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This contribution introduces MOBSY, a fully integrated, autonomous mobile service robot system. It acts as an automatic dialogue-based receptionist for visitors to our institute.
MOBSY incorporates many techniques from different research areas into one working stand-alone system. The techniques involved range from computer vision over speech understanding to classical robotics.
Along with the two main aspects of vision and speech, we also focus on the integration aspect, both on the methodological and on the technical level.
We describe the task and the techniques involved. Finally, we discuss the experiences that we gained with MOBSY during a live performance at our institute.
Die typischerweise gewünschten Einsatzgebiete für Dienstleistungsroboter, z. B. Krankenhäuser oder Seniorenheime, stellen sehr hohe Anforderungen an die Mensch-Maschine-Schnittstelle.
Diese Erfordernisse gehen im Allgemeinen über die Möglichkeiten der Standardsensoren, wie Ultraschalloder Infrarotsensoren, hinaus. Es müssen daher ergänzende Verfahren zum Einsatz kommen.
Aus der Sicht der Mustererkennung sind die Nutzung des Rechnersehens und des natürlichsprachlichen Dialogs von besonderem Interesse. Dieser Beitrag stellt das mobile System MOBS Y vor. MOBS Y ist ein vollkommen integrierter autonomer mobiler Dienstleistungsroboter.
Er dient als ein automatischer dialogbasierter Empfangsservice für Besucher unseres Instituts.
MOBSY vereinigt vielfältige Methoden aus unterschiedlichsten Forschungsgebieten in einem eigenständigen System. Die zum Einsatz kommenden Methoden aus dem Bereich der Bildverarbeitung reichen dabei von Objektklassifikation über visuelle Selbstlokalisierung und Rekalibrierung bis hin zu multiokularer Objektverfolgung.
Die Dialogkomponente umfasst Methoden der Spracherkennung, des Sprachverstehens und die Generierung von Antworten. Im Beitrag werden die zu erfüllende Aufgabe und die einzelnen Verfahren dargestellt.
Unternehmen sind häufig mit Situationen konfrontiert, in denen schnell Entscheidungen bezüglich der Auswahl mehrerer Handlungsalternativen gefunden werden müssen. Mathematische Verfahren können hierbei unterstützen, z. B. für die Ermittlung einer ersten Diskussionsbasis. Verfügbare Softwarelösungen errechnen zwar häufig optimale Ergebnisse, zeigen jedoch Schwächen bei der praktischen Anwendbarkeit. So ist eine Einarbeitung in komplexe Optimierungssoftware für die teilweise sporadisch auftretenden Probleme in der Regel für Unternehmen nicht möglich, unter anderem auch unter Anbetracht der teilweise hohen Kosten der Standardsoftware und dem benötigten hohen Einarbeitungsaufwand. Gerade Problemstellungen in Fachbereichen, die nicht auf mathematische Problemlösung spezialisiert sind, münden daher regelmäßig in Ineffizienzen. Basierend auf den in der Literatur diskutierten Lösungsansätzen wurde ein praxisorientierter Ansatz zur Entscheidungsunterstützung für Rucksackprobleme bzw. 0–1 Probleme mithilfe von Genetischen Algorithmen (GA) entwickelt und technisch in Microsoft Excel® umgesetzt. Ein Praxistest bei einem chinesischen Textilunternehmen belegt erhöhte Effektivität und Effizienz. Die Software kann kostenfrei nach MIT Lizenz unter http://www.solvega.de/ heruntergeladen werden.
We employed the well-established Horton-Strahler, hierarchical, stream-order (ω) scheme to investigate scaling of nutrient loads (P and N) from ~845 wastewater treatment plants (WWTPs) distributed along the river network in urbanized Weser River, the largest national basin in Germany (~46K km2; ~8.4 million population). We estimated hydrologic and water quality impacts at the reach- and basin-scales, at two steady river discharge conditions (median flow, QR50; low-flow, QR90). Of the five WWTPs class-sizes (1 ≤ k ≤ 5), ~68% discharge to small low-order streams (ω < 3). We found large variations in capacity to dilute WWTP nutrient loads because of variability in (1) treated wastewater discharge (QU) within and among different class-sizes, and (2) river discharge (QR) within low-order streams (ω < 3) resulting from differences in drainage areas. For QR50, reach-scale water quality impairment assessed by nutrient concentration was likely at 136 (~16%) locations for P and 15 locations (~2%) for N. About 90% of these locations were lower-order streams (ω < 3). At QR50 and only with dilution, basin-scale cumulative nutrient loads from multiple upstream WWTPs increase impaired locations to 266 (~32% of total) for P. Considering in-stream uptake decreased P-impaired streams to 225 (~27%), suggesting the dominant role of dilution in the Weser River basin. Role of in-stream uptake diminished along the flow paths, while dilution in larger streams (4 ≤ ω ≤ 7) minimizes the impact of WWTP loads. Under QR90 conditions [(QR50/QR90) ~ 2.5], water quality impaired locations will likely double for the basin-scale analyses. Long-term water quality data suggested that diffuse sources are the primary contributors for water quality impairments in large streams. Our data-modeling synthesis approach is transferable to other urbanized river basins and extends understanding of point source impacts on water quality across spatial scales.
Environmental pollution by plastics is a global issue of increasing concern. However, microplastic analysis in complex environmental matrices, such as soil samples, remains an analytical challenge. Destructive mass-based methods for microplastic analysis do not determine plastics’ shape and size, which are essential parameters for reliable ecological risk assessment. By contrast, nondestructive particle-based methods produce such data but require elaborate, time-consuming sample preparation. Thus, time-efficient and reliable methods for microplastic analysis are needed. The present study explored the potential of frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) for rapidly and reliably identifying as well as differentiating plastics and natural materials from terrestrial environments. We investigated the fluorescence spectra of ten natural materials from terrestrial environments, tire wear particles, and eleven different transparent plastic granulates <5 mm to determine the optimal excitation wavelength for identification and differentiation via FD-FLIM under laboratory conditions. Our comparison of different excitation wavelengths showed that 445 nm excitation exhibited the highest fluorescence intensities. 445 nm excitation was also superior for identifying plastic types and distinguishing them from natural materials from terrestrial environments with a high probability using FD-FLIM. We could demonstrate that FD-FLIM analysis has the potential to contribute to a streamlined and time-efficient direct analysis of microplastic contamination. However, further investigations on size-, shape-, color-, and material-type detection limitations are necessary to evaluate if the direct identification of terrestrial environmental samples of relatively low complexity, such as a surface inspection soil, is possible.
Microplastic pollution of the environment has been extensively studied, with recent studies focusing on the prevalence of microplastics in the environment and their effects on various organisms. Identification methods that simplify the extraction and analysis process to the point where the extraction can be omitted are being investigated, thus enabling the direct identification of microplastic particles. Currently, microplastic samples from environmental matrices can only be identified using time-consuming extraction, sample processing, and analytical methods. Various spectroscopic methods are currently employed, such as micro Fourier-transform infrared, attenuated total reflectance, and micro Raman spectroscopy. However, microplastics in environmental matrices cannot be directly identified using these spectroscopic methods. Investigations using frequency-domain fluorescence lifetime imaging microscopy (FD-FLIM) to identify and differentiate plastics from environmental materials have yielded promising results for directly identifying microplastics in an environmental matrix. Herein, two artificially prepared environmental matrices that included natural soil, grass, wood, and high-density polyethylene were investigated using FD-FLIM. Our first results showed that we successfully identified one plastic type in the two artificially prepared matrices using FD-FLIM. However, further research must be conducted to improve the FD-FLIM method and explore its limitations for directly identifying microplastics in environmental samples.
Plastic waste is one of the biggest growing factors contributing to environmental pollution. So far there has been no established method to detect and identify plastics in environmental matrices. Thus, a method based on their characteristic fluorescence behavior is used to investigate whether plastics can be detected and identified in tap water under laboratory conditions. The experiments show that the identification of plastics as a function of water depth is possible. As the identification becomes more difficult with higher water depths, investigations with a highly sensitive imaging method were carried out to obtain an areal integration of the fluorescent light and thus better results.
The effectiveness of hot-melt coating depends on its uniformity and the extent to which the surface is completely covered. Compared with solvent-based coating, spreading is more limited in hot-melt coating; thus, the coating uniformity is more affected by the process parameters. This study presents a new method for identifying and quantifying factors influencing coating uniformity. The proposed method facilitates the determination of coating-thickness distribution and non-covered surface proportion based on micro-computed tomography measurements. The proposed method is based on particles that have undergone hot-melt coating in a fluidized bed, and it is compared to common methods for layer-thickness determination. The influencing factors are quantified in terms of the dependency of coating uniformity on the coating amount and material. Material properties have a significant impact because stearin and palm fat create different coating layers. The proposed method is confirmed to be well suited for analyzing coating qualities.
The necessity for resource-efficient manufacturing technologies requires new developments within the field of plastic processing. Lightweight design using wood fibers as sustainable reinforcement for thermoplastics might be one solution. The processing of wood fibers requires special attention to the applied thermal load. Even at low processing temperatures, the influence of the dwell time, temperature and shear force is critical to ensure the structural integrity of fibers. Therefore, this article compares different compounding rates for polypropylene with wood fibers and highlights their effects on the olfactory, visual and mechanical properties of the injection-molded part. The study compares one-step processing, using an injection-molding compounder (IMC), with two-step processing, using a twin-scew-extruder (TSE), a heating/cooling mixer (HCM) and an internal mixer (IM) with subsequent injection molding. Although the highest fiber length was achieved by using the IMC, the best mechanical properties were achieved by the HCM and IM. The measured oxidation induction time and volatile organic compound content indicate that the lowest amount of thermal damage occurred when using the HCM and IM. The advantage of one-time melting was evened out by the dwell time. The reinforcement of thermoplastics by wood fibers depends more strongly on the structural integrity of the fibers compared to their length and homogeneity
1. Understanding and explaining the structure of communities in response to environmental gradients is a central goal in ecology. Trait‐based approaches are promising but yet rarely applied to understand community dynamics in response to changing environmental conditions.
2. Here, we investigate seasonal succession patterns of functional traits in phytoplankton communities and how nutrient reductions (oligotrophication) alter these patterns. We used phytoplankton data from 40 years of observation from the Rappbode Reservoir (Germany), which underwent a strong shift in trophic conditions, and translated taxonomic composition into functional traits by assigning trait values compiled from the literature.
3. All studied traits (morphological, behavioural and physiological traits) responded to changing environmental conditions and showed consistent, reoccurring seasonal developments. The seasonal succession of phytoplankton communities was shaped by a trade‐off between small‐celled, fast‐growing species that are able to rapidly incorporate existing resources (r ‐strategists) and large‐celled species with more complex and efficient mechanisms to exploit scarce mineral nutrients or acquire previously unexploited nutrient pools (k ‐strategists). In summer, when nutrients were scarce, the k ‐strategy was prevailing (important traits: phosphate affinity, nitrogen fixation, motility and mixotrophy). During the rest of the year, nutrients and turbulence were high and r ‐strategists dominated (important traits: maximum growth rate and light affinity).
4. A comparison between eutrophic and oligotrophic years revealed that the main features of functional trait succession were largely preserved, but intra‐annual fluctuations from spring to summer were stronger during eutrophic years. Nutrient reductions mainly affected functional traits and biomass in spring, while in summer the functional community composition changed little.
5. Synthesis. This study provides for the first time a quantitatively supported functional template for trait‐based succession patterns in lakes under different nutrient conditions. By translating taxonomic composition into trait information, we demonstrate that the quantification of functional characteristics enables ecological interpretation of observed community dynamics and provides not only a testable template but also a powerful tool towards a more mechanistic understanding. The quantification of functional traits further improves the predictability of community shifts in response to changing environmental conditions and thus opens new perspectives for predictive limnology using lake ecosystem models.