Dynamic production system diagnosis and prognosis using model-based data-driven method

Integrating Model-based Diagnosis and Prognosis in Autonomous Production∗

Today’s complex production systems allow to simultaneously build different products following individual production plans. Such plans may fail due to component faults or unforeseen behavior, resulting in flawed products. In this paper, we propose a method to integrate diagnosis with plan assessment to prevent plan failure, and to gain diagnostic information when needed. In our setting, plans ar...

Diagnosis of diabetes by using a data mining method based on native data

Background & Aim: Detecting the abnormal performance of diabetes and subsequently getting proper treatment can reduce the mortality associated with the disease. Also, timely diagnosis will result in irreversible complications for the patient. The aim of this study was to determine the status of diabetes mellitus using data mining techniques. Methods: This is an analytical study and its databas...

A Data-driven Method for Crowd Simulation using a Holonification Model

In this paper, we present a data-driven method for crowd simulation with holonification model. With this extra module, the accuracy of simulation will increase and it generates more realistic behaviors of agents. First, we show how to use the concept of holon in crowd simulation and how effective it is. For this reason, we use simple rules for holonification. Using real-world data, we model the...

Combined Model-based/Data-driven Approach to Failure Prognosis

predicting streamflow using data-driven model and time series

accurate forecasting of streamflows has been one of the most important issues as it plays a key role in allotment of water resources. river flow simulations to determine the future river flows are important and practical. given the importance of flow in the coming years, in this research three stations: haji qooshan, ghare shoor and tamar in gorganrood cachment were simulated in 2002-2011. to s...