Trying to improve the international experience of studying abroad, we focused on a solution that would foster social interactions among students. We created a prototype of a game controller employing interactive floor and vertical display, which would allow players to use their natural body movements and interact with each other during the game. We have designed a high-fidelity prototype of a foot controller for a two-person game and have made a preliminary evaluation of the system.

Edge detection is a fundamental step in many computer vision systems, particularly in image segmentation and feature detection. There are a lot of algorithms for detecting edges of objects in images. This paper proposes a method based on local gradient estimation to detect metallic droplet image edges and compare the results to a contour line obtained from the active contour model of the same images, and to results from crowdsourcing to identify droplet edges at specific points. The studied images were taken at high temperatures, which makes the segmentation process particularly difficult. The comparison between the three methods shows that the proposed method is more accurate than the active contour method, especially at the point of contact between the droplet and the base. It is also shown that the reliability of the data from the crowdsourcing is as good as the edge points obtained from the local gradient estimation method.

With the ongoing digitalization of industry, imaging sensors are becoming increasingly important for industrial process control. In addition to direct imaging techniques such as those provided by video or infrared cameras, tomographic sensors are of interest in the process industry where harsh process conditions and opaque fluids require non-intrusive and non-optical sensing techniques. Because most tomographic sensors rely on complex and often time-multiplexed excitation and measurement schemes and require computationally intensive image reconstruction, their application in the control of highly dynamic processes is often hindered. This article provides an overview of the current state of the art in fast process tomography and its potential for use in industry.

Electrical resistance tomography (ERT) has been used in the literature to monitor the gas–liquid separation. However, the image reconstruction algorithms used in the studies take a considerable amount of time to generate the tomograms, which is far above the time scales of the flow inside the inline separator and, as a consequence, the technique is not fast enough to capture all the relevant dynamics of the process, vital for control applications. This article proposes a new strategy based on the physics behind the measurement and simple logics to monitor the separation with a high temporal resolution by minimizing both the amount of data and the calculations required to reconstruct one frame of the flow. To demonstrate its potential, the electronics of an ERT system are used together with a high-speed camera to measure the flow inside an inline swirl separator. For the 16-electrode system used in this study, only 12 measurements are required to reconstruct the whole flow distribution with the proposed algorithm, 10× less than the minimum number of measurements of ERT (120). In terms of computational effort, the technique was shown to be 1000× faster than solving the inverse problem non-iteratively via the Gauss–Newton approach, one of the computationally cheapest techniques available. Therefore, this novel algorithm has the potential to achieve measurement speeds in the order of 104 times the ERT speed in the context of inline swirl separation, pointing to flow measurements at around 10kHz while keeping the average estimation error below 6 mm in the worst-case scenario.

The performance of multiphase flow processes is often determined by the distribution of phases inside the equipment. However, controllers in the field are typically implemented based on flow variables, which are simpler to measure, but indirectly connected to performance (e.g., pressure). Tomography has been used in the study of the distribution of phases of multiphase flows for decades, but only recently, the temporal resolution of the technique was sufficient for real-time reconstructions of the flow. Due to the strong connection between the performance and distribution of phases, it is expected that the introduction of tomography to the real-time control of multiphase flows will lead to substantial improvements in the system performance in relation to the current controllers in the field. This paper uses a gas–liquid inline swirl separator to analyze the possibilities and limitations of tomography-based real-time control of multiphase flow processes. Experiments were performed in the separator using a wire-mesh sensor (WMS) and a high-speed camera to show that multiphase flows have two components in their dynamics: one intrinsic to its nonlinear physics, occurring independent of external process disturbances, and one due to process disturbances (e.g., changes in the flow rates of the installation). Moreover, it is shown that the intrinsic dynamics propagate from upstream to inside the separator and can be used in predictive and feedforward control strategies. In addition to the WMS experiments, a proportional–integral feedback controller based on electrical resistance tomography (ERT) was implemented in the separator, with successful results in relation to the control of the distribution of phases and impact on the performance of the process: the capture of gas was increased from 76% to 93% of the total gas with the tomography-based controller. The results obtained with the inline swirl separator are extended in the perspective of the tomography-based control of quasi-1D multiphase flows.

We propose a new 3D approach - the TOPO-CUT algorithm - to treat the problem of under-segmentation which can potentially occur when the watershed approach is used to perform the digital separation of objects. This is an important issue to be addressed, especially in the case of granular material, where the contact between objects is inevitable and their separation is necessary to perform local quantitative or numerical analysis during e.g. flowing conditions. The approach adopts topological-based algorithms, such as curvilinear skeletonization and hole closing to perform the separation of at least 2 objects in contact. It supposes that the digital volumetric image of the granular material is composed of nearly-convex objects. The new approach is tested on real 3D images of two types of grain mixtures made of sorghum and rice. The method outperforms the iterative call of the markerbased watershed algorithm and allows up to 13% of the possible problematic objects to be successfully separated in a limited number of iterations.

Swirl-based inline phase separation is a promising approach in the process industry with potential application in oil and gas separation in petroleum industry. To increase the efficiency of the separation, the process may be controlled. In this direction, the position and the diameter of the gas vortex are two parameters that can be used in the control loop, provided that they can be non-intrusively estimated. This article presents a capacitive sensor-based imaging method to extract these geometrical parameters. The proposed method consists in obtaining high-temporal resolution capacitance measurements at the pipe boundary of a test rig, which in turn are used in a reconstruction and extracting the targeted parameters of the gas vortex. The calculated parameters are then used to visually present the swirling flow. The measurement system was evaluated quantitatively by performing experiments with different phantoms of known diameters and positions in the sensing area. Dynamic measurements were also performed in a test rig for liquid-gas swirling flow. The capacitive imaging system is capable of detecting characteristics of the flow for a wide range of gas and liquid flow rates. A qualitative analysis was also carried out by comparing time series of the capacitive images with high-speed camera recording. The geometrical parameters obtained by the proposed approach presents a good agreement with the real data, with a root mean square deviation of 0.76mm for diameter and 0.88mm for vortex position. It can be utilized in future work as an alternative or complementary input for the controlled inline liquid-gas separation system.

Phase separation based centrifugal forces is effective, and thus widely explored by the process industry. In an inline swirl separator, a core of the light phase is formed in the center of the device and captured further downstream. Given the inlet conditions, this gas core created varies in shape and size. To predict the separation behavior and control the process in an optimal way, the gas core diameter should be measured with the minimum possible intrusiveness. Process tomography techniques such as electrical resistance tomography (ERT) allows us to measure the gas core diameter in a fast and non-intrusive way. Due to the soft-field nature and ill-posed problem in solving the inverse problem, especially in the area of low spatial resolution, the reconstructed images often overestimate the diameter of the object under consideration leading to unreliable measurements. To use ERT measurements as an input for the controller, the estimated diameters should be corrected based on secondary measurements, e.g., optical techniques such as high-speed cameras. In this context, image processing and image analysis techniques were adapted to compare the diameter calculated by an ERT system and a fast camera. In this paper, a correction method is introduced to correct the diameter obtained by ERT based on static measurements. The proposed method reduced the ERT error of dynamic measurements of the gas core size from over 300% to below 20%, making it a reliable sensing technique for controlled separation processes.

Gravitational flows, like silo discharge, vary in mode and are complex to identify since they depend on various parameters, ranging from silo structural properties to granular material properties. Moreover, the way cohesionless particles arrange themselves during the flow is still a matter of study and new experimental approaches are needed. Here, the paper introduces a new versatile silo model that was specially designed for in situ X-ray tomography studies of silo discharge for various flow conditions, namely concentric and eccentric. The presented work focuses on organic granular materials, i.e. sorghum and rice, which present relatively similar physicomechanical properties but different elongation. The high-quality tomography images combined with adequate image processing strategy allow to unambiguously analyse individual grains dynamic behaviour during silo discharge for different hopper configurations. The paper focuses on the analysis of packing density, coordination number and grain rotation changes during funnel flow. The quantitative analysis essentially shows that hopper eccentricity and particle elongation influence flow dynamics of funnel flow. The proposed experimental methodology is proven to be solid enough to investigate in more detail dynamic phenomena during silo discharge. It can also serve as a strong baseline for numerical calibration and predictions.

Chemie Ingenieur TechnikVolume 92, Issue 5 p. 492-497 InhaltFree Access Inhalt: Chem. Ing. Tech. 5/2020 First published: 23 April 2020 https://doi.org/10.1002/cite.202070503AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume92, Issue5Special Issue: ReaktionstechnikMay 2020Pages 492-497 RelatedInformation

This paper presents the feasibility study of dynamic flow measurements using the concept of a rotatable electrical capacitance tomography (ECT) sensor. The experiment considered horizontal flow in a pneumatic conveying flow loop in the case of dense phase flow. Slugs and settled layers were imaged and a comparison was made between no rotation or rotation of the sensor for two image reconstruction schemas: linear back projection (LBP) and non-linear iterative back projection. Data were evaluated both qualitatively and quantitatively by estimating the solids concentration level for different hue levels.

Silo discharging and monitoring process for industrial or research application depend on computerized segmentation of different parts of images such as stagnant and flowing zones which is the toughest task. X-ray Computed Tomography (CT) is one of a powerful non-destructive technique for cross-sectional images of a 3D object based on X-ray absorption. CT is the most proficient for investigating different granular flow phenomena and segmentation of stagnant zone as compared to other imaging techniques. In any case, manual segmentation is tiresome and erroneous for further investigations. Hence, automatic and precise strategies are required. In the present work, a U-net architecture is used for segmenting the stagnant zone during silo discharging process. This proposed image segmentation method provides fast and effective outcomes by exploiting a convolutional neural networks technique with an accuracy of 97 percent.

This text structures the application of Wire-Mesh sensors and Electrical Resistance Tomography in the control of an Inline Swirl Separator. It introduces a mechanistic model of the two-phase flow inside the device, which is linearized around an ideal perfect operation, and implemented in a Model Predictive Controller. The whole text is structured aiming at a future real application of the controller, briefly introducing the setup that is going to be used, the sensors and their working principles. The results obtained show a stable controller, able to regulate the process relatively fast in relation to the time resolution of the sensors. The positive response of the approach stimulates further improvements in the model developed, and the implementation of more sophisticated techniques to handle the non-linearities of the process.

DATA OF SWIRLED LIQUID-GAS SEPARATION USING FAST CAMERA IMAGING Muhammad Awais Sattar 1, Matheus Martinez Garcia2, Robert Banasiak1, Luis M. Portela2, and Laurent Babout11 1The Lodz University of Technology, Institute of Applied Computer Science, Stefanowskiego 18/22, 90924 Łódź, Poland 2The Delft University of Technology, Department of Chemical Engineering, Van der Maasweg 9, 2629 HZ Delft, Netherlands

Today's mechanical fluid separators in industry are mostly operated without any control to maintain efficient separation for varying inlet conditions. Controlling inline fluid separators, on the other hand, is challenging since the process is very fast and measurements in the multiphase stream are difficult as conventional sensors typically fail here. With recent improvement of process tomography sensors and increased processing power of smart computers, such sensors can now be potentially used in inline fluid separation. Concepts for tomography-controlled inline fluid separation were developed, comprising electrical tomography and wire-mesh sensors, fast and massive data processing and appropriate process control strategy. Solutions and ideas presented in this paper base on process models derived from theoretical investigation, numerical simulations and analysis of experimental data.

In most industrial products, granular materials are often required to flow under gravity in various kinds of silo shapes and usually through an outlet in the bottom. There are several interrelated parameters which affect the flow, such as internal friction, bulk and packing density, hopper geometry, and material type. Due to the low-spatial resolution of electrical capacitance tomography or scanning speed limitation of standard X-ray CT systems, it is extremely challenging to measure the flow velocity and possible centrifugal effects of granular materials flow effectively. However, ROFEX (ROssendorf Fast Electron beam X-ray tomography) opens new avenues of granular flow investigation due to its very high temporal resolution. This paper aims to track particle movements and evaluate the local grain velocity during silo discharging process in the case of mass flow. The study has considered the use of the Seramis material, which can also serve as a type of tracer particles after impregnation, due to its porous nature. The presented novel image processing and analysis approach allows satisfyingly measuring individual particle velocities but also tracking their lateral movement and three-dimensional rotations.

This study investigates local orientation-based approaches to the complex problem of pattern segmentation in three-dimensional (3D) texture image. The current problem focuses on the extraction of so-called lamellar colonies in titanium alloy, which, from the materials science and engineering point of view, are microstructural features that play a fundamental role on crack propagation and bifurcation during mechanical loading. Methods based on local orientation estimation extend the notion of using local gradient to reveal variation of semi-planar pattern orientation in the 3D image. The study introduces a computational approach that accelerates the calculation of the eigenvectors from the local matrices of inertia of all voxels composing the 3D image. Then different paths are proposed to segment colonies or inter-colony boundaries, i.e. polar orientation map and minimum scalar product map, in order to delimitate regions of similar orientations. The investigated segmentation methods have been compared with other methods that are mainly based on the popular solution of filter banks. Tests, which have been performed on both synthetic and real 3D images, show that the proposed local orientation-based methods better delineate object boundaries than the counterparts.