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Elastic constants of components are usually determined by tensile tests in combination with ultrasonic experiments. However, these properties may change due to e.g. mechanical treatments or service conditions during their lifetime. Knowledge of the actual material parameters is key to the determination of quantities like residual stresses present in the medium. In this work the acoustic nonlinearity parameter (ANP) for surface acoustic waves is examined through the derivation of an evolution equation for the amplitude of the second harmonic. Given a certain depth profile of the third-order elastic constants, the dependence of the ANP with respect to the input frequency is determined and on the basis of these results, an appropriate inversion method is developed. This method is intended for the extraction of the depth dependence of the third-order elastic constants of the material from second-harmonic generation and guided wave mixing experiments, assuming that the change in the linear Rayleigh wave velocity is small. The latter assumption is supported by a 3D-FEM model study of a medium with randomly distributed microcracks as well as theoretical works on this topic in the literature.
Extracting horizon surfaces from key reflections in a seismic image is an important step of the interpretation process. Interpreting a reflection surface in a geologically complex area is a difficult and time-consuming task, and it requires an understanding of the 3D subsurface geometry. Common methods to help automate the process are based on tracking waveforms in a local window around manual picks. Those approaches often fail when the wavelet character lacks lateral continuity or when reflections are truncated by faults. We have formulated horizon picking as a multiclass segmentation problem and solved it by supervised training of a 3D convolutional neural network. We design an efficient architecture to analyze the data over multiple scales while keeping memory and computational needs to a practical level. To allow for uncertainties in the exact location of the reflections, we use a probabilistic formulation to express the horizons position. By using a masked loss function, we give interpreters flexibility when picking the training data. Our method allows experts to interactively improve the results of the picking by fine training the network in the more complex areas. We also determine how our algorithm can be used to extend horizons to the prestack domain by following reflections across offsets planes, even in the presence of residual moveout. We validate our approach on two field data sets and show that it yields accurate results on nontrivial reflectivity while being trained from a workable amount of manually picked data. Initial training of the network takes approximately 1 h, and the fine training and prediction on a large seismic volume take a minute at most.
Wireless sensor networks have found their way into a wide range of applications among which environmental monitoring systems have attracted increasing interests of researchers. The main challenges for the applications are scalability of the network size and energy efficiency of the spatially distributed motes. These devices are mostly battery-powered and spend most of their energy budget on the radio transceiver module. A so-called Wake-On-Radio (WOR) technology can be used to achieve a reasonable balance among power consumption, range, complexity and response time. In this paper, a novel design for integration of WOR into IEEE802.1.5.4 is presented, which flexibly allows trade-offs in energy consumption between sender and receiver station, between real-time capability and energy consumption. For identical behavior, the proposed scheme is significantly more efficient than other schemes, which were proposed in recent publications, while preserving backward compatibility with standard IEEE802.15.4 transceivers.
Wireless sensor networks have recently found their way into a wide range of applications among which environmental monitoring system has attracted increasing interests of researchers. Such monitoring applications, in general, don way into a wide range of applications among which environmental monitoring system has attracted increasing interests of researc latency requirements regarding to the energy efficiency. Also a challenge of this application is the network topology as the application should be able to be deployed in very large scale. Nevertheless low power consumption of the devices making up the network must be on focus in order to maximize the lifetime of the whole system. These devices are usually battery-powered and spend most of their energy budget on radio transceiver module. A so-called Wake-On-Radio (WoR) technology can be used to achieve a reasonable balance among power consumption, range, complexity and response time. In this paper, some designs for integration of WOR into IEEE 802.1.5.4 are to be discussed, providing an overview of trade-offs in energy consumption while deploying the WoR schemes in a monitoring system.
The German Weather Service (DWD) releases a heat warning, when the weather forecast provides a warm, humid, sunny, and windless weather condition during the next days. The heat stress is calculated by the so called Klima-Michel model. If the apparent air temperature exceeds ca. 32°C / 38°C, there is a strong / extreme heat stress. The smallest forecast area is each administrative district. As people (and especially the vulnerable population) stay most of the time indoors, the heat health warning system was extended by the prediction of heat stress in typical rooms. Therewith it is feasible to forecast the heat stress using a combination of the outdoor and indoor heat stress. The prediction for the indoor heat stress is based on the same weather forecast like the Heat Health Warning Systems (HHWS).and calculates the heat stress by the PMV-model (predicted mean vote). Based on a sophisticated data analysis and simulation study, realistic but summer-critical living situations were defined and implemented in the building simulation program ESP-r. As the simulation runs especially for extreme weather conditions, a simplified building model cannot be used. Standardized input/output routines and an adaptive handover of start values provide for short run times for each forecast area. Good building designs and urban planning provide effective measures to reduce heat stress in cities. However, we have to also pay attention to the present building stock under climate change and a higher heat-wave risk. The extended German HHWS provide information for the emergency services to support the social assistants during heat waves.
Extended Performance Measurements of Scalable 6LoWPAN Networks in an Automated Physical Testbed
(2015)
IPv6 over Low power Wireless Personal Area Networks, also known as 6LoWPAN, is becoming more and more a de facto standard for such communications for the Internet of Things, be it in the field of home and building automation, of industrial and process automation, or of smart metering and environmental monitoring. For all of these applications, scalability is a major precondition, as the complexity of the networks continuously increase. To maintain this growing amount of connected nodes a various 6LoWPAN implementations are available. One of the mentioned was developed by the authors' team and was tested on an Automated Physical Testbed for Wireless Systems at the Laboratory Embedded Systems and Communication Electronics of Offenburg University of Applied Sciences, which allows the flexible setup and full control of arbitrary topologies. It also supports time-varying topologies and thus helps to measure performance of the RPL implementation. The results of the measurements prove an excellent stability and a very good short and long-term performance also under dynamic conditions. In all measurements, there is an advantage of minimum 10% with regard to the average times, like global repair time; but the advantage with reagr to average values can reach up to 30%. Moreover, it can be proven that the performance predictions from other papers are consistent with the executed real-life implementations.
The present work describes an extension of current slope estimation for parameter estimation of permanent magnet synchronous machines operated at inverters. The area of operation for current slope estimation in the individual switching states of the inverter is limited due to measurement noise, bandwidth limitation of the current sensors and the commutation processes of the inverter's switching operations. Therefore, a minimum duration of each switching state is necessary, limiting the final area of operation of a robust current slope estimation. This paper presents an extension of existing current slope estimation algorithms resulting in a greater area of operation and a more robust estimation result.
As emissions reach record levels, governments must implement and strengthen climate policies for the global pathway to net‐zero emissions by 2050. Climate finance plays a crucial role in the net‐zero transition. It refers to local, national, or transnational financing seeking to support mitigation and adaptation actions that address climate change. Public export–import banks (EXIMs) and government export credit agencies (ECAs) are highly influential actors for climate action. Although there is no consensus among EXIMs and ECAs on how to define climate finance, 20 institutions assessed in this research give evidence that they strongly support climate‐action‐related transactions: EXIM and ECA financing, guarantees, and insurance amounted to EUR 6.7–8.4 billion in 2020, much more than estimated by the Climate Policy Initiative (CPI). However, the results also reveal that EXIM and ECA lending, guarantee, and insurance activities must rise substantially in order to contribute to climate finance volumes required by 2030 as estimated by CPI. To retain their current proportion relative to other climate finance flows, assessed institutions would need to increase their climate financing 6.8 times to up to EUR 57.4 billion by 2030.
The global pathway to net zero emissions by 2050 requires governments to implement and strengthen climate policies as global emissions are reaching record level. Climate finance plays a crucial role in the net zero transition. It refers to local, national or transnational financing seeking to support mitigation and adaptation actions that address climate change. Public export-import banks (EXIMs) and government export credit agencies (ECAs) are highly influential actors for climate action. Although there is no consensus among EXIMs and ECAs on how to define climate finance, 20 institutions assessed in this report give evidence that they significantly support climate action related transactions: EXIM and ECA financing and insurance amounted to EUR 6.7-8.4 billion in 2020, much more than estimated by the Climate Policy Initiative (CPI). However, the results also show that EXIM and ECA lending and insurance activities must rise substantially in order to contribute to the climate finance volumes required by 2030 as estimated by CPI. To retain their current proportion relative to other climate finance flows, assessed institutions would need to increase their climate financing 6.8 times to between EUR 45.3 billion and EUR 57.4 billion by 2030.