| 31. |
- Carratú, Marco, et al.
(författare)
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A novel IVS procedure for handling Big Data with Artificial Neural Networks
- 2020
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Ingår i: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728144603
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Konferensbidrag (refereegranskat)abstract
- In recent times, thanks to the availability of a large quantity of data coming from the industrial process, several techniques based on a data-driven approach could be developed. Between all the data-driven techniques, as Principle Component Regression, Support Vector Machines, Artificial Neural Networks, Neuro-Fuzzy Systems, and many others, the data on which they rely should be analyzed to find correlations and dependencies that could improve their design. For this reason, the Input variable Selection (IVS) process has become of great interest in the recent period. The classical IVS relies on classical statistics, as Pearson coefficients, able to discover linear dependencies among data; today, due to the significant amount of data available, the challenge of also discovering non-linear dependencies appears to be a necessary skill, mainly for the design and development of a neural network. This paper proposes the use of a novel statistical tool named Maximal Information Coefficient (MIC) for developing an IVS procedure able to discover dependencies in a considerable dataset and guide the IVS designer to the selection of input variables in a data-driven application. As a case study, the procedure will be applied to a real application developed in the context of the Swedish forest industry, in order to choose the input variables of a neural network able to estimate the timber bundles volume, which represents an expensive parameter to measure in this context.
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| 32. |
- Carratu, M., et al.
(författare)
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An innovative method for log diameter measurements 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
- The widespread adoption of Deep Learning techniques for Computer Vision in recent years has brought major changes to the world of industry, contributing greatly to this sector's transition to Industry 4.0, also referred to as Smart Industry. This involves an increasingly predominant role of machines and automation within industrial processes. In this context, the Swedish forest industry is an excellent context for applying these techniques. In particular, this work will deal with automating the measurement of log diameters to date carried out manually by operators in the industry. The proposed methodology will use two object detection neural networks, one deputed to detect logs in the scene and the other for the calibrated target. The latter thus allows the camera calibration to be fully automated, enabling each diameter to be measured without any further operations by the operator. The results obtained are satisfactory and open the way for the industrial application of the proposed methodology.
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| 33. |
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| 34. |
- 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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| 35. |
- Carratù, M., et al.
(författare)
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Vision-Based System for Measuring the Diameter of Wood Logs
- 2023
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Ingår i: IEEE Open Journal of Instrumentation and Measurement. - : IEEE. - 2768-7236. ; 2, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Detecting and measuring objects with vision-based systems in uncontrolled environments is a difficult task that today, thanks to the development of increasingly advanced artificial intelligence-based techniques, can be solved with greater ease. In this context, this article proposes a novel approach for the vision-based measurement of objects in uncontrolled environments using a specific type of convolutional neural network (CNN) named you only look once (YOLO) and a direct linear transformation (DLT) process. The case study concerned designing a novel vision-based system for measuring the diameter of wood logs cut and loaded onto trucks. This problem has been occurring in the Swedish forestry industry. In fact, this operation is not carried out with computer vision algorithms because of the high variability of environmental conditions caused by the changing position of the sun, weather conditions, and the variability of truck positioning. To solve this problem, the YOLO network is proposed to locate logs while attempting to maintain a high Intersection over Union (IoU) value for the correct estimation of log size. Furthermore, in order to obtain accurate measurements, the DLT is used to convert into world coordinates the dimensions of the logs themselves. The proposed CNN-based solution is described after briefly introducing today’s methodologies adopted for wood bundle analysis. Particular attention is paid to both the training and the calibration steps. Results report that for 80% of cases, the error reported has been smaller than 4 cm, representing only 8% of the measurement, considering a mean log diameter for the application of 50 cm.
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| 36. |
- Carratù, Marco, et al.
(författare)
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Wireless Sensor Network Calibration for PM10 Measurement
- 2020
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Ingår i: 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). - : IEEE. - 9781728144337
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Konferensbidrag (refereegranskat)abstract
- The proposal of an Advanced Metering Infrastructure based on short-range communication is suggested for the continuous monitoring of Particulate Matter. A prototype of Automatic Measurement System (AMS), including a low-cost off-the-shelf PM sensor, has been developed as a remote node to be adopted in the radio Local Area Network. The results of the system calibration and comparison with the data quality requirements of the PM measurement according to European regulations, as well as the simulation of a typical Smart City scenario in terms of communication performance, confirm the feasibility of the proposed distributed AMS for an effective adoption within an urban area.
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| 37. |
- Cheng, Peng
(författare)
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Applications of embedded sensors in loader crane positioning and rotor RPM measurement
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis, two novel applications involving embedded sensors arestudied, one dealing with loader crane positioning and the other involving rotorRevolutions Per Minute (RPM) measurement. The thesis presents a generalintroduction to the embedded sensor, its architecture and its use in mechanicalindustry, and provides the reader with an overview of conventional sensortechnologies within the fields of angle sensors and angular speed sensors, coveringtheir working principles, features, advantages and disadvantages and typicalapplications. The particular problems associated with the use of conventionalsensors in both loader crane positioning and rotor RPM measurement aredescribed and these problems provided the motivation for the designs of theembedded sensor systems developed in this thesis.In the case of the loader crane positioning, the origins of the project and thespecial requirements of the application are described in detail. In addition, apreliminary study is conducted in relation to the idea of a contactless joint angularsensor using MEMS inertial sensors in which four different methods, namely, theCommon-Mode-Rejection with Gyro Integration (CMRGI), Common-Mode-Rejection (CMR), Common-Mode-Rejection with Gyro Differentiation (CMRGD)and Distributed Common-Mode-Rejection (DCMR), are conceived, modeled andtested on a custom-designed prototype experimental setup. The results gatheredfrom these four methods are compared and analyzed in order to identify thedifferences in their performances. The methods, which proved to be suitable, arethen further tested using the prototype sensor setup on a loader crane and theperformance results are analyzed in order to make a decision in relation to the twomost suitable methods for the application of the loader crane positioning. Theresults suggested that the two most suitable were the CMRGD and the DCMR. Thepractical design issues relating to this sensor system are highlighted andsuggestions are made in the study. Additionally, possible future work for thisproject is also covered.In the first case for the rotor RPM measurement, the thesis presents themodeling and simulation of the stator-free RPM sensor idea using the Monte Carlomethod, which demonstrated the special features and performance of this sensor.The design aspects of the prototype sensor are described in detail and theprototype is tested on an experimental setup. The conclusions for the stator-freeRPM sensor are then made from the analysis of the experimental results and futurework in relation to this sensor is also proposed.In the second case of the rotor RPM measurement, the thesis presentsanother idea involving the laser mouse RPM sensor and the main focus of thestudy is on the performance characterization of the laser mouse sensor and theverification of the RPM sensor idea. Experiments are conducted using the test setup and results are gathered and analyzed and conclusions are drawn.Possibilities in relation to future work for this laser mouse RPM sensor are alsoprovided.The summary and the conclusion form the final chapter of the thesis andseveral important aspects of the designs relating to both the loader cranepositioning project and the rotor RPM measurement project are discussed.
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| 38. |
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| 39. |
- Ericsson, A., et al.
(författare)
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Comparison of Noise Reduction and MPEG-2 Compression Efficiency for Pre-Processing Video Filters
- 2004
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Ingår i: IWSSIP'04 : international workshop on systems, signals and image processing : ( Poznan, 13-15 September 2004 ). - Poznan, Poland : Polish Society for Theoretical and Applied Electrical Engineering.
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Konferensbidrag (refereegranskat)abstract
- Video information that is input in digital video recorders or distributed over the Internet comes in various different qualities. One possibility to improve the video quality and also to improve the efficiency of the video encoder is to use different types of spatial or temporal video filters. This paper presents a comparison of the noise reduction efficiency for three different video filters. Additionally, the improvement of MPEG-2 encoding efficiency is compared. The results provide an efficiency function that can be used to select an appropriate filter type for a special situation.
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| 40. |
- Fedorov, Igor, et al.
(författare)
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A two-layer 3D reconstruction method and calibration for multi-camera-based volumetric positioning and characterization
- 2021
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - 0018-9456 .- 1557-9662. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- A three-dimensional (3D) reconstruction method and multi-camera calibration using multiple artificial reference markers have been used for precise volumetric surveillance of fast-flying objects. The method uses a two-layer 3D reconstruction that integrates two multi-camera stereo-nodes. The fields of view of stereo nodes are directed at an acute angles to each other to provide greater coverage with the given constraints and to determine the flight characteristics of objects in 3D. The object’s flight reconstruction includes a “rough” estimation of its positions relative to selected artificial reference points in both stereo nodes separately and subsequent “refinement” of calculated positions. In this paper, we describe the proposed method and calibration technique, using a multi-camera system to measure object characteristics in 3D. The proposed method applies to volumetric surveillance in situations where it is necessary to count, track, and analyze the activities of flying objects, especially birds, using high spatial resolution.
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