Semi-supervised segmentation of concrete aggregate using consensus regularisation and prior guidance

In order to leverage and profit from unlabelled data, semi-supervised frameworks for semantic segmentation based on consistency training have been proven to be powerful tools to significantly improve the performance of purely supervised segmentation learning. However, the consensus principle behind consistency training has at least one drawback, which we identify in this paper: imbalanced label distributions within the data. To overcome the limitations of standard consistency training, we propose a novel semi-supervised framework for semantic segmentation, introducing additional losses based on prior knowledge. Specifically, we propose a lightweight architecture consisting of a shared encoder and a main decoder, which is trained in a supervised manner. An auxiliary decoder is added as additional branch in order to make use of unlabelled data based on consensus training, and we add additional constraints derived from prior information on the class distribution and on auto-encoder regularisation. Experiments performed on our concrete aggregate dataset presented in this paper demonstrate the effectiveness of the proposed approach, outperforming the segmentation results achieved by purely supervised segmentation and standard consistency training

Chemo-mechanical characterization of hydrated calcium-hydrosilicates with coupled Raman- and nanoindentation measurements

Celitement is a new type of cement that is based on hydraulic calcium-hydrosilicate (hCHS). It is produced by mechanochemical activation of Calcium-Silicate-Hydrates (C-S-H) in a grinding process. Due to the lack of typical clinker minerals, its CaO/SiO_{2} (C/S) ratio can be minimized from above 3 (as in Ordinary Portland Cement) down to 1, which significantly reduces the amount of CO_{2} released during processing. The reaction kinetics of hCHS differs from that of classical clinker phases due to the presence of highly reactive silicate species, which involve silanol groups instead of pure calcium silicates and aluminates and aluminoferrites. In contrast to Portland cement, no calcium hydroxide is formed during hydration, which otherwise regulates the Ca concentration. Without the buffering role of Ca(OH)_{2} the concentration of the dissolved species c(Ca^2+) and c(SiO_{4}^4−) and the corresponding pH must be controlled to ensure a reproducible reaction. Pure hCHS reacts isochemically with water, resulting in a C-S-H phase with the same chemical composition as a single hydration product, with a homogeneous distribution of the main elements Ca and Si throughout the sample. Here we study via nanoindentation tests, the mechanical properties of two different types of hardened pastes made out of Celitement (C/S = 1.28), with varying amounts of hCHS and variable water to cement ratio. We couple nanoindentation grids with Raman mappings to link the nanoscale mechanical properties to individual microstructural components, yielding in-depth insight into the mechanics of the mineralogical phases constituting the hardened cement paste. We show that we can identify in hardened Celitement paste both fresh C-S-H with varying density, and C-S-H from the raw material using their specific Raman spectra, while simultaneously measuring their mechanical properties. Albeit not suitable for phase identification, supplemental EDX measurements provide valuable information about the distribution of alkalis, thus further helping to understand the reaction pattern of hCHS

The influences of cement hydration and temperature on the thixotropy of cement paste

The rheological properties of fresh cement paste are highly influenced by a large number of parameters, among which the most important factors are the applied shear stress, and the shear history, the age of the sample and the temperature. The effects of these parameters on the yield stress (designated as structural limit stress in this work), the viscosity and the structural recovery rate (i.e., the change in dynamic viscosity with time at rest) were studied. In parallel, the changes in ion composition of the carrier liquid, mineral phase content and granulometry were investigated. The results reveal that all investigated rheological parameters exhibit an approximated bi-linear trend with respect to the degree of hydration, with a period of quasi-constant properties until a degree of hydration of approximately 0.07, followed by a non-linear increase. This increase could be attributed to the formation of calcium hydroxide (CH) and calcium-silicate-hydrate (C-S-H) via calorimetry results. With regard to the effect of the shear history of the sample on the rheological properties, the structural limit stress showed a minor dependency on the shear history immediately after the end of shearing, which, however, vanished within the first minute at rest. The same is true for the structural recovery rate. The presented results give detailed insights into the influences of hydration and shear on the rheological properties—especially the thixotropy—of fresh cement pastes

Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate

A large component of the building material concrete consists of aggregate with varying particle sizes between 0.125 and 32 mm. Its actual size distribution significantly affects the quality characteristics of the final concrete in both, the fresh and hardened states. The usually unknown variations in the size distribution of the aggregate particles, which can be large especially when using recycled aggregate materials, are typically compensated by an increased usage of cement which, however, has severe negative impacts on economical and ecological aspects of the concrete production. In order to allow a precise control of the target properties of the concrete, unknown variations in the size distribution have to be quantified to enable a proper adaptation of the concrete's mixture design in real time. To this end, this paper proposes a deep learning based method for the determination of concrete aggregate grading curves. In this context, we propose a network architecture applying multi-scale feature extraction modules in order to handle the strongly diverse object sizes of the particles. Furthermore, we propose and publish a novel dataset of concrete aggregate used for the quantitative evaluation of our method

Solving the subset-sum problem with a light-based device

We propose a special computational device which uses light rays for solving the subset-sum problem. The device has a graph-like representation and the light is traversing it by following the routes given by the connections between nodes. The nodes are connected by arcs in a special way which lets us to generate all possible subsets of the given set. To each arc we assign either a number from the given set or a predefined constant. When the light is passing through an arc it is delayed by the amount of time indicated by the number placed in that arc. At the destination node we will check if there is a ray whose total delay is equal to the target value of the subset sum problem (plus some constants).Comment: 14 pages, 6 figures, Natural Computing, 200

Exact Cover with light

We suggest a new optical solution for solving the YES/NO version of the Exact Cover problem by using the massive parallelism of light. The idea is to build an optical device which can generate all possible solutions of the problem and then to pick the correct one. In our case the device has a graph-like representation and the light is traversing it by following the routes given by the connections between nodes. The nodes are connected by arcs in a special way which lets us to generate all possible covers (exact or not) of the given set. For selecting the correct solution we assign to each item, from the set to be covered, a special integer number. These numbers will actually represent delays induced to light when it passes through arcs. The solution is represented as a subray arriving at a certain moment in the destination node. This will tell us if an exact cover does exist or not.Comment: 20 pages, 4 figures, New Generation Computing, accepted, 200

Performance and polarization response of slit homogenizers for the GeoCarb mission

The observing strategy of the Geostationary Carbon Observatory (GeoCarb), which is a “step and stare” approach, can lead to distortions in the instrument spectral response function (ISRF) when there are gradients in brightness across instrument field of view. These distortions induce errors in the retrieved trace gases. In order to minimize these errors, the GeoCarb instrument design was modified to include a “slit homogenizer” whose purpose is to scramble the pattern of the incoming light and effectively remove the ISRF distortions caused by the variations in illumination across the slit. As a risk reduction, GeoCarb procured six different homogenizers and had them tested for performance in a benchtop optical system. The major finding is that the homogenizer performance depends strongly on the polarization of the incoming light, with the sensitivity growing as a function of wavelength. The width of the ISRF is substantially smaller when the light is vertically polarized (orthogonal to the slit length) compared to horizontally polarized (parallel to the slit length), and the throughput is accordingly reduced. These effects are due to the effects of the gold coating and high incidence angles present in the GeoCarb homogenizer design, which was verified using a polarization-dependent model generalized from previous homogenizer modeling work. The results strongly recommend controlling the polarization of the light entering a similar implementation using a polarizer, depolarizer, or polarization scrambler for other instruments attempting to mitigate scene illumination non-uniformity effects, as well as a robust characterization of the polarization sensitivity of all key subsystems.</p