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Evaluating the Impact of Model Accuracy for Optimizing Battery Energy Storage Systems
No full textThis study investigates two models of varying complexity for optimizing intraday arbitrage energy trading of a battery energy storage system using a model predictive control approach. Scenarios reflecting different stages of the system’s life-time are analyzed. The findings demonstrate that the equivalent-circuit-model-based non-linear optimization model outperforms the simpler linear model by delivering more accurate predictions of energy losses and system capabilities. This enhanced accuracy enables improved operational strategies, resulting in increased roundtrip efficiency and revenue, particularly in systems with batteries exhibiting high internal resistance, such as second-life batteries. However, to fully leverage the model’s benefits, it is essential to identify the correct parameters
Beyond choice architecture: toward understanding the role of the decoy effect in sustainable tourism recommender systems
No full textThe increasing strain of overtourism on popular destinations calls for data-driven strategies that can subtly influence tourist behavior without limiting freedom of choice. This study investigates the use of digital nudges, particularly the decoy effect, within recommender systems to guide visitor flows spatially or temporally. Through a series of focus group-based experiments, this study examines how introducing asymmetrically dominated options can alter destination preferences. The results suggest that spatial steering benefits more consistently from decoy-based interventions than temporal steering. Furthermore, the findings reveal that while nudging has a measurable impact, individual factors and personal preferences play a substantial role in shaping decisions. The study underscores the value of behaviorally informed recommender systems as tools for promoting sustainable tourism by facilitating more balanced visitor flows. It concludes with a call for further development of adaptive, user-aware digital strategies to address the multifaceted nature of tourist decision-making
Chapter 4 Understanding Crowding Dynamics: The Relationship Between Subjective Crowding and Objective Visitor Numbers in Touristic City Centers
No full textCrowding ist ein häufig diskutiertes Thema im Tourismus, das sowohl Gäste als auch Bewohner/innen von touristischen Stadtzentren betrifft. Crowding als subjektives Konstrukt wird hierbei zunächst eingehend beleuchtet, indem auf beeinflussende Faktoren sowie negative und positive Crowding-Erscheinungen Rücksicht genommen wird. Weiterführend wird in dieser Studie dann die Beziehung zwischen subjektivem Crowding und tatsächlichen Besucherzahlen in der Stadt Füssen im Allgäu untersucht, indem mittels Feldforschung vor Ort das Zusammenspiel objektiver Zähldaten und subjektivem Crowding-Empfinden untersucht wird. Es wird ein mixed-methods Ansatz verwendet, der subjektives Feedback von Gästen über Terminals mit objektiven Zählungen von Kamerasensoren kombiniert. Zusätzlich werden externe Faktoren wie Wetter und Feiertage berücksichtigt, um deren Einfluss auf die Wahrnehnung von Crowding zu untersuchen. Durch die Identifizierung von Mustern in der Crowding Wahrnehmung mithilfe von SHAP Values zielt diese Studie darauf ab, die Forschung in diesem Feld um neue Erkenntnisse eines innovativen Forschungsansatzes zu bereichern sowie das Management von Besucherströmen voranzutreiben. Damit sollen sowohl das touristische Erlebnis als auch die Lebensqualität der Bewohner/innen in touristisch geprägten Stadtzentren verbessert werden.Crowding is a frequently discussed issue in tourism, impacting both visitors and residents of touristic city centers and beyond. Initially, crowding as a subjective construct is examined in detail by taking into account influencing factors as well as negative and positive crowding phenomena. Subsequently, this study examines the relationship between subjective crowding and actual visitor numbers in the town of Füssen in the Allgäu region by investigating the interplay between objective counting data and subjective crowding perceptions with on-site field research. The research employs a mixed-methods approach, combining subjective feedback from visitors via terminals with objective counts from camera sensors. In addition, external factors such as weather and public holidays are taken into account in order to investigate their influence on the perception of crowding using SHAP values. By identifying patterns in crowding perceptions, this study aims to enrich research in this field with new insights from an innovative research approach and to advance the management of visitor flows. Thereby, both tourist experiences and residents’ quality of life in touristic city centers should be enhanced
Standardized Testing of Sensor-Based Object Detection for Autonomous Agricultural Machinery
No full textThe validation of autonomous functions in agricultural machinery faces unique challenges due to highly variable environmental conditions, task diversity, and the open-world nature of agricultural fields. While safety-critical system development in other domains – especially automotive – has led to mature and widely adopted standards, the agricultural domain still lacks a standardized framework for evaluating the performance of sensor systems, particularly for object detection. The aim of this paper is to examine how testing methods and frameworks from adjacent domains, particularly the automotive and industrial sectors, can be adapted and transferred to the agricultural context. The study draws on existing literature and early findings, using a machine specific agricultural operational design domain to describe a procedural concept for deriving test cases for each function and test level
rSRD: An R package for the Sum of Ranking Differences statistical procedure
No full textSum of Ranking Differences (SRD) is a relatively novel, non-para-metric statistical procedure that has become increasingly popular recently. SRD compares solutions via a reference by applying a rank transformation on the input and calculating the distance from the reference in L1 norm. Although the computation of the test statistics is simple, validating the results is cumbersome -- at least by hand. There are two validation steps involved. Comparison of Ranks with Random Numbers, which is a permutation-test, and cross-validation combined with statistical testing. Both options impose computational difficulties albeit different ones. The rSRD package was devised to simplify the validation process by reducing both validation steps into single function calls. In addition, the package provides various useful tools including data preprocessing and plotting. The package makes SRD accessible to a wide audience as there are currently no other software options with such a comprehensive toolkit. This paper aims to serve as a guide for practitioners by offering a detailed presentation of the features
How to Select Algorithms for Predictive Maintenance: An Economic Decision Model and Real-world Instantiation
No full textPredictive maintenance represents a promising application of Artificial Intelligence in the industrial context. The evaluation and selection of predictive maintenance algorithms primarily rely on statistical measures such as absolute and relative prediction errors. However, a purely statistical approach to algorithm selection may not necessarily lead to the optimal economic outcome, as the two types of prediction errors are negatively correlated, thus, cannot be jointly optimized, and are associated with different costs. As the current literature lacks corresponding guidance, we developed a decision model for industrial full-service providers, applying an economic perspective to selecting predictive maintenance algorithms. The decision model was instantiated and evaluated in a real-world setting with a European machinery company providing full-service solutions in the field of car wash systems. Building on sensor data from 4.9 million car wash cycles, the instantiation demonstrates the applicability and effectiveness of the decision model with fidelity to a real-world phenomenon. In sum, the decision model provides economic insights into the trade-off between the algorithms’ error types and enables users to focus on economic concerns in algorithm selection. Our work contributes to the prescriptive knowledge of algorithm selection and predictive maintenance in line with the consideration of different types of cost
Empowering Sustainable Hotels: A Guest-Centric Optimization for Vehicle-To-Building Integration
No full textIn light of global warming, hotels account for one of the highest energy demands within the building sector, offering great decarbonization potential. As electrification increases, so does the demand for electric vehicles (EVs) charging stations at hotels and the proportion of Vehicle-to-Building-capable EVs. Therefore, the study explores the potential of guest-centric energy management. To accomplish this, we develop an optimization model for an energy management system that focuses on either cost-efficiency or carbon dioxide equivalents (CO2)-efficiency, grounded in a real-world case study. Through scenario analyses considering seasons as well as different guest mobility behaviors, this study discusses the expenses associated with CO2 savings using digital solutions. It emphasizes the currently perceived conflict between cost reduction and decarbonization goals to achieve a sustainable design of information systems. Thereby, this study highlights the critical importance of individual mobility behavior in enabling sustainable energy management for hotels
Pneumatische Messtechnik zur Inline-Verschleißermittlung bei Stanzprozessen
No full textEs werden kurz die Grundlagen der pneumatischen Messtechnik beschrieben und darauf aufbauend die Überlegungen für den Aufbau eines Analogieprüfstandes beschrieben. Die Ergebnisse dezidierter wissenschaftlicher Untersuchungen werden vorgestellt und diskutiert
Die Transformation des B2B Social Selling durch Künstliche Intelligenz
No full textDas Dokument analysiert die Rolle von Social Selling im B2B-Vertrieb und untersucht, wie künstliche Intelligenz (KI) diesen Bereich transformiert. Es beschreibt die Grundlagen des Social Sellings, definiert es als beziehungsorientierte Nutzung sozialer Medien und hebt LinkedIn als zentrale Plattform hervor. Zusätzlich werden spezifische Anwendungen von KI wie Social Listening, Lead-Identifikation und personalisierte Kommunikation vorgestellt. Das Arbeitspaper zeigt die Vorteile, Herausforderungen und ethischen Aspekte der Integration von KI in den Vertriebsprozess auf und schließt mit praktischen Empfehlungen und einem Ausblick auf die zunehmende Bedeutung von KI im B2B-Vertrieb
Cycle Time Measurement Using AI-Based Object Detection and Tracking in Industrial Processes
No full textThis paper presents an AI-based system for improving cycle time measurement in industrial environments, leveraging YOLOv8 for object detection and ByteTrack for tracking. Our non-invasive approach analyzes video from an Azure Kinect camera to calculate cycle times by detecting objects and monitoring their state changes. Tested at the University of Applied Sciences Kempten’s demo plant, the system showcased high accuracy against ground truth data, highlighting its potential to enhance production line monitoring and efficiency significantly. This work contributes to industrial automation by offering a real-time, accurate method for cycle time analysis, promising substantial advancements in manufacturing process optimization