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1.
Projekt AC motorjev za golf vozičke
Mitja Sulič, 2005, undergraduate thesis

Abstract: V delu je obravnavano ekonomsko vrednotenje naložbe za proizvodnjo 85.000 enot/leto asinhronsko komutiranih elektromotorjev (v nadaljevaju AC motor), katerega namen je bil ugotoviti smiselnost izvajanja naložbe. AC motorji so tehnološko, tehnično in ekonomsko napredni motorji zadnje generacije, ki nezahtevni za vzdrževanje in hkrati tudi energetsko varčni. Trg je v letu 2006 ocenjen na okrog 170.000 enot v industriji električnih viličarjev, ter na dodatnih 220.000 enot v istem letu v industriji malih električnih avtomobilov. V letu 2006 je cilj Iskre Avtoelektrike ob že planiranih 85.000 ACmotorjih za golf vozičke, prodati vsaj še 16.000 motorjev na različnih svetovnih trgih. Proizvodnja bo potekala na novi opremi. Višina vlaganj v osnovna sredstva bo znašala 2.835.890 EUR. V okviru ekonomske ocene upravičenosti naložbe v industrialiyacijo, so bili narejeni izračuni poslovnega uspeha za obdobje 2006 - 2010. Na osnovi predpostavk v ekonomskem vrednotenju je naložba v proizvodnjo 85.000 enot/leto AC motorjev ekonomsko upravičena, saj je neto sedanja vrednost znaša 78.071 EUR, interna stopnja donosnosti pa je 15,53 %.
Found in: ključnih besedah
Summary of found: ...naložbe za proizvodnjo 85.000 enot/leto asinhronsko komutiranih elektro motorjev (v nadaljevaju AC motor), katerega namen je...
Keywords: diplomske naloge, projekt, AC motor, tržna analiza, terminski plan, interna stopnja donosnosti
Published: 15.10.2013; Views: 2089; Downloads: 191
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2.
Real-time motor unit identification from high-density surface EMG
Vojko Glaser, Aleš Holobar, Damjan Zazula, 2013, original scientific article

Abstract: This study addresses online decomposition of high-density surface electromyograms (EMG) in real-time. The proposed method is based on previouslypublished Convolution Kernel Compensation (CKC) technique and sharesthe same decomposition paradigm, i.e. compensation of motor unit action potentials and direct identification of motor unit (MU) discharges. In contrast to previously published version of CKC, which operates in batch mode and requires ~ 10 s of EMG signal, the real-time implementation begins with batch processing of ~ 3 s of the EMG signal in the initialization stage and continues on with iterative updating of the estimators of MU discharges as blocks of new EMG samples become available. Its detailed comparison to previously validated batch version of CKC and asymptotically Bayesian optimal Linear Minimum Mean Square Error (LMMSE) estimator demonstrates high agreementin identified MU discharges among all three techniques. In the case of synthetic surface EMG with 20 dB signal-to-noise ratio, MU discharges were identified with average sensitivity of 98 %. In the case of experimental EMG, real-time CKC fully converged after initial 5 s of EMG recordings and real-time and batch CKC agreed on 90 % of MU discharges, on average. The real-time CKC identified slightly fewer MUs than its batch version (experimental EMG, 4 MUs versus 5 MUs identified by batch CKC, on average), but required only 0.6 s of processing time on regular personal computer for each second of multichannel surface EMG.
Found in: ključnih besedah
Keywords: discharge pattern, high-density EMG, surface EMG, motor unit, real time decomposition
Published: 05.01.2016; Views: 1787; Downloads: 0

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