A UNIVERSAL NEURAL NETWORK REPRESENTATION FOR HADRON–HADRON INTERACTIONS AT HIGH ENERGY

2000 ◽  
Vol 11 (03) ◽  
pp. 619-628 ◽  
Author(s):  
KHALED. A. EL-METWALLY ◽  
TAREK I. HAWEEL ◽  
MAHMOUD Y. EL-BAKRY
Keyword(s):  
Neural Network ◽  
Multiplicity Distribution ◽  
High Energy ◽  
Training Set ◽  
Charged Multiplicity ◽  
Proton Proton ◽  
Hadron Interaction ◽  
Network Representation ◽  
The Neural Network

An efficient neural network (NN) has been designed to simulate the hadron–hadron interaction at high energy. Two cases have been considered simultaneously, the proton–proton (p–p) and the pion–proton (π-p) interactions. The neural network has been trained to produce the charged multiplicity distribution for both cases based on samples from the overlapping functions. The trained NN shows a good performance in matching the trained distributions. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The robustness of the designed NN in the presence of uncertainties, in the overlapping functions has been demonstrated.

Study on Neural Networks Usage to Analyse Correlation between Spectrum of Vibration Acceleration Signal from Pin of Ball Mill and its Filling Level

2015 ◽  
Vol 770 ◽  
pp. 540-546 ◽  
Author(s):  
Yuri Eremenko ◽  
Dmitry Poleshchenko ◽  
Anton Glushchenko
Keyword(s):  
Neural Network ◽  
Ball Mill ◽  
Signal Amplitude ◽  
Signal Spectrum ◽  
Laboratory Model ◽  
Training Set ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
The Neural Network ◽  
Trained Neural Network

The question about modern intelligent information processing methods usage for a ball mill filling level evaluation is considered. Vibration acceleration signal has been measured on a mill laboratory model for that purpose. It is made with accelerometer attached to a mill pin. The conclusion is made that mill filling level can not be measured with the help of such signal amplitude only. So this signal spectrum processed by a neural network is used. A training set for the neural network is formed with the help of spectral analysis methods. Trained neural network is able to find the correlation between mill pin vibration acceleration signal and mill filling level. Test set is formed from the data which is not included into the training set. This set is used in order to evaluate the network ability to evaluate the mill filling degree. The neural network guarantees no more than 7% error in the evaluation of mill filling level.

Fault Diagnosis of Transformer Based on RBF Neural Network

2014 ◽  
Vol 571-572 ◽  
pp. 201-204
Author(s):  
Jian Li Yu ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
High Energy ◽  
Ratio Method ◽  
Matlab Simulation ◽  
Medium Temperature ◽  
The Neural Network ◽  
Transformer Fault ◽  
Network Diagnosis

According to the characteristics of fault types of the transformer ,RBF neural network is used to diagnose transformer fault. The paper regards six gases as inputs of the neural network and establishes RBF neural network model which can diagnose six transformer faults: low temperature overheat, medium temperature overheat, high temperature overheat, low energy discharge, high energy discharge and partial discharge . The Matlab simulation studies show that transformer fault diagnosis model based on RBF neural network diagnosis for failure beyond the traditional three-ratio method. The rate of the transformer fault diagnosis accuracy reaches 91.67% which is also much higher than the traditional three ratio method.

REPRESENTATION FOR DISPERSION FORMULA OF OPTICAL FIBER USING HYBRID TECHNIQUE

2008 ◽  
Vol 19 (02) ◽  
pp. 205-213 ◽  
Author(s):  
AMR RADI
Keyword(s):  
Neural Network ◽  
Refractive Index ◽  
Optical Fiber ◽  
Hybrid Technique ◽  
Training Set ◽  
Fiber System ◽  
Output Data ◽  
Input And Output ◽  
Dispersion Formula ◽  
The Neural Network

Genetic Algorithm (GA) has been used to find the optimal neural network (NN) solution (i.e., hybrid technique) which represents dispersion formula of optical fiber. An efficient NN has been designed by GA to simulate the dynamics of the optical fiber system which is nonlinear. Without any knowledge about the system, we have used the input and output data to build a prediction model by NN. The neural network has been trained to produce a function that describes nonlinear system which studies the dependence of the refractive index of the fiber core on the wavelength and temperature. The trained NN model shows a good performance in matching the trained distributions. The NN is then used to predict refractive index that is not presented in the training set. The predicted refractive index had been matched to the experimental data effectively.

scholarly journals The Use of Neural Networks in High-Energy Physics

1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
High Quality ◽  
Network Algorithms ◽  
The Past ◽  
The Neural Network ◽  
Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

Prediction of Detonation Pressure of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 641-642 ◽  
pp. 460-463
Author(s):  
Yong Gang Liu ◽  
Xin Tian ◽  
Yue Qiang Jiang ◽  
Gong Bing Li ◽  
Yi Zhou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Pressure ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network(ANN) model was constructed to predict the detonation pressure of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation pressure was used as output. The dataset of 41 aluminized explosives was randomly divided into a training set (30) and a prediction set (11). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [6–9–1], calculated detonation pressures show good agreement with experimental results. It is shown here that ANN is able to produce accurate predictions of the detonation pressure of aluminized explosive.

scholarly journals Power Quality Disturbance Identification and Optimization Based on Machine Learning

2021 ◽  
Author(s):  
Fei Long ◽  
Fen Liu ◽  
Xiangli Peng ◽  
Zheng Yu ◽  
Huan Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Classification Method ◽  
Neural Network Training ◽  
Training Set ◽  
Network Training ◽  
The Neural Network ◽  
Recognition Ability ◽  
Power Quality Disturbance ◽  
Depth Learning

In order to improve the electrical quality disturbance recognition ability of the neural network, this paper studies a depth learning-based power quality disturbance recognition and classification method: constructing a power quality perturbation model, generating training set; construct depth neural network; profit training set to depth neural network training; verify the performance of the depth neural network; the results show that the training set is randomly added 20DB-50DB noise, even in the most serious 20dB noise conditions, it can reach more than 99% identification, this is a tradition. The method is impossible to implement. Conclusion: the deepest learning-based power quality disturbance identification and classification method overcomes the disadvantage of the selection steps of artificial characteristics, poor robustness, which is beneficial to more accurately and quickly discover the category of power quality issues.

scholarly journals Neural Network Effectiveness Evaluation While the Intersection of Images in the Receptor Field

2021 ◽  
Vol 17 ◽  
pp. 172-176
Author(s):  
Siranush Sargsyan ◽  
Anna Hovakimyan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Probabilistic Approach ◽  
Effectiveness Evaluation ◽  
Training Set ◽  
Receptor Field ◽  
Network Effectiveness ◽  
The Neural Network

The study and application of neural networks is one of the main areas in the field of artificial intelligence. The effectiveness of the neural network depends significantly on both its architecture and the structure of the training set. This paper proposes a probabilistic approach to evaluate the effectiveness of the neural network if the images intersect in the receptor field. A theorem and its corollaries are proved, which are consistent with the results obtained by a different path for a perceptron-type neural network.

Prediction of Detonation Velocity of Aluminized Explosive by Artificial Neural Network

2013 ◽  
Vol 790 ◽  
pp. 673-676
Author(s):  
Yue Qiang Jiang ◽  
Yong Gang Liu ◽  
Xin Tian ◽  
Gong Bing Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimal Condition ◽  
Detonation Velocity ◽  
Ann Model ◽  
Training Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

In this study, a three-layer artificial neural network (ANN) model was constructed to predict the detonation velocity of aluminized explosive. Elemental composition and loading density were employed as input descriptors and detonation velocity was used as output. The dataset of 61 aluminized explosives was randomly divided into a training set (49) and a prediction set (12). After optimized by adjusting various parameters, the optimal condition of the neural network was obtained. Simulated with the final optimum neural network [812, calculated detonation velocity show good agreement with experimental results. It is shown that ANN is able to produce accurate predictions of the detonation velocity of aluminized explosive.

High solar activity predictions through an artificial neural network

2017 ◽  
Vol 28 (06) ◽  
pp. 1750075 ◽  
Author(s):  
M. G. Orozco-Del-Castillo ◽  
J. C. Ortiz-Alemán ◽  
C. Couder-Castañeda ◽  
J. J. Hernández-Gómez ◽  
A. Solís-Santomé
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Activity ◽  
Outer Space ◽  
High Energy ◽  
High Solar Activity ◽  
Solar Cycles ◽  
The Neural Network ◽  
Space Electronics ◽  
Artificial Neural

The effects of high-energy particles coming from the Sun on human health as well as in the integrity of outer space electronics make the prediction of periods of high solar activity (HSA) a task of significant importance. Since periodicities in solar indexes have been identified, long-term predictions can be achieved. In this paper, we present a method based on an artificial neural network to find a pattern in some harmonics which represent such periodicities. We used data from 1973 to 2010 to train the neural network, and different historical data for its validation. We also used the neural network along with a statistical analysis of its performance with known data to predict periods of HSA with different confidence intervals according to the three-sigma rule associated with solar cycles 24–26, which we found to occur before 2040.

scholarly journals Térdporc szegmentálása MR-felvételekből mesterséges intelligencia segítségével

2021 ◽  
Vol 162 (9) ◽  
pp. 352-360
Author(s):  
Péter Szoldán ◽  
Zsófia Egyed ◽  
Endre Szabó ◽  
János Somogyi ◽  
György Hangody ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Knee Joint ◽  
Cartilage Tissue ◽  
Osteochondral Allograft ◽  
Mr Images ◽  
Training Set ◽  
Mr Image ◽  
The Neural Network ◽  
Wide Range

Összefoglaló. Bevezetés: A térdízületnek ultrafriss osteochondralis allograft segítségével történő részleges ortopédiai rekonstrukciója képalkotó vizsgálatokon alapuló pontos tervezést igényel, mely folyamatban a morfológia felismerésére képes mesterséges intelligencia nagy segítséget jelenthet. Célkitűzés: Jelen kutatásunk célja a porc morfológiájának MR-felvételen történő felismerésére alkalmas mesterséges intelligencia kifejlesztése volt. Módszer: A feladatra legalkalmasabb MR-szekvencia meghatározása és 180 térd-MR-felvétel elkészítése után a mesterséges intelligencia tanításához manuálisan és félautomata szegmentálási módszerrel bejelölt porckontúrokkal tréninghalmazt hoztunk létre. A mély convolutiós neuralis hálózaton alapuló mesterséges intelligenciát ezekkel az adatokkal tanítottuk be. Eredmények: Munkánk eredménye, hogy a mesterséges intelligencia képes a meghatározott szekvenciájú MR-felvételen a porcnak a műtéti tervezéshez szükséges pontosságú bejelölésére, mely az első lépés a gép által végzett műtéti tervezés felé. Következtetés: A választott technológia – a mesterséges intelligencia – alkalmasnak tűnik a porc geometriájával kapcsolatos feladatok megoldására, ami széles körű alkalmazási lehetőséget teremt az ízületi terápiában. Orv Hetil. 2021; 162(9): 352–360. Summary. Introduction: The partial orthopedic reconstruction of the knee joint with an osteochondral allograft requires precise planning based on medical imaging reliant; an artificial intelligence capable of determining the morphology of the cartilage tissue can be of great help in such a planning. Objective: We aimed to develop and train an artificial intelligence capable of determining the cartilage morphology in a knee joint based on an MR image. Method: After having determined the most appropriate MR sequence to use for this project and having acquired 180 knee MR images, we created the training set for the artificial intelligence by manually and semi-automatically segmenting the contours of the cartilage in the images. We then trained the neural network with this dataset. Results: As a result of our work, the artificial intelligence is capable to determine the morphology of the cartilage tissue in the MR image to a level of accuracy that is sufficient for surgery planning, therefore we have made the first step towards machine-planned surgeries. Conclusion: The selected technology – artificial intelligence – seems capable of solving tasks related to cartilage geometry, creating a wide range of application opportunities in joint therapy. Orv Hetil. 2021; 162(9): 352–360.

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