Zobrazeno 1 - 10
of 72
pro vyhledávání: '"Rhee Man Kil"'
Publikováno v:
Neurocomputing. 360:198-208
This paper presents a novel method of classifying heart conditions from an electrocardiography (ECG) signal. For this purpose, the R-R intervals of ECG signal are analyzed by Gamma distribution parameters and classified into normal (NR) or abnormal (
Publikováno v:
IMCOM
This paper presents a new method of predicting the values of time series using recursive update Gaussian Kernel Function Networks. First, the input structure of time series prediction model is determined by the phase space analysis of time series. Th
Autor:
Young Rok Choi, Rhee Man Kil
Publikováno v:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 30
This paper presents a novel framework to extract highly compact and discriminative features for face video retrieval tasks using the deep convolutional neural network (CNN). The face video retrieval task is to find the videos containing the face of a
Classification of the trained and untrained emitter types based on class probability output networks
Publikováno v:
Neurocomputing. 248:67-75
Modern airplanes and ships are equipped with radars emitting specific patterns of electromagnetic signals. The radar antennas are detecting these patterns which are required to identify the types of emitters. A conventional way of emitter identificat
Publikováno v:
Neural Information Processing ISBN: 9783030367077
ICONIP (1)
ICONIP (1)
This paper presents a new method of classifying speech data in Parkinson’s disease using the class probability output network (CPON) in which the conditional class probabilities are estimated using Beta distributions. In the proposed CPON, the unce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6efdc0c146eaf86d58c4a5312ec3e5b3
https://doi.org/10.1007/978-3-030-36708-4_42
https://doi.org/10.1007/978-3-030-36708-4_42
Publikováno v:
Neural Information Processing ISBN: 9783030042202
ICONIP (5)
ICONIP (5)
This paper proposes a novel method of predicting daily peak power demands using the deep structure of Gaussian kernel function networks (GKFNs). For the prediction model, the whole time series is divided into multiple parts and each part is trained u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a1b0adce09eee8597f8f1c1a2677841
https://doi.org/10.1007/978-3-030-04221-9_11
https://doi.org/10.1007/978-3-030-04221-9_11
Publikováno v:
Neural Networks. 64:19-28
Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically i
Publikováno v:
Neural Information Processing ISBN: 9783319701387
ICONIP (5)
ICONIP (5)
This paper presents a new method of predicting taxi passenger demands in the central city areas of Seoul and New York based on the temporal and spatial information on predicted values. For the efficiency of the city’s taxi system, investigating the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::167094b5cf55ce911ad8ffa65c80eee9
https://doi.org/10.1007/978-3-319-70139-4_27
https://doi.org/10.1007/978-3-319-70139-4_27
Publikováno v:
Neural Information Processing ISBN: 9783319701387
ICONIP (5)
ICONIP (5)
This paper presents a new way of predicting timely air pollution measure such as the PM\(_{10}\) concentration in Seoul based on a new method of combining the global and local estimation models. In the proposed method, the structure of nonlinear dyna
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c4f95fcb8bba0ebfbef0c01c18042f7
https://doi.org/10.1007/978-3-319-70139-4_28
https://doi.org/10.1007/978-3-319-70139-4_28
Publikováno v:
ICTC
Recently, high performance multi-core processor (HMP) called big.LITTLE has been developed, where “big” is high performance core while “LITTLE” is low performance core consuming less energy. It needs efficient scheduler migrating the tasks be