This paper presents a real-time spill detection system that utilizes pretrained deep learning models with RGB and thermal imaging to classify spill vs. no-spill scenarios across varied environments. Using a balanced binary dataset (4,000 images), our experiments demonstrate the advantages of thermal imaging in inference speed, accuracy, and model size. We achieve up to 100% accuracy using lightweight models like VGG19 and NasNetMobile, with thermal models performing faster and more robustly across different lighting conditions. Our system runs on consumer-grade hardware (RTX 4080) and achieves inference times as low as 44 ms with model sizes under 350 MB, highlighting its deployability in safety-critical contexts. Results from experiments with a real robot and test datasets indicate that a VGG19 model trained on thermal imaging performs best.

Role requiring'No experience data provided' months of experience in Jacksonville GENERAL REQUIREMENTS: • Experience with unit testing, release procedures, coding design and documentation protocol as well as change management procedures • Proficiency using React, Material, TypeScript, Webpack, rollup, Redux, boilerplate; • Demonstrated organizational, analytical and interpersonal skills • Flexible team player • Ability to manage tasks independently and take ownership of responsibilities • Ability to learn from mistakes and apply constructive feedback to improve performance • Must demonstrate initiative and effective independent decision-making skills • Ability to communicate technical information clearly and articulately • Ability to adapt to a rapidly changing environment • In-depth understanding of the systems development life cycle o Database knowledge in Mongo DB; REDIS and Jenkins is a plus – but not required. Will be part of a highly motivated and skilled MERN Stack Team that manages central tooling and maintenance of the technology stack. The person will be part of an highly skilled team that develops UI boilerplate code and other frameworks utilized across projects of the organization. The core platform includes best-in-class CRM with responsive and seamless UI that serves 4000 plus individual's with 24 7 uptime. Self-managing (Agile) – the team leads in the creation of best-in-class frameworks and reusable tools for the organization and is a treat to work in.

Battery performance datasets are typically non-normal and multicollinear. Extrapolating such datasets for model predictions needs attention to such characteristics. This study explores the impact of data normality in building machine learning models. In this work, tree-based regression models and multiple linear regressions models are each built from a highly skewed non-normal dataset with multicollinearity and compared. Several techniques are necessary, such as data transformation, to achieve a good multiple linear regression model with this dataset; the most useful techniques are discussed. With these techniques, the best multiple linear regression model achieved an R^2 = 81.23% and exhibited no multicollinearity effect for the dataset used in this study. Tree-based models perform better on this dataset, as they are non-parametric, capable of handling complex relationships among variables and not affected by multicollinearity. We show that bagging, in the use of Random Forests, reduces overfitting. Our best tree-based model achieved accuracy of R^2 = 97.73%. This study explains why tree-based regressions promise as a machine learning model for non-normally distributed, multicollinear data.

In 1913, Henry Ford revolutionized car-making with the first moving assembly line, an innovation that made piecing together new vehicles faster and more efficient. Some hundred years later, Ford is now using artificial intelligence to eke more speed out of today's manufacturing lines. At a Ford Transmission Plant in Livonia, Michigan, the station where robots help assemble torque converters now includes a system that uses AI to learn from previous attempts how to wiggle the pieces into place most efficiently. Ford uses technology from a startup called Symbio Robotics that looks at the past few hundred attempts to determine which approaches and motions appeared to work best. A computer sitting just outside the cage shows Symbio's technology sensing and controlling the arms.

LeddarTech, the Canadian developer of sensors and LiDAR distancing systems for ADAS and other mobile systems, raised $101 million in a Series C funding led by Osram with participation by Delphi, Magneti Marelli, Integrated Device Technology, Fonds de solidarité FTQ, BDC Capital and GO Capital. This round of funding will allow LeddarTech to enhance its ASIC development efforts, expand its R&D team, and accelerate ongoing LiDAR development programs with select Tier-1 automotive customers for rapid market deployment. Innoviz Technologies, the Israeli solid-state LiDAR startup, raised $65 million in a Series B funding. Delphi Automotive PLC and Magna International participated in the round, along with additional new investors including 360 Capital Partners, Glory Ventures, Naver and others. All Series A investors also participated in the round.

Machine-reengineering is a way to automate business processes using machine learning. Although machine-reengineering is new, companies are already seeing striking results with it, particularly in boosts to speed and efficiency. Studying 168 early adopters, we've seen speed improvements of two times or more for most business processes -- and some organizations are reporting speed improvements of 10 times or more. How do companies do it? Our study found that organizations are using machine-reengineering to establish new forms of human-machine collaboration that break through the bottlenecks of complex digital processes.