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This paper describes the development and test of a novel LiDAR based combine harvester steering system using a harvest scenario and sensor point cloud simulation together with an established simulation toolchain for embedded software development. For a realistic sensor behavior simulation, considering the harvesting environment and the sensor mounting position, a phenomenological approach was chosen to build a multilayer LiDAR model at system level in Gazebo and ROS. A software-in-the-loop simulation of the mechatronic steering system was assembled by interfacing the commercial AppBase framework for point cloud processing and feature detection algorithms together with a machine model and control functions implemented in MATLAB/ Simulink. A test of ECUs in a hardware-in-the-loop simulation and as well as HMI elements in a driver-in-the-loop simulation was achieved by using CAN hardware interfaces and a CANoe based restbus simulation.
Hintergrund Die physiotherapeutische Dokumentation spielt im Therapieprozess eine wichtige Rolle, erfolgt jedoch auf unterschiedlichste Weise. Es existieren derzeit eine Vielzahl von Softwarelösungen für die physiotherapeutische Dokumentation, welche sich jedoch in ihren Funktionalitäten stark unterscheiden. Ziel Mithilfe einer Befragung soll ein Konsens von Expertenaus der Physiotherapie im Themengebiet der softwarebasierten Dokumentation ermittelt werden. Anhand der Ergebnisse wird ein Anforderungskatalog für die Entwicklung einer neuartigen und benutzerorientierten Dokumentationssoftware erstellt. Methode Online-Delphi-Befragung mit neun Experten über drei Befragungswellen.
Ergebnisse Hinsichtlich der Anforderungen an die Anamnese konnte ein Konsens erzielt werden. Bei der Gliederung des Befundes kam es zu keiner Übereinstimmung der Experten. Das Ergebnis lässt sich durch unterschiedliche manualtherapeutische Konzepte erklären, die für die Befunderhebung verwendet wurden. Schlussfolgerung Eine softwarebasierte Dokumentation sollte standardisierter als bisher erfolgen, um den ClinicalReasoning-Prozess zu unterstützten. Gleichzeitig ist dabei eine gewisse Flexibilität geboten. Die gesammelten Anforderungen können für die Entwicklung einer neuartigen und benutzerorientierten mobilen Anwendung zur Effizienzsteigerung in der physiotherapeutischen Dokumentation verwendet werden.
Nach dem Auslaufen der 20-jährigen Förderung über eine Einspeisevergütung im Rahmen des Erneuerbare-Energien-Gesetzes (EEG) gibt es für deutsche Biogasanlagen diverse technische Möglichkeiten für einen Weiterbetrieb. Neben der Wirtschaftlichkeit sind die Anlagenbetreibenden ein wesentlicher Entscheidungsfaktor für den Weiterbetrieb der Anlage. Somit ergibt sich die zentrale Fragestellung „Welche Treiber und Hemmnisse für Betreibende von Bestandsbiogasanlagen in Deutschland bestehen in den verschiedenen Nutzungspfaden für Biogas, sowie für kooperative Geschäftsmodelle?“. Die Erkenntnisse können unter anderem dafür genutzt werden, die Situation der Anlagenbetreibenden besser zu verstehen, um notwendige Unterstützung für einen Weiterbetrieb, beispielsweise durch Kommunen, zur Verfügung stellen zu können.
Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.
The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism, and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS-based synchronization model of INET so that it works over UDP/IP. With that, it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds of synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Furthermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.
Knowledge of the small-scale nutrient status of arable land is an important basis for optimizing fertilizer use in crop production. A mobile field laboratory opens up the possibility of carrying out soil sampling and nutrient analysis directly on the field. In addition to the benefits of fast data availability and the avoidance of soil material transport to the laboratory, it provides a future foundation for advanced application options, e.g. a high sampling density, sampling of small sub-fields or dynamic adaptation of the sampling line during field sampling. An innovative key component is the NUTRI-STAT ISFET sensor module. It measures values for the ions "NO3- ”, “H2PO4- " and "K+ " as well as the pH. The ISFET sensor module was specially developed for soil nutrient analysis. The phosphorus measurement was further developed for the project "soil2data". First results from the ISFET sensor module show a measurement signal settling time of significantly less than 100 seconds and a further consistent stable measurement signal. The measurement signal dynamics of approx. 58 mV per factor 10 of concentration change is given for the measured variables pH and K+. For the measured quantities of NO3- and H2PO4- , the measurement signal dynamics are lower.
Der Einsatz paralleler Hardware-Architekturen betrifft alle Software-Entwickler und -Entwicklerinnen: vom Supercomputer bis zum eingebetteten System werden Multi- und Manycore-Systeme inzwischen eingesetzt. Die Herausforderungen an das Software Engineering sind vielfältig. Zum einen ist (wieder) ein stärkeres Verständnis für die Hardware notwendig. Ohne eine skalierbare Partitionierung der Software und parallele Algorithmen bleibt die Rechenleistung ungenutzt. Zum anderen stehen neue Programmiersprachen im Vordergrund, die die Ausführung von parallelen Anweisungen ermöglichen.
Dieses Buch betrachtet unterschiedliche Aspekte bei der Entwicklung paralleler Systeme und berücksichtigt dabei auch eingebettete Systeme. Es verbindet Theorie und praktische Anwendung und ist somit für Studierende und Anwender in der Praxis gleichermaßen geeignet. Durch die programmiersprachenunabhängige Darstellung der Algorithmen können sie leicht für die eigene Anwendung angepasst werden. Viele praktische Projekte erleichtern das Selbststudium und vertiefen das Gelernte.
Simulation von Laserscannern in Pflanzenbeständen für die Entwicklung umfeldbasierter Funktionen
(2018)
Es werden drei Modellierungsansätze zur Simulation von Laserscannern in Pflanzenbeständen für die Entwicklung umfeldbasierter Fahrzeugfunktionen beschrieben. Das Sensorsignal der Distanzmessung wird dabei anhand realer Messwerte oder phänomenologisch und auf der Basis empirisch ermittelter Kennwerte in Abhängigkeit von objekt- und sensorspezifischen Einflussfaktoren abgebildet. Basierend auf den Methoden zur Simulation von Distanzmesssystemen der Open Source Simulationsumgebung Gazebo wurden die Modellierungsansätze als spezifische Sensor- und Umfeldmodelle implementiert. Die Modelle wurden insbesondere für den Einsatz an mobilen landwirtschaftlichen Arbeitsmaschinen und für die Anwendung in der Getreideernte optimiert.
Recent real-time networking developments have enabled ultra reliability, very low latency and high data rates in wired networks. Wireless networking developments have also shown that they can achieve very high data rates with consistency, but they still lack in providing ultra reliability and extremely low latency. Time Sensitive Networking (TSN) developments have brought these capabilities in Industry automation and Automotive industry too. Although TSN is standardized for wired networks for a long time, for wireless networks it will be standardized within the IEEE 802.11be standard for Wi-Fi and 3GPP Release 17 for 5G in the near future. This paper provides an overview of TSN in wired and wireless networks with the aim of comparing different simulators and presenting their offered functionality and shortcomings. These tools can be used to make oneself familiar with TSN algorithms, standards, and for the development and testing of time sensitive networks. Afterwards, the paper discusses open research questions for using TSN over wireless networks.
In modern times, closed-loop control systems (CLCSs) play a prominent role in a wide application range, from production machinery via automated vehicles to robots. CLCSs actively manipulate the actual values of a process to match predetermined setpoints, typically in real time and with remarkable precision. However, the development, modeling, tuning, and optimization of CLCSs barely exploit the potential of artificial intelligence (AI). This paper explores novel opportunities and research directions in CLCS engineering, presenting potential designs and methodologies incorporating AI. Combining these opportunities and directions makes it evident that employing AI in developing and implementing CLCSs is indeed feasible. Integrating AI into CLCS development or AI directly within CLCSs can lead to a significant improvement in stakeholder confidence. Integrating AI in CLCSs raises the question: How can AI in CLCSs be trusted so that its promising capabilities can be used safely? One does not trust AI in CLCSs due to its unknowable nature caused by its extensive set of parameters that defy complete testing. Consequently, developers working on AI-based CLCSs must be able to rate the impact of the trainable parameters on the system accurately. By following this path, this paper highlights two key aspects as essential research directions towards safe AI-based CLCSs: (I) the identification and elimination of unproductive layers in artificial neural networks (ANNs) for reducing the number of trainable parameters without influencing the overall outcome, and (II) the utilization of the solution space of an ANN to define the safety-critical scenarios of an AI-based CLCS.
We describe an automated approach, to easily track patients regaining their walking ability while recovering from neurological diseases like e.g. stroke. Based on captured gait data and objective measures derived out of it the rehabilitation process can be optimized and thus steered. In order to apply such system in clinical practice two key requirements have to be fulfilled: (i) the system needs to be applicable in terms of ease of use and performance; (ii) the derived measures need to be accurate.
Process modeling languages help to define and execute processes and workflows. The Business Process Model and Notation (BPMN) 2.0 is used for business processes in commercial areas such as banks, shops, production and supply industry. Due to its flexible notation, BPMN is increasingly being used in non-traditional business process domains like Internet of Things (IoT) and agriculture. However, BPMN does not fit well to scenarios taking place in environments featuring limited, delayed, intermittent or broken connectivity. Communication just exists for BPMN - characteristics of message transfers, their priorities and connectivity parameters are not part of the model. No backup mechanism for communication issues exists, resulting in error-prone and failing processes. This paper introduces resilient BPMN (rBPMN), a valid BPMN extension for process modeling in unreliable communication environments. The meta model addition of opportunistic message flows with Quality of Service (QoS) parameters and connectivity characteristics allows to verify and enhance process robustness at design time. Modeling of explicit or implicit, decision-based alternatives ensures optimal process operation even when connectivity issues occur. In case of no connectivity, locally moved functionality guarantees stable process operation. Evaluation using an agricultural slurry application showed significant robustness enhancements and prevented process failures due to communication issues.
The effects of reaction parameters on Hurn:xwiley:23670932:media:cptc202000216:cptc202000216-math-0001 production from ethanol photocatalysis in the gas phase have been investigated. The photocatalytic activity evolves from an early mass‐transfer limited regime to an independent one at later irradiation times, which is interpreted in terms of a photocatalytic site activity distribution. Ethanol molar fraction exhibits two different domains, with Hurn:xwiley:23670932:media:cptc202000216:cptc202000216-math-0002 production increasing up to a molar fraction of 0.12, beyond which it plateaus. Hurn:xwiley:23670932:media:cptc202000216:cptc202000216-math-0003 :AcH ratios are very sensitive to reaction conditions, reaching 1.8 at low reactant flows. UV light is converted to Hurn:x-wiley:23670932:media:cptc202000216:cptc202000216-math-0004 with an efficiency of nearly 3 %.
In this experimental work, the quasi static and fatigue properties of a 40 wt.% long carbon fiber reinforced partially aromatic polyamide (Grivory GCL-4H) were investigated. For this purpose, microstructural parameter variations in the form of different thicknesses and different removal directions from injectionmolded plates were evaluated. Mechanical properties decreased by increasing misalignment away from the melt flow direction. By changing the specimen thickness, no change in the general fiber distribution pattern transversal and normal to the axis of melt flow was observed. It has shown that with increasing specimen thickness the quasi static properties along the melt flow direction decreased and vice versa resulting in superior properties normal to the melt flow axis. At around 5 mm, an intersection suggests quasi-isotropic behavior. In addition, the fatigue strength of the material was significantly higher in the flow direction than normal to the flow direction. No change in fatigue life was observed while changing specimen thickness. The Basquin equation seems to describe the effect of stress amplitude on the fatigue strength of this composite. Scanning electron microscopy was used to investigate fracture surfaces of tested specimens. Results show that mechanical properties and morphological structures depend highly on fiber orientation.
Long Range Wide Area Network (LoRaWAN) operates in the ISM band with 868 MHz, where the Time on Air (ToA) is regulated in the EU to 1 %. LoRaWAN nodes use the Adaptive Data Rate (ADR) algorithm to adapt their data rates during operation. The standard ADR algorithm works well with stationary nodes, however is very slow in the adaptation for mobile nodes. This paper introduces a new ADR algorithm for LoRaWAN that is supported by higher level meta-data for sensor streams, namely Quality of Information (QoI). With the help of QoI it is possible to provide additional information to the new ADR algorithm, reducing the convergence time and thus improving the Packet Delivery Ratio (PDR) of data from mobile sensor nodes. The new algorithm requires only modifications on network server side and keeps backwards compatibility with LoRaWAN nodes. Results show a significant better PDR compared to the standard ADR in scenarios with a limited number of mobile nodes.
Due to the resource-constrained nature of embedded systems, it is crucial to support the estimation of their power consumption as early in the development process as possible. Non-functional requirements based on power consumption directly impact the software design, e.g., watt-hour thresholds and expected lifetimes based on battery capacities. Even if software affects hardware behavior directly, these types of requirements are often overlooked by software developers because they are commonly associated with the hardware layer. Modern trends in software engineering such as Model-Driven Development (MDD) can be used in embedded software development to evaluate power consumption-based requirements in early design phases. However, power consumption aspects are currently not sufficiently considered in MDD approaches. In this paper, we present a model-driven approach using Unified Modeling Language profile extensions to model hardware components and their power characteristics. Software m odels are combined with hardware models to achieve a system-wide estimation, including peripheral devices, and to make the power-related impact in early design stages visible. By deriving energy profiles, we provide software developers with valuable feedback, which may be used to identify energy bugs and evaluate power consumption-related requirements. To demonstrate the potential of our approach, we use a sensor node example to evaluate our concept and to identify its energy bugs.
Optimised Nutrient Recovery from Biogas Digestate by Solid/Liquid Separation and Membrane Treatment
(2019)
Anaerobic digestion products of agricultural biogas plants are characterised by high nitrogen, phosphorus, and potassium content. In three scale-up steps, a membrane based digestate treatment process of solid-liquid-separation, ultrafiltration, and reverse osmosis for nutrient recovery was investigated. Lab-scale trials delivered a very good understanding of fluid properties and subsequent ultrafiltration performance, which is the limiting process step in terms of energy demand and operation costs. In semi-technical experiments, optimisation, and design parameters were developed, which were subsequently applied to pilot-scale tests at two full-scale biogas plants. The process optimisation resulted in 50 % energy reduction of the ultrafiltration step. About 36 % of the sludge volume was recovered as dischargeable water, 20 % as solid N/P-fertiliser, and 44 % as liquid N/K-fertiliser.
Pregnancy loss is the most common complication in pregnancy. Yet those who experience it can find it challenging to disclose this loss and feelings associated
with it, and to seek support for psychological and physical recovery. We describe our process for
interleaving interviews, theoretical development, speculative design, and prototyping Not Alone to
explore the design space for online disclosures and
support seeking in the pregnancy loss context.
Interviews with 27 women who had experienced pregnancy loss resulted in theoretical concepts such as
“network-level reciprocal disclosure” (NLRD). We discuss how interview findings informed the design of
the Not Alone prototype, a mobile application aimed at enabling disclosure and social support exchange among those with pregnancy loss experience. The Not Alone prototype embodies concepts that facilitate NLRD: perceptions of homophily, anonymity levels, and selfdisclosure by talking about one’s experience and engaging with others’ disclosures. In future work, we will use Not Alone as a technology probe for exploring
NLRD as a design principle.
Knowledge of the maximum friction coefficient µmax between tire and road is necessary for implementing autonomous driving. As this coefficient cannot be measured via existing serial vehicle sensors, µmax estimation is a challenging field in modern automotive research. In particular, model-based approaches are applied, which are limited in the estimation accuracy by the physical vehicle model. Therefore, this paper presents a data-based µmax estimation using serial vehicle sensors. For this purpose, recurrent artificial neural networks are trained, validated, and tested based on driving maneuvers carried out with a test vehicle showing improved results compared to the model-based algorithm from previous works.