Publikacje naukowe Irmina Durlik
Tytuł: Utilizing recurrent neural networks in sustainable urban transport and logistics
Autor/Autorzy: Irmina Durlik,Tymoteusz Miller, Ewelina Kostecka, Tomasz Pusty , Adrianna Łobodzińska
Miejsce publikacji:
Rok: 2024
Słowa kluczowe: recurrent neural networks, sustainable urban transport, urban logistics, energy transition, machine learning, traffic management, infrastructure planning, zero-emission vehicles, energy efficiency, traffic flow prediction
Abstrakt: Urban centers, replete with diverse amenities and opportunities, simultaneously grapple with the challenges brought on by rapid urbanization, notably in the realms of transport and logistics. A pivotal move towards energy-efficient and sustainable systems is essential to mitigate these challenges. In this landscape, machine learning (ML), and particularly recurrent neural networks (RNNs), emerge as powerful tools for effectively addressing these urban complexities. This comprehensive review zeroes in on the deployment of RNNs within sustainable urban transportation and logistics, highlighting their adeptness in processing sequential data, a critical component in various forecasting and optimization tasks. We commence with a foundational understanding of RNNs, segueing into their successful applications in urban transport and logistics. This review also critically examines the constraints of current methodologies and potential avenues for enhancement. We scrutinize the application of RNNs across several areas, encompassing the energy shift in both passenger and freight transport, logistics management, integration of low- and zero-emission vehicles, and the energy dynamics of transport and logistics. Additionally, the role of RNNs in traffic and infrastructure planning is explored, particularly in forecasting traffic flow, congestion patterns, and optimizing energy usage. The crux of this review is to amalgamate and present the existing knowledge on the instrumental role of RNNs in facilitating the transition to energy-efficient urban transportation and logistics. Our goal is to highlight effective strategies, pinpoint challenges, and map out avenues for future research in this domain.
Adres strony internetowej (link) do pełnego tekstu publikacji: https://repository.am.szczecin.pl/handle/123456789/2827
DOI: 10.17402/609
Tytuł: Advancements in Artificial Intelligence Circuits and Systems (AICAS)
Autor/Autorzy: Tymoteusz Miller, Irmina Durlik, Ewelina Kostecka, Paulina Mitan-Zalewska, Sylwia Sokołowska, Danuta Cembrowska-Lech, Adrianna Łobodzińska
Miejsce publikacji: Electronics
Rok: 2023
Słowa kluczowe: AI circuit design, neuromorphic computing, quantum AI technologies, machine learning algorithms, AI hardware innovations
Abstrakt: In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics.
Adres strony internetowej (link) do pełnego tekstu publikacji: pełny tekst
DOI: 10.3390/app132011217
Tytuł: Predictive modeling of urban lake water quality using machine learning: A 20-year study
Autor/Autorzy: Tymoteusz Miller, Irmina Durlik, Krzemińska Adrianna, Kisiel Anna, Danuta Cembrowska-Lech, Ireneusz Spychalski, Tomasz Tuński
Miejsce publikacji: Sensors
Rok: 2023
Słowa kluczowe: urban lake, water quality, machine learning, prediction, regression, neural networks, random forest
Abstrakt: Water-quality monitoring in urban lakes is of paramount importance due to the direct implications for ecosystem health and human well-being. This study presents a novel approach to predicting the Water Quality Index (WQI) in an urban lake over a span of two decades. Leveraging the power of Machine Learning (ML) algorithms, we developed models that not only predict, but also provide insights into, the intricate relationships between various water-quality parameters. Our findings indicate a significant potential in using ML techniques, especially when dealing with complex environmental datasets. The ML methods employed in this study are grounded in both statistical and computational principles, ensuring robustness and reliability in their predictions. The significance of our research lies in its ability to provide timely and accurate forecasts, aiding in proactive water-management strategies. Furthermore, we delve into the potential explanations behind the success of our ML models, emphasizing their capability to capture non-linear relationships and intricate patterns in the data, which traditional models might overlook.
Adres strony internetowej (link) do pełnego tekstu publikacji: pełny tekst
DOI: 10.3390/app132011217
Tytuł: Navigating the sea of data: A comprehensive review on data analysis in maritime IoT applications
Autor/Autorzy: Irmina Durlik, Tymoteusz Miller, Danuta Cembrowska-Lech, Adrianna Krzemińska, Ewelina Złoczowska, Aleksander Nowak
Miejsce publikacji: Applied Sciences-Basel
Rok: 2023
Słowa kluczowe: maritime industry, Internet of Things (IoT), data analysis, machine learning, predictive maintenance
Abstrakt: The Internet of Things (IoT) is significantly transforming the maritime industry, enabling the generation of vast amounts of data that can drive operational efficiency, safety, and sustainability. This review explores the role and potential of data analysis in maritime IoT applications. Through a series of case studies, it demonstrates the real-world impact of data analysis, from predictive maintenance to efficient port operations, improved navigation safety, and environmental compliance. The review also discusses the benefits and limitations of data analysis and highlights emerging trends and future directions in the field, including the growing application of AI and Machine Learning techniques. Despite the promising opportunities, several challenges, including data quality, complexity, security, cost, and interoperability, need to be addressed to fully harness the potential of data analysis in maritime IoT. As the industry continues to embrace IoT and data analysis, it becomes critical to focus on overcoming these challenges and capitalizing on the opportunities to improve maritime operations.
Adres strony internetowej (link) do pełnego tekstu publikacji: pełny tekst
DOI: 10.3390/app13179742
Tytuł: Revolutionizing marine traffic management: A comprehensive review of machine learning applications in complex maritime systems
Autor/Autorzy: Irmina Durlik, Tymoteusz Miller, Lech Dorobczyński, Polina Kozlovska, Tomasz Kostecki
Miejsce publikacji: Applied Sciences-Basel
Rok: 2023
Słowa kluczowe: machine learning, maritime systems, marine traffic management, predictive analytics, autonomous vessels
Abstrakt: This review article explores the applications and impacts of Machine Learning (ML) techniques in marine traffic management and prediction within complex maritime systems. It provides an overview of ML techniques, delves into their practical applications in the maritime sector, and presents an in-depth analysis of their benefits and limitations. Real-world case studies are highlighted to illustrate the transformational impact of ML in this field. The article further provides a comparative analysis of different ML techniques and discusses the future directions and opportunities that lie ahead. Despite the challenges, ML’s potential to revolutionize marine traffic management and prediction, driving safer, more efficient, and more sustainable operations, is substantial. This review article serves as a comprehensive resource for researchers, industry professionals, and policymakers interested in the interplay between ML and maritime systems.
Adres strony internetowej (link) do pełnego tekstu publikacji: pełny tekst
DOI: 10.3390/app13148099
Tytuł: Statistical model of ship delays on the fairway in terms of restrictions resulting from the port regulations: case study of Świnoujście-Szczecin fairway
Autor/Autorzy: Irmina Durlik, Lucjan Gucma, Tymoteusz Miller
Miejsce publikacji: Applied Sciences
Rok: 2023
Słowa kluczowe: vessel traffic, simulation model, vessels` delays, regression analysis
Abstrakt: The article describes a study of ship delays on the Świnoujście-Szczecin waterway observed by the VTS operator. The research has led to an understanding of the factors that affect delays of ships calling at the ports of Szczecin and Police, as well as the possibilities of predicting and preventing these delays. This article presents the results of the study on the traffic intensity on the investigated waterway and the process of identifying the port regulation that causes the most frequent delays. Based on the obtained results from the statistical analysis and from using multiple regression, a statistical model has been developed that has the ability to estimate expected delays. Additionally, the model has been expanded to calculate financial losses resulting from delays, taking into account the daily cost of maintaining the studied ships. The study took place during ongoing project “Modernization of the Świnoujście-Szczecin waterway to a depth of 12.5 m” but does not include delays resulting from this project.
Adres strony internetowej (link) do pełnego tekstu publikacji: pełny tekst
DOI: 10.3390/app13095271
Tytuł: IoT in Water Quality Monitoring—Are We Really Here?
Autor/Autorzy: Małgorzata Miller, Anna Kisiel, Danuta Cembrowska-Lech, Tymoteusz Miller, Irmina Durlik
Miejsce publikacji: Sensors
Rok: 2023
Słowa kluczowe: IoT, water quality, efficiency
Abstrakt: The Internet of Things (IoT) has become widespread. Mainly used in industry, it already penetrates into every sphere of private life. It is often associated with complex sensors and very complicated technology. IoT in life sciences has gained a lot of importance because it allows one to minimize the costs associated with field research, expeditions,
and the transport of the many sensors necessary for physical and chemical measurements. In the literature, we can find many sensational ideas regarding the use of remote collection of environmental research. However, can we fully say that IoT is well established in the natural sciences?
Adres strony internetowej (link) do pełnego tekstu publikacji: pełny tekst