CZASOPISMA AGH (CN)
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Czasopisma publikowane przez AGH
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Item type:Article, Access status: Open Access , Geometric optimization of two-stage stamping dies for ultra-thin titanium bipolar plates using Sequential Physics-Informed Neural Networks(Wydawnictwa AGH, 2026) Ke, Zijie; Huang, Yiwen; Guo, Ziqiang; Xiao, Yao; Hou, Zeran; Min, JunyingBipolar plates are critical core components in proton exchange membrane fuel cells (PEMFCs). Titanium-based materials are highly favored due to their excellent corrosion resistance and high specific strength. However, the plates often experience severe local thinning and poor consistency in forming dimensions during the two-stage stamping process. Although traditional finite element method (FEM) optimization can mitigate these defects, it comes with high computational costs and time consumption. This study proposes a die design optimization framework based on the Sequential Physics-Informed Neural Network (S-PINN). Unlike traditional single-layer neural network models, S-PINN adopts a sequential architecture that effectively maps the two-stage forming process of the plates. This architecture can explicitly predict the evolution of forming quality from the pre-forming stage to the final stage. By embedding the core physical laws of plastic deformation into the network loss function, the S-PINN model effectively predicts the complex nonlinear relationship between mold geometry and forming quality, while ensuring physical consistency. Experimental and simulation results show that the S-PINN model’s prediction accuracy for dimensional consistency (DC) is 73.8% higher than that of the PINN model and 33.9% higher than that of the S-ANN model. Compared with traditional modeling methods, the S-PINN-optimized die design can reduce the thinning rate and improve channel dimensional consistency.Item type:Journal Issue, Computer Methods in Materials Science2026 - Vol. 26 - No. 1Item type:Article, Access status: Open Access , A Digital Twin for temperature prediction in the laser hardening process of NC10 steel(Wydawnictwa AGH, 2026) Lacki, Piotr; Derlatka, Anna; Lacki, Michał; Lachs, KubaIn this study, Artificial Neural Networks (ANN) were created to develop a Digital Twin (DT) for temperature prediction in the laser hardening process of NC10 steel. The ANN were trained to predict temperature on the top layer during the laser hardening process of NC10 steel samples with different thicknesses and with various laser power and laser scanning speeds. The prediction developed during the project work was based on a parametric numerical model of the laser hardening process for a sample of NC10 steel, using the Finite Element Method (FEM) within the ADINA software. Numerical simulations enabled a detailed analysis of the temperature produced on the surface of each sample, as well as a visualization of the structural changes made to the sample according to the laser hardening process. It is crucial to create data that reflects reality as closely as possible to assess the best setting for each process. A well created DT allows to make automatically important changes along laser hardening process. To obtain a set of the most efficient parameters for the desired result, Genetic Algorithms (GA) were integrated with the developed ANN. As a result, the authors developed an effective and efficient tool to predict the temperature produced along the laser hardening process.Item type:Journal Volume, Computer Methods in Materials ScienceVol. 26 (2026)Item type:Article, Access status: Open Access , Artificial intelligence-enhanced algebraic multigrid for 3D finite element simulations(Wydawnictwa AGH, 2026) Goik, Damian; Banaś, KrzysztofThe paper presents preliminary investigations into a strategy for solving linear systems resulting from 3D finite element simulations, based on the algebraic multigrid (AMG) method, enhanced using artificial intelligence techniques. In particular, we adapt to 3D problems the algorithm presented in Luz et al. (2020) for using a graph neural network to create the prolongation and restriction operators in a way that will improve convergence. The process of training the network proceeds on the basis of a set of system matrices obtainedfor tasks much smaller in scale than the target problem to be solved. Learning is aimed at decreasing the spectral radius of the error propagation matrix after applying modified prolongation and restriction. We describe some implementation details of the solver developed based on the presented strategy and show several numerical examples of its application for medium-sized problems.Item type:Article, Access status: Open Access , FL-MEC: Federated learning for network traffic classification on the network edge(Wydawnictwa AGH, 2025) Paszko, Patryk; Konieczny, Marek; Zieliński, Sławomir; Kwolek, BartoszNowadays, two technological trends, Federated Learning (FL) and Edge Computing (EC), are increasingly important and influential. FL is a decentralized machine learning strategy that allows learning on distributed data. It primarily allows performing learning operations close to the user, where the data is gathered. This approach belongs to the EC domain, where the main goal is to move computation closer to the end user (e.g., from the centralized cloud). In our work, we apply the FL and EC in the context of network flow classification. We achieved an accuracy of 0.957 with the FL model, compared to 0.924 for the best local model. We achieved these results thanks to the federated averaging performed on neural network layers. To verify our approach, we executed all our experiments on a virtualized environment that emulates existing mid-scale EC network infrastructure, including limitations related to resource constraints on edge nodes.Item type:Article, Access status: Open Access , Bielik7B v0.1: Polish language model – development, insights, and evaluation(Wydawnictwa AGH, 2025) Ociepa, Krzysztof; Flis, Łukasz; Wróbel, Krzysztof; Gwoździej, Adrian; Kinas, RemigiuszWe introduce Bielik 7B v0.1 – a seven-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques; these include Weighted Instruction Cross-Entropy Loss (which balances the learning of different instruction types) and Adaptive Learning Rate (which dynamically adjusts the learning rate based on training progress). To evaluate performance, we created the Open PL LLM Leaderboard and Polish MT-Bench – novel frameworks assessing various NLP tasks and conversational abilities. Bielik 7B v0.1 demonstrates significant improvements, achieving a ninepercentage- point increase in its average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench – particularly in the Reasoning (6.15/10) and Role-playing (7.83/10) categories. This model represents a substantial advancement in Polish language AI, offering a powerful tool for diverse linguistic applications and setting new benchmarks in the field.Item type:Article, Access status: Open Access , Toward RAM forensics supported by machine-learning methods(Wydawnictwa AGH, 2025) Jurczyk, Kamil; Topa, Paweł; Faber, ŁukaszIn this article, we propose an enhancement to the computer forensics technique of using Machine-Learning tools to analyze the contents of RAM in order to extract information that is potentially useful during an investigation. In the specific case presented, the use of the extracted information to generate moreoptimal dictionaries for dictionary cryptanalysis is considered. Increasing user awareness is making cryptanalysis of passwords increasingly difficult for law enforcement. Long and complex passwords are impossible to crack – even when high-performance computing platforms are available. A sensible method of optimization is to look for hints to use a dictionary that contains text phrases more likely to be used in the specific case under attack. Such a hint could be an analysis of RAM taken from a suspect computer. Machine-learning methods can significantly facilitate this task. In this article, we also explore the effectiveness of such an approach and its usefulness in practical applications. We also consider applications of the proposed approach for other purposes, such as OSINT.Item type:Article, Access status: Open Access , The benefits of testing software in se research: lessons learned from two phd projects(Wydawnictwa AGH, 2025) Novak, Matija; Mijač, MarkoSoftware engineering (SE) research often involves creating software – either as a primary research output (e.g., in design science research) or as a supporting tool for the traditional research process. Ensuring software quality is essential, as it influences both the research process and the credibility of findings. Integrating software-testing methods into SE research can streamline efforts by addressing the goals of both research and development processes simultaneously. This paper highlights the advantages of incorporating software testing in SE research – particularly for research evaluation. Through qualitative analysis of software artifacts and insights from two PhD projects, we present ten lessons learned. These experiences demonstrate that, when effectively integrated, softwareItem type:Article, Access status: Open Access , A parallel approach for metaheuristics solving the labs problem using CPU and GPU(Wydawnictwa AGH, 2025) Żurek, Dominik; Piętak, Kamil; Pietroń, Marcin; Kisiel-Dorohinicki, MarekThis paper contributes to solving the low autocorrelation binary sequence (LABS) problem that remains an open hard-optimization problem with many applications. The current direction of research is focused on developing algorithms dedicated to parallel architectures such as GPGPU or multi-core CPUs. The paper follows this direction and proposes new heuristics developed from the steepest-descent local search algorithm that extends the notion of a neighborhood of a given sequence. The introduced algorithms utilize the parallel nature of multicore CPUs and provide an effective method for solving the LABS problem. The efficiency levels of SDSL and the new algorithm are presented; to ensure an effective comparison, they were both implemented in the same manner. The comparison shows that exploring the larger neighborhood improves the efficiency of the search method.