---
_id: '7976'
author:
- first_name: Veronika
full_name: Gassenmeier, Veronika
id: '74048'
last_name: Gassenmeier
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Fabian
full_name: Kuhfuß, Fabian
last_name: Kuhfuß
- first_name: André
full_name: Moser, André
last_name: Moser
- first_name: Volker C.
full_name: Hass, Volker C.
last_name: Hass
- first_name: Kim B.
full_name: Kuchemüller, Kim B.
last_name: Kuchemüller
- first_name: Ralf
full_name: Pörtner, Ralf
last_name: Pörtner
- first_name: Johannes
full_name: Möller, Johannes
last_name: Möller
- first_name: George Adrian
full_name: Ifrim, George Adrian
id: '73814'
last_name: Ifrim
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Gassenmeier V, Deppe S, Hernández Rodriguez T, et al. Model-assisted DoE applied
to microalgae processes, Current Research in Biotechnology. Current Research
in Biotechnology. 2022;4:102-119. doi:10.1016/j.crbiot.2022.01.005
apa: Gassenmeier, V., Deppe, S., Hernández Rodriguez, T., Kuhfuß, F., Moser, A.,
Hass, V. C., Kuchemüller, K. B., Pörtner, R., Möller, J., Ifrim, G. A., &
Frahm, B. (2022). Model-assisted DoE applied to microalgae processes, Current
Research in Biotechnology. Current Research in Biotechnology, 4,
102–119. https://doi.org/10.1016/j.crbiot.2022.01.005
bjps: Gassenmeier V et al. (2022) Model-Assisted DoE Applied to Microalgae
Processes, Current Research in Biotechnology. Current Research in Biotechnology
4, 102–119.
chicago: 'Gassenmeier, Veronika, Sahar Deppe, Tanja Hernández Rodriguez, Fabian
Kuhfuß, André Moser, Volker C. Hass, Kim B. Kuchemüller, et al. “Model-Assisted
DoE Applied to Microalgae Processes, Current Research in Biotechnology.” Current
Research in Biotechnology 4 (2022): 102–19. https://doi.org/10.1016/j.crbiot.2022.01.005.'
chicago-de: 'Gassenmeier, Veronika, Sahar Deppe, Tanja Hernández Rodriguez, Fabian
Kuhfuß, André Moser, Volker C. Hass, Kim B. Kuchemüller, u. a. 2022. Model-assisted
DoE applied to microalgae processes, Current Research in Biotechnology. Current
Research in Biotechnology 4: 102–119. doi:10.1016/j.crbiot.2022.01.005,
.'
din1505-2-1: 'Gassenmeier,
Veronika ; Deppe, Sahar ;
Hernández Rodriguez, Tanja ; Kuhfuß, Fabian ; Moser,
André ; Hass, Volker C. ;
Kuchemüller, Kim B. ; Pörtner,
Ralf ; u. a.: Model-assisted DoE applied to microalgae processes,
Current Research in Biotechnology. In: Current Research in Biotechnology
Bd. 4, Elsevier (2022), S. 102–119'
havard: V. Gassenmeier, S. Deppe, T. Hernández Rodriguez, F. Kuhfuß, A. Moser, V.C.
Hass, K.B. Kuchemüller, R. Pörtner, J. Möller, G.A. Ifrim, B. Frahm, Model-assisted
DoE applied to microalgae processes, Current Research in Biotechnology, Current
Research in Biotechnology. 4 (2022) 102–119.
ieee: 'V. Gassenmeier et al., “Model-assisted DoE applied to microalgae processes,
Current Research in Biotechnology,” Current Research in Biotechnology,
vol. 4, pp. 102–119, 2022, doi: 10.1016/j.crbiot.2022.01.005.'
mla: Gassenmeier, Veronika, et al. “Model-Assisted DoE Applied to Microalgae Processes,
Current Research in Biotechnology.” Current Research in Biotechnology,
vol. 4, 2022, pp. 102–19, https://doi.org/10.1016/j.crbiot.2022.01.005.
short: V. Gassenmeier, S. Deppe, T. Hernández Rodriguez, F. Kuhfuß, A. Moser, V.C.
Hass, K.B. Kuchemüller, R. Pörtner, J. Möller, G.A. Ifrim, B. Frahm, Current Research
in Biotechnology 4 (2022) 102–119.
ufg: 'Gassenmeier, Veronika u. a.: Model-assisted DoE applied to microalgae
processes, Current Research in Biotechnology, in: Current Research in Biotechnology
4 (2022), S. 102–119.'
van: Gassenmeier V, Deppe S, Hernández Rodriguez T, Kuhfuß F, Moser A, Hass VC,
et al. Model-assisted DoE applied to microalgae processes, Current Research in
Biotechnology. Current Research in Biotechnology. 2022;4:102–19.
date_created: 2022-05-05T11:14:10Z
date_updated: 2024-08-05T07:10:50Z
department:
- _id: DEP4021
doi: 10.1016/j.crbiot.2022.01.005
intvolume: ' 4'
language:
- iso: eng
page: 102-119
publication: Current Research in Biotechnology
publication_identifier:
eissn:
- '2590-2628 '
publication_status: published
publisher: Elsevier
quality_controlled: '1'
status: public
title: Model-assisted DoE applied to microalgae processes, Current Research in Biotechnology
type: scientific_journal_article
user_id: '83781'
volume: 4
year: '2022'
...
---
_id: '7977'
abstract:
- lang: eng
text: Kinetic growth models are a useful tool for a better understanding of microalgal
cultivation and for optimizing cultivation conditions. The evaluation of such
models requires experimental data that is laborious to generate in bioreactor
settings. The experimental shake flask setting used in this study allows to run
12 experiments at the same time, with 6 individual light intensities and light
durations. This way, 54 biomass data sets were generated for the cultivation of
the microalgae Chlorella vulgaris. To identify the model parameters, a stepwise
parameter estimation procedure was applied. First, light-associated model parameters
were estimated using additional measurements of local light intensities at differ
heights within medium at different biomass concentrations. Next, substrate related
model parameters were estimated, using experiments for which biomass and nitrate
data were provided. Afterwards, growth-related model parameters were estimated
by application of an extensive cross validation procedure.
author:
- first_name: Fabian
full_name: Kuhfuß, Fabian
last_name: Kuhfuß
- first_name: Veronika
full_name: Gassenmeier, Veronika
id: '74048'
last_name: Gassenmeier
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: George Adrian
full_name: Ifrim, George Adrian
id: '73814'
last_name: Ifrim
- first_name: Tanja
full_name: Hernández Rodriguez, Tanja
id: '52466'
last_name: Hernández Rodriguez
- first_name: Björn
full_name: Frahm, Björn
id: '45666'
last_name: Frahm
citation:
ama: Kuhfuß F, Gassenmeier V, Deppe S, Ifrim GA, Hernández Rodriguez T, Frahm B.
View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. Bioprocess
and Biosystems Engineering. 2022;45:15-30. doi:10.1007/s00449-021-02627-2
apa: Kuhfuß, F., Gassenmeier, V., Deppe, S., Ifrim, G. A., Hernández Rodriguez,
T., & Frahm, B. (2022). View on a mechanistic model of Chlorella vulgaris
in incubated shake flasks. Bioprocess and Biosystems Engineering, 45,
15–30. https://doi.org/10.1007/s00449-021-02627-2
bjps: Kuhfuß F et al. (2022) View on a Mechanistic Model of Chlorella
Vulgaris in Incubated Shake Flasks. Bioprocess and Biosystems Engineering
45, 15–30.
chicago: 'Kuhfuß, Fabian, Veronika Gassenmeier, Sahar Deppe, George Adrian Ifrim,
Tanja Hernández Rodriguez, and Björn Frahm. “View on a Mechanistic Model of Chlorella
Vulgaris in Incubated Shake Flasks.” Bioprocess and Biosystems Engineering
45 (2022): 15–30. https://doi.org/10.1007/s00449-021-02627-2.'
chicago-de: 'Kuhfuß, Fabian, Veronika Gassenmeier, Sahar Deppe, George Adrian Ifrim,
Tanja Hernández Rodriguez und Björn Frahm. 2022. View on a mechanistic model of
Chlorella vulgaris in incubated shake flasks. Bioprocess and Biosystems Engineering
45: 15–30. doi:10.1007/s00449-021-02627-2,
.'
din1505-2-1: 'Kuhfuß, Fabian ; Gassenmeier, Veronika ; Deppe,
Sahar ; Ifrim, George Adrian
; Hernández Rodriguez, Tanja ; Frahm, Björn: View on a mechanistic model
of Chlorella vulgaris in incubated shake flasks. In: Bioprocess and Biosystems
Engineering Bd. 45. Berlin, Springer (2022), S. 15–30'
havard: F. Kuhfuß, V. Gassenmeier, S. Deppe, G.A. Ifrim, T. Hernández Rodriguez,
B. Frahm, View on a mechanistic model of Chlorella vulgaris in incubated shake
flasks, Bioprocess and Biosystems Engineering. 45 (2022) 15–30.
ieee: 'F. Kuhfuß, V. Gassenmeier, S. Deppe, G. A. Ifrim, T. Hernández Rodriguez,
and B. Frahm, “View on a mechanistic model of Chlorella vulgaris in incubated
shake flasks,” Bioprocess and Biosystems Engineering, vol. 45, pp. 15–30,
2022, doi: 10.1007/s00449-021-02627-2.'
mla: Kuhfuß, Fabian, et al. “View on a Mechanistic Model of Chlorella Vulgaris in
Incubated Shake Flasks.” Bioprocess and Biosystems Engineering, vol. 45,
2022, pp. 15–30, https://doi.org/10.1007/s00449-021-02627-2.
short: F. Kuhfuß, V. Gassenmeier, S. Deppe, G.A. Ifrim, T. Hernández Rodriguez,
B. Frahm, Bioprocess and Biosystems Engineering 45 (2022) 15–30.
ufg: 'Kuhfuß, Fabian u. a.: View on a mechanistic model of Chlorella vulgaris
in incubated shake flasks, in: Bioprocess and Biosystems Engineering 45
(2022), S. 15–30.'
van: Kuhfuß F, Gassenmeier V, Deppe S, Ifrim GA, Hernández Rodriguez T, Frahm B.
View on a mechanistic model of Chlorella vulgaris in incubated shake flasks. Bioprocess
and Biosystems Engineering. 2022;45:15–30.
date_created: 2022-05-05T11:28:56Z
date_updated: 2024-08-05T07:07:37Z
department:
- _id: DEP4021
doi: 10.1007/s00449-021-02627-2
intvolume: ' 45'
language:
- iso: eng
page: 15-30
place: Berlin
publication: Bioprocess and Biosystems Engineering
publication_identifier:
eissn:
- 1615-7605
issn:
- '1615-7591 '
publication_status: published
publisher: Springer
quality_controlled: '1'
status: public
title: View on a mechanistic model of Chlorella vulgaris in incubated shake flasks
type: scientific_journal_article
user_id: '83781'
volume: 45
year: '2022'
...
---
_id: '1994'
abstract:
- lang: eng
text: In the filling and packaging industry, the trend is towards self-diagnosis,
optimization, and quality monitoring of processes. The aim is to increase production
volumes and the quality. These concepts require continuous monitoring and anomaly
detection of the filling process. In addition, a root cause analysis of the failure
is required because not every failure can be simulated or measured previously.
Standard anomaly detection methods have no integrated root cause analysis. In
this paper a fusion system is utilises for the detection of different unknown
anomalies and also the failure source of them. The performance of this method
is benchmarked with a real-word filling process.
author:
- first_name: Martyna
full_name: Bator, Martyna
id: '46440'
last_name: Bator
- first_name: Alexander
full_name: Dicks, Alexander
id: '1853'
last_name: Dicks
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Bator M, Dicks A, Deppe S, Lohweg V. Anomaly Detection with Root Cause Analysis
for Bottling Process. In: 24nd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA2019) . IEEE; 2019. doi:10.1109/ETFA.2019.8869514'
apa: Bator, M., Dicks, A., Deppe, S., & Lohweg, V. (2019). Anomaly Detection
with Root Cause Analysis for Bottling Process. 24nd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA2019) . 24th IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza,
Spain . https://doi.org/10.1109/ETFA.2019.8869514
bjps: 'Bator M et al. (2019) Anomaly Detection with Root Cause Analysis
for Bottling Process. 24nd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA2019) . Piscataway, NJ: IEEE.'
chicago: 'Bator, Martyna, Alexander Dicks, Sahar Deppe, and Volker Lohweg. “Anomaly
Detection with Root Cause Analysis for Bottling Process.” In 24nd IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA2019) . Piscataway,
NJ: IEEE, 2019. https://doi.org/10.1109/ETFA.2019.8869514.'
chicago-de: 'Bator, Martyna, Alexander Dicks, Sahar Deppe und Volker Lohweg. 2019.
Anomaly Detection with Root Cause Analysis for Bottling Process. In: 24nd IEEE
International Conference on Emerging Technologies and Factory Automation (ETFA2019)
. Piscataway, NJ: IEEE. doi:10.1109/ETFA.2019.8869514,
.'
din1505-2-1: 'Bator, Martyna ; Dicks, Alexander ; Deppe,
Sahar ; Lohweg, Volker: Anomaly
Detection with Root Cause Analysis for Bottling Process. In: 24nd IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA2019) . Piscataway,
NJ : IEEE, 2019'
havard: 'M. Bator, A. Dicks, S. Deppe, V. Lohweg, Anomaly Detection with Root Cause
Analysis for Bottling Process, in: 24nd IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA2019) , IEEE, Piscataway, NJ, 2019.'
ieee: 'M. Bator, A. Dicks, S. Deppe, and V. Lohweg, “Anomaly Detection with Root
Cause Analysis for Bottling Process,” presented at the 24th IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza,
Spain , 2019. doi: 10.1109/ETFA.2019.8869514.'
mla: Bator, Martyna, et al. “Anomaly Detection with Root Cause Analysis for Bottling
Process.” 24nd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA2019) , IEEE, 2019, https://doi.org/10.1109/ETFA.2019.8869514.
short: 'M. Bator, A. Dicks, S. Deppe, V. Lohweg, in: 24nd IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA2019) , IEEE, Piscataway,
NJ, 2019.'
ufg: 'Bator, Martyna u. a.: Anomaly Detection with Root Cause Analysis for
Bottling Process, in: o. Hg.: 24nd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA2019) , Piscataway, NJ 2019.'
van: 'Bator M, Dicks A, Deppe S, Lohweg V. Anomaly Detection with Root Cause Analysis
for Bottling Process. In: 24nd IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA2019) . Piscataway, NJ: IEEE; 2019.'
conference:
end_date: 2019-09-13
location: ' Zaragoza, Spain '
name: 24th IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA)
start_date: 2019-09-10
date_created: 2019-11-22T12:51:15Z
date_updated: 2024-04-15T12:43:21Z
department:
- _id: DEP5023
- _id: DEP1308
doi: 10.1109/ETFA.2019.8869514
language:
- iso: eng
place: Piscataway, NJ
publication: '24nd IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA2019) '
publication_identifier:
eisbn:
- 978-1-7281-0303-7
- 978-1-7281-0302-0
isbn:
- 978-1-7281-0304-4
issn:
- 1946-0759
publication_status: published
publisher: IEEE
status: public
title: Anomaly Detection with Root Cause Analysis for Bottling Process
type: conference
user_id: '83781'
year: '2019'
...
---
_id: '2010'
author:
- first_name: Christian
full_name: Wissel, Christian
id: '59157'
last_name: Wissel
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: Wissel C, Deppe S, Lohweg V. 3D-Inspektion bei 600m/s - Feinste 3D-Strukturen
inline mit hohem Tempo prüfen. SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH,
Marburg. 2018;(1/2).
apa: Wissel, C., Deppe, S., & Lohweg, V. (2018). 3D-Inspektion bei 600m/s -
Feinste 3D-Strukturen inline mit hohem Tempo prüfen. SPS-Magazin Ausgabe 1+2/2018,
TeDo Verlag GmbH, Marburg, (1/2).
bjps: Wissel C, Deppe S and Lohweg V (2018) 3D-Inspektion bei 600m/s - Feinste
3D-Strukturen inline mit hohem Tempo prüfen. SPS-Magazin Ausgabe 1+2/2018,
TeDo Verlag GmbH, Marburg.
chicago: Wissel, Christian, Sahar Deppe, and Volker Lohweg. “3D-Inspektion bei 600m/s
- Feinste 3D-Strukturen inline mit hohem Tempo prüfen.” SPS-Magazin Ausgabe
1+2/2018, TeDo Verlag GmbH, Marburg, no. 1/2 (2018).
chicago-de: Wissel, Christian, Sahar Deppe und Volker Lohweg. 2018. 3D-Inspektion
bei 600m/s - Feinste 3D-Strukturen inline mit hohem Tempo prüfen. SPS-Magazin
Ausgabe 1+2/2018, TeDo Verlag GmbH, Marburg, Nr. 1/2.
din1505-2-1: 'Wissel, Christian ;
Deppe, Sahar ; Lohweg,
Volker: 3D-Inspektion bei 600m/s - Feinste 3D-Strukturen inline mit hohem
Tempo prüfen. In: SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH, Marburg.
Marburg, TeDo-Verl (2018), Nr. 1/2'
havard: C. Wissel, S. Deppe, V. Lohweg, 3D-Inspektion bei 600m/s - Feinste 3D-Strukturen
inline mit hohem Tempo prüfen, SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH,
Marburg. (2018).
ieee: C. Wissel, S. Deppe, and V. Lohweg, “3D-Inspektion bei 600m/s - Feinste 3D-Strukturen
inline mit hohem Tempo prüfen,” SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH,
Marburg, no. 1/2, 2018.
mla: Wissel, Christian, et al. “3D-Inspektion bei 600m/s - Feinste 3D-Strukturen
inline mit hohem Tempo prüfen.” SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH,
Marburg, no. 1/2, TeDo-Verl, 2018.
short: C. Wissel, S. Deppe, V. Lohweg, SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag
GmbH, Marburg (2018).
ufg: 'Wissel, Christian et. al. (2018): 3D-Inspektion bei 600m/s - Feinste
3D-Strukturen inline mit hohem Tempo prüfen, in: SPS-Magazin Ausgabe 1+2/2018,
TeDo Verlag GmbH, Marburg (1/2).'
van: Wissel C, Deppe S, Lohweg V. 3D-Inspektion bei 600m/s - Feinste 3D-Strukturen
inline mit hohem Tempo prüfen. SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH,
Marburg. 2018;(1/2).
date_created: 2019-11-25T08:35:51Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
issue: '1/2 '
language:
- iso: ger
place: Marburg
publication: SPS-Magazin Ausgabe 1+2/2018, TeDo Verlag GmbH, Marburg
publication_identifier:
issn:
- '0935-0187 '
publication_status: published
publisher: TeDo-Verl
status: public
title: 3D-Inspektion bei 600m/s - Feinste 3D-Strukturen inline mit hohem Tempo prüfen
type: journal_article
user_id: '15514'
year: 2018
...
---
_id: '2012'
abstract:
- lang: eng
text: "The rapid growth of optical imaging technologies increased the access and
collection of data, which boosts the demand of data and knowledge discovery. This
is a fast growing topic in several industry and research areas. Nowadays, a large
number of images and signals must be analysed in order to gain and learn proper
knowledge. Detecting images with similar contents without specifying an image,
recently attracts the researches in image processing domain. Motif discovery in
image processing aims to tackle the problem of deriving structures or detecting
regularities in image databases. Most of the motif discovery methods solve this
problem by converting images into one dimensional time series in a pre-processing
step and then applying a motif discovery on these one dimensional time series
for image motifs detection. Nevertheless, this conversion might lead to information
loss and also the problem of inability to discover shifted and multi-scale image
motifs of different size. Contrary to other approaches, here, a method is proposed
to find image motifs of different size in image data sets by employing images
in original dimension (2D) without converting them to one dimensional time series.\r\nThe
proposed approach consists of three steps: Mapping or transformation, feature
extraction and measuring similarities. First, images are inspected by the Complex
Quad Tree Wavelet Packet transform, which provides broad frequency analysis of
an image in various scales. Next, statistical features are extracted from the
wavelet coefficients. Finally, image motifs are detected by measuring the similarity
of the features applying various similarity measures. Here, the performance of
six similarity measures are benchmarked in details. Moreover, the efficiency of
the proposed method is demonstrated on a data set with images from diverse applications
such as hand gesture, text recognition, leaf and plant identification, etc. Additionally,
the robustness of this method is examined with the image data overlaying with
distortions such as noise and blur."
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: Deppe S, Lohweg V. Evaluation of Similarity Measures for Shift-Invariant Image
Motif Discovery. International Journal On Advances in Intelligent Systems.
2017;10(3/4):434-446.
apa: Deppe, S., & Lohweg, V. (2017). Evaluation of Similarity Measures for Shift-Invariant
Image Motif Discovery. International Journal On Advances in Intelligent Systems,
10(3/4), 434–446.
bjps: Deppe S and Lohweg V (2017) Evaluation of Similarity Measures for Shift-Invariant
Image Motif Discovery. International Journal On Advances in Intelligent Systems
10, 434–446.
chicago: 'Deppe, Sahar, and Volker Lohweg. “Evaluation of Similarity Measures for
Shift-Invariant Image Motif Discovery.” International Journal On Advances in
Intelligent Systems 10, no. 3/4 (2017): 434–46.'
chicago-de: 'Deppe, Sahar und Volker Lohweg. 2017. Evaluation of Similarity Measures
for Shift-Invariant Image Motif Discovery. International Journal On Advances
in Intelligent Systems 10, Nr. 3/4: 434–446.'
din1505-2-1: 'Deppe, Sahar ; Lohweg, Volker: Evaluation of Similarity
Measures for Shift-Invariant Image Motif Discovery. In: International Journal
On Advances in Intelligent Systems Bd. 10. [S.l.], IARIA Journals (2017),
Nr. 3/4, S. 434–446'
havard: S. Deppe, V. Lohweg, Evaluation of Similarity Measures for Shift-Invariant
Image Motif Discovery, International Journal On Advances in Intelligent Systems.
10 (2017) 434–446.
ieee: S. Deppe and V. Lohweg, “Evaluation of Similarity Measures for Shift-Invariant
Image Motif Discovery,” International Journal On Advances in Intelligent Systems,
vol. 10, no. 3/4, pp. 434–446, 2017.
mla: Deppe, Sahar, and Volker Lohweg. “Evaluation of Similarity Measures for Shift-Invariant
Image Motif Discovery.” International Journal On Advances in Intelligent Systems,
vol. 10, no. 3/4, IARIA Journals, 2017, pp. 434–46.
short: S. Deppe, V. Lohweg, International Journal On Advances in Intelligent Systems
10 (2017) 434–446.
ufg: 'Deppe, Sahar/Lohweg, Volker (2017): Evaluation of Similarity Measures
for Shift-Invariant Image Motif Discovery, in: International Journal On Advances
in Intelligent Systems 10 (3/4), S. 434–446.'
van: Deppe S, Lohweg V. Evaluation of Similarity Measures for Shift-Invariant Image
Motif Discovery. International Journal On Advances in Intelligent Systems. 2017;10(3/4):434–46.
date_created: 2019-11-25T08:35:54Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
intvolume: ' 10'
issue: 3/4
keyword:
- processing
- Wavelet transformation.
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.iariajournals.org/intelligent_systems/intsys_v10_n34_2017_paged.pdf
oa: '1'
page: 434 - 446
place: ' [S.l.]'
publication: International Journal On Advances in Intelligent Systems
publication_identifier:
issn:
- 1942-2679
publication_status: published
publisher: IARIA Journals
status: public
title: Evaluation of Similarity Measures for Shift-Invariant Image Motif Discovery
type: journal_article
user_id: '45673'
volume: 10
year: 2017
...
---
_id: '2022'
abstract:
- lang: eng
text: 'Nowadays, the boost of optical imaging technologies results in more data
with a faster rate are being collected. Consequently, data and knowledge discovery
science has become an attractive and a fast growing topic in several industry
and research area. Motif discovery in image processing aims to tackle the problem
of deriving structures or detecting regularities in image databases. Most of the
motif discovery methods first convert images into time series and then attempt
to find motifs in such data. This might lead to information loss and also the
problem of inability to detect shifted and multi-scale image motifs of different
size. Here, a method is proposed to find image motifs of different size in image
datasets by applying images in original dimension without converting them to time
series. Images are inspected by the Complex Quad Tree Wavelet Packet transform
which provides broad frequency analysis of an image in various scales. Next, features
are extracted from the wavelet coefficients. Finally, image motifs are detected
by measuring the similarity of the features. The performance of the proposed method
is demonstrated on a dataset with images from diverse applications, such as hand
gesture, text recognition, leaf and plant identification, etc. '
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Deppe S, Lohweg V. Shift-Invariant Motif Discovery in Image Processing “Best
Paper Award.” In: Leister W, ed. PESARO 2017 The Seventh International Conference
on Performance, Safety and Robustness in Complex Systems and Applications.
PESARO 2017; Venice, Italy : The Seventh International Conference on Performance,
Safety and Robustness in Complex Systems and Applications; Special track MAIS:
Machine Learning Algorithms in Image and Signal Processing; 2017:27-32.'
apa: 'Deppe, S., & Lohweg, V. (2017). Shift-Invariant Motif Discovery in Image
Processing “Best Paper Award.” In W. Leister (Ed.), PESARO 2017 The Seventh
International Conference on Performance, Safety and Robustness in Complex Systems
and Applications (pp. 27–32). PESARO 2017; Venice, Italy : The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications;
Special track MAIS: Machine Learning Algorithms in Image and Signal Processing.'
bjps: 'Deppe S and Lohweg V (2017) Shift-Invariant Motif Discovery in Image
Processing ‘Best Paper Award’. In Leister W (ed.), PESARO 2017 The Seventh
International Conference on Performance, Safety and Robustness in Complex Systems
and Applications. PESARO 2017; Venice, Italy : The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications;
Special track MAIS: Machine Learning Algorithms in Image and Signal Processing,
pp. 27–32.'
chicago: 'Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Motif Discovery in Image
Processing ‘Best Paper Award.’” In PESARO 2017 The Seventh International Conference
on Performance, Safety and Robustness in Complex Systems and Applications,
edited by Wolfgang Leister, 27–32. PESARO 2017; Venice, Italy : The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications;
Special track MAIS: Machine Learning Algorithms in Image and Signal Processing,
2017.'
chicago-de: 'Deppe, Sahar und Volker Lohweg. 2017. Shift-Invariant Motif Discovery
in Image Processing „Best Paper Award“. In: PESARO 2017 The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications,
hg. von Wolfgang Leister, 27–32. PESARO 2017; Venice, Italy : The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications;
Special track MAIS: Machine Learning Algorithms in Image and Signal Processing.'
din1505-2-1: 'Deppe, Sahar ; Lohweg, Volker: Shift-Invariant Motif
Discovery in Image Processing „Best Paper Award“. In: Leister,
W. (Hrsg.): PESARO 2017 The Seventh International Conference on Performance,
Safety and Robustness in Complex Systems and Applications. PESARO 2017; Venice,
Italy : The Seventh International Conference on Performance, Safety and Robustness
in Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
in Image and Signal Processing, 2017, S. 27–32'
havard: 'S. Deppe, V. Lohweg, Shift-Invariant Motif Discovery in Image Processing
“Best Paper Award,” in: W. Leister (Ed.), PESARO 2017 The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications,
The Seventh International Conference on Performance, Safety and Robustness in
Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
in Image and Signal Processing, PESARO 2017; Venice, Italy , 2017: pp. 27–32.'
ieee: S. Deppe and V. Lohweg, “Shift-Invariant Motif Discovery in Image Processing
‘Best Paper Award,’” in PESARO 2017 The Seventh International Conference on
Performance, Safety and Robustness in Complex Systems and Applications, Venice,
Italy , 2017, pp. 27–32.
mla: 'Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Motif Discovery in Image
Processing ‘Best Paper Award.’” PESARO 2017 The Seventh International Conference
on Performance, Safety and Robustness in Complex Systems and Applications,
edited by Wolfgang Leister, The Seventh International Conference on Performance,
Safety and Robustness in Complex Systems and Applications; Special track MAIS:
Machine Learning Algorithms in Image and Signal Processing, 2017, pp. 27–32.'
short: 'S. Deppe, V. Lohweg, in: W. Leister (Ed.), PESARO 2017 The Seventh International
Conference on Performance, Safety and Robustness in Complex Systems and Applications,
The Seventh International Conference on Performance, Safety and Robustness in
Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
in Image and Signal Processing, PESARO 2017; Venice, Italy , 2017, pp. 27–32.'
ufg: 'Deppe, Sahar/Lohweg, Volker (2017): Shift-Invariant Motif Discovery
in Image Processing „Best Paper Award“, in: Wolfgang Leister (Hg.): PESARO
2017 The Seventh International Conference on Performance, Safety and Robustness
in Complex Systems and Applications, PESARO 2017; Venice, Italy , S. 27–32.'
van: 'Deppe S, Lohweg V. Shift-Invariant Motif Discovery in Image Processing “Best
Paper Award.” In: Leister W, editor. PESARO 2017 The Seventh International Conference
on Performance, Safety and Robustness in Complex Systems and Applications. PESARO
2017; Venice, Italy : The Seventh International Conference on Performance, Safety
and Robustness in Complex Systems and Applications; Special track MAIS: Machine
Learning Algorithms in Image and Signal Processing; 2017. p. 27–32.'
conference:
end_date: 2017-04-27
location: 'Venice, Italy '
name: 7. International Conference on Performance (PESARO 2017)
start_date: 2017-04-23
date_created: 2019-11-25T09:06:50Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
editor:
- first_name: Wolfgang
full_name: Leister, Wolfgang
last_name: Leister
keyword:
- Motif discovery
- Image processing
- Wavelet transformation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.thinkmind.org/index.php?view=instance&instance=PESARO+2017
oa: '1'
page: 27-32
place: 'PESARO 2017; Venice, Italy '
publication: PESARO 2017 The Seventh International Conference on Performance, Safety
and Robustness in Complex Systems and Applications
publication_identifier:
eisbn:
- 978-1-61208-549-4
eissn:
- 2308-3700
publication_status: published
publisher: 'The Seventh International Conference on Performance, Safety and Robustness
in Complex Systems and Applications; Special track MAIS: Machine Learning Algorithms
in Image and Signal Processing'
status: public
title: Shift-Invariant Motif Discovery in Image Processing 'Best Paper Award'
type: conference
user_id: '15514'
year: 2017
...
---
_id: '2023'
abstract:
- lang: eng
text: Last decades witness a huge growth in medical applications, genetic analysis,and
in performance of manufacturing technologies and automatised productionsystems.
A challenging task is to identify and diagnose the behavior of suchsystems, which
aim to produce a product with desired quality. In order to con-trol the state
of the systems, various information is gathered from differenttypes of sensors
(optical, acoustic, chemical, electric, and thermal). Time seriesdata are a set
of real-valued variables obtained chronologically. Data miningand machine learning
help derive meaningful knowledge from time series.Such tasks include clustering,
classification, anomaly detection andmotif discov-ery. Motif discovery attempts
tofind meaningful, new, and unknown knowledgefrom data. Detection of motifs in
a time series is beneficial for, e.g., discovery ofrules or specific events in
a signal. Motifs provide useful information for theuser in order to model or analyze
the data. Motif discovery is applied to variousareas as telecommunication, medicine, web, motion-capture, and sensornetworks.
This contribution provides a review of the existing publications intime series
motif discovery along with advantages and disadvantages of existingapproaches.
Moreover, the research issues and missing points in thisfield arehighlighted.
The main objective of this focus article is to serve as a glossary forresearchers
in thisfield.
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Deppe S, Lohweg V. Survey on Time Series Motif Discovery. WIREs : Forensic
science. 2017;7(2). doi:
https://doi.org/10.1002/widm.1199'
apa: 'Deppe, S., & Lohweg, V. (2017). Survey on Time Series Motif Discovery.
WIREs : Forensic Science, 7(2). https://doi.org/
https://doi.org/10.1002/widm.1199'
bjps: 'Deppe S and Lohweg V (2017) Survey on Time Series Motif Discovery.
WIREs : Forensic science 7.'
chicago: 'Deppe, Sahar, and Volker Lohweg. “Survey on Time Series Motif Discovery.”
WIREs : Forensic Science 7, no. 2 (2017). https://doi.org/
https://doi.org/10.1002/widm.1199.'
chicago-de: 'Deppe, Sahar und Volker Lohweg. 2017. Survey on Time Series Motif Discovery.
WIREs : Forensic science 7, Nr. 2. doi:
https://doi.org/10.1002/widm.1199, .'
din1505-2-1: 'Deppe, Sahar ; Lohweg, Volker: Survey on Time Series
Motif Discovery. In: WIREs : Forensic science Bd. 7. Danvers, MA ,
Wiley-Blackwell (2017), Nr. 2'
havard: 'S. Deppe, V. Lohweg, Survey on Time Series Motif Discovery, WIREs : Forensic
Science. 7 (2017).'
ieee: 'S. Deppe and V. Lohweg, “Survey on Time Series Motif Discovery,” WIREs :
Forensic science, vol. 7, no. 2, 2017.'
mla: 'Deppe, Sahar, and Volker Lohweg. “Survey on Time Series Motif Discovery.”
WIREs : Forensic Science, vol. 7, no. 2, Wiley-Blackwell , 2017, doi: https://doi.org/10.1002/widm.1199.'
short: 'S. Deppe, V. Lohweg, WIREs : Forensic Science 7 (2017).'
ufg: 'Deppe, Sahar/Lohweg, Volker (2017): Survey on Time Series Motif Discovery,
in: WIREs : Forensic science 7 (2).'
van: 'Deppe S, Lohweg V. Survey on Time Series Motif Discovery. WIREs : Forensic
science. 2017;7(2).'
date_created: 2019-11-25T09:06:51Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: ' https://doi.org/10.1002/widm.1199'
intvolume: ' 7'
issue: '2'
language:
- iso: eng
place: ' Danvers, MA '
publication: ' WIREs : Forensic science'
publication_identifier:
eissn:
- '2573-9468 '
publication_status: published
publisher: 'Wiley-Blackwell '
status: public
title: Survey on Time Series Motif Discovery
type: journal_article
user_id: '15514'
volume: 7
year: 2017
...
---
_id: '2033'
abstract:
- lang: eng
text: Cash machines or automated teller machines (ATMs) are one of the typical ways
to get cash around the world. Such machines are under a variety of criminal attacks.
Most of the manipulations are performed through skimming. In 2014, such attacks
led to a damage of approx. 280 million Euro within the EU. In this paper, we propose
an approach to detect anomalies and attacks on ATMs via motif discovery. Motifs
are frequently unknown occurring sequences or events in a time series signal.
State of the ATM is captured by innovative piezoelectric sensor networks to analyse
the occurring vibrations. The captured signals are inspected by the Complex Quad-Tree
Wavelet Packet transform which provides broad frequency analysis of a signal in
various scales. Next, features are extracted from the selected scale based on
the information content, to detect motifs. Detected motifs provide the prototype
patterns for anomaly detection or classification tasks.
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Alexander
full_name: Dicks, Alexander
id: '1853'
last_name: Dicks
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Deppe S, Dicks A, Lohweg V. Anomaly Detection on ATMs via Time Series Motif
Discovery. In: 21th IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA 2016), Berlin, . ; 2016.'
apa: Deppe, S., Dicks, A., & Lohweg, V. (2016). Anomaly Detection on ATMs via
Time Series Motif Discovery. In 21th IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA 2016), Berlin, .
bjps: Deppe S, Dicks A and Lohweg V (2016) Anomaly Detection on ATMs via
Time Series Motif Discovery. 21th IEEE International Conference on Emerging
Technologies and Factory Automation (ETFA 2016), Berlin, .
chicago: Deppe, Sahar, Alexander Dicks, and Volker Lohweg. “Anomaly Detection on
ATMs via Time Series Motif Discovery.” In 21th IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA 2016), Berlin, , 2016.
chicago-de: 'Deppe, Sahar, Alexander Dicks und Volker Lohweg. 2016. Anomaly Detection
on ATMs via Time Series Motif Discovery. In: 21th IEEE International Conference
on Emerging Technologies and Factory Automation (ETFA 2016), Berlin, .'
din1505-2-1: 'Deppe, Sahar ; Dicks, Alexander ; Lohweg,
Volker: Anomaly Detection on ATMs via Time Series Motif Discovery. In:
21th IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA 2016), Berlin, , 2016'
havard: 'S. Deppe, A. Dicks, V. Lohweg, Anomaly Detection on ATMs via Time Series
Motif Discovery, in: 21th IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA 2016), Berlin, , 2016.'
ieee: S. Deppe, A. Dicks, and V. Lohweg, “Anomaly Detection on ATMs via Time Series
Motif Discovery,” in 21th IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA 2016), Berlin, , 2016.
mla: Deppe, Sahar, et al. “Anomaly Detection on ATMs via Time Series Motif Discovery.”
21th IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA 2016), Berlin, , 2016.
short: 'S. Deppe, A. Dicks, V. Lohweg, in: 21th IEEE International Conference on
Emerging Technologies and Factory Automation (ETFA 2016), Berlin, , 2016.'
ufg: 'Deppe, Sahar et. al. (2016): Anomaly Detection on ATMs via Time Series
Motif Discovery, in: 21th IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA 2016), Berlin, .'
van: 'Deppe S, Dicks A, Lohweg V. Anomaly Detection on ATMs via Time Series Motif
Discovery. In: 21th IEEE International Conference on Emerging Technologies and
Factory Automation (ETFA 2016), Berlin, . 2016.'
date_created: 2019-11-26T14:43:11Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
language:
- iso: eng
publication: '21th IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA 2016), Berlin, '
status: public
title: Anomaly Detection on ATMs via Time Series Motif Discovery
type: conference
user_id: '74004'
year: 2016
...
---
_id: '2123'
abstract:
- lang: eng
text: "The means of data mining and machine learning tasks are important topics
in signal processing fundamentals. An example of such tasks is motif discovery.
This paper presents an efficient method for shift-invariant feature\r\nextraction
in time-series motif discovery. The proposed method initiates from the machine
learning procedure and tackles the drawbacks of existing methods. Moreover, the
efficacy of the novel approach is benchmarked\r\nagainst various algorithms and
data from diverse fields.\r\n"
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Deppe S, Lohweg V. Shift-Invariant Feature Extraction for Time-Series Motif
Discovery. In: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- Und Automatisierungstechnik (GMA). Dortmund; 2015:23-45. doi:10.5445/KSP/1000049620'
apa: Deppe, S., & Lohweg, V. (2015). Shift-Invariant Feature Extraction for
Time-Series Motif Discovery. In 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA) (pp. 23–45). Dortmund. https://doi.org/10.5445/KSP/1000049620
bjps: Deppe S and Lohweg V (2015) Shift-Invariant Feature Extraction for
Time-Series Motif Discovery. 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- Und Automatisierungstechnik (GMA). Dortmund, pp. 23–45.
chicago: Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Feature Extraction for
Time-Series Motif Discovery.” In 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- Und Automatisierungstechnik (GMA), 23–45. Dortmund, 2015. https://doi.org/10.5445/KSP/1000049620.
chicago-de: 'Deppe, Sahar und Volker Lohweg. 2015. Shift-Invariant Feature Extraction
for Time-Series Motif Discovery. In: 25. Workshop Computational Intelligence
VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA), 23–45. Dortmund.
doi:10.5445/KSP/1000049620,
.'
din1505-2-1: 'Deppe, Sahar ; Lohweg, Volker: Shift-Invariant Feature
Extraction for Time-Series Motif Discovery. In: 25. Workshop Computational
Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA).
Dortmund, 2015, S. 23–45'
havard: 'S. Deppe, V. Lohweg, Shift-Invariant Feature Extraction for Time-Series
Motif Discovery, in: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- Und Automatisierungstechnik (GMA), Dortmund, 2015: pp. 23–45.'
ieee: S. Deppe and V. Lohweg, “Shift-Invariant Feature Extraction for Time-Series
Motif Discovery,” in 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA), 2015, pp. 23–45.
mla: Deppe, Sahar, and Volker Lohweg. “Shift-Invariant Feature Extraction for Time-Series
Motif Discovery.” 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- Und Automatisierungstechnik (GMA), 2015, pp. 23–45, doi:10.5445/KSP/1000049620.
short: 'S. Deppe, V. Lohweg, in: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- Und Automatisierungstechnik (GMA), Dortmund, 2015, pp. 23–45.'
ufg: 'Deppe, Sahar/Lohweg, Volker (2015): Shift-Invariant Feature Extraction
for Time-Series Motif Discovery, in: 25. Workshop Computational Intelligence
VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA), Dortmund, S.
23–45.'
van: 'Deppe S, Lohweg V. Shift-Invariant Feature Extraction for Time-Series Motif
Discovery. In: 25 Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess-
und Automatisierungstechnik (GMA). Dortmund; 2015. p. 23–45.'
date_created: 2019-12-03T13:49:19Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
doi: 10.5445/KSP/1000049620
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://publikationen.bibliothek.kit.edu/1000049620
oa: '1'
page: 23-45
place: Dortmund
publication: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und
Automatisierungstechnik (GMA)
status: public
title: Shift-Invariant Feature Extraction for Time-Series Motif Discovery
type: conference
user_id: '68554'
year: 2015
...
---
_id: '2124'
abstract:
- lang: ger
text: Patente schützen das geistige Eigentum von Erfindern und verhindern, dass
ihre neuen Ideen kopiert werden. Sie sind von großer Bedeutung für den wirtschaftlichen
Erfolg eines Unternehmens. Vor einer geplanten Patentanmeldung ist es wichtig
festzustellen, ob eine bestimmte Technik bereits patentiert ist und wie die Erfolgsaussichten
beurteilt werden können. Aber auch die Identifizierung von Verstößen gegen eigene
Patentanmeldungen ist für ein Unternehmen von äußerster Wichtigkeit. Verschiedene
Techniken und Tools sind entwickelt worden, um Patentanalyse-Experten, Managern
und Technologieämtern bei den unterschiedlichsten Anforderungen im Bezug auf eine
Patentrecherche zu unterstützen.
author:
- first_name: Martyna
full_name: Bator, Martyna
id: '46440'
last_name: Bator
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Bator M, Deppe S, Lohweg V. Relevanzbewertung technischer Informationen mittels
Data-Mining Verfahren am Anwendungsfall von Patentdokumenten. In: 25. Workshop
Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik
(GMA). Dortmund; 2015.'
apa: Bator, M., Deppe, S., & Lohweg, V. (2015). Relevanzbewertung technischer
Informationen mittels Data-Mining Verfahren am Anwendungsfall von Patentdokumenten.
In 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA). Dortmund.
bjps: Bator M, Deppe S and Lohweg V (2015) Relevanzbewertung Technischer
Informationen Mittels Data-Mining Verfahren Am Anwendungsfall von Patentdokumenten.
25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik
(GMA). Dortmund.
chicago: Bator, Martyna, Sahar Deppe, and Volker Lohweg. “Relevanzbewertung Technischer
Informationen Mittels Data-Mining Verfahren Am Anwendungsfall von Patentdokumenten.”
In 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik
(GMA). Dortmund, 2015.
chicago-de: 'Bator, Martyna, Sahar Deppe und Volker Lohweg. 2015. Relevanzbewertung
technischer Informationen mittels Data-Mining Verfahren am Anwendungsfall von
Patentdokumenten. In: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA). Dortmund.'
din1505-2-1: 'Bator, Martyna ; Deppe, Sahar ; Lohweg,
Volker: Relevanzbewertung technischer Informationen mittels Data-Mining
Verfahren am Anwendungsfall von Patentdokumenten. In: 25. Workshop Computational
Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA).
Dortmund, 2015'
havard: 'M. Bator, S. Deppe, V. Lohweg, Relevanzbewertung technischer Informationen
mittels Data-Mining Verfahren am Anwendungsfall von Patentdokumenten, in: 25.
Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik
(GMA), Dortmund, 2015.'
ieee: M. Bator, S. Deppe, and V. Lohweg, “Relevanzbewertung technischer Informationen
mittels Data-Mining Verfahren am Anwendungsfall von Patentdokumenten,” in 25.
Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA), 2015.
mla: Bator, Martyna, et al. “Relevanzbewertung Technischer Informationen Mittels
Data-Mining Verfahren Am Anwendungsfall von Patentdokumenten.” 25. Workshop
Computational Intelligence VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik
(GMA), 2015.
short: 'M. Bator, S. Deppe, V. Lohweg, in: 25. Workshop Computational Intelligence
VDI/VDE-Gesellschaft Mess- Und Automatisierungstechnik (GMA), Dortmund, 2015.'
ufg: 'Bator, Martyna et. al. (2015): Relevanzbewertung technischer Informationen
mittels Data-Mining Verfahren am Anwendungsfall von Patentdokumenten, in: 25.
Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA), Dortmund.'
van: 'Bator M, Deppe S, Lohweg V. Relevanzbewertung technischer Informationen mittels
Data-Mining Verfahren am Anwendungsfall von Patentdokumenten. In: 25 Workshop
Computational Intelligence VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA). Dortmund; 2015.'
date_created: 2019-12-03T13:59:40Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.researchgate.net/publication/281933251_Relevanzbewertung_technischer_Informationen_mittels_Data-Mining_Verfahren_am_Anwendungsfall_von_Patentdokumenten
oa: '1'
place: Dortmund
publication: 25. Workshop Computational Intelligence VDI/VDE-Gesellschaft Mess- und
Automatisierungstechnik (GMA)
status: public
title: Relevanzbewertung technischer Informationen mittels Data-Mining Verfahren am
Anwendungsfall von Patentdokumenten
type: conference
user_id: '68554'
year: 2015
...
---
_id: '2125'
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Helene
full_name: Dörksen, Helene
id: '46416'
last_name: Dörksen
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Deppe S, Dörksen H, Lohweg V. Multi-Scale Motif Discovery in Image Processing.
In: Workshop on Probabilistic Graphical Models. Heidelberg, Germany; 2015.'
apa: Deppe, S., Dörksen, H., & Lohweg, V. (2015). Multi-Scale Motif Discovery
in Image Processing. In Workshop on Probabilistic Graphical Models. Heidelberg,
Germany.
bjps: Deppe S, Dörksen H and Lohweg V (2015) Multi-Scale Motif Discovery
in Image Processing. Workshop on Probabilistic Graphical Models. Heidelberg,
Germany.
chicago: Deppe, Sahar, Helene Dörksen, and Volker Lohweg. “Multi-Scale Motif Discovery
in Image Processing.” In Workshop on Probabilistic Graphical Models. Heidelberg,
Germany, 2015.
chicago-de: 'Deppe, Sahar, Helene Dörksen und Volker Lohweg. 2015. Multi-Scale Motif
Discovery in Image Processing. In: Workshop on Probabilistic Graphical Models.
Heidelberg, Germany.'
din1505-2-1: 'Deppe, Sahar ; Dörksen, Helene ; Lohweg,
Volker: Multi-Scale Motif Discovery in Image Processing. In: Workshop
on Probabilistic Graphical Models. Heidelberg, Germany, 2015'
havard: 'S. Deppe, H. Dörksen, V. Lohweg, Multi-Scale Motif Discovery in Image Processing,
in: Workshop on Probabilistic Graphical Models, Heidelberg, Germany, 2015.'
ieee: S. Deppe, H. Dörksen, and V. Lohweg, “Multi-Scale Motif Discovery in Image
Processing,” in Workshop on Probabilistic Graphical Models, 2015.
mla: Deppe, Sahar, et al. “Multi-Scale Motif Discovery in Image Processing.” Workshop
on Probabilistic Graphical Models, 2015.
short: 'S. Deppe, H. Dörksen, V. Lohweg, in: Workshop on Probabilistic Graphical
Models, Heidelberg, Germany, 2015.'
ufg: 'Deppe, Sahar et. al. (2015): Multi-Scale Motif Discovery in Image Processing,
in: Workshop on Probabilistic Graphical Models, Heidelberg, Germany.'
van: 'Deppe S, Dörksen H, Lohweg V. Multi-Scale Motif Discovery in Image Processing.
In: Workshop on Probabilistic Graphical Models. Heidelberg, Germany; 2015.'
date_created: 2019-12-03T14:02:21Z
date_updated: 2023-03-15T13:49:38Z
department:
- _id: DEP5023
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.researchgate.net/publication/283481666_Multi-Scale_Motif_Discovery_in_Image_Processing
oa: '1'
place: Heidelberg, Germany
publication: Workshop on Probabilistic Graphical Models
status: public
title: Multi-Scale Motif Discovery in Image Processing
type: conference_abstract
user_id: '68554'
year: 2015
...
---
_id: '2165'
author:
- first_name: Sahar
full_name: Deppe, Sahar
id: '52121'
last_name: Deppe
- first_name: Volker
full_name: Lohweg, Volker
id: '1804'
last_name: Lohweg
orcid: 0000-0002-3325-7887
citation:
ama: 'Deppe S, Lohweg V. Identification of Multi-Scale Motifs. In: 24. Workshop
Computational Intelligence. Dortmund: VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA); 2014:277-298.'
apa: 'Deppe, S., & Lohweg, V. (2014). Identification of Multi-Scale Motifs.
In 24. Workshop Computational Intelligence (pp. 277–298). Dortmund: VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA).'
bjps: 'Deppe S and Lohweg V (2014) Identification of Multi-Scale Motifs.
24. Workshop Computational Intelligence. Dortmund: VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA), pp. 277–298.'
chicago: 'Deppe, Sahar, and Volker Lohweg. “Identification of Multi-Scale Motifs.”
In 24. Workshop Computational Intelligence, 277–98. Dortmund: VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA), 2014.'
chicago-de: 'Deppe, Sahar und Volker Lohweg. 2014. Identification of Multi-Scale
Motifs. In: 24. Workshop Computational Intelligence, 277–298. Dortmund:
VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA).'
din1505-2-1: 'Deppe, Sahar ; Lohweg, Volker: Identification of Multi-Scale
Motifs. In: 24. Workshop Computational Intelligence. Dortmund : VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA), 2014, S. 277–298'
havard: 'S. Deppe, V. Lohweg, Identification of Multi-Scale Motifs, in: 24. Workshop
Computational Intelligence, VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA), Dortmund, 2014: pp. 277–298.'
ieee: S. Deppe and V. Lohweg, “Identification of Multi-Scale Motifs,” in 24.
Workshop Computational Intelligence, 2014, pp. 277–298.
mla: Deppe, Sahar, and Volker Lohweg. “Identification of Multi-Scale Motifs.” 24.
Workshop Computational Intelligence, VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA), 2014, pp. 277–98.
short: 'S. Deppe, V. Lohweg, in: 24. Workshop Computational Intelligence, VDI/VDE-Gesellschaft
Mess- und Automatisierungstechnik (GMA), Dortmund, 2014, pp. 277–298.'
ufg: 'Deppe, Sahar/Lohweg, Volker (2014): Identification of Multi-Scale Motifs,
in: 24. Workshop Computational Intelligence, Dortmund, S. 277–298.'
van: 'Deppe S, Lohweg V. Identification of Multi-Scale Motifs. In: 24 Workshop Computational
Intelligence. Dortmund: VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik
(GMA); 2014. p. 277–98.'
date_created: 2019-12-04T10:25:35Z
date_updated: 2023-03-15T13:49:39Z
department:
- _id: DEP5023
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://publikationen.bibliothek.kit.edu/1000043427/3301103
oa: '1'
page: 277-298
place: Dortmund
publication: 24. Workshop Computational Intelligence
publication_identifier:
isbn:
- 978-3-7315-0275-3
publication_status: published
publisher: VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA)
status: public
title: Identification of Multi-Scale Motifs
type: conference
user_id: '45673'
year: 2014
...