scholarly journals APPLICATION OF LINEAR SYSTEM MODEL FOR STOCK MANAGEMENT TO PREVENTIVE MAINTENANCE

2016 ◽  
Vol 43 (6Part9) ◽  
pp. 3415-3415
Author(s):  
Y Hu ◽  
R Fueglistaller ◽  
J Rottmann ◽  
M Myronakis ◽  
A Wang ◽  
...  

2008 ◽  
Vol 36 (1) ◽  
pp. 240-251 ◽  
Author(s):  
Bo Zhao ◽  
Jun Zhou ◽  
Yue-Houng Hu ◽  
Thomas Mertelmeier ◽  
Jasmina Ludwig ◽  
...  

2014 ◽  
Vol 41 (6Part4) ◽  
pp. 132-132 ◽  
Author(s):  
B Peng ◽  
A Lubinsky ◽  
A Teymurazyan ◽  
H Zheng ◽  
W Zhao

2014 ◽  
Vol 26 (03) ◽  
pp. 1450036 ◽  
Author(s):  
Mana Sezdi ◽  
Ersin Ozdemir

In this study, a software application was developed to analyze the performance test results of medical devices. The software (BMED) provides a medical device database and analyses problems with medical devices. The BMED application analysis reports incompatible performance test results in accordance with international safety and inspection standards. The BMED also contains performance test measurement history of all medical devices in the BMED medical device database. The main purpose of this study is to make analysis of the performance control of medical devices faster, easier, mobile and more efficient. The increasing number of medical devices in a hospital is another reason to use applications such as this. The BMED software application was tested using sample data collected during performance test results of a total of 1553 medical devices. The devices were defibrillator, electrocardiography, pulse oximeter, anaesthesia unit, vaporizer, ventilator, electrosurgical unit, physiological monitor, sphygmomanometer, surgical aspirator, phototherapy unit and infant incubator. The performance tests of these medical devices were performed by the biomedical personnel in accordance with the inspection and preventive maintenance system (IPM) procedures, which were developed by the emergency care research institute (ECRI institute) and their results were also interpreted in accordance with the same procedures. The results of application testing showed that the BMED is a very successful application used to analyze performance test results of medical devices. Its usage is very easy, fast and comfortable. The main advantage of BMED is that it can be accessed with mobile devices from anywhere. This feature increases the performance and efficiency of biomedical staff. Additionally, this study showed that the BMED application provides consistent data for medical device problems. Spare part stock management and preventive maintenance activities decreased the repair costs and minimized the number of fault device conditions within the BMED application. All the analysis results also affected the selection and purchasing decisions of medical devices and their technologies. In short, the BMED served a manageable solution to combine all distributed data from a large number of medical devices located in different departments of a hospital — the location of the device, the biomedical number, the device name, the manufacturer, the serial number, the interpretation of the performance test results and the analysis results — into one platform. Thus, the management of medical device inventory was made easier. The future study will be the evaluation of the analysis for other medical devices. It is expected that usage of the tool will increase and it will be a very useful application for biomedical personnel.


2014 ◽  
Vol 568-570 ◽  
pp. 1122-1125
Author(s):  
Jian Zhang ◽  
Wan Juan Song

Tracking system is a vital aspect of Virtual Reality and Augmented Reality, the efficiency of tracking system is determined by the implementation of framework and the predictive filtering algorithm. As a result of the better applicability of Bayesian predictive filtering algorithm in simulation of non-linear system model, this paper proposes a framework for Bayesian predictive filter, which includes predictive filtering layer and denotation layer, and according to every layer’s function, analyses the implementation of framework. The optimal simulation count is worked out by the experiment. The results show that in the simulation of non-linear system model, this framework for Bayesian predictive filter can implement the tracking of simple motion and the orientation prediction.


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