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Integrating Industry 4.0 approaches into Low Voltage Grids: A Cost-Efficient Retrofit Strategy Using IoT and Edge Computing 7

Integrating Industry 4.0 approaches into Low Voltage Grids: ...

引用

7th IEEE International Humanitarian Technologies Conference, IHTC 2024

作者： Steinhagen, Bastian Jungh, Timon Hesse, Marc Bielefeld University Cognitronics and Sensor Systems Bielefeld Germany

ISBN: (纸本)9798350354645

This paper presents a cost-efficient retrofit strategy for the digitalization of low voltage grids, leveraging the advancements of Industry 4.0 technologies such as Internet of Things (IoT) and edge computing. Facing the challenges of traditional grid management systems, like scalability, data processing, cybersecurity, and integration of renewable energies, this strategy proposes a practical solution to enhance grid observability. By implementing this approach in a local grid station and two cable distributors, we illustrate the potential for significant improvements in grid management, including real-time monitoring of power consumption and energy feedback. The findings underscore the transformative impact of digital technologies on the energy sector, offering insights into the future of grid management and the integration of renewable energy sources. This study contributes to the evolving discourse on grid digitalization, providing a framework for deploying IoT and edge computing in low voltage grids to achieve a more sustainable and efficient energy future. © 2024 IEEE.

关键词： Power grids

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A Digital Twin Implementation for the AMiRo 29

A Digital Twin Implementation for the AMiRo

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Klarhorst, Christian Quirin, Dennis Hesse, Marc Rückert, Ulrich Bielefeld University Cognitronics & Sensor Systems Bielefeld Germany

ISBN: (纸本)9798350361230

The concept of Asset Administration Shells (AAS), which enables the implementation of digital twins, is important for advancing Industry 4.0 by facilitating the standardized, machine-readable integration of diverse industrial systems. This paper presents the implementation of a Digital Twin for the Autonomous Mini Robot (AMiRo), a modular robot platform used in research and education. The Digital Twin replicates both the physical and software components of AMiRo, enabling centralized management, maintenance, and remote control. Key contributions include the practical application of Digital Twin standards, enhancing student and researcher interaction with the robots, and demonstrating the benefits of standardization in improving interoperability and reducing development efforts. © 2024 IEEE.

关键词： Modular robots

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Fine-grained prediction of cognitive workload in a modern working environment by utilizing short-term physiological parameters 9

Fine-grained prediction of cognitive workload in a modern wo...

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9th International Conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2016 - Part of 9th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2016

作者： Hörmann, Timm Hesse, Marc Christ, Peter Adams, Michael Menßen, Christian Rückert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany

ISBN: (纸本)9789897581700

In this paper we present a method to predict cognitive workload during the interaction with a tablet computer. To set up a predictor that estimates the reflected self-reported cognitive workload we analyzed the information gain of heart rate, electrodermal activity and user input (touch) based features. From the derived optimal feature set we present a Gaussian Process based learner that enables fine-grained and short term detection of cognitive workload. Average inter-subject accuracy in 10-fold cross validation is 74.1 % for the fine-grained 5-class problem and 96.0 % for the binary class problem. Copyright © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： Physiological models

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A resource-efficient multi-camera GigE vision IP core for embedded vision processing platforms

A resource-efficient multi-camera GigE vision IP core for em...

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International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015

作者： Ibraheem, Omar W. Irwansyah, Arif Hagemeyer, Jens Porrmann, Mario Rueckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany

ISBN: (纸本)9781467394062

In vision processing systems, many applications require multi-camera support. For the connection of the cameras to the processing system, multiple interfaces and a platform capable of handling sustained high data rates are essential. To cope with these requirements, a hardware-based solution using FPGA technology is advisable, especially when targeting space and energy constrained embedded systems. The aim of this work is to develop and implement an FPGA-based scalable and resourceefficient multi-camera GigE Vision IP core for video and image processing. To reduce the number of interfaces needed, the IP core supports the connection of multi-camera interfaces to a single Gigabit Ethernet port using an Ethernet switch. The multicamera GigE Vision IP core is able to extract the raw video data from multiple GigE Vision video streams, reconstruct the video frames from every camera and pass these data for further processing. To test the system, four GigE Vision cameras are used. The IP core is implemented on a Xilinx Virtex-4 FPGA and integrated in a complete video processing platform for a full system realization. In addition to the IP core, bilinear interpolation for image demosaicing with Bayer pattern and an automatic white balance algorithm are implemented for evaluation of the platform. Benchmarking of the hardware implementation has been performed with a total resolution of up to 2048x2048 pixels. Achieved frame rates vary from 25 fps to 345 fps depending on the selected resolution and on the number of used cameras. © 2015 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Resource efficient sub-VT level shifter circuit design using a hybrid topology in 28 nm 16

Resource efficient sub-VT level shifter circuit design using...

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Chatterjee, Saikat Rückert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University 33619 Germany

ISBN: (纸本)9783800755899

This paper presents a resource efficient level shifter circuit, which is capable of converting input voltages below subthreshold to above threshold voltages, making it suitable for ultra low power applications such as wireless sensor networks, biomedical implants, environmental sensors, to name a few. The proposed circuit topology has two stages. The first stage comprises of a Wilson current mirror, whereas the second stage has a cross coupled PMOS circuit. The two staged topology helps to overcome the challenges of deep nano process, when operated with ultra low input supply voltage. The circuit presented here, is implemented in 28 nm FDSOI technology from ST Microelectronics. The proposed level shifter is capable of converting an input voltage as low as 150 mV to 1 V. The static power consumption is measured to be 100 pW, when the circuit is operated with the minimum possible input supply and operational frequency of 500 kHz. © VDE VERLAG GMBH.

关键词： Topology

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Hardware accelerated real time classification of hyperspectral imaging data for coffee sorting 20th

Hardware accelerated real time classification of hyperspectr...

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20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012

作者： Backhaus, Andreas Lachmair, Jan Rückert, Ulrich Seiffert, Udo Magdeburg Germany Cognitronics and Sensor Systems University Bielefeld Bielefeld Germany

ISBN: (纸本)9782874190490

Hyperspectral imaging has been proven to be a viable tool for automated food inspection that is non-invasive and on-line capable. In this contribution a hardware implemented Self-Organizing Feature Map with Conscience (CSOM) is presented that is capable of on-line adaptation and recall in order to learn to classify green coffee varieties as well as coffee of different roast stages. The CSOM showed favourable results in some datasets compared to a number of classical supervised neural network classifiers. The massive parallel neural hardware architecture allows for constant processing times at different map sizes. © 2012, *** publication. All rights reserved.

关键词： Spectroscopy

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FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking

FPGA-based circular hough transform with graph clustering fo...

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International Conference on ReConFigurable Computing and FPGAs, ReConFig 2015

作者： Irwansyah, Arif Ibraheem, Omar W. Hagemeyer, Jens Porrmann, Mario Rueckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany

ISBN: (纸本)9781467394062

Shape-based object detection and recognition are frequently used methods in the field of computer vision. A well-known algorithm for circle detection is the Circular Hough Transform (CHT). This Hough Transform algorithm needs a huge memory space and large computational resources. Field Programmable Gate Array (FPGA)-based hardware accelerators can be used to efficiently handle such compute-intensive applications. In this paper, we present a resource-efficient FPGA-based architecture for the CHT algorithm. Additionally, we introduce a unique approach by combining the CHT algorithm with graph clustering. The combination of these algorithms and their implementation on a Xilinx Virtex-4 FPGA is used to support real-time vision-based multi-robot tracking. Furthermore, an efficient architecture is proposed to significantly reduce the required memory in the CHT module. For the Graph Clustering module, a multiplier-less distance calculation unit is implemented, significantly reducing the required FPGA resources. The proposed CHT design can handle multi-robot localization with an accuracy of 97 %, supporting a maximum video resolution of 1024x1024 with 128 frames per second, resulting in 134 MPixel/s. Our design provides significantly higher throughput compared to other implementations on embedded processors, FPGAs, and general purpose CPUs. Compared to an OpenCV implementation on a 3.2 GHz desktop CPU, our implementation achieves a speed-up of more than 5.7. © 2015 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Robust estimation of physical activity by adaptively fusing multiple parameters 12

Robust estimation of physical activity by adaptively fusing ...

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12th IEEE International Conference on Wearable and Implantable Body sensor Networks, BSN 2015

作者： Hormann, Timm Christ, Peter Hesse, Marc Ruckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld33619 Germany

ISBN: (纸本)9781467372015

Raising the awareness of being physically active by utilizing wearable body sensors has become a popular research topic. Recent approaches combine physical and physiological information to obtain a precise prediction of a person;s physical activity ratio. However, the error in the determination of physical activity due to invalid physiological values that are resulting from underlying signal disturbances, has so far not been considered. We therefore present a robust measure of activity that fuses accelerometer data, heart rate and other personalized features, and is adaptively responding to missing physiological sensor data. To set up the model, we make use of regression analysis (MARS). Our findings indicate the need for considering signal quality when estimating physical activity. The predictive model shows close agreement (R2 = 0.97) to the reference from indirect calorimetry, even if the physiological information is partly corrupted. © 2015 IEEE.

关键词： Regression analysis

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Pareto-optimal signal processing on low-power microprocessors

Pareto-optimal signal processing on low-power microprocessor...

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12th IEEE sensorS 2013 Conference

作者： Christ, Peter Sievers, Gregor Einhaus, Julian Jungeblut, Thorsten Porrmann, Mario Ruckert, Ulrich Cognitronics and Sensor Systems Group CITEC Bielefeld University 33619 Bielefeld Germany

ISBN: (纸本)9781467346405

Miniaturised wireless body sensors equipped with low-power microcontrollers are used in various energy-constrained applications. The signal-processing algorithms often require running in real-time on a low computational and memory budget. In this paper we present a framework for the exploration of the design space of resource-efficient signal processing suitable for embedded processors. Using a velocity estimation algorithm for an athlete, we show which configurations of the algorithm perform best in respect to classification accuracy and runtime. Altering the sampling frequency, the feature combination, the classifier (Artificial Neural Network (ANN), Decision Tree (DT)), or the classifier's parametrisation, we obtained 15 Pareto-optimal configurations out of 1008 simulations. The highest classification accuracy of 93.92% was obtained using an ANN, and required 22422 clock cycles per classification. The lowest cycle count of 204 was obtained with a DT configuration which resulted in 84.66 % accuracy. © 2013 IEEE.

关键词： Decision trees

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An approach for determining linear velocities of athletes from acceleration measurements using a neural network

An approach for determining linear velocities of athletes fr...

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6th IASTED International Conference on Biomechanics, BioMech 2011

作者： Christ, Peter Werner, Felix Rückert, Ulrich Mielebacher, Jörg Cognitronics and Sensor Systems Group CITEC Bielefeld University Bielefeld Germany Mielebacher Informatiksysteme Bremen Germany

ISBN: (纸本)9780889869035

With recent technology advancements, miniaturized wireless body sensor (WBS) systems equipped with accelerometers allow the recording of an athlete's actions without impact on their execution. This paper addresses the problem of determining linear velocities of athletes using a single tri-axial accelerometer located in a WBS on a chest strap. We propose using a neural network to associate features extracted from acceleration signals in time and frequency domain with particular velocities. This method neither requires a kinematic model nor information on characteristics of an athlete (e.g. height or weight). In a treadmill experiment with 20 subjects we obtain more than 97% correct classifications for velocities from 3 to 9 km/h. We demonstrate that using a set of simple features (variance, amplitude, RMS) a classification rate of more than 95% can be obtained. Misclassifications are mainly found at higher velocities (9 and 11 km/h) due to severe inter-subject differences in the acceleration signals.

关键词： Neural networks

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