Search Results (65)

This paper presents a dynamic model for full-power converter permanent magnet synchronous wind turbines based on Physics-Informed Neural Networks (PINNs). The model integrates the physical dynamics of the wind turbine directly into the loss function, enabling high-accuracy equivalent modeling with limited data and overcoming the typical “black-box” constraints and large data requirements of traditional data-driven approaches. To enhance the model’s real-time adaptability, we introduce an online update mechanism leveraging Bayesian Neural Networks (BNNs) combined with a clustering-guided strategy. This mechanism estimates uncertainty in the neural network weights in real-time, accurately identifies error sources, and performs local fine-tuning on clustered data. This improves the model’s ability to track real-time errors and addresses the challenge of parameter-specific adjustments. Finally, the data-driven model is integrated into the CloudPSS platform, and its multi-scenario modeling accuracy is validated across various typical cases, demonstrating the robustness of the proposed approach. Full article

This paper presents a real-time remote-control platform for small wind turbines (SWTs) equipped with a permanent magnet synchronous generator (PMSG). The proposed system integrates a DC–DC boost converter controlled by an Arduino^® microcontroller, a Raspberry Pi^® hosting a WebSocket server, and a desktop application developed using MATLAB^® App Designer (version R2024b). The platform enables seamless remote monitoring and control by allowing upper layers to select the turbine’s operating mode—either Maximum Power Point Tracking (MPPT) or Power Curtailment—based on real-time wind speed data transmitted via the WebSocket protocol. The communication architecture follows the IEC 61400-25 standard for wind power system communication, ensuring reliable and standardized data exchange. Experimental results demonstrate high accuracy in controlling the turbine’s operating points. The platform offers a user-friendly interface for real-time decision-making while ensuring robust and efficient system performance. This study highlights the potential of combining open-source hardware and software technologies to optimize SWT operations and improve their integration into distributed renewable energy systems. The proposed solution addresses the growing demand for cost-effective, flexible, and remote-control technologies in small-scale renewable energy applications. Full article

There are two types of noninvasive biomarkers of human embryo developmental potential: those based on a direct assessment of embryo morphology over time and those using spent media after embryo in vitro culture as source of information. Both are derived from previously acquired knowledge on different aspects of pre-implantation embryo development. These aspects include embryo morphology and kinetics, chromosomal ploidy status, metabolism, and embryonic gene transcription, translation, and expression. As to the direct assessment of morphology and kinetics, pertinent data can be obtained by analyzing sequential microscopic images of in vitro cultured embryos. Spent media can serve a source of genomic, metabolomic, transcriptomic and proteomic markers. Methods used in the early pioneering studies, such as microscopy, fluorescence in situ hybridization, autoradiography, electrophoresis and immunoblotting, or enzyme-linked immunosorbent assay, are too subjective, invasive, and/or time-consuming. As such, they are unsuitable for the current in vitro fertilization (IVF) practice, which needs objective, rapid, and noninvasive selection of the best embryo for uterine transfer or cryopreservation. This has been made possible by the use of high-throughput techniques such as time-lapse (for direct embryo evaluation), next-generation sequencing, quantitative real-time polymerase chain reaction, high-performance liquid chromatography, nanoparticle tracking analysis, flow cytometry, mass spectroscopy, Raman spectroscopy, near-infrared spectroscopy, and nuclear magnetic resonance spectroscopy (for spent culture media analysis). In this review, individual markers are presented systematically, with each marker’s history and current status, including available methodologies, strengths, and limitations, so as to make the essential information accessible to all health professionals, even those whose expertise in the matter is limited. Full article

The accuracy of a magnetic gradient tensor (MGT) measured by tri-axial magnetometer cross arrays (TAMCAs) is compromised by inherent errors in individual tri-axial magnetometers (TAMs) and inter-sensor misalignment angles (MAs), both of which degrade the resultant MGT data quality. This paper proposes a novel lossless scalar calibration algorithm that eliminates mathematical approximations while tracking the fluctuation of the reference magnetic intensity (MI). The calibration algorithm is developed to improve TAMCAs’ measurement precision; however it is difficult to provide a completely accurate MGT by experiments. Therefore, we have designed a kind of validation experiment based on a constrained Euler localization to demonstrate the effectiveness of the calibration algorithm. The fundamental principles of the proposed lossless scalar calibration methodology are systematically presented, accompanied by a numerical analysis of relative errors calibrating TAMCA parameters. Key influencing factors are carefully investigated, including the TAM noise level quantified by standard deviation (STD), calibration dataset size, and STD of reference MI fluctuations. In the experiments, to validate the effectiveness of calibrating TAMCAs composed of four fluxgate TAMs (FTAMs), we measured the true geo-MI using a proton magnetometer and regarded an energized circular coil as the alternating current (AC) magnetic source of the constrained Euler localization, respectively. The results indicated that the lossless scalar calibration algorithm significantly improves the measurement accuracy of the geo-MI of the calibration site and MGT of the energized coil. Full article

Variable frequency drives (VFDs) are widely used in industry as an efficient means to control the rotational speed of AC motors by varying the supply frequency to the motor. VFD signatures can be detected in vibration signals in the form of sidebands (modulations) induced on tonal components (carrier frequencies). These sidebands are spaced at twice the “pseudo line” VFD frequency, as the magnetic forces in the motor have two peaks per current cycle. VFD-related signatures are generally less susceptible to interference from other mechanical sources, making them particularly useful for deriving speed variation information and obtaining a “pseudo” tachometer from the motor’s synchronous speed. This tachometer can then be employed to accurately estimate the speed profile and to facilitate order tracking in mechanical systems for vibration analysis purposes. This paper presents a signal processing technique designed to extract a pseudo tachometer from the VFD signature found in a vibration signal. The algorithm was tested on publicly available vibration data from a test rig featuring a two-stage gearbox with seeded bearing faults operating under variable-speed conditions with no load, i.e., with minimal slip between the induction motor’s synchronous and actual speed. The results clearly demonstrate the feasibility of using VFD signatures both to extract an accurate speed profile (root mean square error, RMSE of less than 2.5%) and to effectively perform order tracking, leading to the identification of bearing faults. This approach offers an accurate and reliable tool for the analysis of vibration in mechanical systems driven by AC motors with VFDs. However, it is important to note that some inaccuracies may occur at higher motor slip levels under heavy or variable loads due to the mismatch between the synchronous and actual speeds. Slip-induced variations can further distort tracked order frequencies, compromising the accuracy of vibration analysis for gear mesh and bearing defects. These issues will need to be addressed in future research. Full article

Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals. Full article

Motion analysis is of great interest to a variety of applications, such as virtual and augmented reality and medical diagnostics. Hand movement tracking systems, in particular, are used as a human–machine interface. In most cases, these systems are based on optical or acceleration/angular speed sensors. These technologies are already well researched and used in commercial systems. In special applications, it can be advantageous to use magnetic sensors to supplement an existing system or even replace the existing sensors. The core of a motion tracking system is a localization unit. The relatively complex localization algorithms present a problem in magnetic systems, leading to a relatively large computational complexity. In this paper, a new approach for pose estimation of a kinematic chain is presented. The new algorithm is based on spatially rotating magnetic dipole sources. A spatial feature is extracted from the sensor signal, the dipole direction in which the maximum magnitude value is detected at the sensor. This is introduced as the “maximum vector”. A relationship between this feature, the location vector (pointing from the magnetic source to the sensor position) and the sensor orientation is derived and subsequently exploited. By modelling the hand as a kinematic chain, the posture of the chain can be described in two ways: the knowledge about the magnetic correlations and the structure of the kinematic chain. Both are bundled in an iterative algorithm with very low complexity. The algorithm was implemented in a real-time framework and evaluated in a simulation and first laboratory tests. In tests without movement, it could be shown that there was no significant deviation between the simulated and estimated poses. In tests with periodic movements, an error in the range of 1° was found. Of particular interest here is the required computing power. This was evaluated in terms of the required computing operations and the required computing time. Initial analyses have shown that a computing time of 3 $\mathsf{\mu }$s per joint is required on a personal computer. Lastly, the first laboratory tests basically prove the functionality of the proposed methodology. Full article

Hybrid renewable energy systems (HRES) integrating solar, wind, and storage technologies offer enhanced efficiency and reliability for grid-connected applications. However, existing control methods often struggle with maintaining DC voltage stability and minimizing power fluctuations, particularly under variable load conditions. This paper addresses this research gap by proposing a novel control strategy utilizing a PD (1+PI) regulator that combines proportional–integral (PI) and proportional–derivative (PD) controllers. Integrated into the HRES with maximum power point tracking (MPPT), the system includes solar panels, a storage unit, and a wind system featuring a permanent magnet synchronous generator (PMSG). The PD (1+PI) regulator plays a critical role in stabilizing DC voltages within the storage system and collaborates with predictive direct power control (P-DPC) to improve current quality by mitigating fluctuations in active and reactive power. Comparative analysis against traditional direct power control methods shows that the proposed strategy reduces voltage fluctuation by 30% and improves energy utilization efficiency by 25%, validating its efficacy in managing energy from diverse sources to meet nonlinear load demands. The results demonstrate that integrating the PD (1+PI) regulator with MPPT and P-DPC approaches enhances power stability and optimizes energy utilization in grid-connected HRES, underscoring the effectiveness of this advanced control system. Full article

Magnetomotive ultrasound (MMUS) is a promising imaging modality for detecting magnetic nanoparticles. In MMUS, an external oscillating magnetic field induces the motion of the injected magnetic nanoparticles within tissue, and phase-based tracking algorithms are used to detect the motion. However, the subwavelength scale of these displacements (often a few micrometers) makes direct visualization on conventional ultrasound B-mode images impossible. In this work, we adapt the Eulerian motion magnification technique to create a novel ultrasound display mode for identifying the nanoparticle locations, eliminating the need for displacement tracking algorithms. Phantom and in vivo experiments demonstrate that our technique successfully magnifies magnetomotion and the associated shear wave propagation in ultrasound B-mode imaging and pinpoints the nanoparticle vibration source, even in low-concentration scenarios. Full article

Using multiple input power sources increases the reliability of electric vehicles compared to a single source. However, the inclusion of other sources exhibits complexity in the controller system, such as computing time, program difficulty, and switching speed to connect or disconnect the input power to load. To ensure optimal performance and avoid overloading issues, the EV system needs sophisticated control. This work introduces a machine-learning-based controller using an artificial neural network to solve these problems. This paper describes the detailed power management control methodology using multiple sources like solar PV, fuel cells, and batteries. Novel control with an instantaneous reference current scheme is used to manage the input power sources to satisfy the power demand of electric vehicles. The proposed work executes the power split-up operation with standard and actual drive cycles and maximum power point tracking for PV panels using MATLAB Simulink. Finally, power management with a machine learning technique is implemented in an experimental analysis with the LabVIEW software, and an FPGA controller is used to control a 48 V, 1 kW permanent-magnet synchronous machine. Full article

In the last few years, several studies have emphasized the existence of injury-specific EV “barcodes” that could have significant importance for the precise diagnosis of different organ injuries in polytrauma patients. To expand the research potential of the NTF (network trauma research) biobank of polytraumatized patients, the NTF research group decided to further establish a biobank for EVs. However, until now, the protocols for the isolation, characterization, and storage of EVs for biobank purposes have not been conceptualized. Plasma and serum samples from healthy volunteers (n = 10) were used. Three EV isolation methods of high relevance for the work with patients’ samples (ultracentrifugation, size exclusion chromatography, and immune magnetic bead-based isolation) were compared. EVs were quantified using nanoparticle tracking analysis, EV proteins, and miRNAs. The effects of different isolation solutions; the long storage of samples (up to 3 years); and the sensibility of EVs to serial freezing–thawing cycles and different storage conditions (RT, 4/−20/−80 °C, dry ice) were evaluated. The SEC isolation method was considered the most suitable for EV biobanking. We did not find any difference in the quantity of EVs between serum and plasma-EVs. The importance of particle-free PBS as an isolation solution was confirmed. Plasma that has been frozen for a long time can also be used as a source of EVs. Serial freezing–thawing cycles were found to affect the mean size of EVs but not their amount. The storage of EV samples for 5 days on dry ice significantly reduced the EV protein concentration. Full article

High-dynamic-range integrated magnetometers demonstrate extensive potential applications in fields involving complex and changing magnetic fields. Among them, Diamond Nitrogen Vacancy Color Core Magnetometer has outstanding performance in wide-range and high-precision magnetic field measurement based on its inherent high spatial resolution, high sensitivity and other characteristics. Therefore, an innovative frequency-tracking scheme is proposed in this study, which continuously monitors the resonant frequency shift of the NV color center induced by a time-varying magnetic field and feeds it back to the microwave source. This scheme successfully expands the dynamic range to 6.4 mT, approximately 34 times the intrinsic dynamic range of the diamond nitrogen-vacancy (NV) center. Additionally, it achieves efficient detection of rapidly changing magnetic field signals at a rate of 0.038 T/s. Full article

This paper introduces a medium voltage direct-current (MVDC) system for large spacecraft megawatt-scale (MW) power and propulsion systems intended for interplanetary transport, including missions to the Moon and Mars. The proposed MVDC system includes: (i) A nuclear electric propulsion (NEP) that powers a permanent magnet (PM) generator whose output is rectified and connected to the MVDC bus. (ii) A solar photovoltaic (PV) source that is interfaced to the MVDC bus using a unidirectional boost DC-DC converter. (iii) A backup battery energy storage system (BESS) that connects to the MVDC bus using a bidirectional DC-DC boost converter. (iv) A dual active bridge (DAB) converter that controls the power to the spacecraft’s electric thruster. The NEP serves as the main power source for the spacecraft’s electric thruster, while the solar PV and BESS are intended to provide power for the payload and spacecraft’s low-voltage power system. The paper will (i) provide a review of the spacecraft MVDC power and prolusion system highlighting state-of-the-art main components, (ii) address the control of boost converters for the PV and BESS sources and the DAB converter for the thruster, and (iii) propose an uncertainty and disturbance estimator (UDE) concept based on current control algorithms to mitigate MVDC instability due to unpredictable factors and external disruptions. The proposed UDE can actively estimate and compensate for the system disturbance and uncertainty in real time, and thus, both the system tracking performance and robustness can be improved. Simulation studies have been conducted to substantiate the efficacy of the proposed schemes. Full article

This article presents a magnetic tracking system using on-chip anisotropic magnetoresistive (AMR) sensors. The system consists of four air-core coils sequentially generating four dc magnetic fields. The implemented localization algorithm is quadrilateration, and the accuracy of the system is dependent on the accuracy of the sensors and the simulated field maps. The performance of the system was evaluated using an in-house magnetic field camera (MFC), and the results showed that the system exhibits mean Euclidean errors below 1 mm where the source produces strong gradients. Given the dimensions of the sensors (0.82 × 0.82 mm²), this system is suitable for tracking minimally invasive surgical tools. Full article

Bicycles are rapidly gaining popularity as a sustainable mode of transportation around the world. Furthermore, the smart bicycle paradigm enables increased use through the Internet of Things applications (e.g., GPS tracking systems). This new paradigm introduces energy autonomy as a new challenge. The energy harvesting technology can capture the energy present in the cycling environment (e.g., kinetic or solar) to give this autonomy. The kinetic energy source is more stable and dense in this environment. There are several wheel kinetic harvesters on the market, ranging from low-complexity dynamos used to power bicycle lights to smart harvester systems that harvest kinetic energy while braking and cycling and store it for when it is needed to power sensors and other electronics loads. Perhaps the hub and the “bottle” dynamos are the most commercially successful systems because of their cost-effective design. Furthermore, the bottle generator is very inexpensive, yet it suffers from significant energy losses and is unreliable in wet weather due to mechanical friction and wheel slippage in the wheel/generator contact. This paper proposes a cost-effective bicycle harvester based on a novel kinetic-electromagnetic transducer. The proposed harvester allows for the generation and storage of harnessed kinetic energy to power low-power electronics loads when the user requires it (e.g., cell phone charging, lighting). The proposed harvester is made up of a power processing unit, a battery, and an optimized transducer based on a Halbach magnet array. An extensive full-wave electromagnetic simulation was used to evaluate the proposed transducer. Circuit simulation was also used to validate the proposed power unit. The proposed harvester generates a simulated output power of 1.17 W with a power processing unit efficiency of 45.6% under a constant bicycle velocity of 30 km/h. Full article