357 research outputs found
Bereiding van benzoëzuur door katalytische oxidatie van tolueen in de vloeistoffase
No full textDocument(en) uit de collectie Chemische ProcestechnologieDelftChemTechApplied Science
Supplementary Data from Prognostic Value of Residual Disease after Neoadjuvant Therapy in HER2-Positive Breast Cancer Evaluated by Residual Cancer Burden, Neoadjuvant Response Index, and Neo-Bioscore
No full textSupplementary data to Steenbruggen TG, van Seijen M, Janssen LM, van Ramshorst MS, van Werkhoven E, Vrancken Peeters MJTDF, et al. Prognostic value of residual disease after neoadjuvant therapy in HER2-positive breast cancer evaluated by Residual Cancer Burden, Neoadjuvant Response Index & Neo-Bioscore. Table S1, Table S2, Table S3, Table S4, Text and legend Figure S1</p
Comparison of ecosystem-scale carbon fluxes at agroforestry and adjacent monocropping sites in Germany
No full textSimultaneous pelvic exenteration and liver resection for primary rectal cancer with synchronous liver metastases: results from the PelvEx Collaborative
No full textEditor's ChoiceAbstract not availableGabrielle van Ramshors
Comparison between lower-cost and conventional eddy covariance setups for CO2 and evapotranspiration measurements above monocropping and agroforestry systems
No full textNovel, lower-cost setups of eddy covariance systems may offer a potential solution to the spatial replication problem of single flux towers. Prior to their widespread application, it is essential to conduct comprehensive testing against conventional eddy covariance setups to ensure the accuracy and precision of the measurements. In this study, we performed a comparison between three lower-cost eddy covariance setups based on lower-cost (approximately 33 % of the cost of a conventional infrared gas analyzer) slow-response carbon dioxide (CO2) and relative humidity (RH) sensors and a conventional eddy covariance setup for measuring carbon dioxide and evapotranspiration (ET) fluxes above a monocropping agricultural site in Northern Germany. The fluxes measured by these setups were further compared with a fourth lower-cost eddy covariance setup in an adjacent agroforestry field. The three lower-cost setups demonstrated satisfactory agreement with the conventional eddy covariance setup, with the slopes of the linear regression models for the 30-min flux time series ranging from 0.95 to 1.05 (R2 from 0.88 to 0.92) for CO2 fluxes and from 0.78 to 0.99 (R2 from 0.7 to 0.85) for latent heat (LE) fluxes. All lower-cost setups reproduced well diel and seasonal CO2 flux and ET dynamics. Furthermore, the lower-cost eddy covariance setups were able to measure ecosystem differences between agroforestry and monocropping, with differences in fluxes between both land uses being higher than differences between different setups. Despite the necessity for enhanced spectral corrections and the higher uncertainty associated with the lower-cost setups, the findings of this study illustrate the potential of these lower-cost setups to validate and replicate conventional eddy covariance setups, thereby enhancing the spatial representativity of measurements of energy, trace gases and momentum exchanges between terrestrial ecosystems and the atmosphere.Peer reviewe
Automatic Segmentation of Ships in Digital Images: A Deep Learning Approach
No full textKnowledge on adversaries during military missions at sea heavily influences decision making, making identification of unknown vessels an important task. Identification of surrounding vessels based on visual data offers an alternative to AIS information (Automatic Identification System), the current standard in vessel identification, which can be spoofed. One visual approach employs human expertise and manually identifies vessels guided by a ship catalog. In order to minimize or potentially eliminate human error and performance limitations, there is strong interest in developing an automated vessel classification pipeline. One such pipeline is currently being developed at TNO, capable of classifying over 500 separate classes. A crucial part of the classification pipeline is retrieving an accurate contour of a vessel from a digital image. To address this important challenge, this thesis proposes an advanced deep learning pipeline to automatically segment the vessel image into background (e.g. sky and sea) and the object of interest (a vessel). Deep learning models based on Fully Convolutional Neural Networks (FCNs) have achieved high performance on the task of semantic segmentation. Several networks such as CRF-RNN, PSPNet, DeepLab and Mask R-CNN are employed to determine a baseline performance. We will focus on identifying the cause of poor or failing segmentations and aim to construct a robust network capable of handling these challenges. By sampling disturbances, caused by ship distance and camera noise, augmented data sets are built to tune networks to input from on-site images. Additionally, experiments are done to evaluate the influence of different levels of disturbances. Previous approaches implementing the CRF-RNN network achieved top 1 and top 5 classification accuracies of 31.1% and 44.0% respectively. Employing the DeepLab network, trained to convergence on artificial noise augmented data, we report top 1 and top 5 accuracy of 68.9% and 88.8% respectively. Additionally, implementing an ensemble of classifiers, performance is increased to 73.0% and 91.7% for top 1 and top 5 accuracy respectively. This best result is comparable to the classification results with human annotated ship silhouettes. The human performance accuracy is 73.4% on top 1, and 91.3% on top 5 classification performance. Finally, we show that training on a collection of different levels of image disturbances results in a network that is robust against increasing disturbance in images, while retaining performance on clean images.Mechanical Engineering | Systems and Contro
Wind speed measurements using distributed fiber optics: a wind tunnel study
No full textNear-surface wind speed is typically only measured by point observations. The so-called Actively Heated Fiber-Optic (AHFO) technique, however, has the potential to provide high-resolution distributed observations, allowing for better understanding of different processes. However, before it can be widely used, its performance needs to be tested in a range of settings. Therefore, in this work, experimental results on this novel observational wind-probing technique are presented. We utilized a controlled wind-tunnel setup to assess both the accuracy and the precision of AHFO as well as its potential for outdoor atmospheric operation. The technique allows for wind speed characterization with a spatial resolution of 0.3 m on a 1 s time scale. The flow in the wind tunnel is varied in a controlled manner, such that the mean wind, ranges between 1 and 17 m/s. Comparison of the AHFO measurements with observations from a sonic anemometer shows a high overall correlation, ranging from 0.94-0.99. Also, both precision and accuracy are greater than 95 %. As such, it is concluded that the AHFO has potential to be employed as an outdoor observational technique in addition to existing techniques. In particular, it allows for characterization of spatial varying fields of mean wind in complex terrain, such as in canopy flows or in sloping terrain. In the future the technique could be combined with regular Distributed Temperature Sensing (DTS) for turbulent heat flux estimation in micrometeorological/hydrological applications
Lower-cost eddy covariance for CO2 and H2O fluxes over grassland and agroforestry
No full textOpen-Access-Publikationsfonds 202
Wind speed measurements using distributed fiber optics: a windtunnel study
No full textNear-surface wind speed is typically only measured by point observations. The Actively Heated Fiber-Optic (AHFO)
technique, however, has the potential to provide high-resolution distributed observations of wind speeds, allowing for better
characterization of fine-scale processes. Before AHFO can be widely used, its performance needs to be tested in a range of
settings. In this work, experimental results on this novel observational wind-probing technique are presented. We utilized a
controlled wind-tunnel setup to assess both the accuracy and the precision of AHFO under a range of operational conditions.
The technique allows for wind speed characterization with a spatial resolution of 0.3 m on a 1 s time scale. The flow in the wind
tunnel was varied in a controlled manner, such that the mean wind, ranged between 1 and 17 ms-1. The AHFO measurements
are compared to sonic anemometer measurements and show a high overall correlation (0.85-0.98). Both the precision and
accuracy of the AHFO measurements were also greater than 95%.We conclude that the AHFO has potential to be employed as an outdoor observational technique. It allows for characterization of spatially varying fields of mean wind in complex terrain,
such as in canopy flows or in sloping terrain. In the future, the technique could be combined with conventional Distributed
Temperature Sensing (DTS) for turbulent heat flux estimation in micrometeorological/hydrological applications
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