🙋♂️ Q: EPA's new PFAS rule means you need to choose a new treatment technology, which can take six months to a year. If you don’t have that kind of time, how can you speed up the process? 📢 A: With great modeling tools—and great experts to make sense of them. In just a month, Hazen used both to zero in on a good PFAS removal option for a client on a very tight schedule. One of the models we used was Hazen GAC. This machine learning tool can predict PFAS removal performance in days, delivering results that rival those of lab tests while shaving months off the decision-making process. Learn more: https://lnkd.in/gZWHdG3v
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To regen or not to regen, that is the question. A new AI-based DPF analysis tool is designed to deliver the answer. https://bit.ly/3V7wqfk
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We’re always looking for ways to leverage our physics-based AI to save fleets time and money. Our new DPF solution is designed to take the guesswork out of forced DPF regens and keep soot levels at an optimal level. #Trucking #Intangles #predictiveanalytics #digitaltwintechnology #MachineLearning #ArtificialIntelligence #Mobility
To regen or not to regen, that is the question. A new AI-based DPF analysis tool is designed to deliver the answer. https://bit.ly/3V7wqfk
Intangles targets troublesome DPF issues
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How can you develop several datasets to aid in the development of bin-picking algorithms? You can identify suitable picking surfaces for a vacuum cup, which are defined by some relatively simple rules. However, these rules are almost impossible to consistently apply manually. Read the full case of how we enabled the development of novel algorithms through synthetic data: https://lnkd.in/gaxauMuN #syntheticdata #algorithms #datadrivensolutions
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Process of identification, labeling, and classification of partial discharges using feature extraction and clustering. The result is an identification of the type of anomaly in the PRPD patterns. https://lnkd.in/e4HXXAeB
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Calibration Science, Part IV: Calibration Metrics Previous installments in this series discussed calibration lines, and we now need to discuss how to measure their quality, that is, we need to study calibration metrics. We will discuss variance and how it is measured. Then, using illustrations and equations we will see how to calculate the standard deviation of a data set, which will give us a measure of accuracy; how to calculate the correlation coefficient, which was introduced in the last column; and the F for Regression, a measure of a thing called the robustness of a calibration, which is an obscure but important measure of calibration quality. ➡️ Read the discussion on the importance of calibration metrics and calibration quality: https://ow.ly/jhfi50SOkaI
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Read this PerkinElmer application note to discover the benefits of FT-NIR for hop extract analysis
Transforming hop extract analysis with FT-NIR Spectroscopy and Machine Learning! 🍻 Quick, safe, and accurate - ditch hazardous chemicals for efficient analysis. https://bit.ly/3VhxV9b #SpectroscopyInnovation #SustainableBrewing
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Obvious: Any model is a (likely lossy) data-compression algorithm. Insightful: Predictive models can be trained to be lossless! Trendy: LLMs are predictive models that are more efficient at compression compared to human-generated algorithms ;-). Original paper from DM: https://lnkd.in/g8M2tNY8
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Our next featured #DigitalDiscovery article is from Leonard et al., and demonstrates the value of "shallow learning" algorithms such as decision tree and random forest for chemical predictions. Find out more: https://lnkd.in/e_SacA2t #openaccess #rscdigital
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PEFT algorithms are useful for dealing with LLMs with high parameter counts, as even fine-tuning these models from scratch can be computationally expensive and resource-intensive. A Comprehensive Survey: https://lnkd.in/e6gcm6mD
