Aims and Scope
Research on Intelligent Manufacturing and Assembly (RIMA) (eISSN: 2972-3329) is an international, peer-reviewed, open access journal dedicated to the latest advancements in intelligent manufacturing and assembly. RIMA serves as a critical bridge between cutting-edge research and practical applications, fostering collaboration between the academic community and industry practitioners. The journal aims to publish high-impact research that pushes the boundaries of knowledge in the design, analysis, manufacturing, and operation of intelligent systems and equipment. RIMA focuses on innovative technologies and methodologies that are transforming the manufacturing landscape, driving efficiency, precision, and sustainability in industrial processes. By publishing rigorous research and fostering a vibrant community of scholars and practitioners, RIMA aims to be the go-to resource for advancing the state-of-the-art in intelligent manufacturing and assembly.
Topics of interest include, but are not limited to the following:
• Digital design and manufacturing
• Theories, methods, and systems for intelligent design
• Advanced processing techniques
• Modelling, control, optimization, and scheduling of systems
• Manufacturing system simulation and digital twin technology
• Industrial control systems and the industrial Internet of Things (IIoT)
• Safety and reliability assessment
• Robotics and automation
• Artificial intelligence and machine learning in manufacturing
• Supply chain optimization and management
• Additive manufacturing and materials science
• Cybersecurity and data privacy in manufacturing
• Sustainability and circular economy in manufacturing
• Bio-fabrication and other advanced manufacturing methods
• Digital Workforce and Automation
• etc.
Current Issue
Vol 5 No 2 (2026)
Published: 2026-07-29
Research Article
Pages 383-393
Spectral Semantic Analytics of Local Spaces via Spectral-Semantic Neural Network Metamodel
Evgeniy Bryndin
This paper investigates the development and deployment of spectral-semantic analytical frameworks for local space analysis using neural networks. The proposed paradigm unifies spectral data processing—with measurements collected from spectrometers and thermal imagers—and semantic modeling, which frames spectral features as bearers of structured semantic information. This work elaborates on techniques for translating spectral modality into linguistic-semantic representations; this transformation enables dual characterization of local physical properties: quantitative description via spectral parameters, and qualitative description via semantic profiles and patterns. Special focus is given to building spectral-semantic dictionaries and matching neural architectures that extract and formalize contextual correlations between object spectral signatures and their semantic implications. Diverse spectrogram formats and spectral representations (multiband and hyperspectral data included) are adopted for local space characterization, alongside a neural network metamodel capable of generating domain-specific spectral-semantic models. Attention modules are embedded into these architectures to automatically screen high-value spectral bands and spatial regions pivotal to semantic reasoning.
| eISSN: 2972-3329 Abbreviation: Res Intell Manuf Assem Editor-in-Chief: Prof. Matthew Chin Heng Chua (Singapore) Publishing Frequency: Continuous publication Article Processing Charges (APC): 0 Publishing Model: Open Access |
