PIKE-RAG: sPecIalized KnowledgE and Rationale Augmented Generation

OpenSSA: Small Specialist Agents based on Domain-Aware Neurosymbolic Agent (DANA) architecture for industrial problem-solving

roadmap to applied ai

AI SDK Tutorials helps you to get familiar with AI Software Development Kit (AI SDK) through a set of end-to-end tutorials, how-to jupyter notebooks, and how-to guides.

[CVPRW'25] Official Code For "SK-RD4AD: Skip-Connected Reverse Distillation for One-Class Anomaly Detection"

Build an enterprise-level AI agent operating system enabling cross-departmental and cross-system intelligent collaboration.

This article presents a reference architecture to enhance the compatibility of Siemens Industrial Artificial Intelligence (Industrial AI) products with Microsoft Azure.

Industrial AI Agents using LLMs

Real-time Industrial Anomaly Defect Inference Detection implemented by cpp（实时工业缺陷检测cpp）

Automatically identify whether the sounds produced by industrial machines are normal or anomalous (faulty machines). This is crucial for ensuring efficient and safe operations in the context of AI-based factory automation.

Zero and few-shot industrial image anomaly detection framework comparing AnomalyDINO & MuSc models across MVTec AD, BTAD, and ViSA datasets with MLflow tracking and flexible configuration.

This repository provides code for the paper "Vipul Bansal, Yong Chen, Shiyu Zhou, Component-Wise Markov Decision Process for Solving Condition Based Maintenance of Large Multi-Component Systems with Economic Dependence"

Build an enterprise-level AI agent operating system enabling cross-departmental and cross-system intelligent collaboration.

[CVPR 2025 Workshop] SK-RD4AD: Skip-Connected Reverse Distillation for One-Class Anomaly Detection

Industrial LLM Engine is an innovative and scalable solution engineered to empower large language models to understand and analyze industrial telemetry data, transforming raw measurements into actionable insights.

A GitOps-driven pipeline for automated prediction of industrial equipment efficiency. Includes Dockerized ML models, CI/CD integration with Jenkins, and infrastructure-as-code practices for scalable deployment.

Real-time anomaly detection system using the Numenta Anomaly Benchmark (NAB) dataset, Isolation Forest model, and an interactive Streamlit dashboard with live metrics & anomaly visualization.

The prototype, CemGenie, is a Generative AI–powered platform designed to autonomously optimize cement plant operations. It integrates real-time process data from raw material handling, grinding, clinkerization, and utilities into a unified AI control layer.

Custom-trained YOLOv11 model for industrial fault detection in manufacturing environments.

Predicting product quality in hot forming processes using interpretable machine learning models. Includes preprocessing, feature engineering, model selection and evaluation in a reproducible Docker setup. Based on a master’s thesis in Industrial Data Science / Engineering.