15 Years of Intelligent Container Research

Jedermann, Reiner; Lang, Walter

doi:10.1007/978-3-030-88662-2_11

Cited by 5 publications

(7 citation statements)

References 44 publications

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“…During these tests, we already evaluated required Internet of Things (IoT) technologies for DTs such as wireless sensors, communication gateways, and data transfer via Wi-Fi, satellite, and cellular networks. The last three field tests also included artificial ripening of bananas inside the container (Jedermann and Lang, 2021).…”

Section: Scope and Motivationmentioning

confidence: 99%

Digital twin concepts for linking live sensor data with real-time models

Jedermann

Singh

Lang

et al. 2023

J. Sens. Sens. Syst.

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Abstract. For taking most advantage of live or real-time sensor measurements, data have to be processed by a single or even by a chain of models on the fly, in contrast to earlier offline simulation solutions. This requirement can be best met by concepts developed under the general term “digital twin” (DT). The step from the Internet of Things (IoT) to a full exploitation of DT solutions entails new challenges but also provides new features, which we discuss based on our example DT solution for remote monitoring of fruit during ocean transportation. A crucial challenge is the transformation of models into an updateable format, necessary to keep the physical object and its modelled representation in sync. A basic new feature of DTs is new software solutions for easy and flexible linking of different models through a streaming platform by implementing an event-driven architecture. We demonstrate a solution for controlling model execution during multiple life cycle phases of the fruit as physical object. An evaluation of response times showed that server performance is sufficient to handle more than 100 DT instances per second.

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Section: Scope and Motivationmentioning

confidence: 99%

Digital twin concepts for linking live sensor data with real-time models

Jedermann

Singh

Lang

et al. 2023

J. Sens. Sens. Syst.

Self Cite

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“…We have worked for several years on a remote-sensing and quality-prediction system for refrigerated ocean containers with fruits [ 17 ]. We are currently attempting to enhance the system by taking the most advantage of new DT concepts and features.…”

Section: Examples For Linked Model Structuresmentioning

confidence: 99%

“…Our test scenario is based on real-life data, which we collected during earlier experiments for the sea transportation of bananas and their further processing in Europe [ 17 ]. It was more useful to play back these data for scenario testing rather than using new real-world live data by repeated tests.…”

Section: Demonstration and Testingmentioning

confidence: 99%

“…The training data must contain sufficient instances of all combinations of input factors. Otherwise, the machine learning overfits to single deviating events [ 17 ]. Physical modelling, e.g., computational fluid dynamics [ 15 ], are less demanding in regard to the size of training data.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we describe the steps that were necessary to develop our new concept of generic wrapper functions. A set of models for the monitoring of cool chain processes was developed in our earlier research [ 17 ], including models for temperature-related quality changes, analysis, and prediction of temperature changes over time, and estimation of heat production of fruits during ripening as ‘hidden’ or not-directly-measurable state.…”

Section: Introductionmentioning

confidence: 99%

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Wrapper Functions for Integrating Mathematical Models into Digital Twin Event Processing

Jedermann

Lang

2022

Sensors

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Analog sensors often require complex mathematical models for data analysis. Digital twins (DTs) provide platforms to display sensor data in real time but still lack generic solutions regarding how mathematical models and algorithms can be integrated. Based on previous tests for monitoring and predicting banana fruit quality along the cool chain, we demonstrate how a system of multiple models can be converted into a DT. Our new approach provides a set of generic “wrapper functions”, which largely simplify model integration. The wrappers connect the in- and outputs of models to the streaming platform and, thus, require only minor changes to the model software. Different scenarios for model linking structures are considered, including simultaneous processing of multiple models, sequential processing of life-cycle-specific models, and predictive models, based on data from the current and previous life cycles. The wrapper functions can be easily adapted to host models or microservices from various applications fields, to predict the future system behavior and to test what-if scenarios.

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