Enhancing trust in automated 3D point cloud data interpretation through explainable counterfactuals

Information Fusion, Год журнала: 2025, Номер unknown, С. 103032 - 103032

Опубликована: Фев. 1, 2025

Язык: Английский

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MASFNet: Multi-level attention and spatial sampling fusion network for pine wilt disease trees detection

Ecological Indicators, Год журнала: 2025, Номер 170, С. 113073 - 113073

Опубликована: Янв. 1, 2025

Язык: Английский

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Advances in Cable Yarding: a Review of Recent Developments in Carriers for Mobile Skyline Cable Yarding

Current Forestry Reports, Год журнала: 2025, Номер 11(1)

Опубликована: Март 4, 2025

Язык: Английский

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A Novel Framework of Human–Computer Interaction and Human-Centered Artificial Intelligence in Learning Technology

Cognitive systems monographs, Год журнала: 2025, Номер unknown, С. 387 - 431

Опубликована: Янв. 1, 2025

Язык: Английский

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On the disagreement problem in Human-in-the-Loop federated machine learning

Journal of Industrial Information Integration, Год журнала: 2025, Номер unknown, С. 100827 - 100827

Опубликована: Март 1, 2025

Язык: Английский

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Global-scale improvement of terrestrial gross primary productivity estimation by integrating optical remote sensing with meteorological data

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113429 - 113429

Опубликована: Апрель 1, 2025

Язык: Английский

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MODEL DEVELOPMENT OF DYNAMIC REPRESENTATION A MODEL DESCRIPTION PARAMETERS FOR THE ENVIRONMENT OF A COLLABORATIVE ROBOT MANIPULATOR WITHIN THE INDUSTRY 5.0 FRAMEWORK

Системи управління навігації та зв’язку Збірник наукових праць, Год журнала: 2025, Номер 1(79), С. 42 - 48

Опубликована: Март 12, 2025

The article presents a study on the development of model for dynamic representation environmental description parameters collaborative robot manipulator within Industry 5.0 requirements. main focus is mathematical that allows to quickly adapt changes in workspace, ensuring effective and safe interaction with humans. proposed takes into account data from various sensor systems, such as lidars, cameras, ultrasonic sensors, continuously update information about environment. also considers algorithms optimize process collection processing improve accuracy prediction response robot. results work are aimed at increasing efficiency robots production environments, improving level automation harmonious cooperation between humans machines modern cyber manufacturing systems.

Язык: Английский

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Usability in human-robot collaborative workspaces

Universal Access in the Information Society, Год журнала: 2024, Номер unknown

Опубликована: Окт. 25, 2024

Abstract This study explores the usability of human-robot collaboration in previously under-researched field forestry and agroforestry. The robotic platforms used were Boston Dynamics Spot Agile X Bunker, latter equipped with a movable arm. research was conducted an experimental test park, simulating real-world scenarios relevant to agriculture. focus this is on use these robots as collaborative (cobots). Usability, central characteristic human-computer interaction, evaluated using well-established System Usability Scale (SUS). results demonstrate potential systems enhance productivity safety, while also underscoring importance user-centered design development tools. A key finding work that successful integration AI-driven technologies sectors such agriculture requires human-centered AI which includes good usability, accessibility, emphasizing concept universal access.

Язык: Английский

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Integrating Belief-Desire-Intention agents with large language models for reliable human–robot interaction and explainable Artificial Intelligence

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 141, С. 109771 - 109771

Опубликована: Дек. 13, 2024

Язык: Английский

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Developing an AI Tool for Forest Monitoring: Introducing SylvaMind AI

Bulletin of the Transilvania University of Brasov Series II Forestry • Wood Industry • Agricultural Food Engineering, Год журнала: 2024, Номер unknown, С. 39 - 54

Опубликована: Дек. 16, 2024

Global forests face increasing threats from deforestation, biodiversity loss, and climate change, necessitating innovative tools for effective monitoring management. Traditional forest methods, which rely heavily on manual fieldwork labor-intensive data processing, are often inadequate addressing the scale complexity of these challenges. Advanced leveraging artificial intelligence (AI) remote sensing have emerged as critical solutions, offering timely, accurate, actionable insights to enable efficient ecosystem monitoring, threat detection, sustainable management practices. This paper introduces SylvaMind AI, an advanced platform that integrates satellite imagery, deep learning frameworks, geospatial analysis within a user-friendly interface, was built using Python backend systems pipelines, alongside like Pandas, Rasterio, TensorFlow preprocessing predictive modelling. The processes high-resolution Sentinel-2 Landsat missions feature extraction AI offers two modelling approaches: automated option non-technical users customizable researchers with specialized needs. Using approaches, we developed canopy height model study area. results demonstrated platform's ability capture underlying patterns provide detailed into distribution, particularly medium high canopies (>25m). underscores its strength in modeling structural dense forests. However, showed limitations representing smaller trees, attributed insufficient training data. holds immense potential transforming by data, intuitive design address challenges

Язык: Английский

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