Quantitative infrared thermography resolved leakage current problem in cathodic protection system

Leakage current problem can happen in Cathodic Protection (CP) system installation. It could affect the performance of underground facilities such as piping, building structure, and earthing system. Worse can happen is rapid corrosion where disturbance to plant operation plus expensive maintenance cost. Occasionally, if it seems, tracing its root cause could be tedious. The traditional method called line current measurement is still valid effective. It involves isolating one by one of the affected underground structures. The recent methods are Close Interval Potential Survey and Pipeline Current Mapper were better and faster. On top of the mentioned method, there is a need to enhance further by synthesizing with the latest visual methods. Therefore, this paper describes research works on Infrared Thermography Quantitative (IRTQ) method as resolution of leakage current problem in CP system. The scope of study merely focuses on tracing the root cause of leakage current occurring at the CP system lube base oil plant. The results of experiment adherence to the hypothesis drawn. Consequently, res

Method of lines and runge-kutta method in solving partial differential equation for heat equation

Solving the differential equation for Newton’s cooling law mostly consists of several fragments formed during a long time to solve the equation. However, the stiff type problems seem cannot be solved efficiently via some of these methods. This research will try to overcome such problems and compare results from two classes of numerical methods for heat equation problems. The heat or diffusion equation, an example of parabolic equations, is classified into Partial Differential Equations. Two classes of numerical methods which are Method of Lines and Runge-Kutta will be performed and discussed. The development, analysis and implementation have been made using the Matlab language, which the graphs exhibited to highlight the accuracy and efficiency of the numerical methods. From the solution of the equations, it showed that better accuracy is achieved through the new combined method by Method of Lines and Runge-Kutta method

Redes neuronales artificiales en el control de procesos por variables: aplicación en la fabricación de tableros de partículas

Artificial neural networks are an efficient tool for modelling production control processes using data from the actual production as well as simulated or design of experiments data. In this study two artificial neural networks were combined with the control process charts and it was checked whether the data obtained by the networks were valid for variable process control in particleboard manufacture. The networks made it possible to obtain the mean and standard deviation of the internal bond strength of the particleboard within acceptable margins using known data of thickness, density, moisture content, swelling and absorption. The networks obtained met the acceptance criteria for test values from non-standard test methods, as well as the criteria for using these values in statistical process control.Las redes neuronales artificiales son una herramienta eficaz para el modelado de los procesos de control de producción, tanto partiendo de datos de la propia producción como de datos simulados o procedentes de diseños de experimentos. En este estudio se han combinado dos redes neuronales artificiales con los gráficos de control de procesos y se ha comprobado si los datos obtenidos con ellas eran válidos para el control de producción por variables en la fabricación de tableros de partículas. Las redes han permitido obtener valores de la media y la desviación típica de la cohesión interna del tablero de partículas dentro de unos márgenes aceptables a partir de datos conocidos de espesor, densidad, contenido de humedad, hinchazón y absorción. Las redes obtenidas han cumplido con los requisitos de aceptación de valores de ensayo por métodos alternativos al normalizado y con los requisitos impuestos para su utilización en el control estadístico de procesos

Portable marketing set

Folding table (Figure 8.1) that consist of chair or also known as “Portable Table” is an item that have been widely used all over the world. However, in Malaysia this concept of idea is still new and needs publicities. This portable table usually consist of a rectangular table and chairs around it. The development of the table is based from the dining table at home to gather family member and for eating. Folding tables are produced in many sizes, design and configuration and it can be made from plastic, metal, plastic and other material. Mostly special material will be used by engineer to produces the product

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

Slipping into Sleep: neurodynamics of alertness transitions in humans and fruit flies

The ability to react to events in the external world determines the fate of every living organism, this general state of readiness is called as ’alertness’. What happens to neurodynamics in the brain when alertness fades away as we fall asleep? How is behaviour affected? These questions will help us understand the organizing principles in the brain and functions of sleep itself. Here I use two distant animal models, the richness in behaviour and complexity of the human brain to understand how alertness transitions affects attention; and the experimental flexibility of the fruit fly, to understand its effect over longer time intervals. I first develop an objective method to track alertness using Electroencephalography (EEG). Then, I investigate the behavioural dynamics using an auditory spatial attention task while participants fall asleep. By using multilevel modelling and psychophysics, I show that participants systematically misclassify tones from the left side when drowsy, and further with a hierarchical drift diffusion model (HDDM) show how drift-rate (evidence accumulation) explains errors. Then, I show convergent evidence in the neural dynamics using multivariate pattern analysis (MVPA). Next, I probe the effect of handedness on the same task. Handedness affects behaviour only under drowsy condition and I show how neural dynamics are affected by a combination of handedness and alertness. To approach alertness transitions in a system with reduced neural complexity, I explore those dynamics in the fruit fly (Drosophila melanogaster), using both single and multichannel local field potential (LFP) data to show how alertness transitions and sleep modulate different regions of the fly brain. Further, I validate the results by converging evidence from causal manipulations. Finally, I discuss how the mapping of alertness transitions -under natural conditions- can help us understand fundamental questions in neuroscience such as the functions of sleep or the mechanisms of general anaesthesia.Gates Cambridg

A Neurophysiologic Study Of Visual Fatigue In Stereoscopic Related Displays

Two tasks were investigated in this study. The first study investigated the effects of alignment display errors on visual fatigue. The experiment revealed the following conclusive results: First, EEG data suggested the possibility of cognitively-induced time compensation changes due to a corresponding effect in real-time brain activity by the eyes trying to compensate for the alignment. The magnification difference error showed more significant effects on all EEG band waves, which were indications of likely visual fatigue as shown by the prevalence of simulator sickness questionnaire (SSQ) increases across all task levels. Vertical shift errors were observed to be prevalent in theta and beta bands of EEG which probably induced alertness (in theta band) as a result of possible stress. Rotation errors were significant in the gamma band, implying the likelihood of cognitive decline because of theta band influence. Second, the hemodynamic responses revealed that significant differences exist between the left and right dorsolateral prefrontal due to alignment errors. There was also a significant difference between the main effect for power band hemisphere and the ATC task sessions. The analyses revealed that there were significant differences between the dorsal frontal lobes in task processing and interaction effects between the processing lobes and tasks processing. The second study investigated the effects of cognitive response variables on visual fatigue. Third, the physiologic indicator of pupil dilation was 0.95mm that occurred at a mean time of 38.1min, after which the pupil dilation begins to decrease. After the average saccade rest time of 33.71min, saccade speeds leaned toward a decrease as a possible result of fatigue on-set. Fourth, the neural network classifier showed visual response data from eye movement were identified as the best predictor of visual fatigue with a classification accuracy of 90.42%. Experimental data confirmed that 11.43% of the participants actually experienced visual fatigue symptoms after the prolonged task

Adversarial training to improve robustness of adversarial deep neural classifiers in the NOvA experiment

The NOvA experiment is a long-baseline neutrino oscillation experiment. Consisting of two functionally identical detectors situated off-axis in Fermilab’s NuMI neutrino beam. The Near Detector observes the unoscillated beam at Fermilab, while the Far Detector observes the oscillated beam 810 km away. This allows for measurements of the oscillation probabilities for multiple oscillation channels, ν_µ → ν_µ, anti ν_µ → anti ν_µ, ν_µ → ν_e and anti ν_µ → anti ν_e, leading to measurements of the neutrino oscillation parameters, sinθ_23, ∆m^2_32 and δ_CP. These measurements are produced from an extensive analysis of the recorded data. Deep neural networks are deployed at multiple stages of this analysis. The Event CVN network is deployed for the purposes of identifying and classifying the interaction types of selected neutrino events. The effects of systematic uncertainties present in the measurements on the network performance are investigated and are found to cause negligible variations. The robustness of these network trainings is therefore demonstrated which further justifies their current usage in the analysis beyond the standard validation. The effects on the network performance for larger systematic alterations to the training datasets beyond the systematic uncertainties, such as an exchange of the neutrino event generators, are investigated. The differences in network performance corresponding to the introduced variations are found to be minimal. Domain adaptation techniques are implemented in the AdCVN framework. These methods are deployed for the purpose of improving the Event CVN robustness for scenarios with systematic variations in the underlying data