up to 98% of passings are detected successfully with time delays between approach and detection of less than 0.5 s.
Ključne besede: warehousing, indoor positioning systems, bluetooth low energy, particle swarm optimization, nature–inspired algorithm, intralogistics
Objavljeno v DKUM: 17.08.2023; Ogledov: 349; Prenosov: 35
.pdf Celotno besedilo (2,42 MB)
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Algorithmic linearization improves Syringe-based extrusion in elastic systems using Hydrogel-based materials
Jernej Vajda, Luka Banović, Mihael Miško, Igor Drstvenšek, Marko Milojević, Uroš Maver, Boštjan Vihar, 2023, izvirni znanstveni članek

Opis: Accuracy and precision are essential in extrusion-based material handling such as three-dimensional (3D) bioprinting. However, the elasticity of components, backlash, variability of nozzle and cartridge shapes, etc. can lead to unpredictable printing results, which is further complicated by the wide range of rheologically diverse materials and complex sample designs. To address this issue, we present an algorithmic approach to compensate for the discrepancies between piston motion and material extrusion in syringe-based extrusion systems. This approach relies on cyclical, iterative optimization through rapid piston movements, which are adjusted based on extrusion analysis. In this work we establish a general theoretical framework for extrusion and link the rheological properties of prepared hydrogels with shear rates in a typical bioprinting process. The determined properties are compared with the success of the developed algorithm to modify machine instructions for precise material deposition of short, interrupted lines, as well as multi-layered scaffold structures. Overall, our approach provides a means of improving the accuracy and precision of complex extrusion-based bioprinting systems, without prior knowledge of set-up or material properties, making it highly versatile and suitable for a wide range of applications, particularly when the combined set-up and material properties are too complex for solely predictive approaches.
Ključne besede: 3D bioprinting, syringe extrusion, optimization, algorithm
Objavljeno v DKUM: 21.04.2023; Ogledov: 555; Prenosov: 86
.pdf Celotno besedilo (3,38 MB)
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9.
Optimizacija Chaboche materialnih parametrov z genetskim algoritmom : magistrsko delo
Nejc Dvoršek, 2022, magistrsko delo

Opis: The basis of this thesis is research and development of a genetic algorithm for material parameters optimization. It is written in collaboration with AVL, which already has a solution for this problem, but is looking into better alternatives. Chaboche material model is a nonlinear isotropic and kinematic hardening model which can describe elasto-viscoplastic constitutive relations. Parameters of such complex nature do not have a physical interpretation in the real-world and must be defined with inverse analysis. Genetic algorithms (GA) are a promising tool to help with such tasks. They have been widely used and recognized for various optimization problems. Material data available are low cycle fatigue (LCF), creep, and tensile experiments. For each experiment a corresponding finite element model in Abaqus is prepared. Comparing experimental and simulation data is the objective function GA will try to minimize. For this reason, a corresponding fitness function was developed to score each individual. It makes use of similarity measure algorithm proposed in this paper [10]. GA was implemented in Python with Pygad library. Instead of bits, genes are represented with real-valued numbers with defined limits. Performance of developed GA was tested based on various population sizes, mutation probabilities, and crossover operators. The main parameter that impacts algorithms performance is population size. Paired with right mutation probability the algorithm can find a global minimum of described optimization problem. Making it a viable alternative to existing approach used at AVL.
Ključne besede: Chaboche material model, parameter optimization, genetic algorithm, finite element method
Objavljeno v DKUM: 16.12.2022; Ogledov: 738; Prenosov: 0
.pdf Celotno besedilo (1,90 MB)

10.
Automatic compiler/interpreter generation from programs for domain-specific languages using semantic inference : doktorska disertacija
Željko Kovačević, 2022, doktorska disertacija

Opis: 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.
Ključne besede: Grammatical Inference, Semantic Inference, Genetic Programming, Attribute Grammars, Memetic Algorithm, Domain-Specific Languages
Objavljeno v DKUM: 17.02.2022; Ogledov: 1114; Prenosov: 115
.pdf Celotno besedilo (3,59 MB)

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