2026· SINTEZA· pp. 638-644· 0 citations· 17 references
TL;DR
It is demonstrated how specialized AI agents—including agents for visual analysis, technical diagnostics, compliance verification, and interactive communication—can autonomously identify defects, calculate the remaining service life, and generate valid reports for registries such as CROPP.
Abstract
: This paper investigates the transformation of the traditional pressure equipment (PE) inspection process into an intelligent digital workflow through the application of advanced artificial intelligence technologies. The research focuses on overcoming the issues of inspector cognitive overload and the inefficient management of extensive documentation during the interpretation of the Pressure Equipment Directive (PED) and national regulations. An innovative multimodal architecture is proposed, based on a Multi-Agent System (MAS) and Vision-Large Language Models (VLLMs) utilizing a Retrieval-Augmented Generation (RAG) mechanism for dynamic compliance validation. Through a case study of a Liquefied Petroleum Gas (LPG) tank inspection, it is demonstrated how specialized AI agents—including agents for visual analysis, technical diagnostics, compliance verification, and interactive communication—can autonomously identify defects, calculate the remaining service life, and generate valid reports for registries such as CROPP.
Inspection and testing underpin product safety and regulatory compliance across industries such as manufacturing, healthcare and food and beverage. However, conventional engineering test processes that mainly depend on manual, offline actions, failed to adapt to next generation, automated, digital and environmental-sensitive testing, resulting in very low productivity and high cost for most firms. The automation and digitalization of inspection and testing processes have become a research hotspot in both academia and industry. The natural language processing and computer interpretation of test results have been a focus of AI research as well. Nonetheless, due to the lack of real data, the verification and simulation of real inspection and testing environments are still difficult for researchers. As a solution, we manage to develop a multimodal cognitive test system that fuses the textual regulatory documents and instrument data, through a series of modules and processing flows. Our test system is based on a so-called multimodal cognitive agent, which includes large language model, vision module, knowledge graph and retrieval-augmented generation. We introduce the design, development and application of our test system, which used for rubber heater in a glass factory, and layout the future challenges for the exploration of multimodal agent technology in the test engineering. The project is supported by the Guangxi Key Research and Development Program. This paper was partially presented at the IEEE 2023 International Conference on Intelligent Commerce (ICIC).
Zhiruo Li, Wenlin You, Xinrong Zhang et al.· Frontiers in Computing and I...· 0 citations
We present the first deployment of an end-to-end autonomous control system driven by a large language model (LLM) on an operational solar telescope—the Solar Full-disk Multi-layer Magnetograph, named JW-ASTClaw. This system employs a multi-agent framework adopting a decoupled three-layer architecture (perception–decision–execution) interconnected through the Model Context Protocol, which addresses real-time adaptive scheduling under complex environmental conditions while achieving high portability: the perception and decision logic are reused unchanged across instruments, requiring only telescope-specific command interfaces to be adapted. Three perception agents—data-quality-agent, cloud-analyzer-agent, and flare-detector-agent—encode senior observer expertise, including wind jitter detection via limb-ring standard deviation, projected-circle zonal cloud analysis, and multi-band active region identification, as LLM-callable rules, while a central reasoning engine performs multi-source fusion and conflict resolution. The system supports graceful degradation from cloud LLM to local inference and finally to rule-based fallback, designed for remote field stations with unstable connectivity. Cross-season validation on archival data demonstrates 100% cloud detection with zero false positives across 10 distinct observation dates, with active-region counts and positions closely matching the NOAA Solar Region Summary (SRS) reports (102 versus 100 across 10 separate validation dates). These capabilities significantly improve scientific-intent-driven observation accessibility, enable rapid flare response for space weather monitoring, enhance data usability under adverse conditions, and increase observability during partially cloudy periods. This work represents the first concrete engineering step toward the embodied intelligent solar telescope concept, providing a validated foundation for the transition from automated scheduling to AI-driven autonomous observation.
Liyue Tong, Jiaben Lin, Yuanyong Deng et al.· Research in Astronomy and As...· 1 citation
Artificial intelligence (AI) is transforming smart manufacturing by enabling intelligent automation, data-driven decisions, and stronger collaboration between humans and manufacturing systems. The widespread adoption of collaborative robots, the industrial internet of things, and cyber-physical systems is driving demand for manufacturing environments that are safer, more flexible, and more efficient. Despite AI’s broad application in manufacturing, few studies have combined adaptive safety and intelligent task allocation within a single human-centered framework. This review offers a comprehensive look at AI applications that support these two complementary functions. Literature from Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and the ACM Digital Library was systematically reviewed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and analysed thematically. The findings show that machine learning, deep learning, computer vision, reinforcement learning, knowledge-driven approaches, digital twins, and explainable AI contribute to improvements in predictive maintenance, quality inspection, production scheduling, adaptive safety, and collaborative decision-making. That said, challenges remain - such as interoperability, explainability, limited access to high-quality manufacturing data, industrial validation, and integrating multiple AI technologies. This review gives researchers and practitioners a holistic perspective and highlights integrated, human-centered AI frameworks as key enablers of resilient, efficient, and sustainable Industry 5.0 manufacturing systems.
Zaliha Baso, N. Yadav, P. Faujdar· Cureus Journal of Computer S...· 0 citations
This study formally evaluates the effectiveness of AI models over multiple iterations of the models’ architecture for the domain-specific application of automated construction hazard assessment from multimodal inputs and introduces and validates high-fidelity, game engine-based synthetic images as a solution.
Trevor Neece, A. Fascetti· Journal of computing in civi...· 1 citation· ⚡1
The rapid evolution of intelligent transportation systems has positioned conditionally automated vehicles (CAVs, SAE Level 3) at the forefront of automotive innovation. These vehicles represent a critical transition between human-driven and fully autonomous systems, in which safety and reliability depend on effective management of takeover control (TOC) events. This paper introduces a Case-Based Reasoning (CBR) framework to model, evaluate, and improve decision-making during control transitions using empirical and contextual data from both human and vehicular agents. The framework follows the CBR cognitive cycle of retrieval, reuse, revision, and retention to compare new TOC scenarios with previously observed cases. Each case integrates multimodal information, including driver personal traits, non-driving-related tasks, traffic density, and takeover urgency, as well as temporal and spatial performance metrics such as takeover time and steering behaviour. The time budget to system limitation is used as the determining outcome variable. By capturing and reusing experiential knowledge, the proposed framework enables adaptive and interpretable decision-making for Level 3 automation. It supports bidirectional learning between drivers and automated systems and provides a foundation for future Levels 4 and 5 vehicles to incorporate human-like reasoning in safety-critical decisions.
A. Mostafavi, Wenge Xu, O. Carsten et al.· AHFE International· 0 citations
Artificial intelligence (AI) is increasingly central to power and energy systems, supporting modeling, forecasting, optimization, and control. Yet most existing works emphasize specialized applications and offer little reusable material for newcomers or interdisciplinary learners, who increasingly rely on large language models rather than building their own. This gap points to a need for engineering-grounded AI (EGAI), in which AI workflows follow established engineering and power-system domain rules rather than acting as task-agnostic black boxes. Motivated by a community survey of researchers and practitioners, which shows 92% report at least one barrier before running an AI model and 94% want a power-specific hands-on course. This paper presents a framework consisting of open, executable module library that lowers the entry barrier for AI in power systems. The modules follow a progressive difficulty ladder that maps core AI concepts onto representative power-system tasks: (i) foundational deep neural network (DNN) templates for function approximation and load-curve fitting; (ii) a domain-coupled convolutional neural network (CNN) power-flow surrogate for a 5-bus system; and (iii) frontier modules on DNN-assisted optimization, deep reinforcement learning (DRL) for battery storage control, and physics-informed neural networks (PINNs) for the swing equation. All modules are released as Jupyter notebooks that run locally or on Google Colab and are delivered through an IEEE online course and IEEE Power&Energy Society (PES) webinar series. The webinar drew more than 590 live attendees, which is among the ten most-attended IEEE PES webinars, and over 344 repository visits within two weeks, reinforcing the survey-based motivation.
Junjie Yin, Buxin She, Xinyu Feng et al.· 1 citation
