Zobrazeno 1 - 10
of 174
pro vyhledávání: '"shoorehdeli, Mahdi Aliyari"'
False and nuisance alarms in industrial fault detection systems are often triggered by uncertainty, causing normal process variable fluctuations to be erroneously identified as faults. This paper introduces a novel encoder-based residual design that
Externí odkaz:
http://arxiv.org/abs/2408.13526
The Fisher Information Matrix (FIM) provides a way for quantifying the information content of an observable random variable concerning unknown parameters within a model that characterizes the variable. When parameters in a model are directly linked t
Externí odkaz:
http://arxiv.org/abs/2406.05395
In brain neural networks, Local Field Potential (LFP) signals represent the dynamic flow of information. Analyzing LFP clinical data plays a critical role in improving our understanding of brain mechanisms. One way to enhance our understanding of the
Externí odkaz:
http://arxiv.org/abs/2405.06732
Autor:
Modarres, Ardavan, Eivaghi, Vahid Mohammad-Zadeh, Shoorehdeli, Mahdi Aliyari, Moosavian, Ashkan
Due to the incapability of one sensory measurement to provide enough information for condition monitoring of some complex engineered industrial mechanisms and also for overcoming the misleading noise of a single sensor, multiple sensors are installed
Externí odkaz:
http://arxiv.org/abs/2311.02282
This paper deals with the trajectory tracking control problem for a class of bilinear systems with unmeasurable states and unknown parameters. Firstly, a full-information controller is suggested that guarantees global tracking under a persistency of
Externí odkaz:
http://arxiv.org/abs/2309.01002
There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data-based fault detection methods proposed by artificial intelligence app
Externí odkaz:
http://arxiv.org/abs/2301.04049
In order to gain access to networks, different types of intrusion attacks have been designed, and the attackers are working on improving them. Computer networks have become increasingly important in daily life due to the increasing reliance on them.
Externí odkaz:
http://arxiv.org/abs/2211.16304
Autor:
Kalbasi, AmirAli, Jamali, Shole, Shoorehdeli, Mahdi Aliyari, Behzadnia, Alireza, Haghparast, Abbas
Addiction is a major public health concern characterized by compulsive reward-seeking behavior. The excitatory glutamatergic signals from the hippocampus (HIP) to the Nucleus accumbens (NAc) mediate learned behavior in addiction. Limited comparative
Externí odkaz:
http://arxiv.org/abs/2211.08288
Publikováno v:
2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1-6
Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov function for online robot dynamic identification. This paper suggests
Externí odkaz:
http://arxiv.org/abs/2210.15055
Publikováno v:
In Journal of Process Control March 2024 135
