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Recently, magnetic levitation systems have been applied and studied in various industrial fields. In particular, in-tracktype magnetic levitation conveyor systems are actively studied since they can effectively minimize electromagnetic effects in processes that require a highly clean environment. In this type of system, diverse and multiple sensors are structurally required so that the control performance of an integrated system is primarily governed by the slowest measuring sensor. This paper proposes a multisensor fusion compensator to integrate the outputs obtained from various sensors into one output with the single fastest time rate. Since the state of the system is estimated at a fast time rate, the optimal controller also guarantees fast performance and stability. The computation of electromagnetic fields and the control performance of the considered superconducting hybrid system were analyzed using a computer simulation based on finite element methods.

The increasing size and complexity of wind turbines have intensified the need for reliable real-time condition monitoring and health assessment. However, conventional numerical models often involve high computational demand, limiting their applicability for real-time digital twin implementation. This paper proposes a physics-based digital twin framework for the real-time health monitoring of a 3 MW class wind turbine. A physics-based numerical model was developed using Modelica 4.0.0 to simulate the electrical and mechanical behaviors of the wind turbine based on supervisory control and data acquisition (SCADA) inputs. Data preprocessing and wind speed calibration strategies were applied to reconcile nacelle-measured SCADA data with the turbine design specifications. Furthermore, reduced-order models (ROMs) were integrated with the physics-based numerical model to predict the thermal states of the generator and gearbox. Key operational parameters were selected through correlation analysis to enable accurate temperature prediction. Validation results demonstrate that the proposed digital twin accurately reproduces the dynamic behavior of the wind turbine, with the ROM-based temperature predictions showing agreement with SCADA measurements. The overall framework achieves a computation time within one second, indicating its suitability for real-time diagnostic and predictive maintenance applications.

In this study, real-scale high-temperature superconducting (HTS) field coils for a 10 MW class rotating machine were designed, fabricated, and experimentally evaluated. The aim was to propose a cost-effective winding strategy by combining two types of HTS wires with different angular dependencies of critical current. The 3D FEM simulations were performed to determine the coil layout by considering the magnetic field magnitude and incidence angle. Based on this design, two HTS field coils were fabricated, one wound with two different types of wire and the other with a single wire type. For application to an actual HTS generator, the coil was equipped with an iron core to evaluate its influence on critical current and magnetic field distribution. Experimental results at 77 K showed that the coil combined with two types of HTS wire achieved 112 A without the core and 105 A with the core, while the single-wire coil reached 101 A and 93 A, respectively. The measured results showed good agreement with the simulations, with deviations within 3.7% for the combined-wire coil and 1.9% for the coil equipped with the iron core. These findings indicate that the proposed winding method can maintain high performance while lowering material cost, providing useful guidelines for the design of large-scale HTS rotating machines.

The primary challenge facing silicon-based electronics, crucial for modern technological progress, is difficulty in dimensional scaling. This stems from a severe deterioration of transistor performance due to carrier scattering when silicon thickness is reduced below a few nanometres. Atomically thin two-dimensional (2D) semiconductors still maintain their electrical characteristics even at sub-nanometre scales and offer the potential for monolithic three-dimensional (3D) integration. Here we explore a strategic shift aimed at addressing the scaling bottleneck of silicon by adopting 2D semiconductors as new channel materials. Examining both academic and industrial viewpoints, we delve into the latest trends in channel materials, the integration of metal contacts and gate dielectrics, and offer insights into the emerging landscape of industrializing 2D semiconductor-based transistors for monolithic 3D integration. This Review explores adopting 2D semiconductors to overcome the scaling bottleneck of Si-based electronics. Recent trends and potential approaches for the development of 2D materials as a channel are discussed. Following this, the prerequisites, obstacles and feasible technologies for integrating contacts and gate dielectrics with 2D semiconductor-based channels are examined. The Review also provides an industrial perspective towards facilitating monolithic 3D integration.

Recently, optical dielectric metasurfaces, ultrathin optical skins with densely arranged dielectric nanoantennas, have arisen as next-generation technologies with merits for miniaturization and functional improvement of conventional optical components. In particular, dielectric metalenses capable of optical focusing and imaging have attracted enormous attention from academic and industrial communities of optics. They can offer cutting-edge lensing functions owing to arbitrary wavefront encoding, polarization tunability, high efficiency, large diffraction angle, strong dispersion, and novel ultracompact integration methods. Based on the properties, dielectric metalenses have been applied to numerous three-dimensional imaging applications including wearable augmented or virtual reality displays with depth information, and optical sensing of three-dimensional position of object and various light properties. In this paper, we introduce the properties of optical dielectric metalenses, and review the working principles and recent advances in three-dimensional imaging applications based on them. The authors envision that the dielectric metalens and metasurface technologies could make breakthroughs for a wide range of compact optical systems for three-dimensional display and sensing.

Continuous, comfortable, convenient (C3), and accurate blood pressure (BP) measurement and monitoring are needed for early diagnosis of various cardiovascular diseases. To supplement the limited C3 BP measurement of existing cuff-based BP technologies, though they may achieve reliable accuracy, cuffless BP measurement technologies, such as pulse transit/arrival time, pulse wave analysis, and image processing, have been studied to obtain C3 BP measurement. One of the recent cuffless BP measurement technologies, innovative machine-learning and artificial intelligence-based technologies that can estimate BP by extracting BP-related features from photoplethysmography (PPG)-based waveforms have attracted interdisciplinary attention of the medical and computer scientists owing to their handiness and effectiveness for both C3 and accurate, i.e., C3A, BP measurement. However, C3A BP measurement remains still unattainable because the accuracy of the existing PPG-based BP methods was not sufficiently justified for subject-independent and highly varying BP, which is a typical case in practice. To circumvent this issue, a novel convolutional neural network(CNN)- and calibration-based model (PPG2BP-Net) was designed by using a comparative paired one-dimensional CNN structure to estimate highly varying intrasubject BP. To this end, approximately 70 % , 20 % , and 10 % of 4185 cleaned, independent subjects from 25,779 surgical cases were used for training, validating, and testing the proposed PPG2BP-Net, respectively and exclusively (i.e., subject-independent modelling). For quantifying the intrasubject BP variation from an initial calibration BP, a novel ‘standard deviation of subject-calibration centring (SDS)’ metric is proposed wherein high SDS represents high intrasubject BP variation from the calibration BP and vice versa. PPG2BP-Net achieved accurately estimated systolic and diastolic BP values despite high intrasubject variability. In 629-subject data acquired after 20 minutes following the A-line (arterial line) insertion, low error mean and standard deviation of 0.209 ± 7.509 and 0.150 ± 4.549 mmHg for highly varying A-line systolic and diastolic BP values, respectively, where their SDSs are 15.375 and 8.745. This study moves one step forward in developing the C3A cuffless BP estimation devices that enable the push and agile pull services.

Quantitative phase imaging (QPI) enables non-invasive analysis of transparent specimens across biomedicine, materials science, and neuroscience. However, conventional hardware relies on complex architectures with multi-shot acquisition that preclude compact, real-time operation, while phase-retrieval software often yields degraded image quality, is environmentally sensitive, and runs slowly. Here, we introduce a compact, fast, high-resolution QPI platform that addresses these challenges by integrating nanophotonic metasurfaces with artificial intelligence (AI). Our metasurface optics simplifies the optical architecture by replacing bulky optics and modulators, enabling single-shot acquisition and a drastic reduction in form factor. We develop physics-informed AI models that correct optical aberrations, compensate for imperfections in nanofabrication and alignment, and restore nanoscale quantitative phase information in real-time. The system achieves nanoscale resolution better than 840 nm at 74 Hz within a single, thin optical layer. Our unique combination of nanophotonic hardware and AI algorithms advances QPI technology towards portable, precise, real-time phase imaging. The authors introduce a compact, fast and high-resolution quantitative phase imaging system which integrates nanophotonic metasurfaces with artificial intelligence (AI)-driven algorithms. They demonstrate nanoscale resolution better than 840 nm at a 74 Hz frame rate, all within a single thin optical layer.

Predicting the output power of wind generators is essential to improve grid flexibility, which is vulnerable to power supply variability and uncertainty. Digital twins can help predict the output of a wind turbine using a variety of environmental data generated by real-world systems. This paper dealt with the development of a physics-based output prediction model (P-bOPM) for a 10 MW floating offshore wind turbine (FOWT) for a digital twin. The wind power generator dealt with in this paper was modeled considering the NREL 5 MW standard wind turbine with a semi-submersible structure. A P-bOPM of a 10 MW FOWT for a digital twin was designed and simulated using ANSYS Twin Builder. By connecting the P-bOPM developed for the digital twin implementation with an external sensor through TCP/IP communication, it was possible to calculate the output of the wind turbine using real-time field data. As a result of evaluating the P-bOPM for various marine environments, it showed good accuracy. The digital twin equipped with the P-bOPM, which accurately reflects the variability of the offshore wind farm and can predict the output in real time, will be a great help in improving the flexibility of the power system in the future.

With the increased usage of hydrocarbon-based fossil fuels, air pollution and global warming have accelerated. To solve this problem, renewable energy, such as hydrogen technology, has gained global attention. Hydrogen has a low volumetric density and thus requires compression technologies at high pressures to reduce storage and transportation costs. Techniques for compressing hydrogen include using mechanical and electrochemical hydrogen compressors. Mechanical compressors require higher specific energy consumption than electrochemical hydrogen compressors. Here, we used an electrochemical hydrogen compressor as a pseudo-two-dimensional model focused on electroosmotic drag, water back-diffusion, and hydrogen crossover flux at various temperatures, polymer electrolyte membrane thicknesses, and relative humidity conditions. To date, there have been few studies based on various operating conditions to find the optimal conditions. This study was conducted to determine the optimal parameters under various operating conditions. A numerical analysis demonstrated that the specific energy consumption was low in a specific current density section when the temperature was decreased. At the above-mentioned current density, the specific energy consumption decreased as the temperature increased. The polymer electrolyte membrane thickness yielded similar results. However, according to the relative humidity, it was confirmed that the higher the relative humidity, the lower the specific energy consumption in all of the current density sections. Therefore, when comparing temperatures of 30 °C and 80 °C at 145 A/m2, operating at 30 °C reduces the specific energy consumption by 12.12%. At 3000 A/m2 and 80 °C, the specific energy consumption is reduced by 11.7% compared to operating at 30 °C. Using N117 compared to N211 at 610 A/m2 for polymer electrolyte membranes can reduce specific energy consumption by 10.4%. Using N211 in the 1500 A/m2 condition reduces the specific energy demand by 9.6% compared to N117.

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.