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To maximize the travel distances of battery electric vehicles such as cars or buses for a given amount of stored energy, their powertrains are optimized energetically. One key part within optimization models for electric powertrains is the efficiency map of the electric motor. The underlying function is usually highly nonlinear and nonconvex and leads to major challenges within a global optimization process. To enable faster solution times, one possibility is the usage of piecewise linearization techniques to approximate the nonlinear efficiency map with linear constraints. Therefore, we evaluate the influence of different piecewise linearization modeling techniques on the overall solution process and compare the solution time and accuracy for methods with and without explicitly used binary variables.
In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.
High-intensity discharge lamps can be driven by radio-frequency signals in the ISM frequency band at 2.45 GHz, using a matching network to transform the impedance of the plasma to the source impedance. To achieve an optimal operating condition, a good characterization of the lamp in terms of radio frequency equivalent circuits under operating conditions is necessary, enabling the design of an efficient matching network. This paper presents the characterization technique for such lamps and presents the design of the required matching network. For the characterization, a high-intensity discharge lamp was driven by a monofrequent large signal at 2.45 GHz, whereas a frequency sweep over 300 MHz was performed across this signal to measure so-called small-signal hot S-parameters using a vector network analyzer. These parameters are then used as an equivalent load in a circuit simulator to design an appropriate matching network. Using the measured data as a black-box model in the simulation results in a quick and efficient method to simulate and design efficient matching networks in spite of the complex plasma behavior. Furthermore, photometric analysis of high-intensity discharge lamps are carried out, comparing microwave operation to conventional operation.
Today, the assembly of laser systems requires a large share of manual operations due to its complexity regarding the optimal alignment of optics. Although the feasibility of automated alignment of laser optics has been shown in research labs, the development effort for the automation of assembly does not meet economic requirements – especially for low-volume laser production. This paper presents a model-based and sensor-integrated assembly execution approach for flexible assembly cells consisting of a macro-positioner covering a large workspace and a compact micromanipulator with camera attached to the positioner. In order to make full use of available models from computer-aided design (CAD) and optical simulation, sensor systems at different levels of accuracy are used for matching perceived information with model data. This approach is named "chain of refined perception", and it allows for automated planning of complex assembly tasks along all major phases of assembly such as collision-free path planning, part feeding, and active and passive alignment. The focus of the paper is put on the in-process image-based metrology and information extraction used for identifying and calibrating local coordinate systems as well as the exploitation of that information for a part feeding process for micro-optics. Results will be presented regarding the processes of automated calibration of the robot camera as well as the local coordinate systems of part feeding area and robot base.
The assessment of the right ventricle (RV) is a challenge in today's cardiology, but of growing clinical impact regarding patient prognosis in different cardiac diseases. The detection and differentiation of small wall motion abnormalities may help to enhance the differentiation of cardiomyopathies including Arrhythmogenic Rightventricular Cardiomyopathy. Cardiovascular magnetic resonance (CMR) at 1.5T is the accepted gold standard for RV quantification. The higher spatial resolution achievable at ultrahigh field strength (UHF) offers the potential to gain new insights into the structure and function of the RV. To approach this goal accurate RV chamber quantification at 7T has to be proven. Consequently this study examines the feasibility of assessment of RV dimensions and function at 7T using improved spatial resolution enabled by the intrinsic sensitivity gain of UHF CMR. For this purpose, a dedicated 16 channel TX/RX RF coil array is used together with 2D CINE fast gradient echo (FGRE) imaging. For comparison RV chamber quantification is conducted at 1.5T using a SSFP based state of the art clinical protocol.
With its need for high SNR and short acquisition times, Cardiac MRI (CMR) is an intriguing target application for ultrahigh field MRI. Due to the sheer size of the upper torso, however, the known RF issues of 7T MRI are also most prominent in CMR. Recent years brought substantial progress but the full potential of the ultrahigh field for CMR is yet to be exploited. Parallel transmission (pTx) is a promising approach in this context and several groups have already reported B1 shimming for 7T CMR. In such a static pTx application amplitudes and phases of all Tx channels are adjusted individually but otherwise imaging techniques established in current clinical practice 1.5 T and 3 T are applied. More advanced forms of pTx as spatially selective excitation (SSE) using Transmit SENSE promise additional benefits like faster imaging with reduced fields of view or improved SAR control. SSE requires the full dynamic capabilities of pTx, however, and for the majority of today's implemented pTx hardware the internal synchronization of the Tx array does not easily permit external triggering as needed for CMR. Here we report a software solution to this problem and demonstrate the feasibility of CINE CMR at 7 T using a Tx array.