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Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is prevalent in all other steps of developing AI, from data analysis via data selection and cleaning to performance evaluation. During AI architecture design, HMI can immediately highlight unproductive layers of the architecture so that lightweight network architecture for embedded applications can be created easily. We show that by using this HMI, users can instantly distinguish which AI architecture should be trained and evaluated first since a high accuracy on the task could be expected. This approach reduces the resources needed for AI development by avoiding training and evaluating AI architectures with unproductive layers and leads to lightweight AI architectures. These resulting lightweight AI architectures will enable HMI while running the AI on an edge device. By enabling HMI during an AI uses inference, we will introduce the AI-in-the-loop concept that combines AI's and humans' strengths. In our AI-in-the-loop approach, the AI remains the working horse and primarily solves the task. If the AI is unsure whether its inference solves the task correctly, it asks the user to use an appropriate HMI. Consequently, AI will become available in many applications soon since HMI will make AI more reliable and explainable.
Currently, soil nutrient analysis involves two separate processes for soil sampling and nutrient analysis: 1. field soil sampling and 2. laboratory analysis. These two - separate - main work processes are combined and conceptualised for a mobile field laboratory so that soil sampling and analysis can be carried out simultaneously in the field. The module-based field laboratory "soil2data" can carry out these two main work processes in parallel and consists of 5 different task-specific modules that build on each other: app2field, field2soil, app2liquid, liquid2data and data2app. The individual modules were designed and built for the sub-process steps and adapted to the special features of the mobile field laboratory "soil2data". The biggest advantage is that the analysis results are available immediately, and a fertiliser recommendation can be generated instantly. For further analyses, the results are stored in the data cloud. The soil material remains in the field. In the ongoing project "Prototypes4soil2data", the mobile field laboratory soil2data is being further developed into a prototype with a modular structure.
While developing traffic-based cognitive enhancement technology (CET), such as bike accident prevention systems, it can be challenging to test and evaluate them properly. After all, the real-world scenario could endanger the subjects’ health and safety. Therefore, a simulator is needed, preferably one that is realistic yet low cost. This paper introduces a way to use the video game Grand Theft Auto V (GTA V) and its sophisticated traffic system as a base to create such a simulator, allowing for the safe and realistic testing of dangerous traffic situations involving cyclists, cars, and trucks. The open world of GTA V, which can be explored on foot and via various vehicles, serves as an immersive stand-in for the real world. Custom modification scripts of the game give the researchers control over the experiment scenario and the output data to be evaluated. An off-the-shelf bicycle equipped with three sensors serves as a realistic input device for the subject’s movement direction and speed. The simulator was used to test two early-stage CET concepts enabling cyclists to sense dangerous traffic situations, such as trucks approaching from behind the cyclist. Thus, this paper also presents the user evaluation of the cycling simulator and the CET used by the subjects to sense dangerous traffic situations. With the knowledge of the first iteration of the user-centered design (UCD) process, this paper concludes by naming improvements for the cycling simulator and discussing further research directions for CET that enable users to sense dangerous situations better.
Knowledge of the small-scale nutrient status of a field is an important basis for decision-making when it comes to optimising the fertiliser use in crop production. Currently, the traditional method involves soil sampling in the field and soil sample analysis in the laboratory as two separate working processes.
The previous research project "soil2data" developed a mobile field laboratory for different carrier vehicles. In the follow-up project "prototypes4soil2data", the results of soil2data are further developed. A mixed soil sample is collected during the drive on the field. The soil sample is then wet-chemically prepared and analysed. The overall soil sampling and analysis process is divided into the following process steps: soil sampling planning, soil sampling, soil preparation, soil analysis and data management. The process steps are modified for the mobile field laboratory and the process steps run in parallel. The new soil extraction method is based on official German methods (VDLUFA) to ensure the interoperability of the analysis results with the VDLUFA fertiliser recommendations. An innovative key component is the NUTRISTAT analysis module (lab-on-chip with ISFET measurement technology). It can measure pH, the nutrients NO3-, H2PO4-, K+ and the electrical conductivity. In addition to the advantages of rapid data availability and no need to transport soil material to the laboratory, it provides a future basis for new application, e.g. verification of current results in the field during soil sampling with existing results or dynamic adjustment of soil sampling during work in the field.
The expiry of national subsidies for biogas in Germany means that new business models are needed. Furthermore, hydrogen is expected to make a significant contribution to the energy transition in the future. Therefore, potentials for the production of hydrogen from biogas are identified in this study. A joint upgrading infrastructure is developed that models the collaborative upgrading of biogas to hydrogen for existing biogas plants with subsequent gas grid injection. Furthermore, regions are identified that are particularly suitable as pioneer regions in Germany due to a high potential for green hydrogen production and comparatively low costs for hydrogen production. The modeling shows that collaborative upgrading achieves significant cost savings compared to single-farm upgrading. Furthermore, the potential for hydrogen production from biogas and the costs of upgrading differ significantly within the administrative districts in Germany.
Today's development of client-side web applications is based on one of the JavaScript-frameworks, such as Angular or React. The excessive dependencies that arise in the ecosystem from the Node-Package-Manager increase the security risk and the dependency of your own web application on third-party packages. Moreover, the frameworkless approach proposes a renaissance of classic web development, because it strives to avoid external dependencies as far as possible and to fall back on the standards. Whether the implementation achieves maintainability and security of frameworks is questionable. Therefore, it makes sense to research which core concepts of the frameworks meet the requirements for maintainability and security and how these are implemented. The novelty is that the concepts to be explored are moved to a standard in order to ensure the developer efficiency, security, performance and maintainability in the long term. This allows existing approaches to focus on other essential features.
The Internet of Things (IoT) is the enabler for new innovations in several domains. It allows the connection of digital services with real, physical entities. These entities are devices of different categories and range in size from large machinery to tiny sensors. In the latter case, devices are typically characterized by limited resources in terms of computational power, available memory and sometimes limited power supply. As a consequence, the use of security algorithms requires expert knowledge in order for them to work within the limited resources. That means to find a suitable configuration for the algorithms to perform properly on the device. On the other side, there is the desire to protect valuable assets as strong as possible. Usually, security goals are captured in security policies, but they do not consider resource availability on the involved device and their consumption while executing security algorithms. This paper presents a resource aware information exchange model and a generation tool that uses high-level security policies as input. The model forms the conceptual basis for an automated security configuration recommendation system.
Power consumption has become a major design constraint, especially for battery-powered embedded systems. However, the impact of software applications is typically considered in later phases, where both software and hardware parts are close to their finalization. Power-related issues must be detected in early stages to keep the development costs low, satisfy time-to-market, and avoid cost-intensive redesign loops. Moreover, the variety of hardware components, architectures, and communication interfaces make the development of embedded software more challenging. To manage the complexity of software applications, approaches such as model-driven development (MDD) may be used. This article proposes a power-estimation approach in MDD for software application models in early development phases. A unified modeling language (UML) profile is introduced to model power-related properties of hardware components. To determine the impact of software applications, we defined two analysis methods using simulation data and a novel in-the-loop concept. Both methods may be applied at different development stages to determine an energy trace, describing the energy-related behavior of the system. A novel definition of energy bugs is provided to describe power-related misbehavior. We apply our approach to a sensor node example, demonstrate an energy bug detection, and compare the runtime and accuracy of the analysis methods.
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.
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.
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 %.
The simulation of the residual stress field achieved by shot peening cannot be carried out on component-large models. Hence, an efficient unit cell model for the simulation of the shot peening process is developed. The model allows both, the simple inclusion of a pre-stress and the evaluation of the up-arching of the Almen strip. For this purpose, generalized coupling constraints for the periodic boundaries of the unit cell are developed. These allow for displacement and rotation of the coupled boundaries relative to each other. In the coupling constraints, this is accomplished by respective variables, which can either be prescribed to the analysis or read out as a result from the analysis. Hence, the unit cell can expand, shear, bend and twist under driving forces like, e. g., residual stresses or thermal effects. At the same time, deformations of the cell’s periodic boundary pairs are kept congruent by the generalized coupling. The ability to cover expansion is novel regarding known periodic boundary conditions. Also, the application of a generalized unit cell to shot peening is new.
Results obtained with the generalized unit cell are displayed, demonstrating its capabilities: A fundamental analysis of the residual stress field from shot peening shows inhomogeneities at a fatigue relevant level to be inevitable. A validation of the model was done by comparison with experimental Almen strip shot peening tests reported in literature. Shot peening under pre-stress is demonstrated and its results in terms of residual stress are evaluated. The application of the generalized unit cell is not limited to shot peening.
Various overoxidized poly(1H-pyrrole) (PPy), poly(N-methylpyrrole) (PMePy) or poly(3,4-ethylenedioxythiophene) (PEDOT) membranes incorporated into an acrylate-based solid polymer electrolyte matrix (SPE) were directly electrosynthesized by a two-step in situ procedure. The aim was to extend and improve fundamental properties of pure SPE materials. The polymer matrix is based on the cross-linking of glycerol propoxylate (1PO/OH) triacrylate (GPTA) with poly(ethylene glycol) diacrylate (PEGDA) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as a conducting salt. A self-standing and flexible polymer electrolyte film is formed during the UV-induced photopolymerization of the acrylate precursors, followed by an electrochemical polymerization of the conducting polymers to form a 3D-IPN. The electrical conductivity of the conducting polymer is destroyed by electrochemical overoxidation in order to convert the conducting polymer into an ion-exchange membrane by introduction of electron-rich groups onto polymer units. The resulting polymer films were characterized by scanning electron microscopy, cyclic voltammetry, electrochemical impedance spectroscopy, differential scanning calorimetry, thermal analysis and infrared spectroscopy. The results of this study show that the combination of a polyacrylate-matrix with ion selective properties of overoxidized CPs leads to new 3D materials with higher ionic conductivity than SPEs and separator or selective ion-exchange membrane properties with good stability by facile fabrication.
A recently published study of high temperature nitridation of iron chromium aluminum alloys (FeCrAl) at 900°C in N2–H2 has redundantly shown the formation of locally confined corrosion pockets reaching several microns into the alloy. These nitrided pockets form underneath chromia islands laterally surrounded by the otherwise protective alumina scale. Chromia renders a nitrogen‐permeable defect under the given conditions and the presence of aluminum in the alloy. In light of these findings on FeCrAl, a focused ion beam–scanning electron microscope tomography study has been undertaken on an equally nitrided FeNiCrAl sample to characterize its nitridation corrosion features chemically and morphologically. The alloy is strengthened by a high number of chromium carbide precipitates, which are also preferential chromia formation sites. Besides the confirmation of the complete encapsulation of the corrosion pocket from the alloy by a closed and dense aluminum nitride rim, very large voids have been found in the said pockets. Furthermore, metallic particles comprising nickel and iron are deposited on top of the outer oxide scale above such void regions.
With the increasing size and complexity of embedded systems, the impact of software on energy consumption is becoming more important. Previous research focused mainly on energy optimization at the hardware level. However, little research has been carried out regarding energy optimization at the software design level. This paper focuses on the software design level and addresses the gap between software and hardware design for embedded systems. This is achieved by proposing a framework for software design patterns, which takes aspects of power consumption and time behavior of the hardware level into account. We evaluate the expressiveness of the framework by applying it to well-known and novel design patterns. Furthermore, we introduce a dimensionless numerical efficiency factor to make possible energy savings quantifiable.
The objective of this review is a global assessment of the economics of second‐generation biorefineries, with a focus on the use of food waste and agricultural residues for chemical production by applying biotechnological processes. Analyses are conducted on feedstock and product distribution, applied economic models, and profitability figures for the period 2013–2018. In a study of 163 articles on different biorefinery systems, the production of chemicals is identified as the second major product class, after bioenergy. Bagasse and straw are frequently analyzed second‐generation feedstocks. Based on the evaluation of 22 articles, second‐generation biorefineries producing chemicals by applying biotechnological processes proves to be economically feasible. On average, both the internal rate of return (IRR) and the return on investment (ROI) are 20% and the payback period (PP) is 6 years. The cost share of feedstock in biorefineries is between 0–50%. The price of the end product and the fermentation yields have the most impact on profitability. The processing of food waste that has industrial and municipal origins appears more economical than the processing of agricultural residues. Scientists, policy makers and entrepreneurs with an appropriate risk tolerance are advised to pay particular attention to municipal food waste and the potential economic production of carboxylic acids. For various economic issues related to biorefineries, dynamic‐deterministic models are recommended, which can be extended by a stochastic model. This review provides an initial overview of the economic feasibility of second‐generation biorefineries. Further techno‐economic analyses are required to produce statistically significant statements on key profitability figures. © 2020 The Authors. Biofuels, Bioproducts, and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.
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.
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.
This article proposes the concept of a simulation framework for environmental sensors with multilevel abstraction in agricultural scenarios. The implementation case study is a simulation of a grain-harvesting scenario enabled by LiDAR sensors. Environmental sensor models as well as kinematics and dynamic behavior of machines are based on the robotics simulator Gazebo. Models for powertrain, machine process aggregates and peripheral simulation components are implemented with the help of MATLAB/ Simulink and with the robotics middleware Robot Operating System (ROS). This article deals with the general concept of a multilevel simulation framework and in particular with sensor and environmental modeling.