Improving the resolution of Non-Invasive Time Domain Reflectometry

Non invasive time domain reflectometry (TDR) may be used to estimate the volumetric moisture content, θᵥ, with depth for a variety of sample materials. The forward physical model is couched in terms of a moments method where integration is performed over a discretised sample space to estimate the measured propagation time, tp down a pair of parallel transmission lines. We show that inverse solution to this, which recovers relative permittivity and thus θᵥ, is greatly facilitated by a simplification of the system geometry via, 1) realistically modeling the prior density of the sample, 2) using this prior with the inherent system symmetry to reduce the number of required discretisation cells, and 3) determining a physically meaningful reduction operator to allow a coarse discretisation mesh to be used. The observational equation is expressed in the Bayesian paradigm with the most accurate and robust solution obtained using the conditional mean of the posterior distribution constructed via a Monte Carlo method. Results of simulation show that the method is capable of providing accurate estimates of the moisture density profile down to a depth of 100 mm with an error < 4%. Further, the reduction in the number of discretised cells required to accurately estimate these profiles means that the inversion procedure is quick enough to enable the real time application of the equipment, a fundamental requirement in the development

Designing an adaptive production control system using reinforcement learning

Modern production systems face enormous challenges due to rising customer requirements resulting in complex production systems. The operational efficiency in the competitive industry is ensured by an adequate production control system that manages all operations in order to optimize key performance indicators. Currently, control systems are mostly based on static and model-based heuristics, requiring significant human domain knowledge and, hence, do not match the dynamic environment of manufacturing companies. Data-driven reinforcement learning (RL) showed compelling results in applications such as board and computer games as well as first production applications. This paper addresses the design of RL to create an adaptive production control system by the real-world example of order dispatching in a complex job shop. As RL algorithms are “black box” approaches, they inherently prohibit a comprehensive understanding. Furthermore, the experience with advanced RL algorithms is still limited to single successful applications, which limits the transferability of results. In this paper, we examine the performance of the state, action, and reward function RL design. When analyzing the results, we identify robust RL designs. This makes RL an advantageous control system for highly dynamic and complex production systems, mainly when domain knowledge is limited

Исследование остаточных углеводородов в ходе деструкции гептана углеводородокисляющими микроорганизмами рода Pseudomonas и Rodococcus

Molding of micro structures by injection molding leads to special requirements for the molds e.g. regarding wear resistance and low release forces of the molded components. At the same time it is not allowed to affect the replication precision. Physical vapor deposition (PVD) is one of the promising technologies for applying coatings with adapted properties like high hardness, low roughness, low Young's modulus and less adhesion to the melt of polymers. Physical vapor deposition technology allows the deposition of thin films on micro structures. Therefore, the influence of these PVD layers on the contour accuracy of the replicated micro structures has to be investigated. For this purpose injection mold inserts were laser structured with micro structures of different sizes and afterwards coated with two different coatings, which were deposited by a magnetron sputter ion plating PVD technology. After deposition, the coatings were analyzed by techniques regarding hardness, Young's modulus and morphology. The geometries of the micro structures were analyzed by scanning electron microscopy before and after coating. Afterwards, the coated mold inserts were used for injection molding experiments. During the injection molding process, a conventional and a variothermal temperature control of the molds were used. The molded parts were analyzed regarding roughness, structure height and structure width by means of laser microscopy