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Neural network based approach for time to crash prediction to cope with software aging

Journal of Systems Engineering and Electronics 2015年第2期26卷 407-414页

作者： Moona Yakhchi Javier Alonso mahdi fazeli Amir Akhavan Bitaraf Ahmad Patooghy School of Computer Science Institute for Research in Fundamental Sciences (IPM) Institute of Advanced Studies on Cybersecurity University of Leon Department of Computer Engineering Iran University of Technology

Recent studies have shown that software is one of the main reasons for computer systems unavailability. A growing ac- cumulation of software errors with time causes a phenomenon called software aging. This phenomenon can result in system per- formance degradation and eventually system hang/crash. To cope with software aging, software rejuvenation has been proposed. Software rejuvenation is a proactive technique which leads to re- moving the accumulated software errors by stopping the system, cleaning up its internal state, and resuming its normal operation. One of the main challenges of software rejuvenation is accurately predicting the time to crash due to aging factors such as me- mory leaks. In this paper, different machine learning techniques are compared to accurately predict the software time to crash un- der different aging scenarios. Finally, by comparing the accuracy of different techniques, it can be concluded that the multilayer per- ceptron neural network has the highest prediction accuracy among all techniques studied.

关键词： software reliability,software rejuvenation,machine learning

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The Novel Adaptive Robust Speed Tracking Controller for a PM Synchronous Motor Drive Based on Input-Output Feedback Linearization

The Novel Adaptive Robust Speed Tracking Controller for a PM...

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11th International Conference on Electrical Machines and Systems

作者： Sayed mahdi fazeli Hossein Abootorabi Zarchi Jafar Soltani Hew Wooi Ping Faculty of Engineering University of MalayaKuala-LumpurMalaysia Faculty of Electrical and Computer Engineering Isfahan University of TechnologyIsfahan-Iran

＜正＞A nonlinear adaptive robust speed tracking controller is presented for a three-phase surface mounted permanent magnet synchronous motor（SPMSM）considering the control strategy of maximum torque control（MTC）related to this motor.Ignoring the motor iron losses,the proposed controller is designed based on combination of input-output feedback linearization（IOFL）control and adaptive sliding mode technique.The proposed adaptive sliding mode controller estimates the unknown uncertainties without using sign（.）or sat（.）function.Hence,it reduces chattering or steady state error phenomenon.In addition,in order to make the drive system control robust to load torque disturbance,the stator current reference signal is predicted by another adaptive sliding mode controller that has no reaching phase and has a low sliding mode chattering.Finally,the effectiveness and feasibility of the proposed control approach is demonstrated by computer simulation.The results obtained confirm that the desired speed reference command is perfectly tracked in spite of motor parameter uncertainties and load torque disturbance.

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