AGH (Akademia Górniczo-Hutnicza) University of Science and Technology: Journals
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Magazynowanie energii w kontekście transformacji energetycznej – od materiałów po systemy. Wybrane kierunki badań i zastosowanie oraz współpraca z operatorami sieci i przemysłem
Full text linkMagazynowanie energii odgrywa kluczową rolę w procesie transformacji energetycznej, umożliwiając efektywną integrację odnawialnych źródeł energii, zwiększenie elastyczności systemów energetycznych oraz poprawę niezawodności dostaw energii. W artykule przedstawiono wybrane kierunki badań prowadzonych w Akademii Górniczo-Hutniczej w Krakowie koncentrujących się na zagadnieniach związanych z magazynowaniem energii i funkcjonowaniem magazynów. Część rozdziałów zawiera niezależne opracowania dotyczące szerokiego spektrum zagadnień technologicznych i inżynierskich. Obejmują one aspekty elektrochemiczne, materiałowe, cieplne, geotermalne, mechaniczne oraz elektroenergetyczne. Artykuł ma charakter interdyscyplinarny, aplikacyjny i wdrożeniowy, podkreśla znaczenie prowadzenia badań nad rozwojem i integracją nowoczesnych technologii magazynowania energii oraz innowacyjnych rozwiązań technicznych z potrzebami i wyzwaniami współczesnych systemów elektroenergetycznych
A Proposal of Digital Contents Copyright Protection by using Blockmarking Technique
Full text linkRecently, blockmarking technique \cite{blockmarking} is proposed for a new hybrid model based on the combination of blockchain and watermarking method. In this model, it not only achieves the goal of image copyright protection but also stores the image into the blockchain network such as IPFS system. In this paper, we propose a new DRM system by inheriting the idea of blockmarking. The copyright contents can be distributed via IPFS blockchain, then be restored by using the reconstruction license for each legal user. Also, in our method, based on the reconstruction licenses, the distributed contents can be reconstructed from IPFS with various watermarking patterns. It helps us can manage the legal users and trace the traitor if a dispute occurs. The experimental results show that our method successfully achieved the purpose of digital copyright protection
Character/Word Modelling: A Two-Step Framework for Text Recognition in Natural Scene Images
Full text linkText recognition from images is a complex task in computer vision. Traditional text recognition methods typically rely on Optical Character Recognition (OCR); however, their limitations in image processing can lead to unreliable results. However, recent advancements in deep-learning models have provided an effective alternative for recognizing and classifying text in images. This study proposes a deep-learning-based text recognition system for natural scene images that incorporates character/word modeling, a two-step procedure involving the recognition of characters and words. In the first step, Convolutional Neural Networks (CNN) are used to differentiate individual characters from image frames. In the second step, the Viterbi search algorithm employs lexicon-based word recognition to determine the optimal sequence of recognized characters, thereby enabling accurate word identification in natural scene images. The system is tested using the ICDAR 2003 and ICDAR 2013 datasets from the Kaggle repository, and achieved accuracies of 79.8% and 81.5%, respectively
Transgenerational Holocaust Memory In Slovakia: From Forgetting to Ambivalence About the Roots of Hatred
Full text linkThis article explores Holocaust memory in Slovakia, shedding light on how Slovak citizens perceive this past and its transgenerational transmission. The data presented were gathered in 2023 through ethnographic fieldwork and focus group interviews with informants belonging to three generations (between ages of 18 and 95), in three different locations across the country: Krupina, Prešov, and Bratislava. The initial findings show that Slovakia has been moving from indifference towards the Holocaust to the limited capability of realizing the actual causes and effects of atrocities, while at the same time officially accepting the commemorative centrality of the Holocaust.This article explores Holocaust memory in Slovakia, shedding light on how Slovak citizens perceive this past and its transgenerational transmission. The data presented were gathered in 2023 through ethnographic fieldwork and focus group interviews with informants belonging to three generations (between ages of 18 and 95), in three different locations across the country: Krupina, Prešov, and Bratislava. The initial findings show that Slovakia has been moving from indifference towards the Holocaust to the limited capability of realizing the actual causes and effects of atrocities, while at the same time officially accepting the commemorative centrality of the Holocaust
The interdisciplinarity of the publications of the Medical University of Silesia in Katowice based on the analysis of the co-occurrence of issues specific to medicine and computer science
No full textOver the years, the study of the interdisciplinarity of publications has taken various forms, from its identification based on the disciplines represented by the authors, through the examination of citations used when writing the article, to the analysis of the publication text itself. The last of these approaches seems to be the most reliable in the context of verifying the real integration between disciplines in a specific text. The approach utilized in the conducted research facilitates a deeper analysis of integration not only between disciplines in general but also between specific issues within their domains, aiding the examination of the intensity of such connections. The research was aimed at analyzing publications affiliated with the Medical University of Silesia in Katowice in terms of their connection with issues included in the area of Computer Science. OpenAlex, a bibliographic database supported by data mainly from Scopus, WoS and Google Scholar, which uses concepts that make up the Wikidata knowledge base to describe the content of publications was used. A list of 14,136 publications from the Medical University of Silesia in Katowice was downloaded from the OpenAlex bibliographic database including such data as: publication id, title, author, abstract, journal, date of publication, ISSN number or concepts. Overall, the most prevalent concepts in the publications were concepts regarding the field of the medicine (medicine, internal medicine, cardiology). The most prevalent concepts concerning computer science in the publications were: computer science, logistic regression and artificial intelligence. The strenght of the connections between concepts regarding medicine and computer science was calculated by calculating the arithmetic mean of the score value for each pair of IT and medical concepts contained in a single publication. The study showed the importance of computer Science issues in the medical publications and highligted the growing importance of AI in the field of medicine
Significant and Trivial Dependencies Separation in Data Tensor by the Projection Method
Full text linkThe problem of data structure analysis through their multidimensional representation as a d-dimensional tensor is considered to assess dependencies on influencing factors in the decision-making process. The higher-order Singular Value Decomposition (SVD) is developed as a d-SVD schema to identify significant and trivial dependencies. The d-SVD includes the SVD of the tensor reshaped as a matrix and the SVDs of reduced size of the previous SVD vectors reshaped as matrices. The entropy of the distribution of the Singular Values (SVs) of the vectors’ decomposition is used for the separation of the significant and trivial vectors, in contrast to the commonly used approach based on the magnitude analysis of SVs. The singular projection in the significant vector space in selected dimensions gives the tensor’s low-rank approximation without loss of information in comparison with the truncated SVD. The tensor projection on a vector subspace of reduced dimension can be obtained by using a part of the SVs and the corresponding vectors as an alternative to the commonly used averaging. It was shown that data prediction in the subspace of the significant vectors allows stable assessments of the predicted values to be obtained
BIELIK 7B V0.1: POLISH LANGUAGE MODEL - DEVELOPMENT, INSIGHTS, AND EVALUATION
Full text linkWe introduce Bielik 7B v0.1, a 7-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 9 percentage point increase in average score compared to Mistral-7B-v0.1 on the RAG Reader task. It also excels in the Polish MT-Bench, particularly in 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
Research trends and trajectories in quality management in the age of Industry 4.0 – the current state of knowledge
No full textThis study aims to analyse publication activity and identify development directions of scientific research themes in the area of enterprise quality management in the context of Industry 4.0, based on a bibliometric analysis of scientific literature from the Web of Science and Scopus databases up to the end of 2023. The article employs a systematic literature review method. The research included a bibliometric analysis of scientific literature sourced from the Web of Science and Scopus databases. Based on a predefined combination of keywords, a set of articles was selected, which – after initial screening and justified exclusions – was accepted for further analysis. The study enabled the characterization of, among others, publications production, sources of publications, time of publication, main authors and leading topics. Next, a word co-occurrence analysis and longitudinal thematic map analysis were conducted to examine the research field in depth and to identify research trends and trajectories in quality management in the age of Industry 4.0.The conducted research shows that the number of publications on quality management in the context of Industry 4.0 is constantly growing, and the topics are progressively evolving towards issues such as: the Internet of Things (IoT), Artificial Intelligence (AI), Big Data, digitalization. These technologies are seen as key enablers for enterprises to cope with the growing uncertainty and complexity of the business environment and to achieve a competitive advantage. The study indicates that AI, IoT, big data analytics, and sustainable development are among the most rapidly growing and prominent themes in the literature on quality management in the Industry 4.0 context. Identifying research gaps and potential directions for scientific development in the field of quality management may contribute to the creation or improvement of new tools, approaches, and concepts including those based on Industry 4.0 technologies
PSO-WESRGAN: A NOVEL DOCUMENT IMAGE SUPER RESOLUTION
No full textOne of the major challenges of document images that can hinder readability and the analysis of information is low resolution, typically caused by low-pixel density scanning or excessive compression to save storage space. This results in images lacking fine details, making it difficult to recognize important information. Super-resolution techniques are essential to addressing these issues. These techniques enhance image quality by increasing resolution while maintaining fine details. The PSO-WESRGAN is an innovative method, which combines wavelet processing, deep transfer learning, and particle swarm optimization (PSO). Wavelet processing analyzes image detail at diverse scales and orientations, while transfer-based deep learning advantages pre-trained models on vast image datasets. By integrating PSO, the method’s efficiency is enhanced through optimal exploration of the solution space to identify the best parameters for the super-resolution model. Experimental results demonstrate the effectiveness of this approach and pave the way for future advances in document image resolution