Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Enhancing environmental safety: Innovations in radiation monitoring and security with machine learning

Durlik Irmina, Miller Tymoteusz, Kostecka Ewelina, Sokołowska Sylwia, Kostecki Tomasz, Zwolak Rafał

Zeszyty Naukowe Politechniki Morskiej w Szczecinie

2025

nr 84 (156)

34--45

In this article, we delve into the fusion of machine learning (ML) and Internet of Things (IoT) technologies to redefine environmental radiation monitoring and security. By harnessing these advanced technologies, this work presents a novel approach to radiation safety, emphasizing enhanced real-time monitoring, precision in detection, and improved regulatory compliance. Through an in-depth analysis of various case studies and methodologies, it uncovers the potential of ML and IoT in overcoming traditional challenges, such as data accuracy and privacy concerns. The discussion extends to the implications of these technologies on environmental safety, offering a forward-looking perspective on the evolution of radiation monitoring systems. This article not only addresses the technical and ethical challenges but also highlights the transformative impact of ML and IoT integration on public health and environmental protection, paving the way for innovative solutions in the domain of environmental safety and security.

Utilizing recurrent neural networks in sustainable urban transport and logistics

Durlik Irmina, Miller Tymoteusz, Kostecka Ewelina, Pusty Tomasz, Łobodzińska Adrianna

Zeszyty Naukowe Politechniki Morskiej w Szczecinie

2024

nr 78 (150)

131--149

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.