This paper investigates the application of machine learning regression algorithms Kernel Ridge Regression (KRR), Huber Regressor (HR), and Gaussian Process Regression (GPR) for predicting sound power levels of gensets, offering significant value for marketing and sales teams during the early bidding process. When engine sizes and genset enclosure dimensions are tentative, and measured noise data is unavailable, these algorithms enable reliable noise level estimation for unbuilt gensets. The study utilizes high fidelity datasets from over 100 experiments conducted at Cummins Acoustics Technology Center (ATC) in a hemi-anechoic chamber, adhering to ISO 3744 standards. By using readily available information from the bidding and initial design stages, KRR predicts sound power with an average accuracy of within 5 dBA. While HR and GPR show slightly higher prediction errors, all models effectively capture the overall noise trends across various genset configurations. These findings present a promising method for early-stage noise estimation in genset design.

FOX Business correspondent Lydia Hu has the latest on jobs at risk as AI further develops on'America's Newsroom.' President Biden will meet with science and technology advisers on Wednesday to discuss the "risks and opportunities" that artificial intelligence technologies pose for Americans and national security. A White House official said the president would focus on discussing the importance of protecting rights and safety to ensure there are appropriate safeguards and innovation is responsible. Furthermore, Biden will call on Congress to pass bipartisan legislation to protect children and to limit the personal data tech companies collect. The Council of Advisors on Science and Technology, or PCAST, is a federal advisory committee composed of experts outside the federal government charged with making science, technology and innovation policy recommendations to the White House.