| 1. |
- Carratù, Marco, et al.
(författare)
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Design and Evaluation of a Soft Sensor for Snow Weight Measurement
- 2024
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Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- Snow accumulations, especially if of great intensity, as is the case in northern countries, for example, can be very damaging, especially if they occur in urban environments. The damage provoked by snow is not only related to the weight of the accumulations, causing damage to structures but also to the pollution retained by the structure of the snowflakes. However, snow weight monitoring is a complex task, both because of the placement of the sensors and the specific operating ranges they must have in terms of operating temperature. These complications can be overcome by the design and use of a soft sensor, that is, a sensor capable of making indirect measurements from other parameters related to the measurement under consideration. This paper presents the design and metrological validation of a soft sensor for indirect weight measurement of snow accumulations. The designed soft sensor has been based on Artificial Neural Network and achieved, as a result, a Root-Mean-Square Error (RMSE) of 114g and a maximum extended uncertainty, evaluated by Monte Carlo simulation, of 300g in a measurement range from 150g to 5200g.
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| 2. |
- Carratu, M., et al.
(författare)
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Earthquake Magnitude Estimation with Single Seismic Station using Deep Learning
- 2024
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Ingår i: 2024 IEEE International Symposium on Measurements and Networking, M and N 2024 - Proceedings. - : IEEE conference proceedings. - 9798350370539
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Konferensbidrag (refereegranskat)abstract
- Prediction and early estimation of earthquake hazards have always been a subject of research. Indeed, the ability to promptly understand the energy released by a given earthquake event is of utmost importance for rapidly estimating the extent of damage to property and people. Estimating the Magnitude of an earthquake event, and thus the energy released by it, is, however, a slow process, requiring knowledge of the location of the epicenter and, therefore, necessitating the analysis of measurements from several seismic stations. The goal of this work has, hence, been to develop a model that succeeds in providing a coarse estimate, through the use of Artificial Neural Networks, of the Magnitude of a seismic event from the measurements of a single station, with the aim then of refining the estimate in a network of stations. The focus was directed toward estimating the classification uncertainty of the results based on the input measurements' uncertainty to assess the results' reliability.
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| 3. |
- Gallo, Vincenzo, et al.
(författare)
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Design and Characterization of a Powered Wheelchair Autonomous Guidance System
- 2024
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 24:5
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Tidskriftsartikel (refereegranskat)abstract
- The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver’s feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms.
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| 4. |
- Gallo, Vincenzo, et al.
(författare)
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Metrological Characterization of a Clip Fastener assembly fault detection system based on Deep Learning
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- In a time when Artificial Intelligence (AI) technologies are nearly ubiquitous, railway construction and maintenance systems have not fully grasped the capabilities of such technologies. Traditional railway inspection methods rely on inspection from experienced workers, making such tasks costly from both, the monetary and the time perspective. From an overview of the state-of-the-art research in this area regarding AI-based systems, we observed that their main focus was solely on detection accuracy of different railway components. However, if we consider the critical importance of railway fastening in the overall safety of the railway, there is a need for a thorough analysis of these AI-based methodologies, to define their uncertainty also from a metrological perspective. In this article we address this issue, proposing an image-based system that detects the rotational displacement of the fastened railway clips. Furthermore, we provide an uncertainty analysis of the measurement system, where the resulting uncertainty is of 0.42°, within the 3° error margin defined by the clip manufacturer.
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| 5. |
- Gatner, Ola, et al.
(författare)
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Method for Capturing Measured LiDAR Data with Ground Truth for Generation of Big Real LiDAR Data Sets
- 2024
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Ingår i: Conference Record - IEEE Instrumentation and Measurement Technology Conference. - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- The development of machine learning has resulted in data gaining a pivotal role in the technological advancement, especially data where the ground truth of targeted parameters can be efficiently captured. This requires the development of methods that facilitate accurate data collection with ground truth. Under this perspective, Time of Flight sensors pose a high complexity due to the multifaceted nature of noise in the captured data. To enable the use of such sensors in a wide range of applications including Artificial Intelligence, we need to provide also accurate ground truth data. In this article, we present a method for automated data capturing from a LiDAR sensor together with ground truth data generation. This method will facilitate generating big datasets from LiDAR sensors with high accuracy ground truth data. In addition, we provide a dataset that aside from depth sensor data contains also RGB, confidence and infrared data captured from the LiDAR sensor. As a result, the proposed method not only facilitates data capturing but it enables to generate accurate ground truth data, with RMSE of only 0.04 m at 1.3 m distance.
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| 6. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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A Study on the Correlation between Change in the Geometrical Dimension of a Free-Falling Molten Glass Gob and Its Viscosity
- 2022
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 22:2, s. 661-661
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- To produce flawless glass containers, continuous monitoring of the glass gob is required. It is essential to ensure production of molten glass gobs with the right shape, temperature, viscosity and weight. At present, manual monitoring is common practice in the glass container industry, which heavily depends on previous experience, operator knowledge and trial and error. This results in inconsistent measurements and consequently loss of production. In this article, a multi-camera based setup is used as a non-invasive real-time monitoring system. We have shown that under certain conditions, such as keeping the glass composition constant, it is possible to do in-line measurement of viscosity using sensor fusion to correlate the rate of geometrical change in the gob and its temperature. The correlation models presented in this article show that there is a strong correlation, i.e., 0.65, between our measurements and the projected viscosity.
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| 7. |
- Hussain, Mazhar, 1980-, et al.
(författare)
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Selection of optimal parameters to predict fuel consumption of city buses using data fusion
- 2022
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Ingår i: 2022 IEEE Sensors Applications Symposium (SAS). - : IEEE. - 9781665409810
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Konferensbidrag (refereegranskat)abstract
- The study aims to explore the fuel consumption of city buses with data fusion using a dataset with multiple parameters such as travelled distance, weekday, hour of the day, drivers, buses, and routes, that influence the trip fuel consumption. In this study, manipulated parameters such as modified driver, bus and route identification numbers are used together with original parameters to identify the optimal combination of parameters that can be used to enhance the accuracy of the prediction model. Two regression methods, i.e. cubic SVM and artificial neural networks (ANN), are used to demonstrate the performance of the proposed approach. Results shows that a combination of original parameters and processed parameters increases the performance.
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| 8. |
- Krug, Silvia, et al.
(författare)
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A Case Study on Energy Overhead of Different IoT Network Stacks
- 2019
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Ingår i: 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). - : IEEE. - 9781538649817 - 9781538649800 ; , s. 528-529
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Konferensbidrag (refereegranskat)abstract
- Due to the limited energy budget for sensor nodes in the Internet of Things (IoT), it is crucial to develop energy efficient communications amongst others. This need leads to the development of various energy-efficient protocols that consider different aspects of the energy status of a node. However, a single protocol covers only one part of the whole stack and savings on one level might not be as efficient for the overall system, if other levels are considered as well. In this paper, we analyze the energy required for an end device to maintain connectivity to the network as well as perform application specific tasks. By integrating the complete stack perspective, we build a more holistic view on the energy consumption and overhead for a wireless sensor node. For better understanding, we compare three different stack variants in a base scenario and add an extended study to evaluate the impact of retransmissions as a robustness mechanism. Our results show, that the overhead introduced by the complete stack has an significant impact on the nodes energy consumption especially if retransmissions are required.
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| 9. |
- Nie, Yali, et al.
(författare)
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Multi-Path Interference Denoising of LiDAR Data Using a Deep Learning Based on U-Net Model
- 2024
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Ingår i: 2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE conference proceedings. - 9798350380903
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Konferensbidrag (refereegranskat)abstract
- Eliminating Multi-Path Interference (MPI) stands as a significant unresolved challenge in the domain of depth estimation using Time-of-Flight (ToF) cameras. ToF data is typically influenced by significant noise and artifacts stemming from MPI. Although a variety of conventional methods have been suggested to enhance ToF data quality, the application of machine learning techniques has been infrequent, primarily due to the scarcity of authentic training data with accurate depth information. This paper introduces an approach that eliminates the dependency on labeled real-world data within the learning framework. We employ a U-Net trained on the data with ground truth in a supervised manner, enabling it to leverage multi-frequency ToF data for MPI correction. Concurrently, we compare three channels as input with one channel and two channels. Our experimental results convincingly showcase the effectiveness of this approach in reducing noise in real-world data.
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| 10. |
- Sánchez Leal, Isaac, et al.
(författare)
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Impact of input data on intelligence partitioning decisions for IoT smart camera nodes
- 2021
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Ingår i: Electronics. - : MDPI AG. - 2079-9292. ; 10:16
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Tidskriftsartikel (refereegranskat)abstract
- Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis.
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