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aciona mento de velocidade
Teoria
Teoria
Luiz Otavio Da Silva Ladeira
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Luiz Otavio Da Silva Ladeira
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profibus PA
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Luiz Otavio Da Silva Ladeira
rede profibus dp
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Profibusdp 140503095743-phpapp01
Luiz Otavio Da Silva Ladeira
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Luiz Otavio Da Silva Ladeira
apostila rede HART
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1hart 140503100312-phpapp02
Luiz Otavio Da Silva Ladeira
modulo 1
S7 1200
S7 1200
Luiz Otavio Da Silva Ladeira
tratamento de erros
Pro3 06 e_tratamento_de_erros
Pro3 06 e_tratamento_de_erros
Luiz Otavio Da Silva Ladeira
endereçamento indereto
Pro3 04 e_enderecamento_indireto
Pro3 04 e_enderecamento_indireto
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dados complexos
Pro3 03 e_dados_complexos
Pro3 03 e_dados_complexos
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jumper e acumuladores
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Pro2 05 e_instrucoes_de_jump_e_de_acumuladores
Luiz Otavio Da Silva Ladeira
micro master
Cp mm4 apostila - portugues
Cp mm4 apostila - portugues
Luiz Otavio Da Silva Ladeira
scl tia portal
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04 tia portal hands on - scl v11 -v1
Luiz Otavio Da Silva Ladeira
According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Seizure stage detection of epileptic seizure using convolutional neural networks
Seizure stage detection of epileptic seizure using convolutional neural networks
IJECEIAES
A Coordinate Measuring Machine (CMM) is a precision measuring metrology machine used in manufacturing and quality control processes to precisely measure the geometric characteristics of objects. It operates on the principle of coordinate geometry, employing a probe to collect data points from the surface of an object to determine the dimensions, shapes, and positions of features, such as holes, slots, and surfaces. CMMs are essential tools for ensuring that manufactured parts meet design specifications and quality standards.
What is Coordinate Measuring Machine? CMM Types, Features, Functions
What is Coordinate Measuring Machine? CMM Types, Features, Functions
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A one hour lecture delivered in May 2024 to the Intro to Mechatronics unit at The University of Sydney, Australia. By Dr. Ricardo Sosa
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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well timedomain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.
Seizure stage detection of epileptic seizure using convolutional neural networks
Seizure stage detection of epileptic seizure using convolutional neural networks
IJECEIAES
A Coordinate Measuring Machine (CMM) is a precision measuring metrology machine used in manufacturing and quality control processes to precisely measure the geometric characteristics of objects. It operates on the principle of coordinate geometry, employing a probe to collect data points from the surface of an object to determine the dimensions, shapes, and positions of features, such as holes, slots, and surfaces. CMMs are essential tools for ensuring that manufactured parts meet design specifications and quality standards.
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We investigated the applicability and efficiency of the MLMC approach to the Henry-like problem with uncertain porosity, permeability and recharge. These uncertain parameters were modelled by random fields with three independent random variables. Permeability is a function of porosity. Both functions are time-dependent, have multi-scale behaviour and are defined for two layers. The numerical solution for each random realisation was obtained using the well-known ug4 parallel multigrid solver. The number of random samples required at each level was estimated by calculating the decay of the variances and the computational cost for each level. The MLMC method was used to compute the expected value and variance of several QoIs, such as the solution at a few preselected points $(t,\bx)$, the solution integrated over a small subdomain, and the time evolution of the freshwater integral. We have found that some QoIs require only 2-3 mesh levels and samples from finer meshes would not significantly improve the result. Other QoIs require more grid levels.
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