Computer Vision Algorithms, Remote Sensing Data Fusion Techniques, and Mapping and Navigation Tools in the Industry 4.0-Based Slovak Automotive Sector

Nagy, Marek; Lăzăroiu, George

doi:10.3390/math10193543

Cited by 45 publications

(19 citation statements)

References 106 publications

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“…Autonomous Vehicles and Traffic Management: In the realm of autonomous vehicles, YOLO plays a crucial role in object detection for obstacle avoidance and navigation [49]. Its rapid identification and classification of objects contribute to the safe and efficient operation of autonomous vehicles [50].…”

Section: Real-world Applications and Impactmentioning

confidence: 99%

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO

Hussain

2024

IEEE Access

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This paper implements a systematic methodological approach to review the evolution of YOLO variants. Each variant is dissected by examining its internal architectural composition, providing a thorough understanding of its structural components. Subsequently, the review highlights key architectural innovations introduced in each variant, shedding light on the incremental refinements. The review includes benchmarked performance metrics, offering a quantitative measure of each variant's capabilities. The paper further presents the performance of YOLO variants across a diverse range of domains, manifesting their real-world impact. This structured approach ensures a comprehensive examination of YOLOs journey, methodically communicating its internal advancements and benchmarked performance before delving into domain applications. It is envisioned, the incorporation of concepts such as federated learning can introduce a collaborative training paradigm, where YOLO models benefit from training across multiple edge devices, enhancing privacy, adaptability, and generalisation.

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Section: Real-world Applications and Impactmentioning

confidence: 99%

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO

Hussain

2024

IEEE Access

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“…Sample plot inventory [44], model inversion [45], and flux observation [46] are included in this category. Simple AI methods have been extensively applied for NPP estimation over large scales as well [47]- [50]. However, simple AI models yield quite different results from traditional methods, so their validity is questionable; simple AI models are more controversial in terms of NPP estimation [26].…”

Section: A Carbon Neutralization In Citymentioning

confidence: 99%

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

et al. 2023

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Carbon storage capacity can be estimated to establish evaluation standards and statistics for carbon neutrality. Existing estimation methods including machine learning system have weakness modeling ability, and they are unable to deal with the complex topographies and temporal changes in vegetation in urban zones. However, a deep neural network has the potential ability to face such complex scenes because of its nonlinear fitting properties which has been widely used in industry. In this study, a novel and powerful neural network learning system named CiSL-NPP is proposed, to integrate multi-source data as an accurate, efficient, simple, long-term, city-scale carbon storage capacity measurement method. We use an unsupervised generation model spatio-temporal Masked AutoEncoder (st-MAE) and lightweight RNN model to efficiently obtain high-quality data that contain time-series information such as seasons; this minimizes any undue impact of meteorological, temporal, and spatial factors on the measurement owing to the multi-source, multi-modal nature of the data. In the MAE, we add seasonal coding to make it time-series sensitive; we also embed road network information to accurately perceive the complex topography of the city. Results for 16 cities in China and Europe revealed that the proposed method shows: 1) Higher-quality generated data (MSE is 0.13-0.29); 2) Accurate coverage of time series and complex geographical features; 3) Satisfaction of estimation demands with only 316 RMB cost; 4) Capability to evolve a long-term trend of urban vegetation carbon storage capacity in 4.2 days; and 5) Easily interpretable results which could, in practice, provide sound guidance for urban planning and decision-making processes. INDEX TERMSNeural network Learning System, Unsupervised generation model, Spatio-temporal Masked AutoEncoder, Carbon Storage Capacity Measurement I. INTRODUCTION M ORE than 90% of human economic activity takes place in cities [1], which directly contribute at least 85% of global carbon emissions [2] and are regarded as the world's main battlefields of carbon neutrality [3]. Currently, carbon mitigation and carbon sink increment are the primary approaches to achieving urban carbon neutrality. The carbon mitigation approach, a complex systematic undertaking, is difficult to implement [4]. Further, strict policies towards emissions may have consequences such as carbon leakage [5], [6]. The carbon sink increment approach shows more potential to achieve urban carbon neutrality. For example, studies indicate that the carbon sink of urban vegetation can offset more than 22.45% of carbon emissions [7], already achieving carbon neutrality in some areas [8]. In city, limited to a fixed-size space, the effective way to increase carbon sink is to use green resources (e.g., municipal parks) rationally according to their vegetation carbon storage capacity. Net Primary Productivity (NPP), a crucial metric reflecting the storage effect of vegetation , is the key role for increasing carbon storage capacity in the city ...

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“…Using stateof-the-art technologies, Industry 4.0 digitally transforms production networks [27][28][29]. Moreover, the goal of Industry 4.0 is to improve managerial decision-making via the use of real-time data related to energy consumption, production status, machines, customer orders, and material flows using technologies such as artificial intelligence, the IoT, big data analytics, and robotics [5,[30][31][32]. There is a lack of consensus in the existing literature on the technologies that fall within the Industry 4.0 umbrella [33].…”

Section: Industry 40mentioning

confidence: 99%

The Nexus of Industry 4.0 and Circular Procurement: A Systematic Literature Review and Research Agenda

Rejeb

Appolloni

2022

Sustainability

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In the wake of Industry 4.0, circular supply chain management has undergone radical transformations. Organizations have been able to boost their efficiency thanks to incorporating Industry 4.0 technologies such as big data analytics, the internet of things (IoT), blockchain, cloud computing, artificial intelligence, etc., into various supply chain functions. Since circular procurement (CP) is an essential part of circular supply chain management, it can provide fresh chances for organizations to become more efficient and sustainable. With the support of Industry 4.0 technologies, CP can provide extra opportunities for accelerating the shift to the circular economy. Although research into the intersection of procurement and Industry 4.0 continues to advance, no review study has examined the implications of Industry 4.0 in CP. Therefore, this research aims to address this knowledge gap by systematically reviewing the existing literature about the applications of Industry 4.0 technologies in CP. A total of 89 journal articles were selected from Scopus and Web of Science databases to identify the themes discussed and set an agenda for future research. A conceptual framework is also developed to evaluate the antecedents, enablers, and performance outcomes of Industry-4.0-enabled CP. Finally, the review concludes by highlighting the theoretical and practical implications.

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Computer Vision Algorithms, Remote Sensing Data Fusion Techniques, and Mapping and Navigation Tools in the Industry 4.0-Based Slovak Automotive Sector

Cited by 45 publications

References 106 publications

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO

YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO

A Spatio-Temporal Neural Network Learning System for City-Scale Carbon Storage Capacity Estimating

The Nexus of Industry 4.0 and Circular Procurement: A Systematic Literature Review and Research Agenda

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