On‐line turbidimeters are currently used for monitoring particle concentration in water and gas treatment processes. For small particle concentration, the intensity of scattered light is a linear function of the particle concentration, as long as a number of other parameters are kept constant: the refractive indexes of the particles and the surrounding medium, size, measuring angle and wavelength of the light. An international standard has been created in order to define the characteristics of the turbidimeters and the calibration suspension. The effects of the parameters are described and quantified. The special aspects of the measurement of very low turbidity values in water are treated: the zero value of water and the definition of the calibration conditions. A measuring method is presented which grants long‐term stability without recalibration. The first industrial application for on‐line turbidimeters was the monitoring of beer filtration in breweries, which started about 50 years ago. In the meantime, turbidimeters have been used in very diversified fields such as dust measurements in stacks, visibility in road tunnels and filtration control in the chemical industry. However, their main application today is the monitoring of drinking water treatment plants: control of the flocculation process by measuring the variations in the raw water (rivers, lakes, ground‐water, sources) and the resulting properties, sand filtration survey and final quality control.
The present paper offers an overview of the potential of ion cyclotron resonance heating (ICRH) or radio frequency (RF) heating for the DEMO machine. It is found that various suitable heating schemes are available. Similar to ITER and in view of the limited bandwidth of about 10M Hz that can be achieved to ensure optimal functioning of the launcher, it is proposed to make core second harmonic tritium heating the key ion heating scheme, assisted by fundamental cyclotron heating 3 He in the early phase of the discharge; for the present design of DEMO-with a static magnetic field strength of B o = 5.855T-that places the T and 3 He layers in the core for f = 60M Hz and suggests to center the bandwidth around that main operating frequency. In line with earlier studies for hot, dense plasmas in large-size magnetic confinement machines it is shown that good single pass absorption is achieved but that the size as well as operating density and temperature of the machine cause the electrons to absorb a non-negligible fraction of the power away from the core when core ion heating is aimed at. Current drive and alternative heating options are briefly discussed and a dedicated computation is done for the traveling wave antenna, proposed for DEMO in view of its compatibility with substantial antenna-plasma distances. The various tasks that ICRH can fulfill are briefly listed. Finally, the impact of transport and the sensitivity of the obtained results to changes in the machine parameters is commented on.
Abstract-In this work, we present a Hidden Markov Model (HMM) based workflow analysis of an assembly task jointly performed by a human and an assistive robotic system. In an experiment subjects had to assemble a tower by combining six cubes with several bolts for their own without the influence of a robot or any other technical device. To estimate the current action of the human, we have trained composite HMMs. After the successful evaluation on disjunct experimental data sets, the models are transferred to the assistive robotic system JAHIR, where the same assembly tasks was executed. A new 3D occupancy grid approach was used to determine the hand positions of the worker. The positions were then used to compute the inputs of the analysis HMMs. The workflow of the right hand could be recognized with an accuracy of 92.26 % which is nearly as good as the recognition rate of reference experiments.
A: Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays. K : Computerized Tomography (CT) and Computed Radiography (CR); Plasma diagnostics -interferometry, spectroscopy and imaging 1Corresponding author. 2See the author list of Overview of the JET preparation for Deuterium-Tritium Operation by E. Joffrin et al. in Nucl.
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