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- ItemComparing Posture Classification: A Human Lying Posture Pressure-Map Dataset(IEEE, 2025-04-11) Husák, Michal; Mihálik, Ondrej; Arm, Jakub; Mesárošová, Michaela; Kaczmarczyk, Václav; Bradáč, ZdeněkWe discuss methods and algorithms for classifying the posture of a patient in their bed. The actual classification tasks are performed with a measurement chain including an in-house designed pressure mattress, a matrix of 30×11 sensing spots, a data concentrator, and a cloud-based service. Utilizing a survey of open-source datasets that facilitate such classifying operations, we designed a relevant experiment. A Human Lying Posture Pressure-Map dataset (HLPPDat) formed during the research is publicly available. Involving 20 subjects in 64 defined postures, the classification output was separated into four basic groups included prone posture. The data enabled us to analyze multiple classification methods developed with the state-of-the-art concepts of Machine Learning (ML), sparse representation, and artificial intelligence represented by Transfer Learning (TL). The analysis included both data measured and data experimentally corrupted in a manner that would most probably occur due to a partial measurement error. Regarding the techniques and options tested, feature extraction via the Histogram of Oriented Gradient (HOG) and the K-Nearest Neighbors (KNN) tools appeared to be the most beneficial, yielding an accuracy of over 99.5% in the leave-one-subject-out crossvalidation. The research confirmed that an accurate classification is feasible at a matrix sensor resolution markedly lower than the limits regularly presented in the literature. The system allows monitoring how long a bedridden person has remained in the same posture, and thus it has a potential to help prevent decubitus in both hospitals and the home care.
- ItemOnline Monitoring of Interturn Short Circuit Current in PMSMs(IEEE, 2025-03-10) Zezula, Lukáš; Blaha, PetrThis paper extends the previously published parameter estimation-based approach to interturn short circuit diagnostics in permanent magnet synchronous motors by real-time monitoring of hidden machine states after fault occurrence. The designed monitoring method relies on an adaptive formulation of the Kalman filter, which assumes interdependence between measurement and process noise variables. A variable forgetting factor not only mitigates the impact of the process model uncertainty but also facilitates the simultaneous operation of the monitoring algorithm and fault indicator estimation. Furthermore, contributions of fault current and healthy machine model to stationary reference frame currents are estimated from an advanced discrete-time motor description reflecting a stator winding arrangement inside a motor's case. The monitoring algorithm is validated in steady state, torque load transient, and velocity transient laboratory experiments with diverse fault severity values.
- ItemParallel Computing Utilization in Nonlinear Model Predictive Control of Permanent Magnet Synchronous Motor(IEEE, 2024-09-09) Kozubík, Michal; Veselý, Libor; Aufderheide, Eyke; Václavek, PavelPermanent Magnet Synchronous Motor (PMSM) drives are widely used for motion control industrial applications and electrical vehicle powertrains, where they provide a good torque-to-weight ratio and a high dynamical performance. With the increasing usage of these machines, the demands on exploiting their abilities are also growing. Usual control techniques, such as field-oriented control (FOC), need some workaround to achieve the requested behavior, e.g., field-weakening, while keeping the constraints on the stator currents. Similarly, when applying the linear model predictive control, the linearization of the torque function and defined constraints lead to a loss of essential information and sub-optimal performance. That is the reason why the application of nonlinear theory is necessary. Nonlinear Model Predictive Control (NMPC) is a promising alternative to linear control methods. However, this approach has a major drawback in its computational demands. This paper presents a novel approach to the implementation of PMSMs' NMPC. The proposed controller utilizes the native parallelism of population-based optimization methods and the supreme performance of field-programmable gate arrays to solve the nonlinear optimization problem in the time necessary for proper motor control. The paper presents the verification of the algorithm's behavior both in simulation and laboratory experiments. The proposed controller's behavior is compared to the standard control technique of FOC and linear MPC. The achieved results prove the superior quality of control performed by NMPC in comparison with FOC and LMPC. The controller was able to follow the Maximal Torque Per Ampere strategy without any supplementary algorithm, altogether with constraint handling.
- ItemVarroa destructor detection on honey bees using hyperspectral imagery(Elsevier, 2024-09-01) Duma, Zina-Sabrina; Zemčík, Tomáš; Bilík, Šimon; Sihvonen, Tuomas; Honec, Peter; Reinikainen, Satu-Pia; Horák, KarelHyperspectral (HS) imagery in agriculture is becoming increasingly common. These images have the advantage of higher spectral resolution. Advanced spectral processing techniques are required to unlock the information potential in these HS images. The present paper introduces a method rooted in multivariate statistics designed to detect parasitic Varroa destructor mites on the body of western honey bee Apis mellifera, enabling easier and continuous monitoring of the bee hives. The present paper is the first to utilize hyperspectral imagery for the task, previous studies existing only for multispectral imagery. The methodology explores unsupervised (K-means++) and recently developed supervised (Kernel Flows-Partial Least-Squares, KF-PLS) methods for parasitic identification. Additionally, in light of the emergence of custom-band multispectral cameras, the present research outlines a strategy for identifying the specific wavelengths necessary for effective bee-mite separation, suitable for implementation in a custom-band camera. Illustrated with a real-case dataset, our findings demonstrate that as few as four spectral bands are sufficient for accurate parasite identification.
- ItemIndustry 4.0 demonstrator: COMBED(ELSEVIER, 2024-08-14) Braun, Vlastimil; Zezulka, František; Marcoň, Petr; Jirsa, Jan; Fiedler, Petr; Kaczmarczyk, Václav; Arm, Jakub; Bradáč, Zdeněk; Dohnal, PřemyslThe paper discusses the manufacturing procedures and modes that will be employed in smart factories in the course of the next stage of technology development within the 4th Industrial Revolution. The manufacturing modes integrate the principles of Industry 4.0 (I4.0), presented through specifications by German, French, and Italian standardization committees. At present, the individual aspects of the entire system, which comprises technological means, functions, services, management structures, and requirements on manufacturing components and products in factories of the future, are being standardized. In our research, this 14.0 -based mode of production is demonstrated on a virtual testbed. Compared to the costly physical testbeds, whose application is limited physically as well as in terms of funding and time, the virtual approach allows showing the 14.0 manufacturing principles effectively and cheaply. For this purpose, we employ two versions of the COMBED, a virtual testbed; in this context, the principles are exposed as facilitating consistent production management decentralization via standardized Asset Administration Shell (AAS) digital twins in both the individual manufacturing components and the actual product. The paper is complemented with viderecordings that expose the model functions of a smart factory producing simple plastic models of various cars by using robots, automated machines and stores, 3D printers, and other manufacturing components equipped with standardized digital twins (AASs). Copyright (c) 2024 The Authors.