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Řízení střídavých elektrických pohonů pomocí algoritmů NMPC
(Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, ) Kozubík, Michal; Václavek, Pavel; Prokop, Roman; Lettl, Jiří
Tato dizertační práce se zabývá využitím nelineárního prediktivního řízení pro řízení motoru s permanentními magnety. V teoretické části práce je představena teorie prediktivního řízení, optimalizace a paralelních výpočtů. Znalosti nabyté v teoretické části jsou aplikovány v části praktické, která prezentuje navržené algoritmy prediktivního řízení. Tyto algoritmy jsou rozděleny do dvou kategorií – s konečnou a se spojitou řídicí množinou. Algoritmy byly navrženy s ohledem na paralelizaci výpočtů, což umožnilo dosáhnout výpočetních časů potřebných pro řízení motoru v reálném čase. V praktické části jsou algoritmy nejprve testovány v simulačním prostředí. Zde jsou také diskutovány možné komplikace, jako například změny parametrů motoru v čase, a jejich potenciální řešení. Závěrečná část práce je věnována laboratornímu ověření jednoho z navržených algoritmů a jeho porovnání s klasickou kaskádní řídicí strukturou. Výsledky tohoto srovnání ukazují výhody nelineárního prediktivního řízení oproti tradičním lineárním přístupům.
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Samočinně se nastavující regulátory elektrických pohonů
(Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, ) Bartík, Ondřej; Blaha, Petr; Schlegel, Miloš; Grepl, Robert
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The Dispersion-Strengthening Effect of TiN Nanoparticles Evoked by Ex Situ Nitridation of Gas-Atomized, NiCu-Based Alloy 400 in Fluidized Bed Reactor for Laser Powder Bed Fusion
(MDPI, 2024-10-01) Roth, Jan-Philipp; Šulák, Ivo; Gálíková, Markéta; Duval, Antoine; Boissonnet, Germain; Pedraza, Fernando; Krupp, Ulrich; Jahns, Katrin
Throughout recent years, the implementation of nanoparticles into the microstructure of additively manufactured (AM) parts has gained great attention in the material science community. The dispersion strengthening (DS) effect achieved leads to a substantial improvement in the mechanical properties of the alloy used. In this work, an ex situ approach of powder conditioning prior to the AM process as per a newly developed fluidized bed reactor (FBR) was applied to a titanium-enriched variant of the NiCu-based Alloy 400. Powders were investigated before and after FBR exposure, and it was found that the conditioning led to a significant increase in the TiN formation along grain boundaries. Manufactured to parts via laser-based powder bed fusion of metals (PBF-LB/M), the ex situ FBR approach not only revealed a superior microstructure compared to unconditioned parts but also with respect to a recently introduced in situ approach based on a gas atomization reaction synthesis (GARS). A substantially higher number of nanoparticles formed along cell walls and enabled an effective suppression of dislocation movement, resulting in excellent tensile, creep, and fatigue properties, even at elevated temperatures up to 750 degrees C. Such outstanding properties have never been documented for AM-processed Alloy 400, which is why the demonstrated FBR ex situ conditioning marks a promising modification route for future alloy systems.
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A Novel Tree Model-based DNN to Achieve a~High-Resolution DOA Estimation via Massive MIMO Receive Array
(Radioengineering society, 2024-12) Li, Y.; Shu, F.; Song, Y.; Wang, J.
To satisfy the high-resolution requirements of direction-of-arrival (DOA) estimation, conventional deep neural network (DNN)-based methods using grid idea need to significantly increase the number of output classifications and also produce a huge high model complexity. To address this problem, a multi-level tree-based DNN model (TDNN) is proposed as an alternative , where each level takes small-scale multi-layer neural networks (MLNNs) as nodes to divide the target angular interval into multiple sub-intervals, and each output class is associated to a MLNN at the next level. Then the number of MLNNs is gradually increasing from the first level to the last level, and so increasing the depth of tree will dramatically raise the number of output classes to improve the estimation accuracy. More importantly, this network is extended to make a multi-emitter DOA estimation. Simulation results show that the proposed TDNN performs much better than conventional DNN and root multiple signal classification algorithm (root-MUSIC) at extremely low signal-to-noise ratio (SNR) with massive multiple input multiple output (MIMO) receive array, and can achieve Cramer-Rao lower bound (CRLB). Additionally, in the multi-emitter scenario, the proposed Q-TDNN has also made a substantial performance enhancement over DNN and Root-MUSIC, and this gain grows as the number of emitters increases.
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Innovative TCAM Solutions for IPv6 Lookup: Don't Care Reduction and Data Relocation Techniques
(Radioengineering society, 2024-12) Pham, A.; Bui, D.; Nguyen, P. T. P.; Tran, L.
Ternary Content-Addressable Memory (TCAM) enables high-speed searches by comparing search data with all stored data in a single clock cycle, using ternary logic ("0", "1", "X" for "don't care") for flexible matching. This makes TCAM ideal for applications like network routers and lookup tables. However, TCAM's speed increases silicon area and limits memory capacity. This paper introduces a low-area, enhanced-capacity TCAM for IPv6 lookup tables using Don't Care Reduction (DCR) and Data Relocation (DR) techniques. The DCR technique requires only (N + log_2(N))-bit memory for an N-bit IP address, reducing the need for 2N-bit memory. The DR technique improves TCAM storage capabilities by classifying the IPv6 into 4 different prefix length types and relocating the data in the prefix bit into the "X" cells. The design features a 256x128-bit TCAM (eight 32x128-bit memory banks) on a 65 nm process with a 1.2 V operation voltage. Results show a 71.47% increase in area efficiency per stored IP value compared to conventional TCAM and a 20.97% increase compared to data-relocation TCAM.