Metres Research Articles

Multi 2D-CNN-based model for short-term PV power forecast embedded with Laplacian Attention

Amid the bloom of Renewable energy (RE) integrated into the grid, an accurate Photovoltaic(PV) power forecast is considered to be a crucial task in maintaining the reliability and stability of the power systems since this technology strongly depends on various external factors, causing the fluctuation in the output power. However, the poor quality of input data, which is very common in practical circumstances owing to the low-cost measurement and data acquisition devices, poses an enormous challenge for the predictive model to deeply extract the spatial and temporal correlation of the input data. This study proposes a Multi Two-Dimensional Convolutional Neural Network (2D-CNN) for short-term PV power forecast embedded with Laplacian Attention mechanism. By viewing the input sequences in a 2D form, the input map is constructed, and the interconnected feature among variables can be captured by convolution operation. Moreover, with the multiple CNN layers working in parallel architecture, different representations hidden inside the input map can be detected, enabling the proposed model to bring out promising performance across forecast time-step without modifying its initial parameters. In order to reduce the decay impact of irrelevant variables existing inside the input data, the Laplacian Attention mechanism is employed. The Attention matrix is dynamically modified during the training process to produce an accurate attention matrix, which represents the correlation between variables. Therefore, the model is able to focus on informative features and ignore negative ones. The experiments conducted on two different datasets with opposite characteristics provide deep insights into the strength of the proposed model over the baseline model, which strongly demonstrates the efficiency of the proposed model, especially when dealing with datasets bearing tough characteristics.

A novel approach for the replacement of accuracy and precision measurements with the visual instrument agreement scale (VIAS) in evaluating the performance of four intraoral scanners and one shade measurement device

ObjectivesThe terms 'accuracy' and 'precision' are tightly defined in color science but are often used ambiguously in dental research. This study introduces the visual instrument agreement scale (VIAS), a new method for determining visual-instrumental agreement in dental colorimetry by comparing visually perceived and measured color differences. Materials and MethodsIn-vivo tooth color measurements were taken from 16 participants using four intraoral scanners (Primescan, Medit i700, Carestream CS3700, Trios 3) and one spectrophotometer (Vita Easyshade V). Visual shade assessment was also performed by one expert observer using the 3D Master shade guide. Statistical significance testing was conducted using the STRESS index to calculate VIAS, and the F-statistic was used to evaluate device performance. ResultsCarestream CS3700 achieved the highest visual-instrumental agreement with a VIAS score of 82%, performing significantly better than the other devices. Primescan, Medit i700, and Trios 3 showed scores of 76%, 75%, and 72%, respectively, with no significant differences between them. Vita Easyshade V scored 57%, performing significantly worse than the other devices. ConclusionsThe overall performance of the intraoral scanners was strong, with Carestream CS3700 approaching excellent performance. The VIAS method offers a practical, color science-based framework for evaluating visual-instrumental agreement and can be easily replicated using the freely available toolbox. Clinical SignificanceIntraoral scanners performed surprisingly better than a spectrophotometer specifically designed for tooth color measurement and which is often regarded as the gold standard. Additionally, VIAS offers a new, scientifically grounded approach for testing visual-instrumental agreement in dental colorimetry.

Exploring External Peak Demands: The Influence of Contextual Factors on Male Basketball Players

Understanding the external load, particularly the peak demands (PDs), experienced by players is essential. Several factors, such as the playing time, team venue, score outcome, and match nature, can influence basketball performance. Accelerometry-based methods allow for the estimation of the load borne by athletes and the peaks they reach. The aim of this study was to analyze the influence of these factors on PDs across different time windows (30 s, 1 min, and 3 min). Eleven national-level athletes were monitored during 29 matches using measurement devices. Inertial movement units were used to measure peak PlayerLoad™ (PL) across any time window. Mixed linear models were used to compare the groups, and effect size was calculated to quantify the differences. Significant differences (p < 0.05, small effects) were found between occasional players (who play fewer minutes) and core players (who play intermediate minutes), with the latter reaching higher PDs, though with small effect sizes. Core players also reached higher PDs than top players (who play the most minutes), but the differences were not significant (p > 0.05, trivial effects). In summary, core players reach higher external PDs during competition compared to occasional players, while other contextual factors did not show a significant influence on external PDs.

Research on Wireless Power Transmission Technology for Underground Deformation 3-D Measurement Device

Research on Wireless Power Transmission Technology for Underground Deformation 3-D Measurement Device

Study on the excess seeds removing performance of a potato precision seed metering device.

In the potato mechanical planting industry, the high multi-seeding rate and miss-seeding rate are problems that exist in many potato precision seed metering devices. The main potato seeds in China are cut potatoes with different shapes and sizes. However, there is a lack of research on precision seeding technology for cut potatoes, and the adaptability of many existing potato seeding devices on cut potato is poor. The mechanized precision seeding of potato can effectively reduce labor consumption, improve the seeding effect (decrease multi-seeding rate and miss-seeding rate), improve the improve production of potato per acre and improve the quality commodity potatoes. In response to the above issues, a precision seed metering device with novel scoops, which can adapt to various sizes and shapes of cut potatoes, was developed. In this study, the different seed collecting scenarios (SCSs) were constructed. Through the statics and kinematic analysis, the excess seeds removing (ESR) process was divided in to three areas. The kinematic model of seed potato was established to study the influence of different SCSs and sprocket rotational speeds on the ESR performance, and preliminary determined the appropriate sprocket rotational speed range for ESR process. Then, the coupled simulation results of EDEM and RecurDyn revealed that different sprocket speeds and SCSs had a significant influence on the ESR performance. Finally, the bench tests and field tests were conducted to verify the actual performance of the precision seed metering device. The results showed that, with the increase of the sprocket rotational speed, the ESR performance of the device improved, the multi-seeding rate significantly decreased, but the miss-seeding rate increased. High sprocket rotational speed will remove more seed potatoes and cause high miss-seeding rate. The bench tests showed the appropriate sprocket rotational speed for medium and large potato was 35 r/ min. The field tests obtained the best qualified-seeding rate of 91.54%, the miss-seeding rate of 3.08% and the multi-seeding rate of 5.38%. This study can provide theoretical reference for the design of potato precision seed metering device.

Assessment of measurement variability across automated biometry devices

Purpose: To evaluate measurement variability between different readily available automated biometric devices for the purposes of improving surgeon decision making for phakic intraocular lens (IOL) sizing. Setting: Private practice (Houston, TX, USA) Design: Prospective, single center, bilateral, non-randomized, open-label, observational study. Methods: This study included healthy eyes, with no prior eye surgery, and with a spherical equivalent of at least -1.00D. Orbscan II was compared to Argos, Atlas 9000, caliper, IOLMaster 500, IOLMaster 700, iTrace, Lenstar LS900, Pentacam HR, and Pentacam AXL Wave. Preoperative measurements included white to white (WTW), anterior chamber depth (ACD), and central corneal thickness (CCT). Linear mixed effect models were created to determine adjustment factor between the Orbscan II and other devices for WTW, ACD, and CCT measurements. Results: A total of 204 subjects (408 eyes) completed the study. All mean WTW measurements were significantly different compared to the Orbscan II (p < 0.01), except for Pentacam AXL Wave. All mean ACD and CCT measurements were significantly different compared to the Orbscan II (p < 0.01). Adjustment factors for WTW ranged from -0.65 mm to 0.24 mm. Adjustment factors for ACD ranged from -0.21 mm to -0.16 mm. Adjustment factors for CCT ranged from 19.9 µm to -36.0 µm. Conclusions: The biometers tested were not interchangeable with the Orbscan II. Adjustment factors for WTW and ACD may compensate for differences between devices and adapt ICL sizing recommendations.

Non-axisymmetric coupled non-stationary problem of thermoelectroelasticity for a long piezoceramic cylinder

A new closed solution to the non-axisymmetric coupled non-stationary problem of thermoelectroelasticity was constructed for a long piezoceramic cylinder for the case of satisfaction of the first and the third kind boundary conditions. Cylindrical surfaces were made as electrodes and connected to a measurement device with large input resistance. Limitation of a temperature change “load” rate made it possible to include equations of statics, electrostatics and thermal conductivity in the initial formula. The finite biorthogonal transforms are applying to explore a non-selfadjoint system of differential equations and to develop a closed solution. The obtained relations made it possible to determine the temperature and electric fields, and the stress-strain state in the piezoceramic cylinder, as well as the potential difference between cylindrical surfaces (electrodes) under non-stationary non-axisymmetric temperature impact.

Development of high-speed precision maize metering device for dense planting pattern with standard ridges

This study proposed a high-speed precision dual-chamber maize metering device for the dense planting pattern with standard ridge promoted in China. Through theoretical analysis of the sowing process, parameters for the key components have been designed. The metering device is capable of planting two rows in a single pass for high-speed precision seeding. The effect of operating speed and negative pressure on seed metering quality was investigated. A high-speed camera was used to capture the trajectory of maize seed at different operating speeds, and it was found that intra-row shifts were caused by collisions at the mouth of the seed guide tube and rotation of the seed as it fell. Employing a two-factor, five-level orthogonal rotation test, Investigated the optimal operating parameters for the maize metering device. Response surface analysis showed that optimum seed metering quality was achieved at 14.2 km/h and 13.5 kPa. Validation tests showed a qualified rate of 97.93%, with a coefficient variation of 8.97% for this method. Additionally, an energy consumption analysis indicated a reduction in operating energy consumption of approximately 32% compared to conventional air suction seed metering devices for dense planting with large ridges on the same area of farmland. This study provides insights for reducing energy consumption in the seeding process, contributing to the sustainable development of agricultural resources.

A Measurement Device for the Normal Spectral Emissivity of Materials Under Low-Temperature and Vacuum Conditions

Based on parallel light, a device for measuring sample emissivity under vacuum and low temperature was established. In this paper, a new emissivity measurement formula was designed, which replaces the derivation of Kirchhoff’s law of thermal radiation. The device is designed with a light-weight blackbody, an improved cooling speed, and an improved PID temperature control system to achieve a good temperature stability, close to 1 mK. The device is capable of measuring within a wavelength range of 4 μm to 16 μm. The results of the uncertainty assessment show that the uncertainty of the normal emissivity is better than 1.6% in the range of 4 μm to 7 μm, and better than 0.6% in the range of 7 μm to 14 μm (k = 1) at 0 °C.

Methods and equipment for analysis and diagnosis of marine engines during navigation

ABSTRACT Expert diagnostic systems are required for the development of intelligent marine engines and other ship systems. Measurement and analysis of marine engine parameters are fundamental in ensuring the efficient operation of these key propulsion systems. This paper introduces a novel measurement device and method capable of real-time monitoring of cylinder pressure and emissions in marine engines. The experimental procedure involved measuring critical engine parameters and emissions, such as NOx, CO2, and particulate matter, under various operating conditions. The device provided accurate real-time data acquisition, enabling detailed analysis of combustion processes and emission profiles under various operating conditions. Furthermore, a numerical model was developed, like digital twins, which utilized the measured data to simulate engine performance, predict potential failures, and optimize operational parameters to enhance efficiency and reduce environmental impact. The two most important parameters: the indicated cylinder pressure and NOx emissions were simulated and validated. Measured NOx was measured 889 ppm at 90% and simulated results was 985 ppm, which is 10% difference. Specific fuel consumption has less than 1% difference between measured and simulated data. Novelty of this work is the methodology used in the testing and calibration processes. Current actual parameters of engine operation are monitored, based on which a comparative analysis is made in real time. Previous diagnostic control systems did not provide diagnostics, comparative analysis and optimization in real time. The integration with digital twin allowed potential wear and tear predictions, fuel consumption optimization, and emission reduction strategies, showcasing the potential of this technology to improve marine engine diagnostics and maintenance. The device and its virtual counterpart worked well together to provide a complete understanding of the engine’s response. This facilitates proactive maintenance strategies and contributes to eco-efficiency in the maritime sector.

Thermophysical Properties of Silicon Oxide Nanoparticles in Water and Ethylene Glycol–Water Dispersions

Measurements of transmission as well as thermophysical properties have been carried out for different concentrations of SiO2 nanoparticles (0, 1, 2, 5, 10, and 20 wt.%) in pure water (W) and ethylene glycol–water mixture (EG/W) in a weight ratio of 25/75, from 298 to 323 K at 100 kPa. In particular, the density, specific heat capacity, and thermal diffusivity are measured by a density meter, differential scanning calorimetry, and the laser flash method. In the case of 20 wt.% SiO2, transmission in the visible range is reduced by 9.3%. Simultaneously, the density rises linearly to 12.3% (in W) and 11.3% (in EG/W). The specific heat capacity decreases to 15.9% (in W) and 17.3% (in EG/W), while the thermal diffusivity rises to 16.4% (in W) and 20.4% (in EG/W). While the density measurements are in very good agreement with the literature, the measured values of the specific heat capacity deviate more than 5%, especially for concentrations below 5 wt.% SiO2. Moreover, it is shown that the thermal conductivity increases less for fluids in small gaps compared to other authors, which could be due to the suppression of the Brownian motion. Based on the measurement results, temperature- and concentration-dependent correlations for the investigated thermophysical properties are developed using two adjustable parameters. In general, these correlations show deviations of less than 4% from the experimental results, which will help to fill the gaps in the variation of experimental results due to size, SiO2 nanofluid production, and different measurement devices, and thus optimize solar thermal applications with SiO2 nanofluid.

Establishment of a measuring unit based on Arduino board for recording vibrations on an agricultural tractor during tillage process

When it comes to agricultural tractor ergonomics, vibration is one of the most important factors to consider. However, little research has been done to evaluate this parameter and its impact on workers’ health. Therefore, the aim of this study was to fabricate a portable vibration measurement unit (PVMU) based on an Arduino microcontroller and a vibration sensor (accelerometer ADXL335). The PVMU performance was tested in combination with a commercially available three-axis USB vibration measurement device (VB10). Furthermore, the vibration measurements were used to calculate the tractor driver’s whole-body vibration load according to ISO 2631-1 along three perpendicular axes (X, Y, and Z) during soil tillage using a disk harrow under different tillage speeds of 4.0, 5.5, and 7.0 km/h at a tillage depth of 15 cm in a sandy loam soil. Performance tests of the PVMU and the commercially available three-axis USB vibration meter (VB10) revealed a highly significant level of agreement based on a correlation coefficient of 0.9822. It was found that the vibration level increases with increasing tillage speed. The highest vibration level, 0.80 m/sec2, was observed in the vertical direction (Z-axis) at a tillage speed of 7.0 km/h. The lowest vibration level, 0.12 m/sec2, was observed in the lateral direction (Y-axis) at a tillage speed of 4.0 km/h. The dominant frequency range of vibration transmitted to the tractor seat was found to be between 2.04 Hz and 20.14 Hz. The daily exposure values that is, A (8) determined during this study to assess the health hazards due to vibration for tractor drivers ranged from 0.01 m/sec2 to 0.06 m/sec2 lower than the recommended exposure action value (EAV) limit that is, 0.5 m/sec2. This means that there is no health risk for the driver during the soil tillage using a disk harrow plow. The PVMU proposed in this study is very reliable and can be used as a vibration assessment tool for other agricultural tractor-implement combinations.

Study on heat transfer law of low temperature oxidation of coal under convection condition

Coal oxidation in the goaf generates heat accumulation influenced by airflow, leading to heat transfer within the porous coal structure. This study experimentally investigates the heat transfer characteristics of coal under convective conditions. A temperature migration rate measurement device was developed, and a formula for calculating the temperature migration rate was derived using the steady-state heat conduction differential equation and experimental data. The study examines the effects of temperature and airflow on the temperature migration rate and heat transfer characteristics. The experimental results indicate that the heat transfer effect of coal at low temperatures is minimal and volatile. As the temperature increases, efficiency improves, and heat transfer stabilizes. Airflow facilitates coal's heat transfer, causing the temperature generated by coal oxidation to concentrate on the downwind side. Additionally, airflow inhibits coal oxidation at low temperatures, while high temperatures promote it. Furthermore, the temperature migration rate of coal decreases with increasing temperature, initially decreases and then increases with rising airflow at low temperatures, whereas the opposite trend is observed at high temperatures.

Device-independent certification of desirable properties with a confidence interval

In the development of quantum technologies, a reliable means for characterizing quantum devices, be it a measurement device, a state-preparation device, or a transformation device, is crucial. However, the conventional approach based on, for example, quantum state tomography or process tomography relies on assumptions that are often not necessarily justifiable in a realistic experimental setting. Although the device-independent (DI) approach to this problem bypasses the shortcomings above by making only minimal, justifiable assumptions, most of the theoretical proposals to date only work in the idealized setting where independent and identically distributed (i.i.d.) trials are assumed. Here, we provide a versatile solution for rigorous device-independent certification that does not rely on the i.i.d. assumption. Specifically, we describe how the prediction-based ratio (PBR) protocol and martingale-based protocol developed for hypothesis testing can be applied in the present context to achieve a device-independent certification of desirable properties with confidence interval (CI). To illustrate the versatility of these methods, we demonstrate how we can use them to certify—with finite data—the underlying negativity, Hilbert space dimension, entanglement depth, and fidelity to some target pure state. In particular, we provide examples showing how the amount of certifiable negativity and fidelity scales with the number of trials and how many experimental trials one needs to certify a qutrit state space or the presence of genuine tripartite entanglement. Overall, we have found that the PBR protocol and the martingale-based protocol often offer similar performance, even though the latter does have to presuppose any witness (Bell-like inequality). In contrast, our findings also show that the performance of the martingale-based protocol may be severely affected by one’s choice of Bell-like inequality. Intriguingly, a Bell function useful for self-testing does not necessarily give the optimal confidence-gain rate for certifying the fidelity to the corresponding target state.

Radar-based contactless heart beat detection with a modified Pan–Tompkins algorithm

Background.Using radar for non-contact measuring human vital signs has garnered significant attention due to its undeniable benefits. However, achieving reasonably good accuracy in contactless measurement senarios is still a technical challenge.Materials and methods.The proposed method includes two stages. The first stage involves the process of datasegmentation and signal channel selection. In the next phase, the raw radar signal from the chosen channel is subjected to modified Pan-Tompkins.Results.The experimental findings from twelve individuals demonstrated a strong agreement between the contactless radar and contact electrocardiography (ECG) devices for heart rate measurement, with correlation coefficient of 98.74 percentage; and the 95% limits of agreement obtained by radar and those obtained by ECG were 2.4 beats per minute.Conclusion.The results showed high agreement between heart rate calculated by radar signals and heart rate by electrocardiograph. This research paves the way for future applications using non-contact sensors to support and potentially replace contact sensors in healthcare.

Robotized Mobile Platform for Non-Destructive Inspection of Aircraft Structures

The robotization of the non-destructive inspection of aircraft is essential for improving the accuracy and duration of performed inspections, being an integral part of inspection and data management systems within the currently developed NDT 4.0 concept. In this paper, the authors presented the design and testing of a universal mobile platform with interchangeable sensing systems for the non-destructive inspection of aircraft structures with various angles of inclination. As a result of the performed studies, a low-cost approach of automation of existing measurement devices used for inspection was proposed. The constructed prototype of the mobile platform was equipped with eddy current testing probe and successfully passed both laboratory and environmental tests, demonstrating its performance in various conditions. The presented approach confirms the effectiveness of the automation of the inspection process using climbing robots and defining the directions of possible development of automation in non-destructive testing in aviation.

Developing the optimized control scheme for digital light processing 3D printing by combining numerical simulation and machine learning-guided temperature prediction

Digital light processing (DLP) 3D printing has attracted significant attention for its rapid printing speed, high accuracy, and diverse applications. However, the continuous DLP printing process releases substantial heat, resulting in a swift temperature rise in the curing area, which may lead to printing failures. Due to the lack of effective means to measure real-time temperature changes of the curing surface during continuous DLP 3D printing, the prevailing approach is to predict temperature variations during printing via numerical simulation. Nevertheless, temperature prediction methods relying solely on numerical simulation tend to be slow and overlook heat exchange dynamics during printing, potentially resulting in prediction inaccuracies, particularly for complex models. To address these issues, this paper proposes a method to combine numerical simulation and a machine learning approach for temperature prediction in the DLP 3D printing process, along with a printing control scheme generation method. Firstly, the m+nth order autocatalytic kinetic model considering the light intensity and the Beer–Lambert law are employed to formulate the heat calculation equation for the photopolymer resin curing reaction. Subsequently, a heat exchange calculation equation is established based on Fourier heat conduction law and Newton’s cooling equation. A numerical simulation model for temperature changes during the printing process is then developed by integrating the heat calculation equation, heat exchange calculation equation, and measurement data from Photo-DSC. Furthermore, a temperature measurement device for the printing process is designed to validate the accuracy of the numerical simulation. Following this, an improved Long Short-term Memory (LSTM) network is proposed, using temperature change data generated by the numerical simulation model to train the network for rapid (2×10−4s/layer) prediction of temperature changes during printing. Finally, aiming for the shortest printing time, an optimized control scheme planning algorithm and a target function are designed based on the model’s temperature change data and the monomer’s flash point to ensure the temperature remains below this threshold. This algorithm can automatically generate the optimal printing control scheme for any model. Experimental results demonstrate that the proposed temperature prediction method can predict temperature variation accurately. Based on this, the generated printing control scheme can guarantee efficient and high-quality manufacturing for anymodel.

A multi-task learning based line parameter identification method for medium-voltage distribution network

Accurate line parameters are critical for and dispatch in distribution systems. External operating condition variations affect line parameters, reducing the accuracy of state estimation and power flow calculations. While many methods have been proposed and obtained results rather acceptable, there is room for improvement as they don’t fully consider line connections in known topologies. Furthermore, inaccuracies in measurement devices and data acquisition systems can introduce noise and outliers, impacting the reliability of parameter identification. To address these challenges, we propose a line parameter identification method based on Graph Attention Networks and Multi-gate Mixture-of-Experts. The topological structure of the power grid and the capabilities of modern data acquisition equipment are utilized to capture. We also introduce a multi-task learning framework to enable joint training of parameter identification across different branches, thereby enhancing computational efficiency and accuracy. Experiments show that the GAT-MMoE model outperforms traditional methods, with notable improvements in both accuracy and robustness.

An adaptive distance protection scheme considering fault resistance, injected current, and structural changes in the power system

AbstractFaults in power systems occur for various reasons, such as aging or natural disasters. Detecting, locating, and promptly clearing these faults is crucial for maintaining the safety and reliability of transmission lines (TLs). Distance relays (DRs), which protect TLs, detect faults, estimate their location, and send the required commands. However, these relays may experience mis‐detection due to manipulated impedance arising from both internal and external factors. These factors include measurement device errors, network topology changes, the presence of fault resistance (FR), and injected currents from remote line terminals. To address this challenge, an innovative adaptive protection scheme that considers FR, changes in network topology, and injected current from the opposite end of the line is proposed. By estimating the equivalent circuit impedances (ECIs) of the network connected to the terminal of the TL, this protection scheme utilizes impedance estimation techniques at the line terminals and offline network information. Simulation studies (tested on the IEEE 39‐bus standard network) show that the proposed scheme accurately estimates fault location (FL) and FR with high precision. The simulation results demonstrate its effectiveness in improving the performance of conventional distance protection relays in both the first and second protection zones ( and ).

Broadband generation and tomography of non-Gaussian states for ultra-fast optical quantum processors

Quantum information processors benefit from high clock frequencies to fully harness quantum advantages before they are lost to decoherence. All-optical systems offer unique benefits due to their inherent 100-THz carrier frequency, enabling the development of THz-clock frequency processors. However, the bandwidth of quantum light sources and measurement devices has been limited to the MHz range, with nonclassical state generation rates in the kHz range. In this study, we demonstrated broadband generation and quantum tomography of non-Gaussian states using an optical parametric amplifier (OPA) as a squeezed light source and an optical phase-sensitive amplifier (PSA). Our system includes a 6-THz squeezed-light source, a 6-THz PSA, and a 66-GHz homodyne detector. We successfully generated non-Gaussian states at a 0.9 MHz rate with sub-nanosecond wave packets using a continuous-wave laser. The performance is currently limited by the jitter of superconducting detectors, restricting the usable bandwidth to 1 GHz. Our technique extends the bandwidth to GHz, potentially increasing non-Gaussian state generation rates for practical optical quantum processors using OPAs.