A new family of  lifetime distributions in terms of cumulative hazard rate function

Kharazmi, Omid; Jahangard, Shahla

doi:10.31801/cfsuasmas.439069

Cited by 11 publications

(4 citation statements)

References 8 publications

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“…In the past few decades, a variety of mathematical computational approaches have been implemented in different research fields such as fluid mechanic engineering [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], chemical engineering , electrical engineering , robotics and automation , urban planning engineering [73][74][75][76], petroleum engineering [77][78][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94], energy engineering [95,96], mathematics [97][98][99][100][101][102][103][104], environmental engineering [105][106]…”

Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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The main required organ of the biological system is the Central Nervous System (CNS), which can influence the other basic organs in the human body. The basic elements of this important organ are neurons, synapses, and glias (such as astrocytes, which are the highest percentage of glias in the human brain). Investigating, modeling, simulation, and hardware implementation (realization) of different parts of the CNS are important in case of achieving a comprehensive neuronal system that is capable of emulating all aspects of the real nervous system. This paper uses a basic neuron model called the Izhikevich neuronal model to achieve a high copy of the primary nervous block, which is capable of regenerating the behaviors of the human brain. The proposed approach can regenerate all aspects of the Izhikevich neuron in high similarity degree and performances. The new model is based on Look-Up Table (LUT) modeling of the mathematical neuromorphic systems, which can be realized in a high degree of correlation with the original model. The proposed procedure is considered in three cases: 100 points LUT modeling, 1000 points LUT modeling, and 10,000 points LUT modeling. Indeed, by removing the high-cost functions in the original model, the presented model can be implemented in a low-error, high-speed, and low-area resources state in comparison with the original system. To test and validate the proposed final hardware, a digital FPGA board (Xilinx Virtex-II FPGA board) is used. Digital hardware synthesis illustrates that our presented approach can follow the Izhikevich neuron in a high-speed state (more than the original model), increase efficiency, and also reduce overhead costs. Implementation results show the overall saving of 84.30% in FPGA and also the higher frequency of the proposed model of about 264 MHz, which is significantly higher than the original model, 28 MHz.

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Section: Introductionmentioning

confidence: 99%

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

et al. 2022

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“…In the past few decades, various advanced computational approaches, e.g., finite element, numerical linear algebra, statistics, numerical analysis, tensor analysis, and artificial intelligence, have been applied in various fields of study, such as fluid mechanic engineering [26][27][28][29][30][31][32][33][34], chemical engineering [35][36][37][38][39][40][41], electrical and computer engineering , petrochemical engineering [73][74][75][76][77], petroleum engineering [78][79][80][81][82][83][84][85][86][87][88][89][90][91][92], mathematics and physics [93][94][95][96][97][98][99][100][101][102], and environmental engineering [103]…”

Section: Mlp Neural Networkmentioning

confidence: 99%

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

et al. 2022

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One of the factors that significantly affects the efficiency of oil and gas industry equipment is the scales formed in the pipelines. In this innovative, non-invasive system, the inclusion of a dual-energy gamma source and two sodium iodide detectors was investigated with the help of artificial intelligence to determine the flow pattern and volume percentage in a two-phase flow by considering the thickness of the scale in the tested pipeline. In the proposed structure, a dual-energy gamma source consisting of barium-133 and cesium-137 isotopes emit photons, one detector recorded transmitted photons and a second detector recorded the scattered photons. After simulating the mentioned structure using Monte Carlo N-Particle (MCNP) code, time characteristics named 4th order moment, kurtosis and skewness were extracted from the recorded data of both the transmission detector (TD) and scattering detector (SD). These characteristics were considered as inputs of the multilayer perceptron (MLP) neural network. Two neural networks that were able to determine volume percentages with high accuracy, as well as classify all flow regimes correctly, were trained.

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“…The purpose of training a perceptron is to find the values of its weights, so that the perceptron generates the correct values for the training examples. Different kinds of numerical calculations [68][69][70][71][72][73][74][75][76][77][78] and soft computing [79][80][81][82][83][84][85][86] have been used in various fields such as electrical engineering problems [87][88][89][90][91][92][93][94][95][96][97][98][99][100][101][102], computer sciences problems [103,104], and basic sciences [105][106][107][108][109], etc. In this paper, the perceptron learning algorithm is as follows-this algorithm is shown in Figure 4 as a flowchart:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

et al. 2022

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In the current paper, a novel technique is represented to control the liquid petrochemical and petroleum products passing through a transmitting pipe. A simulation setup, including an X-ray tube, a detector, and a pipe, was conducted by Monte Carlo N Particle-X version (MCNPX) code to examine a two-by-two mixture of four diverse petroleum products (ethylene glycol, crude oil, gasoline, and gasoil) in various volumetric ratios. As the feature extraction system, twelve time characteristics were extracted from the received signal, and the most effective ones were selected using correlation analysis to present reasonable inputs for neural network training. Three Multilayers perceptron (MLP) neural networks were applied to indicate the volume ratio of three kinds of petroleum products, and the volume ratio of the fourth product can be feasibly achieved through the results of the three aforementioned networks. In this study, increasing accuracy was placed on the agenda, and an RMSE < 1.21 indicates this high accuracy. Increasing the accuracy of predicting volume ratio, which is due to the use of appropriate characteristics as the neural network input, is the most important innovation in this study, which is why the proposed system can be used as an efficient method in the oil industry.

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A new family of lifetime distributions in terms of cumulative hazard rate function

Cited by 11 publications

References 8 publications

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

An Investigation on Spiking Neural Networks Based on the Izhikevich Neuronal Model: Spiking Processing and Hardware Approach

Application of Neural Network and Time-Domain Feature Extraction Techniques for Determining Volumetric Percentages and the Type of Two Phase Flow Regimes Independent of Scale Layer Thickness

Extraction of Time-Domain Characteristics and Selection of Effective Features Using Correlation Analysis to Increase the Accuracy of Petroleum Fluid Monitoring Systems

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