Writing.
Engineering notes, research, and news from the Arpuro team.
Blog for longer pieces. Field Notes for technical writing. Newsroom for announcements, releases, and press.
Perspective
Blog
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Diffuse emissions, PM, and industry 4.0: a wry look at development of SOTA 2019-2026
In this post I provide a BTS perspective on development of SOTA for PM measurements 2019-2026, based on experiences from a number of R&D projects. This is done on the backdrop of the 2023 report by NEA and Norce about "Monitoring of diffuse emissions to air from industry".
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The quest for tangible RoI from the use of generative AI for interpreting dust sensor data in metallurgical industry
Grim Gjønnes pushes back on the MIT finding that 95% of organisations see no ROI from generative AI. Using Arpuro's dust-monitoring deployments as case studies, he sketches concrete returns in heavy industry: cleaning costs, avoided failures, smarter capex.
From the engineering team
Field Notes
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Prediction of dynamic mooring responses of a floating wind turbine using an artificial neural network
A neural-network surrogate for mooring-line tensions on the OC3-Hywind floating wind turbine. Trained on 32 wave states, it reaches ~71% correlation with the reference solver at a fraction of the runtime — useful in early-stage design and fatigue iteration.
F.A. Bjørni, S. Lien, T. Aa. Midtgarden, Geir Kulia, A. Verma, Z. Jiang. IOP Conf. Series, 2021. 2021.
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Real-time passive control of wave energy converters using the Hilbert–Huang transform
A passive-control strategy for wave-energy converters that adapts damping to instantaneous wave frequency rather than using static tuning. HHT supplies the frequency at every sample; the adaptive controller improves energy absorption over fixed-gain in irregular seas.
Paula B. Garcia-Rosa, Geir Kulia, John V. Ringwood, Marta Molinas. IFAC-PapersOnLine (Elsevier). 2017.
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Understanding instantaneous frequency detection: a discussion of Hilbert–Huang Transform versus Wavelet Transform
A head-to-head between the Hilbert–Huang transform and discrete wavelet decomposition for instantaneous frequency in non-stationary signals. Tests on synthetic data and real EEG recordings show HHT yielding more interpretable oscillatory components.
Maximiliano Bueno-López, Marta Molinas, Geir Kulia. ITISE 2017. 2017.
News
Newsroom
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Arpuro Labs takes over from Signal Analysis Lab
Arpuro Labs is now wholly owned by the engineers who built the dust-monitoring platform at Signal Analysis Lab. The new structure dedicates the company entirely to industrial particulate-matter monitoring.
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Industrial dust sensors with ±15% accuracy and no drift after six months
Arpuro PM sensor delivers ±15% accuracy with no drift after 6 months in harsh industrial sites. Site-specific calibration adapts to varied dust chemistries.
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Real-time dust monitoring for safer, more sustainable industry
Business Norway profiles our work on continuous particulate-matter monitoring for industrial operations — connecting worker-health protection, environmental compliance, and operational efficiency into a single instrumented platform.