Showing papers by "Mahdi Aliyari Shoorehdeli published in 2019"
TL;DR: Coil current and contact travel waveforms are focused on as significant signals that bear helpful information about the fault occurrence for a typical EDF, 72.5 kV, SF6 HVCB.
Abstract: High-voltage circuit breakers (HVCBs) play a substantial protection role in power networks. The reliable operation of these critical components leads to an increment of resiliency and safety of power systems. It is essential to design a fault diagnostic system that detects the defects in preliminary levels and identify the origins to establish a precise maintenance task. This paper focuses on coil current and contact travel waveforms as significant signals that bear helpful information about the fault occurrence for a typical EDF, 72.5 kV, SF6 HVCB. Healthy and faulty signals simulated based on Michael Stanek's HVCB model in MATLAB, with performing some modifications in the actuating coil and operating mechanism. In the first step, to arrange an efficient fault recognition system, neural network and support vector machine (SVM) have been designed using the information of 475 simulated healthy and faulty HVCBs and verified for 200 new samples. In the second step, to improve the classification results, an additional distinction algorithm has been recommended for the cases in which two failure modes are detected by the classifier. Since any failure mode's impact on the selected features is different, the proposed diagnostic method makes a decision, between two classes of faults, based on the extracted pattern of each failure mode. The recommended method, which is a combination of commonly used classification techniques and the defined algorithm, leads to the more accurate diagnosis.
44 citations
TL;DR: An improved Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy was achieved, and inserting only the plausible firing patterns and eliminating unrealistic ones were inserted, reducing the modeling complexity.
Abstract: Introduction Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biologically-plausible models, i.e. capable of capturing most recognized firing patterns of neurons. This property makes the model efficient in simulating the large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations. Methods Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male Wistar rats, and spike sorting was conducted by Plexon offline sorter. Further analyses were performed through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters, a genetic algorithm was used. In this algorithm, optimization tools, like crossover and mutation, provide the basis for generating model parameters populations. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution. Results In the present study, the possible firing patterns of the real single neurons of the HIP and BLA were identified. Additionally, an improved Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern of these regions' neurons and the corresponding cases of the Izhikevich neuron spiking pattern were adjusted with great accuracy. Conclusion This study was conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural networks simulations, as well as reducing the modeling complexity. This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones.
6 citations
14 Jul 2019
TL;DR: This paper suggests stable updating rules to drive neural networks inspiring from model reference adaptive paradigm to drive robot dynamic terms individually through three parallel self-driving neural networks.
Abstract: 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 stable updating rules to drive neural networks inspiring from model reference adaptive paradigm. Network structure consists of three parallel self-driving neural networks which aim to estimate robot dynamic terms individually. Lyapunov candidate is selected to construct energy surface for a convex optimization framework. Learning rules are driven directly from Lyapunov functions to make the derivative negative. Finally, experimental results on 3-DOF Phantom Omni Haptic device demonstrate efficiency of the proposed method.
5 citations
TL;DR: It is demonstrated that not only does the proposed model have advantages of the previously proposed models potentially, but also it can be used as a technique for regularization of neural network weights and faster convergence.
4 citations
TL;DR: A new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems and includes Auto-Encoders as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features.
Abstract: Applying more features gives us better accuracy in modeling; however, increasing the inputs causes the curse of dimensions. In this paper, a new structure has been proposed for fault detecting and identifying (FDI) of high-dimensional systems. This structure consist of two structure. The first part includes Auto-Encoders (AE) as Deep Neural Networks (DNNs) to produce feature engineering process and summarize the features. The second part consists of the Local Model Networks (LMNs) with LOcally LInear MOdel Tree (LOLIMOT) algorithm to model outputs (multiple models). The fault detection is based on these multiple models. Hence the residuals generated by comparing the system output and multiple models have been used to alarm the faults. To show the effectiveness of the proposed structure, it is tested on single-shaft industrial gas turbine prototype model. Finally, a brief comparison between the simulated results and several related works is presented and the well performance of the proposed structure has been illustrated.
4 citations
TL;DR: In this article, an improved Izhikevich model was proposed for adapting to the neuronal activity of the rat brain with great accuracy, which would make the model effective for future neural network implementations.
Abstract: Introduction- Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. The Izhikevich model is one of the simplest biologically plausible models, i.e. capable of capturing the most recognized firing patterns of neurons. This property makes the model efficient in simulating the large-scale networks of neurons. Improving the Izhikevich model for adapting to the neuronal activity of the rat brain with great accuracy would make the model effective for future neural network implementations. Methods- Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male rats, and spike sorting was conducted by Plexon offline sorter. Further analyses were performed through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters, a genetic algorithm was used. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution. Results- In the present study, the possible firing patterns of the real single neurons of the HIP and BLA were identified. Additionally, an improved Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern of these regions neurons and the corresponding cases of the Izhikevich neuron spiking pattern were adjusted with great accuracy. Conclusion- This study was conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural network simulations, as well as reducing the modeling complexity. This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns, and eliminating unrealistic ones.
2 citations
1 citations
Posted Content•
TL;DR: Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations, as well as reducing the modeling complexity.
Abstract: Introduction: Identifying the potential firing patterns following by different brain regions under normal and abnormal conditions increases our understanding of what is happening in the level of neural interactions in the brain. On the other hand, it is important to be capable of modeling the potential neural activities, in order to build precise artificial neural networks. The Izhikevich model is one of the simple biologically plausible models that is capable of capturing the most known firing patterns of neurons. This property makes the model efficient in simulating large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations. Methods: Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter. Further data analyses are done through NeuroExplorer and MATLAB software. In order to optimize the Izhikevich model parameters, the genetic algorithm is used. Results: In the present study, the possible firing patterns of the real single neurons of the HIP and BLA are identified. Additionally, improvement of the Izhikevich model is achieved. As a result, the real neuronal spiking pattern of these regions neurons, and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with great accuracy. Conclusion: This study is conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large scale neural networks simulations, as well as reducing the modeling complexity. This aim is achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones, as the results of this study.