Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Karl Bäckström"'
Publikováno v:
IEEE Transactions on Industry Applications. 56:3250-3260
In this article, a new approach to compensate both the response and reaction times of active power filters (APF) for special cases of highly time-varying harmonics and interharmonics of electric arc furnace (EAF) currents is proposed. Instead of usin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccf1cc0ba1e0416e48000bd6ffe28a62
https://doi.org/10.1109/tia.2020.2976722
https://doi.org/10.1109/tia.2020.2976722
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030948757
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b8bac804b198bd8e16c0428dd7dbd0c
https://doi.org/10.1007/978-3-030-94876-4_4
https://doi.org/10.1007/978-3-030-94876-4_4
Publikováno v:
IPDPS
Stochastic gradient descent (SGD) is an essential element in Machine Learning (ML) algorithms. Asynchronous parallel shared-memory SGD (AsyncSGD), including synchronization-free algorithms, e.g. HOGWILD!, have received interest in certain contexts, d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::118e8cf9e5361740a6a1d7f7256bf7fd
Publikováno v:
AITest
Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge. In current practice, there are two major techniques of input stimulation. O
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07093da44d09fcc87e45b110db488a43
http://arxiv.org/abs/2002.06611
http://arxiv.org/abs/2002.06611
Publikováno v:
IEEE BigData
Stochastic Gradient Descent (SGD) is very useful in optimization problems with high-dimensional non-convex target functions, and hence constitutes an important component of several Machine Learning and Data Analytics methods. Recently there have been
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e4f4198feffce390b33254a27dd7fba
http://arxiv.org/abs/1911.03444
http://arxiv.org/abs/1911.03444
Autor:
Marina Papatriantafilou, Victor Gustafsson, Vincenzo Gulisano, Karl Bäckström, Hampus Renberg Nilsson
Publikováno v:
DEBS
Streaming applications are used for analysing large volumes of continuous data. Achieving efficiency and effectiveness in data streaming imply challenges that gen all the more important when different parties (i) define applications' semantics, (ii)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0654ecc7fface81afdb51e5c0c6ee2e0
https://doi.org/10.1145/3328905.3332516
https://doi.org/10.1145/3328905.3332516
Publikováno v:
IAS
In this research work, time- and frequency-domain Deep Learning (DL) based methods have been developed to pre-detect harmonic and interharmonic components of a current waveform of an Electric Arc Furnace (EAF) application. In the time-domain DL based
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e13f1dc8e335efe7a4a82e2f4f2336e
https://aperta.ulakbim.gov.tr/record/70537
https://aperta.ulakbim.gov.tr/record/70537
Publikováno v:
ISBI
Automatic extraction of features from MRI brain scans and diagnosis of Alzheimer's Disease (AD) remain a challenging task. In this paper, we propose an efficient and simple three-dimensional convolutional network (3D ConvNet) architecture that is abl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c203dea9e39b29b85cafced1c38b6602
https://doi.org/10.1109/isbi.2018.8363543
https://doi.org/10.1109/isbi.2018.8363543