Consistency Based Diagnosis

Consistency-based diagnosis (CBD) aims to identify the root cause of system malfunctions by detecting inconsistencies between observed behavior and expected behavior based on a model. Current research focuses on integrating machine learning, particularly deep learning and reinforcement learning, to improve the efficiency and accuracy of CBD, addressing challenges like data discretization, imbalanced datasets, and the need for automated model generation. This approach holds significant promise for diverse applications, including improving the reliability of medical diagnoses, enhancing the safety and efficiency of cyber-physical systems, and optimizing resource allocation in complex systems like nuclear power plants and educational settings.

Papers

November 27, 2023

Diagnosis driven Anomaly Detection for CPS
Henrik S. Steude, Lukas Moddemann, Alexander Diedrich, Jonas Ehrhardt, Oliver Niggemann
Anomaly Detection Medical Diagnosis Cyber Physical System Deep Learning Based Anomaly Detection Diagnosis Algorithm Consistency Based Diagnosis

November 6, 2023

Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis
Lukas Moddemann, Henrik Sebastian Steude, Alexander Diedrich, Oliver Niggemann
Deep Learning Time Series Data Discretization Model Based Diagnosis Consistency Based Diagnosis

February 20, 2023

Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
Zheng Yu, Yikuan Li, Joseph Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang
Deep Reinforcement Learning Policy OpTimization Adaptive Policy Consistency Based Diagnosis

October 6, 2022

Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks
Chenxi Sun, Hongyan Li, Moxian Song, Derun Cai, Baofeng Zhang, Shenda Hong
Deep Neural Network Prognosis Prediction Prognostic Model Sepsis Prediction Supervised Classification Consistency Based Diagnosis

August 3, 2022

Post-hoc Interpretability based Parameter Selection for Data Oriented Nuclear Reactor Accident Diagnosis System
Chengyuan Li, Meifu Li, Zhifang Qiu
Raw Data Diagnostic Model Diagnosis System Parameter Selection Diagnostic Accuracy Post Hoc Interpretability Consistency Based Diagnosis

April 13, 2022

Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features
Alex Doboli, Simona Doboli, Ryan Duke, Sangjin Hong, Wendy Tang
Machine Learning Cognitive Learning Emotional Feature Student Learning Class Diversity Adaptive Teaching Consistency Based Diagnosis

December 6, 2021

Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows
Elena Williams, Sebastian Niehaus, Janis Reinelt, Alberto Merola, Paul Glad Mihai, Kersten Villringer, Konstantin Thierbach, Evelyn Medawar, Daniel Lichterfeld, Ingo Roeder, Nico Scherf, Maria del C. Valdés Hernández
New Machine Learning Based Magnetic Resonance Imaging Data Brain Segmentation Quality Control Healthcare Workflow Seamless Integration Improved Box Dice Consistency Based Diagnosis

Consistency Based Diagnosis

Papers

Diagnosis driven Anomaly Detection for CPS

Discret2Di -- Deep Learning based Discretization for Model-based Diagnosis

Deep Reinforcement Learning for Cost-Effective Medical Diagnosis

Continuous Diagnosis and Prognosis by Controlling the Update Process of Deep Neural Networks

Post-hoc Interpretability based Parameter Selection for Data Oriented Nuclear Reactor Accident Diagnosis System

Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features

Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows