@article{UlbrichtMohrAltenburgetal.2021, author = {Ulbricht, Alexander and Mohr, Gunther and Altenburg, Simon J. and Oster, Simon and Maierhofer, Christiane and Bruno, Giovanni}, title = {Can potential defects in LPBF be healed from the laser exposure of subsequent layers?}, series = {Metals : open access journal}, volume = {11}, journal = {Metals : open access journal}, number = {7}, publisher = {MDPI}, address = {Basel}, issn = {2075-4701}, doi = {10.3390/met11071012}, pages = {14}, year = {2021}, abstract = {Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 mu m) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.}, language = {en} } @phdthesis{Ohrnberger2001, author = {Ohrnberger, Matthias}, title = {Continuous automatic classification of seismic signals of volcanic origin at Mt. Merapi, Java, Indonesia}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0000028}, school = {Universit{\"a}t Potsdam}, year = {2001}, abstract = {Aufgrund seiner nahezu kontinuierlichen eruptiven Aktivit{\"a}t z{\"a}hlt der Merapi zu den gef{\"a}hrlichsten Vulkanen der Welt. Der Merapi befindet sich im Zentralteil der dicht bev{\"o}lkerten Insel Java (Indonesien). Selbst kleinere Ausbr{\"u}che des Merapi stellen deswegen eine große Gefahr f{\"u}r die ans{\"a}ssige Bev{\"o}lkerung in der Umgebung des Vulkans dar. Die am Merapi beobachtete enge Korrelation zwischen seismischer und vulkanischer Aktivit{\"a}t erlaubt es, mit Hilfe der {\"U}berwachung der seismischen Aktivit{\"a}t Ver{\"a}nderungen des Aktivit{\"a}tszustandes des Merapi zu erkennen. Ein System zur automatischen Detektion und Klassifizierung seismischer Ereignisse liefert einen wichtigen Beitrag f{\"u}r die schnelle Analyse der seismischen Aktivit{\"a}t. Im Falle eines bevorstehenden Ausbruchszyklus bedeutet dies ein wichtiges Hilfsmittel f{\"u}r die vor Ort ans{\"a}ssigen Wissenschaftler. In der vorliegenden Arbeit wird ein Mustererkennungsverfahren verwendet, um die Detektion und Klassifizierung seismischer Signale vulkanischen Urprunges aus den kontinuierlich aufgezeichneten Daten in Echtzeit zu bewerkstelligen. Der hier verwendete A nsatz der hidden Markov Modelle (HMM) wird motiviert durch die große {\"A}hnlichkeit von seismischen Signalen vulkanischen Ursprunges und Sprachaufzeichnungen und den großen Erfolg, den HMM-basierte Erkennungssysteme in der automatischen Spracherkennung erlangt haben. F{\"u}r eine erfolgreiche Implementierung eines Mustererkennungssytems ist es notwendig, eine geeignete Parametrisierung der Rohdaten vorzunehmen. Basierend auf den Erfahrungswerten seismologischer Observatorien wird ein Vorgehen zur Parametrisierung des seismischen Wellenfeldes auf Grundlage von robusten Analyseverfahren vorgeschlagen. Die Wellenfeldparameter werden pro Zeitschritt in einen reell-wertigen Mustervektor zusammengefasst. Die aus diesen Mustervektoren gebildete Zeitreihe ist dann Gegenstand des HMM-basierten Erkennungssystems. Um diskrete hidden Markov Modelle (DHMM) verwenden zu k{\"o}nnen, werden die Mustervektoren durch eine lineare Transformation und nachgeschaltete Vektor Quantisierung in eine diskrete Symbolsequenz {\"u}berf{\"u}hrt. Als Klassifikator kommt eine Maximum-Likelihood Testfunktion zwischen dieser Sequenz und den, in einem {\"u}berwachten Lernverfahren trainierten, DHMMs zum Einsatz. Die am Merapi kontinuierlich aufgezeichneten seismischen Daten im Zeitraum vom 01.07. und 05.07.1998 sind besonders f{\"u}r einen Test dieses Klassifikationssystems geeignet. In dieser Zeit zeigte der Merapi einen rapiden Anstieg der Seismizit{\"a}t kurz bevor dem Auftreten zweier Eruptionen am 10.07. und 19.07.1998. Drei der bekannten, vom Vulkanologischen Dienst in Indonesien beschriebenen, seimischen Signalklassen konnten in diesem Zeitraum beobachtet werden. Es handelt sich hierbei um flache vulkanisch-tektonische Beben (VTB, h < 2.5 km), um sogenannte MP-Ereignisse, die in direktem Zusammenhang mit dem Wachstum des aktiven Lavadoms gebracht werden, und um seismische Ereignisse, die durch Gesteinslawinen erzeugt werden (lokaler Name: Guguran). Die spezielle Geometrie des digitalen seismischen Netzwerkes am Merapi besteht aus einer Kombination von drei Mini-Arrays an den Flanken des Merapi. F{\"u}r die Parametrisierung des Wellenfeldes werden deswegen seismische Array-Verfahren eingesetzt. Die individuellen Wellenfeld Parameter wurden hinsichtlich ihrer Relevanz f{\"u}r den Klassifikationsprozess detailliert analysiert. F{\"u}r jede der drei Signalklassen wurde ein Satz von DHMMs trainiert. Zus{\"a}tzlich wurden als Ausschlussklassen noch zwei Gruppen von Noise-Modellen unterschieden. Insgesamt konnte mit diesem Ansatz eine Erkennungsrate von 67 \% erreicht werden. Im Mittel erzeugte das automatische Klassifizierungssystem 41 Fehlalarme pro Tag und Klasse. Die G{\"u}te der Klassifikationsergebnisse zeigt starke Variationen zwischen den individuellen Signalklassen. Flache vulkanisch-tektonische Beben (VTB) zeigen sehr ausgepr{\"a}gte Wellenfeldeigenschaften und, zumindest im untersuchten Zeitraum, sehr stabile Zeitmuster der individuellen Wellenfeldparameter. Das DHMM-basierte Klassifizierungssystem erlaubte f{\"u}r diesen Ereignistyp nahezu 89\% richtige Entscheidungen und erzeugte im Mittel 2 Fehlalarme pro Tag. Ereignisse der Klassen MP und Guguran sind mit dem automatischen System schwieriger zu erkennen. 64\% aller MP-Ereignisse und 74\% aller Guguran-Ereignisse wurden korrekt erkannt. Im Mittel kam es bei MP-Ereignissen zu 87 Fehlalarmen und bei Guguran Ereignissen zu 33 Fehlalarmen pro Tag. Eine Vielzahl der Fehlalarme und nicht detektierten Ereignisse entstehen jedoch durch eine Verwechslung dieser beiden Signalklassen im automatischen Erkennnungsprozess. Dieses Ergebnis konnte aufgrund der {\"a}hnlichen Wellenfeldeigenschaften beider Signalklassen erkl{\"a}rt werden, deren Ursache vermutlich in den bekannt starken Einfl{\"u}ssen des Mediums entlang des Wellenausbreitungsweges in vulkanischen Gebieten liegen. Insgesamt ist die Erkennungsleistung des entwickelten automatischen Klassifizierungssystems als sehr vielversprechend einzustufen. Im Gegensatz zu Standardverfahren, bei denen in der Seismologie {\"u}blicherweise nur der Startzeitpunkt eines seismischen Ereignisses detektiert wird, werden in dem untersuchten Verfahren seismische Ereignisse in ihrer Gesamtheit erfasst und zudem im selben Schritt bereits klassifiziert.}, language = {en} } @article{SchirrmannLandwehrGiebeletal.2021, author = {Schirrmann, Michael and Landwehr, Niels and Giebel, Antje and Garz, Andreas and Dammer, Karl-Heinz}, title = {Early detection of stripe rust in winter wheat using deep residual neural networks}, series = {Frontiers in plant science : FPLS}, volume = {12}, journal = {Frontiers in plant science : FPLS}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-462X}, doi = {10.3389/fpls.2021.469689}, pages = {14}, year = {2021}, abstract = {Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 x 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90\%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77\%. Even in a stage with very low disease spreading (0.5\%) at the very beginning of the Pst outbreak, a detection accuracy of 57\% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4\% of Pst disease spreading, detection accuracy with 76\% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.}, language = {en} } @article{PrieskeChaabeneKullmannetal.2022, author = {Prieske, Olaf and Chaabene, Helmi and Kullmann, Niclas and Granacher, Urs}, title = {Effects of Individualized Versus Traditional Power Training on Strength, Power, Jump Performances, and Body Composition in Young Male Nordic Athletes}, series = {International journal of sports physiology and performance}, volume = {17}, journal = {International journal of sports physiology and performance}, number = {4}, publisher = {Human Kinetics Publ.}, address = {Champaign}, issn = {1555-0265}, doi = {10.1123/ijspp.2021-0074}, pages = {541 -- 548}, year = {2022}, abstract = {Purpose: This study aimed to examine the effects of individualized-load power training (IPT) versus traditional moderate-load power training (TPT) on strength, power, jump performance, and body composition in elite young Nordic athletes. Methods: In a randomized crossover design, 10 young male athletes (ski jumpers, Nordic combined athletes) age 17.5 (0.6) years (biological maturity status: +3.5 y postpeak height velocity) who competed on a national or international level performed 5 weeks of IPT (4 x 5 repetitions at 49\%-72\% 1-repetiton maximum [RM]) and TPT (5 x 5 repetitions at 50\%-60\% 1-RM) in addition to their regular training. Testing before, between, and after both training blocks comprised the assessment of muscle strength (loaded back squat 3-RM), power (maximal loaded back squat power), jump performance (eg, drop-jump height, reactive strength index), and body composition (eg, skeletal muscle mass). Results: Significant, large-size main effects for time were found for muscle strength (P < .01; g = 2.7), reactive strength index (P = .03; g= 1.6), and drop jump height (P = .02; g= 1.9) irrespective of the training condition (IPT, TPT). No significant time-by-condition interactions were observed. For measures of body composition, no significant main effects of condition and time or time-by-condition interactions were found. Conclusions: Our findings demonstrate that short-term IPT and TPT at moderate loads in addition to regular training were equally effective in improving measures of muscle strength (loaded back squat 3-RM) and vertical jump performance (reactive strength index, drop jump, and height) in young Nordic athletes.}, language = {en} } @article{RaveGranacherBoullosaetal.2020, author = {Rav{\´e}, Guillaume and Granacher, Urs and Boullosa, Daniel and Hackney, Anthony C. and Zouhal, Hassane}, title = {How to Use Global Positioning Systems (GPS) Data to Monitor Training Load in the "Real World" of Elite Soccer}, series = {Frontiers in Physiology}, volume = {11}, journal = {Frontiers in Physiology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00944}, pages = {11}, year = {2020}, language = {en} } @misc{RaveGranacherBoullosaetal.2020, author = {Rav{\´e}, Guillaume and Granacher, Urs and Boullosa, Daniel and Hackney, Anthony C. and Zouhal, Hassane}, title = {How to Use Global Positioning Systems (GPS) Data to Monitor Training Load in the "Real World" of Elite Soccer}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {663}, issn = {1866-8364}, doi = {10.25932/publishup-48055}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-480558}, pages = {13}, year = {2020}, language = {en} } @article{WilhelmsBoersigYangetal.2022, author = {Wilhelms, Andre and B{\"o}rsig, Nicolas and Yang, Jingwei and Holbach, Andreas and Norra, Stefan}, title = {Insights into phytoplankton dynamics and water quality monitoring with the BIOFISH at the Elbe River, Germany}, series = {Water}, volume = {14}, journal = {Water}, number = {13}, publisher = {MDPI}, address = {Basel}, issn = {2073-4441}, doi = {10.3390/w14132078}, pages = {20}, year = {2022}, abstract = {Understanding the key factors influencing the water quality of large river systems forms an important basis for the assessment and protection of cross-regional ecosystems and the implementation of adapted water management concepts. However, identifying these factors requires in-depth comprehension of the unique environmental systems, which can only be achieved by detailed water quality monitoring. Within the scope of the joint science and sports event "Elbschwimmstaffel" (swimming relay on the river Elbe) in June/July 2017 organized by the German Ministry of Education and Research, water quality data were acquired along a 550 km long stretch of the Elbe River in Germany. During the survey, eight physiochemical water quality parameters were recorded in high spatial and temporal resolution with the BIOFISH multisensor system. Multivariate statistical methods were applied to identify and delineate processes influencing the water quality. The BIOFISH dataset revealed that phytoplankton activity has a major impact on the water quality of the Elbe River in the summer months. The results suggest that phytoplankton biomass constitutes a substantial proportion of the suspended particles and that photosynthetic activity of phytoplankton is closely related to significant temporal changes in pH and oxygen saturation. An evaluation of the BIOFISH data based on the combination of statistical analysis with weather and discharge data shows that the hydrological and meteorological history of the sampled water body was the main driver of phytoplankton dynamics. This study demonstrates the capacity of longitudinal river surveys with the BIOFISH or similar systems for water quality assessment, the identification of pollution sources and their utilization for online in situ monitoring of rivers.}, language = {en} } @misc{CoppalleRaveMoranetal.2021, author = {Coppalle, Sullivan and Rav{\´e}, Guillaume and Moran, Jason and Salhi, Iyed and Ben Abderrahman, Abderraouf and Zouita, Sghaeir and Granacher, Urs and Zouhal, Hassane}, title = {Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period}, series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe}, number = {694}, issn = {1866-8364}, doi = {10.25932/publishup-48974}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-489745}, pages = {12}, year = {2021}, abstract = {This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players' RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg's 0-10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12-15.9 km/h; 16-19.9 km/h; 20-24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.}, language = {en} } @article{CoppalleRaveMoranetal.2021, author = {Coppalle, Sullivan and Rav{\´e}, Guillaume and Moran, Jason and Salhi, Iyed and Ben Abderrahman, Abderraouf and Zouita, Sghaeir and Granacher, Urs and Zouhal, Hassane}, title = {Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period}, series = {International Journal of Environmental Research and Public Health}, volume = {18}, journal = {International Journal of Environmental Research and Public Health}, number = {2}, publisher = {MDPI AG}, address = {Basel}, issn = {1660-4601}, doi = {10.3390/ijerph18020558}, pages = {10}, year = {2021}, abstract = {This study aimed to compare the training load of a professional under-19 soccer team (U-19) to that of an elite adult team (EAT), from the same club, during the in-season period. Thirty-nine healthy soccer players were involved (EAT [n = 20]; U-19 [n = 19]) in the study which spanned four weeks. Training load (TL) was monitored as external TL, using a global positioning system (GPS), and internal TL, using a rating of perceived exertion (RPE). TL data were recorded after each training session. During soccer matches, players' RPEs were recorded. The internal TL was quantified daily by means of the session rating of perceived exertion (session-RPE) using Borg's 0-10 scale. For GPS data, the selected running speed intensities (over 0.5 s time intervals) were 12-15.9 km/h; 16-19.9 km/h; 20-24.9 km/h; >25 km/h (sprint). Distances covered between 16 and 19.9 km/h, > 20 km/h and >25 km/h were significantly higher in U-19 compared to EAT over the course of the study (p = 0.023, d = 0.243, small; p = 0.016, d = 0.298, small; and p = 0.001, d = 0.564, small, respectively). EAT players performed significantly fewer sprints per week compared to U-19 players (p = 0.002, d = 0.526, small). RPE was significantly higher in U-19 compared to EAT (p = 0.001, d = 0.188, trivial). The external and internal measures of TL were significantly higher in the U-19 group compared to the EAT soccer players. In conclusion, the results obtained show that the training load is greater in U19 compared to EAT.}, language = {en} } @article{HeineFranckeRogassetal.2014, author = {Heine, Iris and Francke, Till and Rogass, Christian and Medeiros, Pedro Henrique Augusto and Bronstert, Axel and F{\"o}rster, Saskia}, title = {Monitoring seasonal changes in the water surface areas of reservoirs using TerraSAR-X time series data in semiarid northeastern Brazil}, series = {IEEE journal of selected topics in applied earth observations and remote sensing}, volume = {7}, journal = {IEEE journal of selected topics in applied earth observations and remote sensing}, number = {8}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {1939-1404}, doi = {10.1109/JSTARS.2014.2323819}, pages = {3190 -- 3199}, year = {2014}, abstract = {The 933 km(2) Bengue catchment in northeastern Brazil is characterized by distinct rainy and dry seasons. Precipitation is stored in variously sized reservoirs, which is essential for the local population. In this study, we used TerraSAR-X SM(HH) data for an one-year monitoring of seasonal changes in the reservoir areas from July 2011 to July 2012. The monitoring was based on acquisitions in the ascending pass direction, complemented by occasional descending-pass images. To detect water surface areas, a histogram analysis followed by a global threshold classification was performed, and the results were validated using in situ GPS data. Distinguishing between small reservoirs and similar looking dark areas was difficult. Therefore, we tested several approaches for identifying misclassified areas. An analysis of the surface area dynamics of the reservoirs indicated high spatial and temporal heterogeneities and a large decrease in the total water surface area of the reservoirs in the catchment by approximately 30\% within one year.}, language = {en} }