Hasil untuk "Technological innovations. Automation"

Bongumsa Mendu, Nhlanhla Mbuli

Unmanned aerial vehicles (UAVs) make power line inspections more safe, efficient, and cost-effective, replacing risky manual checks and expensive helicopter surveys while overcoming challenges like stability and regulations. The aim of this study is to conduct a systematic review of the application of UAVs for power line inspections. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology is implemented to ensure a structured and comprehensive review process. The Scopus database is used to identify relevant publications, and after screening and applying eligibility criteria, 75 documents were selected for further analysis. The study results show a shift toward predictive maintenance, multi-UAV operations, and real-time data analysis. However, challenges remain, including UAV–grid connectivity, resilience to extreme weather, and large-scale automation. This work provides key insights into technological and algorithmic advancements and research trends on UAV-based power line inspections while pointing out gaps in the existing literature. Finally, future research directions to advance UAV-based power line inspections are suggested.

Sadia Afrin, Shobnom Roksana, Riad Akram

The rapid technological growth in recent decades due to the integration of robust technologies and automation have led to the rise of digital services and the emergence of Industry 4.0. This paper explores the concept and potential of AI-powered intelligent automation based on the synergistic use of Robotic Process Automation (RPA) and Artificial Intelligence (AI) to enhance organizational and business processes across various sectors. RPA automates routine, rules-based tasks, thereby allowing human workers to engage in more innovative activities. When integrated with AI, RPA systems gain the capacity to analyze data, identify patterns, classify information and forecast which leads to significant improvement in accuracy and productivity. This literature review investigates the current state of RPA and AI integration while highlighting its applications in different sectors such as manufacturing, agriculture, healthcare, finance, and retail. Along with discussing the drawbacks and restrictions, such as technological issues and moral dilemmas, this paper also discusses the advantages of this integration, which include decreased costs, increased output, and simplified operations. By leveraging AI techniques such as classification, text mining of neural network, RPA technologies optimize business operations and advance Industry 4.0. This study also illustrates the challenges and limitations of this integration such as technical difficulties and ethical considerations. The aim of this review is to provide a comprehensive understanding of the synergistic potential of RPA and AI while offering insights into their contribution in shaping the future of intelligent automation.

Li Jiang, Boyan Xu, Naveed Husnain et al.

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production.

Obinna Joshua Ochulor, Oludayo Olatoye Sofoluwe, Ayemere Ukato et al.

Subsea maintenance and production represent critical aspects of offshore operations, vital for sustaining energy production and ensuring operational efficiency. However, these endeavors face numerous challenges, including the complexities of deepwater environments, harsh weather conditions, and the high costs associated with traditional maintenance methods. To address these challenges, this paper explores the integration of technological innovations and optimized work methods in subsea operations. Technological innovations play a pivotal role in revolutionizing subsea maintenance and production. Remote monitoring and control systems enable real-time data collection and decision-making, enhancing operational visibility and efficiency. Autonomous underwater vehicles (AUVs) offer the capability to conduct inspections and repairs in remote and hazardous environments, reducing human intervention and associated risks. Robotics and automation further streamline maintenance tasks, improving accuracy and reducing downtime. Advanced materials and coatings enhance equipment durability and corrosion resistance, prolonging asset lifecycles and reducing maintenance requirements. In parallel, optimized work methods offer strategic approaches to enhance subsea operations' effectiveness. Predictive maintenance strategies leverage data analytics and machine learning to anticipate equipment failures, enabling proactive interventions and minimizing downtime. Condition-based monitoring facilitates real-time assessment of asset health, enabling timely maintenance interventions and cost savings. Integrated asset management systems provide holistic insights into asset performance and facilitate informed decision-making. Lean operations and continuous improvement methodologies further optimize workflows, driving operational excellence and cost efficiency. Through case studies and industry examples, this paper highlights the successful implementation of technological innovations and optimized work methods in subsea maintenance and production. Furthermore, it explores future trends, regulatory considerations, and the importance of industry collaborations in shaping the future of subsea operations. Ultimately, the integration of these approaches offers a pathway towards enhanced operational efficiency, reduced costs, and sustainable subsea operations. Keywords: Technological Innovations, Optimized Work Methods, Subsea Maintenance and Production.

Eline de Jong, Sebastian De Haro

Technological understanding is not a singular concept but varies depending on the context. Building on De Jong and De Haro's (2025) notion of technological understanding as the ability to realise an aim by using a technological artefact, this paper further refines the concept as an ability that varies by context and degree. We extend its original specification for a design context by introducing two additional contexts: operation and innovation. Each context represents a distinct way of realising an aim through technology, resulting in three types (specifications) of technological understanding. To further clarify the nature of technological understanding, we propose an assessment framework based on counterfactual reasoning. Each type of understanding is associated with the ability to answer a specific set of what-if questions, addressing changes in an artefact's structure, performance, or appropriateness. Explicitly distinguishing these different types helps to focus efforts to improve technological understanding, clarifies the epistemic requirements for different forms of engagement with technology, and promotes a pluralistic perspective on expertise.

Ahmad Farooq, Kamran Iqbal

Reinforcement Learning (RL) has become a critical tool for optimization challenges within automation, leading to significant advancements in several areas. This review article examines the current landscape of RL within automation, with a particular focus on its roles in manufacturing, energy systems, and robotics. It discusses state-of-the-art methods, major challenges, and upcoming avenues of research within each sector, highlighting RL's capacity to solve intricate optimization challenges. The paper reviews the advantages and constraints of RL-driven optimization methods in automation. It points out prevalent challenges encountered in RL optimization, including issues related to sample efficiency and scalability; safety and robustness; interpretability and trustworthiness; transfer learning and meta-learning; and real-world deployment and integration. It further explores prospective strategies and future research pathways to navigate these challenges. Additionally, the survey includes a comprehensive list of relevant research papers, making it an indispensable guide for scholars and practitioners keen on exploring this domain.

Chinmay Vilas Samak, Tanmay Vilas Samak, Bing Li et al.

Simulation-based design, optimization, and validation of autonomous vehicles have proven to be crucial for their improvement over the years. Nevertheless, the ultimate measure of effectiveness is their successful transition from simulation to reality (sim2real). However, existing sim2real transfer methods struggle to address the autonomy-oriented requirements of balancing: (i) conditioned domain adaptation, (ii) robust performance with limited examples, (iii) modularity in handling multiple domain representations, and (iv) real-time performance. To alleviate these pain points, we present a unified framework for learning cross-domain adaptive representations through conditional latent diffusion for sim2real transferable automated driving. Our framework offers options to leverage: (i) alternate foundation models, (ii) a few-shot fine-tuning pipeline, and (iii) textual as well as image prompts for mapping across given source and target domains. It is also capable of generating diverse high-quality samples when diffusing across parameter spaces such as times of day, weather conditions, seasons, and operational design domains. We systematically analyze the presented framework and report our findings in terms of performance benchmarks and ablation studies. Additionally, we demonstrate its serviceability for autonomous driving using behavioral cloning case studies. Our experiments indicate that the proposed framework is capable of bridging the perceptual sim2real gap by over 40%.

Enrico Saccon, Davide De Martini, Matteo Saveriano et al.

We present a novel framework that integrates Large Language Models (LLMs) with automated planning and formal verification to streamline the creation and use of Markov Decision Processes (MDP). Our system leverages LLMs to extract structured knowledge in the form of a Prolog knowledge base from natural language (NL) descriptions. It then automatically constructs an MDP through reachability analysis, and synthesises optimal policies using the Storm model checker. The resulting policy is exported as a state-action table for execution. We validate the framework in three human-robot interaction scenarios, demonstrating its ability to produce executable policies with minimal manual effort. This work highlights the potential of combining language models with formal methods to enable more accessible and scalable probabilistic planning in robotics.

Paolo Magaudda

This paper examines the emergence of the ‘gravel bike’, a new and successful category of sports bicycles that gained prominence in the global cycling industry in the late 2010s, to advance the understanding of the role of users in the processes of sociotechnical innovation. The study traces the development of gravel cycling and the gravel bike within the framework of science and technology studies (STS), introducing the concept of ‘user betrayal’ to highlight how innovations initially driven by users can later diverge from their original values and needs. The development of the gravel bike represents a case where users’ input played a crucial role in creating an alternative cycling culture that directly supported the introduction of a new, successful bicycle model. However, the commercialization and institutionalization of gravel cycling, driven by industries, institutions and sporting bodies, has led to a significant shift away from the values that motivated early enthusiasts. This case reveals the tensions between user-driven innovation and the forces of commodification, emphasizing how marketing and institutional pressures can undermine the original needs and ideals of user collectives.

John A. Mathews

Mauro Caselli, Andrea Fracasso, Arianna Marcolin et al.

Chen Zhi, Siyu Huang, Zihan Cheng

This study explores the multifaceted impact of economic indicators, financial market dynamics, and technological innovations on supply chain efficiency and resilience. Through comprehensive quantitative analyses, including panel data regression, multivariate regression, and time-series analysis, we dissect the relationship between GDP growth rates, inflation, unemployment rates, interest rate fluctuations, exchange rate volatility, stock market trends, and supply chain performance metrics. Our findings highlight a significant correlation between economic growth and supply chain efficiencies, the exacerbating effect of inflation and unemployment on supply chain costs and demand, and the nuanced impacts of financial market dynamics on supply chain financing and operational strategies. Moreover, we delve into the transformative potential of digitalization, automation, blockchain technology, and advanced analytics in mitigating risks associated with economic fluctuations and financial uncertainties. The empirical evidence suggests that technological innovations not only enhance supply chain resilience but also offer strategic advantages in navigating the complexities of global economic landscapes. This study underscores the critical need for adaptive strategies that leverage technological advancements to sustain supply chain competitiveness in an era of economic and financial volatility.

O. Oluokun, Oluwadayomi Akinsooto, O. B. Ogundipe et al.

Energy efficiency in mining operations has become a critical focus due to the sector's significant energy consumption and environmental impact. This paper explores the intersection of policy frameworks and technological innovations aimed at enhancing energy efficiency in mining. The study examines current policies promoting energy efficiency, such as government regulations, incentives, and industry standards, alongside the adoption of advanced technologies like smart sensors, automation, and machine learning for predictive maintenance. Key innovations, including energy-efficient mining equipment, optimization algorithms, and renewable energy integration, are analyzed for their potential to reduce operational costs and minimize the carbon footprint of mining activities. Additionally, the paper discusses the challenges associated with implementing these policies and technologies, such as high initial costs, regulatory compliance, and the need for skilled labor. By assessing case studies of successful energy efficiency programs in mining operations worldwide, the paper provides insights into best practices and strategies for overcoming these challenges. The findings suggest that a combination of robust policy support and cutting-edge technologies can significantly improve energy efficiency in mining, contributing to more sustainable and cost-effective operations. The paper concludes by recommending policy actions and technological pathways that can further drive energy efficiency in the mining industry, emphasizing the need for continuous innovation and collaboration among stakeholders.

Oghenekome Urefe, Theodore Narku Odonkor, Edith Ebele Agu

This review paper explores the transformative impact of technological innovations on financial reporting accuracy and compliance efficiency within legal firms. It examines the current challenges legal firms face in maintaining accurate financial records and adhering to regulatory requirements, highlighting issues such as manual errors, complex transactions, and evolving regulations. The paper delves into the roles of automation, artificial intelligence (AI), data analytics, and blockchain technology in addressing these challenges, demonstrating how these tools can enhance financial precision, transparency, and operational efficiency. Additionally, it discusses the future trends in regulatory technology (RegTech), integrated compliance management systems, and real-time monitoring and reporting, emphasizing their potential to streamline compliance processes further. Practical recommendations are provided for legal firms to adopt and integrate these emerging technologies successfully. The paper concludes by underscoring the critical importance of technological innovation in enhancing legal firms' financial and compliance capabilities, ensuring they remain competitive and compliant in a dynamic regulatory environment. Keywords:  Financial Reporting, Compliance Efficiency, Legal Firms, Artificial Intelligence, Blockchain Technology, RegTech.

R. Aguilar, Warren Cris Arcolar, Christian Earl David Calmerin et al.

The research investigated the impact of modern technology on seafarers within the maritime industry, aiming to understand how technological advancements influence their work lives through a narrative phenomenology qualitative research approach. The findings revealed a significant transformation in seafarers’ professional experiences, identifying key themes such as automation, communication evolution, continuous training, enhanced maintenance, and improved emergency response. Seafarers reported positive attitudes towards the efficiency brought by automation, while communication technologies provided global connectivity but raised concerns about constant updates. Job satisfaction was shaped by diverse roles, adaptation challenges, productivity gains, and career advancement opportunities. Continuous learning emerged as essential, underscoring the need for seafarers to stay current with evolving technologies. Adaptation strategies included developing soft skills, complying with regulations, and anticipating future trends. Recommendations emphasized continuous learning, effective time management for internet use, upgrading educational institutions, specific equipment training, and proficiency in software applications. These recommendations offer a roadmap for seafarers, educational institutions, and industry stakeholders to navigate and leverage technological advancements in the maritime sector, ensuring safety, satisfaction, and success amid ongoing technological evolution.

Irina V. Grigorieva, Leysan M. Sungatullina, Osman M. Minaev

The study discusses the significance of innovations in automated manufacturing systems for modern industry. Describes the importance of using advanced technologies such as artificial intelligence, machine learning, robotics, Internet of Things and augmented/virtual reality in manufacturing processes. The advantages and problems associated with the implementation of these innovations are analyzed, and possible solutions to them are proposed. In conclusion, the importance of priority implementation of innovations to ensure the competitiveness of enterprises in modern industry is emphasized.

Lirui Guo, Michael G. Burke, Wynita M. Griggs

Public acceptance is critical to the adoption of Shared Autonomous Vehicles (SAVs) in the transport sector. Traditional acceptance models, primarily reliant on Structural Equation Modeling, may not adequately capture the complex, non-linear relationships among factors influencing technology acceptance and often have limited predictive capabilities. This paper introduces a framework that combines Machine Learning techniques with chord diagram visualizations to analyze and predict public acceptance of technologies. Using SAV acceptance as a case study, we applied a Random Forest machine learning approach to model the non-linear relationships among psychological factors influencing acceptance. Chord diagrams were then employed to provide an intuitive visualization of the relative importance and interplay of these factors at both factor and item levels in a single plot. Our findings identified Attitude as the primary predictor of SAV usage intention, followed by Perceived Risk, Perceived Usefulness, Trust, and Perceived Ease of Use. The framework also reveals divergent perceptions between SAV adopters and non-adopters, providing insights for tailored strategies to enhance SAV acceptance. This study contributes a data-driven perspective to the technology acceptance discourse, demonstrating the efficacy of integrating predictive modeling with visual analytics to understand the relative importance of factors in predicting public acceptance of emerging technologies.

Jonathan Külz, Michael Terzer, Marco Magri et al.

In situ robotic automation in construction is challenging due to constantly changing environments, a shortage of robotic experts, and a lack of standardized frameworks bridging robotics and construction practices. This work proposes a holistic framework for construction task specification, optimization of robot morphology, and mission execution using a mobile modular reconfigurable robot. Users can specify and monitor the desired robot behavior through a graphical interface. In contrast to existing, monolithic solutions, we automatically identify a new task-tailored robot for every task by integrating \acf{bim}. Our framework leverages modular robot components that enable the fast adaption of robot hardware to the specific demands of the construction task. Other than previous works on modular robot optimization, we consider multiple competing objectives, which allow us to explicitly model the challenges of real-world transfer, such as calibration errors. We demonstrate our framework in simulation by optimizing robots for drilling and spray painting. Finally, experimental validation demonstrates that our approach robustly enables the autonomous execution of robotic drilling.

Yunho Kim, Jeong Hyun Lee, Choongin Lee et al.

For reliable autonomous robot navigation in urban settings, the robot must have the ability to identify semantically traversable terrains in the image based on the semantic understanding of the scene. This reasoning ability is based on semantic traversability, which is frequently achieved using semantic segmentation models fine-tuned on the testing domain. This fine-tuning process often involves manual data collection with the target robot and annotation by human labelers which is prohibitively expensive and unscalable. In this work, we present an effective methodology for training a semantic traversability estimator using egocentric videos and an automated annotation process. Egocentric videos are collected from a camera mounted on a pedestrian's chest. The dataset for training the semantic traversability estimator is then automatically generated by extracting semantically traversable regions in each video frame using a recent foundation model in image segmentation and its prompting technique. Extensive experiments with videos taken across several countries and cities, covering diverse urban scenarios, demonstrate the high scalability and generalizability of the proposed annotation method. Furthermore, performance analysis and real-world deployment for autonomous robot navigation showcase that the trained semantic traversability estimator is highly accurate, able to handle diverse camera viewpoints, computationally light, and real-world applicable. The summary video is available at https://youtu.be/EUVoH-wA-lA.

Caterina Conigliani, Martina Iorio, Salvatore Monni

According to the UN's Sustainable Development Agenda, to effectively achieve sustainable development, strategies for building economic growth should also address social needs, including access to essential services. Sustainable integrated management of water resources for both primary use and energy production is crucial, especially in territories such as the Amazonian State of Pará, where a primary good like fresh water is also the main source of electricity. However, the territorial transformations occurring in Pará over installing new hydroelectric plants have jeopardised local development. This was mainly caused by the top-down approach underlying national strategic projects that have paid little attention to local needs, thus paving the way for detrimental conditions for implementing the UN's 2030 Agenda. This paper aims to analyse the relationship between a municipality's level of development and quality of life and the most relevant key determinants of sustainable development in Pará. To this end, we consider a spatial regression analysis, with particular attention devoted to the role of access to both energy and water. The presence of significant spillover effects implies that providing public services on a geographically broad basis could induce self-reinforcing benefits.