Central nervous system: overall considerations based on hardware realization of digital spiking silicon neurons (dssns) and synaptic coupling

The Central Nervous System (CNS) is the part of the nervous system including the brain and spinal cord. The CNS is so named because the brain integrates the received information and influences the activity of different sections of the bodies. The basic elements of this important organ are: neurons, synapses, and glias. Neuronal modeling approach and hardware realization design for the nervous system of the brain is an important issue in the case of reproducing the same biological neuronal behaviors. This work applies a quadratic-based modeling called Digital Spiking Silicon Neuron (DSSN) to propose a modified version of the neuronal model which is capable of imitating the basic behaviors of the original model. The proposed neuron is modeled based on the primary hyperbolic functions, which can be realized in high correlation state with the main model (original one). Really, if the high-cost terms of the original model, and its functions were removed, a low-error and high-performance (in case of frequency and speed-up) new model will be extracted compared to the original model. For testing and validating the new model in hardware state, Xilinx Spartan-3 FPGA board has been considered and used. Hardware results show the high-degree of similarity between the original and proposed models (in terms of neuronal behaviors) and also higher frequency and low-cost condition have been achieved. The implementation results show that the overall saving is more than other papers and also the original model. Moreover, frequency of the proposed neuronal model is about 168 MHz, which is significantly higher than the original model frequency, 63 MHz

Application of feature extraction and artificial intelligence techniques for increasing the accuracy of x-ray radiation based two phase flow meter

The increasing consumption of fossil fuel resources in the world has placed emphasis on flow measurements in the oil industry. This has generated a growing niche in the flowmeter industry. In this regard, in this study, an artificial neural network (ANN) and various feature extractions have been utilized to enhance the precision of X-ray radiation-based two-phase flowmeters. The detection system proposed in this article comprises an X-ray tube, a NaI detector to record the photons, and a Pyrex-glass pipe, which is placed between detector and source. To model the mentioned geometry, the Monte Carlo MCNP-X code was utilized. Five features in the time domain were derived from the collected data to be used as the neural network input. Multi-Layer Perceptron (MLP) was applied to approximate the function related to the input-output relationship. Finally, the introduced approach was able to correctly recognize the flow pattern and predict the volume fraction of two-phase flow’s components with root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of less than 0.51, 0.4 and 1.16%, respectively. The obtained precision of the proposed system in this study is better than those reported in previous works

Applications of discrete wavelet transform for feature extraction to increase the accuracy of monitoring systems of liquid petroleum products

This paper presents a methodology to monitor the liquid petroleum products which pass through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a pipe was established using a Monte Carlo n-particle X-version transport code to investigate a two-by-two mixture of four different petroleum products, namely, ethylene glycol, crude oil, gasoline, and gasoil, in deferent volumetric ratios. After collecting the signals of each simulation, discrete wavelet transform (DWT) was applied as the feature extraction system. Then, the statistical feature, named the standard deviation, was calculated from the approximation of the fifth level, and the details of the second to fifth level provide appropriate inputs for neural network training. Three multilayer perceptron neural networks were utilized to predict the volume ratio of three types of petroleum products, and the volume ratio of the fourth product could easily be obtained from the results of the three presented networks. Finally, a root mean square error of less than 1.77 was obtained in predicting the volume ratio, which was much more accurate than in previous research. This high accuracy was due to the use of DWT for feature extraction

Estimation of void fraction for homogenous regime of two-phase flows in unstable operational conditions using gamma-ray and neural networks

 Almost all the multi-phase flow meters (MPFMs) using gamma-ray attenuation, are calibrated for liquid and gas phases with constant density and pressure. When operational conditions such as temperature and pressure change in pipelines, the radiation-based multi-phase flowmeters would measure the flow rate with error. Therefore, performance of MPFMs would be improved by eliminating any dependency on the fluid properties such as density. In this work, a method based on dual modality densitometry combined with Artificial Neural Network (ANN) is proposed in order to estimate the void fraction in homogenous regime of gas-liquid two-phase flows in unstable operational conditions (changeable temperature and pressure) in oil industry. An experimental setup was implemented to generate the optimum required input data for training the network. ANNs were trained on the registered counts of the transmission and scattering detectors in various liquid phase densities and void fractions. Void fractions were predicted by ANNs with mean relative error of less than 0.78% in density variations range of 0.735 up to 0.98 g/cm

The Role of Perceived Risk in the Adoption of Internet of Things Technology in Sports

The Internet of Things (IoT) is one technology that can revolutionize traditional methods and transform sports infrastructure. To promote the adoption of IoT effectively, it's vital for sports manager to understand the factors that positively and negatively affect it. The aim of this study was to investigate how perceived risk impacts people's willingness to use IoT technology in sports. This quantitative study used a survey method and applied a descriptive approach. The statistical population included Iranian athletes, and 394 individuals completed questionnaires using a non-probability sampling method. Data analysis was performed using Smart PLS3 software. Results showed that perceived risk has a direct and negative impact on perceived ease of use, willingness to use, and perceived usefulness. However, its effect on attitude towards use was insignificant. The study also confirmed the positive impact of perceived ease of use on perceived usefulness and attitude towards use. Perceived usefulness had a greater effect on the latter variable. Additionally, perceived usefulness had a significant positive impact on willingness to use IoT technology in sports, as did attitude towards use. Attitude towards use also had a significant positive impact on willingness to use IoT technology in sports. The study recommends implementing strategies to enhance and improve perceived usefulness, ease of use, and attitude towards use while reducing perceived risk to increase acceptance and willingness to use IoT technology in sports

Introducing a precise system for determining volume percentages independent of scale thickness and type of flow regime

When fluids flow into the pipes, the materials in them cause deposits to form inside the pipes over time, which is a threat to the efficiency of the equipment and their depreciation. In the present study, a method for detecting the volume percentage of two-phase flow by considering the presence of scale inside the test pipe is presented using artificial intelligence networks. The method is non-invasive and works in such a way that the detector located on one side of the pipe absorbs the photons that have passed through the other side of the pipe. These photons are emitted to the pipe by a dual source of the isotopes barium-133 and cesium-137. The Monte Carlo N Particle Code (MCNP) simulates the structure, and wavelet features are extracted from the data recorded by the detector. These features are considered Group methods of data handling (GMDH) inputs. A neural network is trained to determine the volume percentage with high accuracy independent of the thickness of the scale in the pipe. In this research, to implement a precise system for working in operating conditions, different conditions, including different flow regimes and different scale thickness values as well as different volume percentages, are simulated. The proposed system is able to determine the volume percentages with high accuracy, regardless of the type of flow regime and the amount of scale inside the pipe. The use of feature extraction techniques in the implementation of the proposed detection system not only reduces the number of detectors, reduces costs, and simplifies the system but also increases the accuracy to a good extent

Design and Construction of Zana Robot for Modeling Human Player in Rock-paper-scissors Game using Multilayer Perceptron, Radial basis Functions and Markov Algorithms

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies

Introducing effective parameters for predicting job burnout using a self-organizing method based on group method of data handling neural network.

In addition to affecting people's bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach's alpha confirmed the polls' dependability, and an expert panel confirmed the surveys' authenticity. The GMDH neural network's inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network's output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned

Experimental Study of Void Fraction Measurement Using a Capacitance-Based Sensor and ANN in Two-Phase Annular Regimes for Different Fluids

One of the most severe problems in power plants, petroleum and petrochemical industries is the accurate determination of phase fractions in two-phase flows. In this paper, we carried out experimental investigations to validate the simulations for water–air, two-phase flow in an annular pattern. To this end, we performed finite element simulations with COMSOL Multiphysics, conducted experimental investigations in concave electrode shape and, finally, compared both results. Our experimental set-up was constructed for water–air, two-phase flow in a vertical tube. Afterwards, the simulated models in the water–air condition were validated against the measurements. Our results show a relatively low relative error between the simulation and experiment indicating the validation of our simulations. Finally, we designed an Artificial Neural Network (ANN) model in order to predict the void fractions in any two-phase flow consisting of petroleum products as the liquid phase in pipelines. In this regard, we simulated a range of various liquid–gas, two-phase flows including crude oil, oil, diesel fuel, gasoline and water using the validated simulation. We developed our ANN model by a multi-layer perceptron (MLP) neural network in MATLAB 9.12.0.188 software. The input parameters of the MLP model were set to the capacitance of the sensor and the liquid phase material, whereas the output parameter was set to the void fraction. The void fraction was predicted with an error of less than 2% for different liquids via our proposed methodology. Using the presented novel metering system, the void fraction of any annular two-phase flow with different liquids can be precisely measured