Systemic And Hole Semantics In Human-Machine Language Interfaces

Giachos, Ioannis; Papakitsos, Evangelos C.; Antonopoulos, Ioannis; Laskaris, Nikolaos A.

doi:10.1109/emes58375.2023.10171635

2023 17th International Conference on Engineering of Modern Electric Systems (EMES) 2023

DOI: 10.1109/emes58375.2023.10171635

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Systemic And Hole Semantics In Human-Machine Language Interfaces

Ioannis Giachos

Evangelos C. Papakitsos

Ioannis Antonopoulos

et al.

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Cited by 2 publications

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Natural Language Processing in Knowledge-Based Support for Operator Assistance

Besharati Moghaddam,

Lopez,

De Vuyst

et al. 2024

Applied Sciences

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Manufacturing industry faces increasing complexity in the performance of assembly tasks due to escalating demand for complex products with a greater number of variations. Operators require robust assistance systems to enhance productivity, efficiency, and safety. However, existing support services often fall short when operators encounter unstructured open questions and incomplete sentences due to primarily relying on procedural digital work instructions. This draws attention to the need for practical application of natural language processing (NLP) techniques. This study addresses these challenges by introducing a domain-specific dataset tailored to assembly tasks, capturing unique language patterns and linguistic characteristics. We explore strategies to process declarative and imperative sentences, including incomplete ones, effectively. Thorough evaluation of three pre-trained NLP libraries—NLTK, SPACY, and Stanford—is performed to assess their effectiveness in handling assembly-related concepts and ability to address the domain’s distinctive challenges. Our findings demonstrate the efficient performance of these open-source NLP libraries in accurately handling assembly-related concepts. By providing valuable insights, our research contributes to developing intelligent operator assistance systems, bridging the gap between NLP techniques and the assembly domain within manufacturing industry.

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Natural Language Processing in Knowledge-Based Support for Operator Assistance

Besharati Moghaddam,

Lopez,

De Vuyst

et al. 2024

Applied Sciences

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Investigation of Sediment Pollution in the Gulf of Elefsina Using Environmental Indicators

Paschaliori,

Palmos,

Papakitsou

et al. 2023

International Journal of Environmental Engineering and Development

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In this paper, the sediment pollution in the Gulf of Elefsina, over the period 1986-2010, has been measured and presented. The pollution had been measured regarding heavy metals, like cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), manganese (Mn), nickel (Ni) and zinc (Zn). Initially, the measured area is presented, along with the sources of pollution in the historical background and the wider region. Three pollutant indicators had been calculated for the investigation of Sediment Pollution: The Pollution Load Index (PLI), the Enrichment Factor (EF) and the Geo-accumulation index (Igeo). The results of the Pollution Control and Environmental Quality Office measurements had been used for the calculation of these indicators, taken at six locations of the coast and three locations in the center of the Gulf of Elefsina. The study of these indicators reveals that there is heavy metal pollution at all sampling locations, while the most heavily affected areas of the Gulf are these near Skaramangas Shipyards and Elefsis Shipyards. More intense pollution is recorded for Cd, Cu, Fe, Pb, Zn. More specifically, high values of Cd had been measured at all sampling points, while for Cu, Fe, Pb and Zn high values had been measured in areas that are active in shipbuilding-repair and dismantling units. In contrast, there is no pollution for Mn.

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Systemic And Hole Semantics In Human-Machine Language Interfaces

Cited by 2 publications

References 4 publications

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Natural Language Processing in Knowledge-Based Support for Operator Assistance

Investigation of Sediment Pollution in the Gulf of Elefsina Using Environmental Indicators

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