🌟 Introducing ECGiT's All Employee Featuring Month! 🌟 At ECGiT, we're more than just a team; we're a family! To celebrate our incredible employees and their unique contributions, we're excited to announce the ECGiT All Employee Featuring Month, starting October 19, 2023. 🔦 What's the Spotlight Month? 🔦 Throughout this month, we'll showcase one exceptional ECGiT team member every day. We believe in celebrating the talent, dedication, and diverse skills that make our organization extraordinary. 🌐 Why Are We Doing This? 🌐 Our team is the heart and soul of ECGiT, and we want to share their stories, achievements, and experiences. This initiative is not just about recognizing individuals; it's about inspiring others, fostering connections, and building a stronger ECGiT community. 🤝 How Can You Get Involved? 🤝 Follow our LinkedIn page to stay updated. Engage with our daily posts - show your appreciation, ask questions, and connect with your colleagues. Share the spotlight with your network! Tag your friends who might be interested in our journey. 🎉 Join Us in Celebrating Our Team! 🎉 We can't wait to showcase the incredible people who drive ECGiT's success. Stay tuned for daily employee spotlights, insights, and behind-the-scenes glimpses into the amazing work happening here. Let's make this month memorable together. 🙌 #ECGiTTeamSpotlight #EmployeeRecognition #CompanyCulture
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excellent work !!
Multivariate Batch Evolution Models (BEMs) use control limits based on reducing many process variables into a smaller set of key factors. These key factors are chosen based on their strong correlation with the batch process over time. This works well in batch processing, where most variables are highly related and can be combined into a single representation. BEMs can spot out-of-control or drifting processes by comparing these key factors with averages and standard deviations from previous batches. What's notable is that BEMs are effective even with a small number of batches (~10-15), unlike other machine learning methods that need larger samples. Operators can trace back BEM control limits to specific process variables using a Variable Importance Plot, which helps identify which variables are causing deviations. To successfully deploy BEMs, a lot of metadata is needed—like process parameters, sampling rates, data aggregation methods, and historical tests. For scalable use, this metadata must be standardized across a site or the entire organization. Real-time analytics with BEMs require interactive dashboards that let operators not only monitor the process but also investigate issues quickly. These dashboards aren’t static—they dynamically link data, helping identify root causes. In summary, BEMs don’t just show what is happening in a process, but also explain why, allowing operators to take fast, informed action. #questdb #iiot #batch_evolution_model #golden_batch #univariate #multivariate #pls #grafana #control_limit #lolo #hihi #ml #machine_learning
