Tagged articles

closed-loop control

5 articles · Page 1 of 1

Sep 30, 2026 · Industry Insights

Three Steps to Smart Factory: From Rule Checks to Autonomous Agent Loops

The article outlines three maturity layers of AI in manufacturing — rule-based inspection, AI visual inspection, and autonomous agent closed-loops — using real-world cases like Deli Group's 42x efficiency gain and PetroChina's 90% root-cause accuracy to show why most factories stall at layer two and what it takes to reach true self-optimizing production.

AI agentsAI in manufacturingcase study

0 likes · 13 min read

Three Steps to Smart Factory: From Rule Checks to Autonomous Agent Loops

Chen Xiaogong's Room

Sep 28, 2026 · Industry Insights

How Cement Raw Meal Is Batched: Chemistry, Control & Closed-Loop Systems

This article explains how cement plants dynamically batch raw materials like limestone, clay, and iron ore to achieve target chemical moduli (KH, SM, IM) through continuous sampling, computational models, and automated feeding, while addressing moisture correction, coal ash inclusion, silo flow issues, and time-delay challenges in closed-loop control.

alumina moduluscement manufacturingclosed-loop control

0 likes · 30 min read

How Cement Raw Meal Is Batched: Chemistry, Control & Closed-Loop Systems

Random Bulletin

Sep 24, 2026 · Operations

Second-Level Fault Mitigation at 10M QPS: From Minutes to Seconds

This article details how to achieve second-level fault mitigation in 10M QPS systems by building a closed-loop control system with layered signals, tiered actions, independent control planes, pre-authorized automation, automated verification, blast radius management, and progressive drills, moving beyond manual response to automated risk convergence.

MTTMSREautomation

0 likes · 38 min read

Second-Level Fault Mitigation at 10M QPS: From Minutes to Seconds

Machine Heart

Sep 9, 2026 · Artificial Intelligence

Action Map Policy: Pixel Classification for High-Precision Robot Manipulation

Action Map Policy (AMP) reframes robot manipulation as pixel-level classification by projecting 3D keypoint trajectories onto 2D image planes, enabling cross-entropy loss to model multi-modal action distributions with sub-millimeter precision and single-forward-pass inference.

Action Map PolicyImitation LearningMulti-View Transformer

0 likes · 12 min read

Action Map Policy: Pixel Classification for High-Precision Robot Manipulation

Random Bulletin

Sep 5, 2026 · Operations

Why Static 80% Thresholds Fail at 10M QPS: Dynamic Water Level Management

The article explains why fixed thresholds like 80% CPU are inadequate for large-scale systems, and introduces dynamic water level management that combines load, resource, service, and resilience signals with capacity profiling, trend prediction, and action latency to drive automated scaling, scheduling, rate limiting, and degradation in a closed loop.

10M QPSAuto ScalingSLO

0 likes · 31 min read

Why Static 80% Thresholds Fail at 10M QPS: Dynamic Water Level Management