Details

Kombinirana metoda enodimenzionalnega razreza materiala : magistrsko delo
ID Trkman, Peter (Author), ID Gradišar, Miro (Mentor) More about this mentor... This link opens in a new window

URLURL - Presentation file, Visit http://www.cek.ef.uni-lj.si/magister/trkman63.pdf This link opens in a new window

Language:Slovenian
Keywords:produkcija, materialno poslovanje, informacijski sistemi, programiranje, operacijsko raziskovanje
Work type:Master's thesis
Typology:2.09 - Master's Thesis
Organization:EF - School of Economics and Business
Place of publishing:Ljubljana
Publisher:[P. Trkman]
Year:2002
Number of pages:74 str.
PID:20.500.12556/RUL-3303 This link opens in a new window
UDC:519.8
COBISS.SI-ID:13039334 This link opens in a new window
Publication date in RUL:11.07.2014
Views:3960
Downloads:328
Metadata:XML DC-XML DC-RDF
:
TRKMAN, Peter, 2002, Kombinirana metoda enodimenzionalnega razreza materiala : magistrsko delo [online]. Master’s thesis. Ljubljana : P. Trkman. [Accessed 14 June 2025]. Retrieved from: http://www.cek.ef.uni-lj.si/magister/trkman63.pdf
Copy citation
Share:Bookmark and Share

Secondary language

Language:Unknown
Keywords:production, materials management, information systems, programming, operations research

Similar documents

Similar works from RUL:
  1. Automatic image captioning using deep neural networks
  2. Iris recognition using deep learning
  3. Object detection and classification in aquatic environment using convolutional neural networks
  4. DEEP LEARNING METHODS FOR BIOMETRIC RECOGNITION BASED ON EYE INFORMATION
  5. Convolutional neural networks for lesion segmentation in brain magnetic resonance images
Similar works from other Slovenian collections:
  1. Predicting GPS tracks with deep neural networks
  2. Time series classification based on convolutional neural networks
  3. Zaznavanje sentimenta v novicah z globokimi nevronskimi mrežami
  4. The preparation of photos' dataset and its classification using deep neural networks
  5. Prediction of geospatial raster data using convolutional neural networks

Back