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Online grocery shopping (OGS) has significantly risen due to accelerated retail digitization and reshaped consumer shopping behaviors over the last years. Despite this trend, the German online grocery market lags behind its international counterparts. Notably, with almost half of the German population aged over 50 and the 55–64 age group emerging as the largest user segment in e-commerce, the over-50 demographic presents an attractive yet relatively overlooked audience for the expansion of the online grocery market. However, research on OGS behavior among German over-50s is scarce. This study addresses this gap, empirically investigating OGS adoption factors within this demographic through an online survey with 179 respondents. Our findings reveal that over a third of the over-50 demographic has embraced OGS, indicating a growing receptivity for OGS among the over-50s. Notably, home delivery, product variety, convenience, and curiosity emerged as primary drivers for OGS adoption among this demographic. Surprisingly, most adopters did not increase online grocery orders since 2020 and a not inconsiderable proportion have even stopped buying groceries online again. For potential OGS adopters, regional product availability turned out as a motivator, signaling substantial growth potential and providing online grocers with strategic opportunities to target this demographic. In light of our research, we offer practical suggestions to online grocery retailers, aiming to overcome barriers and capitalize on key drivers identified in our study for sustained growth in the over-50 market segment.
Der Online-Handel verzeichnet seit Jahren ein stetiges Wachstum. Durch die COVID-19-Pandemie kaufen nun auch Nutzende, die zuvor physische Kanäle bevorzugten, vermehrt online ein. Der Anbietererfolg hängt dabei wesentlich von der Kenntnis über die Kund*innen ab. Allerdings dominieren einige große Anbieter den Markt, während kleinere Online-Shops Schwierigkeiten haben, ihre Angebote zu personalisieren. Eine Lösung bietet der Ansatz selbstbestimmter Identitäten. Dieser ermöglicht Kund*innen, ihre eigenen Shoppingdaten zu kontrollieren und sie selektiv mit Online-Shops zu teilen. Dadurch können individuelle Wünsche und Anforderungen der Kund*innen in Online-Shops berücksichtigt und ein personalisiertes Angebot sowie eine gute Nutzungserfahrung geboten werden. Trotz des großen Potenzials selbstbestimmter Identitäten ist der Ansatz in Deutschland kaum verbreitet. Dieser Beitrag beleuchtet den Einsatz selbstbestimmter Identitäten im Online-Handel. Mithilfe eines menschenzentrierten Gestaltungsprozesses wurden Personas und Ist-Szenarien erstellt, sowie daraus resultierend Anforderungen erhoben und Potenziale identifiziert. Auf Basis dessen konnte ein Daten- und Architekturmodell zur Integration von selbstbestimmten Identitäten im Online-Handel entwickelt werden.
Design and Implementation of a Camera-Based Tracking System for MAV Using Deep Learning Algorithms
(2023)
In recent years, the advancement of micro-aerial vehicles has been rapid, leading to their widespread utilization across various domains due to their adaptability and efficiency. This research paper focuses on the development of a camera-based tracking system specifically designed for low-cost drones. The primary objective of this study is to build up a system capable of detecting objects and locating them on a map in real time. Detection and positioning are achieved solely through the utilization of the drone’s camera and sensors. To accomplish this goal, several deep learning algorithms are assessed and adopted because of their suitability with the system. Object detection is based upon a single-shot detector architecture chosen for maximum computation speed, and the tracking is based upon the combination of deep neural-network-based features combined with an efficient sorting strategy. Subsequently, the developed system is evaluated using diverse metrics to determine its performance for detection and tracking. To further validate the approach, the system is employed in the real world to show its possible deployment. For this, two distinct scenarios were chosen to adjust the algorithms and system setup: a search and rescue scenario with user interaction and precise geolocalization of missing objects, and a livestock control scenario, showing the capability of surveying individual members and keeping track of number and area. The results demonstrate that the system is capable of operating in real time, and the evaluation verifies that the implemented system enables precise and reliable determination of detected object positions. The ablation studies prove that object identification through small variations in phenotypes is feasible with our approach.
An in-depth study of U-net for seismic data conditioning: Multiple removal by moveout discrimination
(2024)
Seismic processing often involves suppressing multiples that are an inherent component of collected seismic data. Elaborate multiple prediction and subtraction schemes such as surface-related multiple removal have become standard in industry workflows. In cases of limited spatial sampling, low signal-to-noise ratio, or conservative subtraction of the predicted multiples, the processed data frequently suffer from residual multiples. To tackle these artifacts in the postmigration domain, practitioners often rely on Radon transform-based algorithms. However, such traditional approaches are both time-consuming and parameter dependent, making them relatively complex. In this work, we present a deep learning-based alternative that provides competitive results, while reducing the complexity of its usage, and, hence simplifying its applicability. Our proposed model demonstrates excellent performance when applied to complex field data, despite it being exclusively trained on synthetic data. Furthermore, extensive experiments show that our method can preserve the inherent characteristics of the data, avoiding undesired oversmoothed results, while removing the multiples from seismic offset or angle gathers. Finally, we conduct an in-depth analysis of the model, where we pinpoint the effects of the main hyperparameters on real data inference, and we probabilistically assess its performance from a Bayesian perspective. In this study, we put particular emphasis on helping the user reveal the inner workings of the neural network and attempt to unbox the model.
Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In the past years, there has been a remarkable increase of machine-learning-based solutions that have addressed the aforementioned issues. In particular, deep-learning practitioners have usually relied on heavily fine-tuned, customized discriminative algorithms. Although, these methods can provide solid results, they seem to lack semantic understanding of the provided data. Motivated by this limitation, in this work, we employ a generative solution, as it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for three seismic applications: demultiple, denoising and interpolation. To that end, we run experiments on synthetic and on real data, and we compare the diffusion performance with standardized algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.
Maschinelles Lernen verändert zusehends die Arbeitswelt. Auch in der Produktionsplanung und -steuerung finden sich vielversprechende Anwendungsfälle. In diesem Beitrag sollen ausgewählte Anwendungsbereiche und Ansätze vorgestellt werden, die anhand einer umfangreichen Untersuchung wissenschaftlicher Veröffentlichungen identifiziert wurden. Als Strukturierungshilfe dient das Aachener PPS-Modell.
The present work ties in with the problem of bicycle road assessment that is currently done using expensive special measuring vehicles. Our alternative approach for road condition assessment is to mount a sensor device on a bicycle which sends accelerometer and gyroscope data via WiFi to a classification server. There, a prediction model determines road type and condition based on the sensor data. For the classification task, we compare different machine learning methods with each other, whereby validation accuracies of 99% can be achieved with deep residual networks such as InceptionTime. The main contribution of this work with respect to comparable work is that we achieve excellent accuracies on a realistic dataset classifying road conditions into nine distinct classes that are highly relevant for practice.