Temat: neural simulation - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Measurement of off-shell Higgs boson production in the $H^{*}\to ZZ\to 4\ell$ decay channel using a neural simulation-based inference technique in 13 TeV pp collisions with the ATLAS detector The ATLAS Collaboration
Autorzy:: Gil, Damian
Volkotrub, Yuriy
Przygoda, Witold
Richter-Wąs, Elżbieta
Współwytwórcy:: Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2873
Opis:: A measurement of off-shell Higgs boson production in the $H^{*}\to ZZ\to 4\ell$ decay channel is presented. The measurement uses 140 fb$^{−1}$ of proton–proton collisions at $\sqrt{s}$ = 13 TeV collected by the ATLAS detector at the Large Hadron Collider and supersedes the previous result in this decay channel using the same dataset. The data analysis is performed using a neural simulation-based inference method, which builds per-event likelihood ratios using neural networks. The observed (expected) off-shell Higgs boson production signal strength in the $ZZ\to 4\ell$ decay channel at 68% CL is $0.87^{+0.75}_{-0.54} (1.00^{+1.04}_{-0.95})$. The evidence for off-shell Higgs boson production using the $ZZ\to 4\ell$ decay channel has an observed (expected) significance of 2.5σ (1.3σ). The expected result represents a significant improvement relative to that of the previous analysis of the same dataset, which obtained an expected significance of 0.5σ. When combined with the most recent ATLAS measurement in the $ZZ\to 2\ell2\nu$ decay channel, the evidence for off-shell Higgs boson production has an observed (expected) significance of 3.7σ (2.4σ). The off-shell measurements are combined with the measurement of on-shell Higgs boson production to obtain constraints on the Higgs boson total width. The observed (expected) value of the Higgs boson width at 68% CL is $4.3^{+2.7}_{-1.9}(4.1^{+3.5}_{-3.4})$ MeV.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 2.

Tytuł:: An implementation of neural simulation-based inference for parameter estimation in ATLAS
Autorzy:: Przygoda, Witold
Richter-Wąs, Elżbieta
Volkotrub, Yuriy
Gil, Damian
Współwytwórcy:: Współautorami artykułu są członkowie ATLAS Collaboration w liczbie 2873
Opis:: Neural simulation-based inference (NSBI) is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. This work develops a NSBI framework for statistical inference, using neural networks to estimate probability density ratios, which enables the application to a full-scale analysis. It incorporates a large number of systematic uncertainties, quantifies the uncertainty due to the finite number of events in training samples, develops a method to construct confidence intervals, and demonstrates a series of intermediate diagnostic checks that can be performed to validate the robustness of the method. As an example, the power and feasibility of the method are assessed on simulated data for a simplified version of an off-shell Higgs boson couplings measurement in the four-lepton final states. This approach represents an extension to the standard statistical methodology used by the experiments at the Large Hadron Collider, and can benefit many physics analyses.
Dostawca treści:: Repozytorium Uniwersytetu Jagiellońskiego

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Skocz do pozycji: 3.

Tytuł:: Wpływ degradacji urządzeń pomiarowych na pozyskiwanie symptomów niesprawnej pracy złożonych obiektów energetycznych
Influence of measuring equipment degradation on gaining of symptoms of large power units inefficient operation
Autorzy:: Głuch, J.
Tematy:: symulacja neuronowa
diagnostyka cieplno-przepływowa
turbina parowa
neural simulation
thermal diagnostics
flow diagnostics
steam power plants
measuring system; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Powiązania:: https://bibliotekanauki.pl/articles/327488.pdf Link otwiera się w nowym oknie
Opis:: Opisywana jest możliwość budowania relacji cieplno-przepływowych diagnostycznych z zastosowaniem metody sztucznych sieci neuronowych. Są one zastosowane do detekcji zdegradowanych urządzeń pomiarowych w złożonych systemach pomiarowych. Przedstawiono to na przykładzie bloku energetycznego dużej mocy. Wykorzystano obliczenia symulacyjne degradacji. Rozważano zarówno degradacje samego systemu pomiarowego jak i degradacje geometrii urządzeń składowych. Pokazano dobrą jakość określania symptomów degradacji. Wykorzystano przykłady z praktyki eksploatacyjnej.
Possibility of building of diagnostic relations with usage of artificial neural networks ANN is described in the paper. The relations are applied for detection of the degraded measuring devices in steam power cycles of complex electricity generation systems. The example of the large steam turbine power plant is shown in the paper. Neuronal diagnostic relations are created on the basis of simulation calculations. There are taking into account both degradations of that of measuring equipment as well as simultaneously occurring degradations of measuring equipment and components of thermal cycle. Good quality of neuronal calculations is stated. Application of these relations is shown on some examples from exploitation practice.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Deep learning classification and recognition method for milling surface roughness combined with simulation data
Autorzy:: Lu, Lingli
Yi, Huaian
Shu, Aihua
Qin, Jianhua
Lu, Enhui
Tematy:: milling surface
classification
deep neural network
simulation; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2203367.pdf Link otwiera się w nowym oknie
Opis:: To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Self-learning control algorithms used to manage the operating of an internal combustion engine
Autorzy:: Graba, Mariusz
Mamala, Jarosław
Bieniek, Andrzej
Tematy:: transport
simulation
combustion engines
environmental protection
neural network; Pokaż więcej
Wydawca:: Instytut Techniczny Wojsk Lotniczych
Powiązania:: https://bibliotekanauki.pl/articles/242942.pdf Link otwiera się w nowym oknie
Opis:: The article presents the possibility of using self-learning control algorithms to manage subassemblies of an internal combustion engine in order to reduce exhaust emissions to the natural environment. In compression ignition (CI) engines, the issue of emissions mainly concerns two components: particulate matter (PM) and nitrogen oxides (NOx). The work focuses mainly on the possibility of reducing the emission of nitrogen oxides. It is assumed that the particularly problematic points when it comes to excessive emission of harmful substances are the dynamic states in which combustion engines operate constantly. In dynamically changing operating points, it is very difficult to choose the right setting of actuators such as the exhaust gas recirculation (EGR) valve to ensure the correct operation of the unit and the minimum emission of these substances. In the light of the above, an attempt was made to develop a selflearning mathematical model, which can predict estimated emission levels of selected substance basing on current measurement signals (e.g. air, pressure, crankshaft rotational speed, etc.). The article presents the results of the estimation of nitrogen oxides by the trained neural network in comparison to the values measured with the use of a sensor installed in the exhaust system. The presented levels of estimated and measured results are very similar to each other and shifted over time in favour of neural networks, where the information about the emission level appears much earlier. On the basis of the estimated level, it shall be possible to make an appropriate decision about specific settings of recirculation system components, such as the EGR valve. It is estimated that by using the chosen control method it is possible significantly to reduce the emission of harmful substances into the natural environment while maintaining dynamic properties of the engine.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Examples of Simulation of the Alloying Elements Effect on Austenite Transformations During Continuous Cooling
Autorzy:: Trzaska, Jacek
Tematy:: CCT diagram
simulation
neural network
heat treatment
steel; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/2049560.pdf Link otwiera się w nowym oknie
Opis:: The article shows examples of simulation of the chemical composition effect on austenite transformation during continuous cooling. The calculations used own neural model of CCT (Continuous Cooling Transformation) diagrams describing austenite transformations that occur during continuous cooling. The model allows to calculate a CCT diagrams of structural steels and engineering steels based on chemical composition of steel and austenitizing temperature. Examples of simulation shown herein are related to the effect of selected elements on the temperatures of phase transformations, hardness and volume fraction of ferrite, pearlite, bainite and martensite in steel.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Neural networks for function approximation in dynamic modelling
Autorzy:: Nedbálek, J.
Tematy:: reliability
Monte Carlo
RBF neural network
simulation
temperature; Pokaż więcej
Wydawca:: Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Powiązania:: https://bibliotekanauki.pl/articles/2069707.pdf Link otwiera się w nowym oknie
Opis:: The paper demonstrates the comparsion of Monte Carlo simulation (MC) algorithm with the Radial Basis Function (RBF) neural network enhancement of the same algorithm in the reliability case study. In our test, we dispose of the tank containing liquid water – its temperature process variable evolves deterministicly according to the differential equation, which solution is known. All component failures are considered as a stochastic events. In the case of surpassing temperature treshhold of the liquid inside the tank, we interpret the situation as the system failure. With regard to process dynamics, we attempt to evaluate the tank system unreliability related to the initiative input parameters setting. The neural network is used in equation coeficients calculation, which is executed in each transient state. Due to the neural networks, for some of the initial component settings, we can achieve the results of computation faster than in classical way of coeficients calculating and substituting into the equation.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Artificial neural network modelling of cutting force components during AZ91HP alloy milling
Autorzy:: Kulisz, M.
Zagórski, I.
Semeniuk, A.
Tematy:: simulation
cutting force
artificial neural networks
magnesium alloys; Pokaż więcej
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Powiązania:: https://bibliotekanauki.pl/articles/118253.pdf Link otwiera się w nowym oknie
Opis:: The paper presents simulation of the cutting force components for ma-chining of magnesium alloy AZ91HP. The simulation employs the Black Box model. The closest match to (input and output) data obtained from the machining process was determined. The simulation was performed with the use of the Statistica programme with the application of neural networks: RBF (Radial Basis Function) and MLP (Multi-Layered Perceptron).
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Research on the risk classification of cruise ship fires based on an attention-BP neural network
Autorzy:: Xiong, Zhenghua
Xiang, Bo
Chen, Ye
Chen, Bin
Tematy:: cruise fire
simulation modeling
ensemble learning
BP neural network; Pokaż więcej
Wydawca:: Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Powiązania:: https://bibliotekanauki.pl/articles/32912853.pdf Link otwiera się w nowym oknie
Opis:: Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 10.

Tytuł:: Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland
Autorzy:: Dzik-Walczak, Aneta
Odziemczyk, Maciej
Tematy:: convolutional neural networks
machine learning
simulation
bankruptcy prediction
financial indicators; Pokaż więcej
Wydawca:: Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
Powiązania:: https://bibliotekanauki.pl/articles/1965119.pdf Link otwiera się w nowym oknie
Opis:: The paper deals with the topic of modelling the probability of bankruptcy of Polish enterprises using convolutional neural networks. Convolutional networks take images as input, so it was thus necessary to apply the method of converting the observation vector to a matrix. Benchmarks for convolutional networks were logit models, random forests, XGBoost, and dense neural networks. Hyperparameters and model architecture were selected based on a random search and analysis of learning curves and experiments in folded, stratified cross-validation. In addition, the sensitivity of the results to data preprocessing was investigated. It was found that convolutional neural networks can be used to analyze cross-sectional tabular data, especially for the problem of modelling the probability of corporate bankruptcy. In order to achieve good results with models based on parameters updated by a gradient (neural networks and logit), it is necessary to use appropriate preprocessing techniques. Models based on decision trees have been shown to be insensitive to the data transformations used.
Dostawca treści:: Biblioteka Nauki

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