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Increased Signal-to-Noise Ratio of Sensorless Control Using Current Oversampling
Bastian Weber,Karsten Wiedmann,Axel Mertens 전력전자학회 2015 ICPE(ISPE)논문집 Vol.2015 No.6
This paper presents a novel approach based on current oversampling with low computational effort that significantly increases the signal-to-noise ratio with regard to sensorless control of permanent magnet synchronous machines at low and zero speed. The novel method is based on calculating arithmetic averages of all current samples taken during a period of pulse width modulation (PWM). This algorithm is implemented in a field programmable gate array (FPGA). Compared to a conventional current measurement with single sampling, the arithmetic average of the current samples has a much higher signal-to-noise ratio than the current samples themselves. The current derivative, which is used for sensorless control, is then calculated from the difference between two consecutive current averages. Experimental results validate the functionality of the novel approach.
Synthesis and Characterization of Graphene/ITO Nanoparticle Hybrid Transparent Conducting Electrode
Hemasiri, Bastian Waduge Naveen Harindu,Kim, Jae-Kwan,Lee, Ji-Myon Springer Berlin Heidelberg 2018 Nano-micro letters Vol.10 No.1
<P>The combination of graphene with conductive nanoparticles, forming graphene–nanoparticle hybrid materials, offers a number of excellent properties for advanced engineering applications. A novel and simple method was developed to deposit 10 wt% tin-doped indium tin oxide (ITO) nanoparticles on graphene. The method involved a combination of a solution-based environmentally friendly electroless deposition approach and subsequent vacuum annealing. A stable organic-free solution of ITO was prepared from economical salts of In(NO<SUB>3</SUB>)<SUB>3</SUB><SUP>·</SUP>H<SUB>2</SUB>O and SnCl<SUB>4</SUB>. The obtained ITO nanostructure exhibited a unique architecture, with uniformly dispersed 25–35 nm size ITO nanoparticles, containing only the crystallized In<SUB>2</SUB>O<SUB>3</SUB> phase. The synthesized ITO nanoparticles–graphene hybrid exhibited very good and reproducible optical transparency in the visible range (more than 85%) and a 28.2% improvement in electrical conductivity relative to graphene synthesized by chemical vapor deposition. It was observed that the ITO nanoparticles affect the position of the Raman signal of graphene, in which the D, G, and 2D peaks were redshifted by 5.65, 5.69, and 9.74 cm<SUP>−1</SUP>, respectively, and the annealing conditions had no significant effect on the Raman signatures of graphene.</P><P/>
Harrach, Bastian,Jin Keun Seo,Eung Je Woo IEEE 2010 IEEE transactions on medical imaging Vol.29 No.11
<P>Time-difference electrical impedance tomography (tdEIT) requires two data sets measured at two different times. The difference between them is utilized to produce images of time-dependent changes in a complex conductivity distribution inside the human body. Frequency-difference EIT (fdEIT) was proposed to image frequency-dependent changes of a complex conductivity distribution. It has potential applications in tumor and stroke imaging since it can visualize an anomaly without requiring any time-reference data obtained in the absence of an anomaly. In this paper, we provide a rigorous analysis for the detectability of an anomaly based on a constructive and quantitative physical correlation between a measured fdEIT data set and an anomaly. From this, we propose a new noniterative frequency-difference anomaly detection method called the factorization method (FM) and elaborate its physical justification. To demonstrate its practical applicability, we performed fdEIT phantom imaging experiments using a multifrequency EIT system. Applying the FM to measured frequency-difference boundary voltage data sets, we could quantitatively evaluate indicator functions inside the imaging domain, of which values at each position reveal presence or absence of an anomaly. We found that the FM successfully localizes anomalies inside an imaging domain with a frequency-dependent complex conductivity distribution. We propose the new FM as an anomaly detection algorithm in fdEIT for potential applications in tumor and stroke imaging.</P>