Actual Measurements Research Articles

Leveraging machine learning to minimize experimental trials and predict hot deformation behaviour in dual phase high entropy alloys

In recent time, high entropy alloys (HEAs) are widely used due to their wide design space and remarkable properties allowing a vast range of property variations with myriads of possible combinations of constituent elements. This research utilizes machine learning techniques, including Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Decision Tree (DT), and Support Vector Regressor (SVR), to predict the flow stress behavior of the dual FCC phase CoCrCu1.2FeNi high entropy alloy (HEA) at new temperature and strain rates to reduce the experiments dependency. The RF and KNN models were identified as top performers, with R² values of 0.987 and 0.986, respectively. These models successfully predicted flow stresses, aligning closely with actual measurements. A new flow stress-strain curve was generated at new temperature of 1123K, demonstrating good performance metrics for RF and KNN. with R² values of 0.97 and 0.958 respectively. This approach has the potential to significantly reduce the need for extensive experimental work in determining the mechanical behaviour of HEA, offering substantial savings in time, cost, and energy, and marks a significant advancement in the development of such alloys.

Fabrication and Evaluation of an Electromagnetic Shielding Material Made of Randomly Chopped Tape Carbon Fiber/Polyamide 6 Composites

Abstract Environmental pollution generated by electromagnetic wave radiation has significantly increased including in automotive applications. Thus, material technology, including polymer composite, has attracted a reduction of the negative effect of electromagnetic radiation. In this study, carbon fiber reinforced polyamide thermoplastic (PA-6/CF) made of carbon fiber with polyamide 6 tape, in the shape of a half-box, was produced using the hot compression method with different tape-length, and then the electrical conductivity and electromagnetic shielding performance were evaluated. The electrical conductivity was obtained from the resistivity measured by using a digital multimeter. Whilst the effectiveness of electromagnetic interference shielding (EMI-SE) was investigated using two methods, calculation, and actual measurement. The result shows that there is a different electrical conductivity between base and slope area of the PA-6/CF box composite. It also was observed that the actual EMI shielding performance shows a corresponding increase with the length of chopped carbon fiber tape and has a higher value compared to the calculated EMI-SE. Then, the highest value obtained for actual EMI-SE was 54 dB for the PA6/CF composite with 20-mm tape length, in the frequency range 0.3 – 1 GHz, and on horizontal antenna polarization. Whereas the composite with 10-mm and 15-mm tape length was the lowest EMI- SE (38 dB) on vertical antenna polarization, in the same frequency range. Furthermore, the microscopy analysis depicted that there are more voids exist in the slope area compared with the base area of the PA-6/CF composites. Finally, the molded PA-6/CF composites can be used as the battery enclosures of electrical vehicles in automobiles.

Walking the talk? The educational agenda of the Austrian populist radical right: From discourse to policymaking

This paper explores the educational policy positions in programmatic and parliamentary discourse by the Austrian Freedom Party (FPÖ) and their actual policy measures implemented in executive office. The FPÖ is one of the most successful populist radical right parties (PRRPs) in Western Europe and also served in two periods of government with the centre-right Austrian People’s Party (ÖVP) (2000–2005; 2017–2019), providing a fruitful record over time. Our analysis over three decades (1990–2018) reveals that even though it’s not a highly salient policy issue, education policy has indeed become an ideological battleground for the FPÖ. At large, it uses education policy claims to substantiate the ideological core pillars of PRRPs, yet it also delivers on many of these claims once in government. This is particularly true for its culturalist claims, which trigger an increasing convergence by the centre-right partner. In contrast, the always-existing congruence of redistributive claims results in several policy decisions in both periods of government.

On the accuracy of the segmentation process and transcatheter heart valve dimensions in TAVI patients

Accurate segmentation of medical images is critical for generating patient-specific models suitable for computational analyses, particularly in the context of transcatheter aortic valve implantation (TAVI). This study aimed to quantify the accuracy of the segmentation process from medical images of TAVI patients to understand the uncertainty in patient-specific geometries. We also quantified discrepancies between actual and CT-related diameter measurements due to artifacts and intra-observer variability. Segmentation accuracy was assessed using both synthetic phantom models and patient-specific data. The impact of voxelization and CT scanner resolution on segmentation accuracy was evaluated, while the intersection over union (IoU) metric was used to compare the consistency of different segmentation methodologies. The voxelization process introduced a marginal error (<1%) in phantom models relative to CAD models. CT scanner resolution impacted segmented model accuracy only after a 7.5-fold increase in voxel size compared to the baseline medical image. IoU analysis revealed higher segmentation accuracy for calcification (93.4 ± 3.1 %) compared to the aortic wall (85.4 ± 8.4 %) and native valve leaflets (75.5 ± 6.3 %). Discrepancies in THV diameter measurements highlighted a ∼5 % error due to metallic artifacts, with variability among observers and at different THV heights. Errors due to voxel size, segmentation methodologies and CT-related artifacts can impact the reliability of patient-specific geometries and ultimately computational predictions used to asses clinical outcomes and enhance decision-making. This study underscores the importance of accurate segmentation and its standardization for patient-specific modeling of TAVI simulations.

Experimental research of helicopter blade load in flight based on optical sensing

Abstract The actual flight measurement of blade load is an extremely important part of the new helicopter. In order to solve the problems existing in the traditional electrical measurement method, such as weak anti-interference ability, short life, and large added mass, a flight measurement method of blade load based on optical sensing is proposed. Firstly, the strain mode distribution law is obtained by calculating the dynamic response of the real blade. The optimal layout of the optical fiber measurement points is determined. Through theoretical analysis of strain transferring and experimental verification, a highly reliable fusion scheme of blade and fiber is designed to meet the flight requirements. Based on the power line carrier technology, the optical sensing test system for helicopter flight test is developed. The flight test of blade load is completed successfully. The results of optical fiber measurement are consistent with the results of traditional electrical measurement, which indicates that the optical sensing technology has good applicability in the measurement of helicopter blade load. At the same time, it has been proved that the optical measurement system set is reliable and durable. It is of great significance for the leap from electric measurement to optical measurement.

Is cardiovascular risk profiling from UK Biobank retinal images using explicit deep learning estimates of traditional risk factors equivalent to actual risk measurements? A prospective cohort study design

ObjectiveDespite extensive exploration of potential biomarkers of cardiovascular diseases (CVDs) derived from retinal images, it remains unclear how retinal images contribute to CVD risk profiling and how the results can...

Hybrid building energy modeling method with parameterized prototype models and rapid calibration

Building energy models were widely used in building energy performance analysis and efficiency improvement, such as building demand response assessment and energy system optimization. However, building energy modeling was a time-consuming and complex process. Therefore, this paper proposed a hybrid building energy modeling method based on parameterized prototype models and rapid calibration. The main feature of the method was the rapid generation of target building energy models using the prototype building energy performance database. The parameterized prototype building energy model considered 13 uncertainty parameters. Based on the range of 13 uncertainty parameters, such as wall U-value and solar heat gain coefficient, 1000 simulations were performed by Monte Carlo sampling to generate the prototype building energy performance database. The target building energy model was quickly calibrated by learning from this database. It filtered the models that met the requirements based on the actual building measurement data. Based on this hybrid modeling method, the rapid modeling tool AutoBPS-Hybrid was developed in a ruby environment. Shopping mall buildings located in three different climate zones were selected as case studies for this study. The results showed that if only one building energy model meeting the percentage errors was needed, only 3–4 simulations were required. If it was necessary to match the real uncertainty parameter distributions, an average of about 53 simulations was needed. The building energy models were applied to plug load and lighting control in buildings. The shopping mall buildings in Harbin, Beijing and Chengdu could reduce energy consumption by 10.18–13.33 kWh/m2, 14.43–18.15 kWh/m2 and 11.54–14.69 kWh/m2 per year, respectively.

Application of indoor thermal energy cycle and visual communication simulation scene based on Machine vision and optical image enhancement

Traditional thermal analysis methods have limitations in complex environments, and innovative technologies are urgently needed to improve the accuracy of indoor thermal monitoring and optimization. In this paper, machine vision and optical image enhancement technology are combined to simulate the indoor heat energy cycle process to achieve more efficient heat management and visual communication, so as to improve the energy utilization efficiency and residential comfort of buildings. In this paper, a high-resolution camera is used to capture real-time images of indoor environment, image quality is enhanced by image processing algorithm, and heat distribution characteristics are extracted. The machine learning model is used to analyze the dynamic changes of the thermal energy cycle, and the visual model of the indoor thermal energy cycle is constructed by combining the fluid dynamics theory. The experimental results show that the proposed method can effectively identify and display the key areas of indoor heat circulation and their changing trends. The simulation results show that the optical enhanced images can show different temperature regions more clearly, directly reflect the heat flow situation, and have a high agreement with the actual measurement data. Therefore, the indoor thermal energy cycle simulation method based on machine vision and optical image enhancement not only improves the accuracy and real-time performance of thermal energy analysis, but also provides visual support for indoor environmental management.

Open-set Communication Scene Recognition Based on Metric Learning

Abstract In this paper, we study the problem of recognizing communication scenes under open set protocols, i.e., making the extracted features of communication scenes sufficiently separable under a suitable metric space. To achieve it, we design a feature fusion model based on a convolutional neural network to extract the features of communication scenes, and we take the real and imaginary parts of the channel frequency response as model inputs to extract the scene features sufficiently. Furthermore, in order to optimize the distribution of scene features in angular space, a Linear-Softmax loss function is designed to restrict the inter-class angles of extracted features to be larger than the intra-class angles by two hyperparameters. Meanwhile, a cosine distance-based communication scene recognition algorithm is designed to complete the communication scene recognition by calculating the cosine distance between features. Finally, it is tested on actual measurement data sets, and the experimental results show that compared with the state-of-the-art loss functions, the proposed Linear-Softmax loss function enables the learning of the maximum angular margin. The Linear-Softmax loss function combined with the proposed communication scenes recognition algorithm can complete the recognition of both closed-set and open-set test data, with a recognition accuracy as high as 97.28% on the closed-set test set and 85.65% on the open-set test set.

On-orbit update and scrubbing design of SRAM FPGA for spacecraft

Abstract In the design of spacecraft products, improving the anti-irradiation ability of products, especially the anti-single Event Upsets ability of electronic products, is an important research area to improve the reliability of products. As a programmable device, FPGA is widely used in electronic products. In this paper, the reliability improvement method of Xilinx Company’s SRAM FPGA is studied, and a scheme for on-orbit update and scrubbing refresh of SRAM FPGA is designed using anti-fuse FPGA. This scheme can support on-orbit updates and scrubbing of multiple SRAM FPGAs at the same time, which can meet specific engineering applications. Through simulation verification and actual measurement verification, it can effectively improve the ability of spacecraft on-orbit fault handling and function upgrading. It provides a way to improve the reliability of electronic products.

Design and Implementation of a Ka-Band Frequency Source Based on DS+DDS Hybrid Method

Abstract This article proposes a design scheme for a broadband agile frequency conversion Ka-band frequency source, which adopts a mixed frequency synthesis method of DS+DDS. This method combines the advantages of DDS such as fast frequency switching speed, small frequency step, and wide DS frequency bandwidth, and uses segmented filtering to effectively suppress the disadvantage of DDS spurious error. DS adopts an up conversion and double frequency doubling scheme to broaden the frequency band, with simple design and low cost. The deterioration of phase noise is offset by selecting a low phase noise PLL. Actual measurement shows that the output bandwidth of the frequency source is 8GHz, with spurious suppression ≤ −61dBc, frequency switching time ≤ 140ns, output power ≥ 15dBm, step frequency of 1MHz, and phase noise ≤ −100dBc/Hz@1kHz, meet the design requirements.

The One-Dimensional Flow Pressure Loss Correction Model Based on the Particle Flow through Concrete Bend

The rheological behavior of concrete pumping in the bend section is complex. The existing conversion calculation for pumping pressure loss based on the straight pipe section often fails to meet engineering precision requirements. This paper develops a pressure loss correction model for a concrete pumping bend section based on a new one-dimensional flow model for the straight pipe section simulation of particle flow; the study analyzes the impact of parameters such as elbow shape and pumping flow rate on pressure loss. An equivalent conversion relationship between the bend section and the straight section is established. The comparison with the measured pressure values from the project shows that the relative error between the pressure loss values calculated by the correction model and the actual measurements is 15.8%.

Using well log data to predict rock compressibility and elasticity in Zubair formation/ southern of Iraq

The mechanical characteristics of rocks such as elasticity (Young's modulus, Poisson's ratio, and unconfined compressive strength) play an essential in sand production analysis, well design, and borehole stability assessment. In this study, the mechanical characteristics of rocks were indirectly estimated using gamma ray, density, and acoustic (compressional and shear) log data from well RU-X in the Rumaila oil field, specifically for the Zubair Formation. These estimated properties were compared to direct measurements obtained from triaxial and uniaxial mechanical tests conducted on well RU-X. The results showed a significant similarity between the indirect estimates and the direct measurements, indicating their reliability for sand production analysis and their valuable contribution to constructing the geomechanical model. Moreover, the static profile of Poisson's ratio was validated using laboratory core test results, demonstrating reasonable agreement. The validation process involved comparing laboratory-derived measurements with actual field measurements in the Zubair Formation. The higher Poisson's ratio observed in the shale was attributed to the slower propagation of acoustic waves, resulting in a good matching with an R2 value of 0.77. On the other hand, the lower Young's modulus in the shaly formations indicated lower resistance to deformation, with a comparative ratio of R²=0.96. Static measurements, which consider various influencing factors, provide a more realistic representation of rock behavior under different conditions. Regarding unconfined compressive strength, the comparative ratio was R²=0.83.

Thermal Monitoring of an Internal Combustion Engine for Lightweight Fixed-Wing UAV Integrating PSO-Based Modelling with Condition-Based Extended Kalman Filter

The internal combustion engines of long-endurance UAVs are optimized for cruises, so they are prone to overheating during climbs, when power requests increase. To counteract the phenomenon, step-climb maneuvering is typically operated, but the intermittent high-power requests generate repeated heating–cooling cycles, which, over multiple missions, may promote thermal fatigue, performance degradation, and failure. This paper deals with the development of a model-based monitoring of the cylinder head temperature of the two-stroke engine employed in a lightweight fixed-wing long-endurance UAV, which combines a 0D thermal model derived from physical first principles with an extended Kalman filter capable to estimate the head temperature under degraded conditions. The parameters of the dynamic model, referred to as nominal condition, are defined through a particle-swarm optimization, minimizing the mean square temperature error between simulated and experimental flight data (obtaining mean and peak errors lower than 3% and 10%, respectively). The validated model is used in a so-called condition-based extended Kalman filter, which differs from a conventional one for a correction term in section prediction, leveraged as degradation symptom, based on the deviation of the model-state derivative with respect to the actual measurement. The monitoring algorithm, being executable in real-time and capable of identifying incipient degradations of the thermal flow, demonstrates applicability for online diagnostics and predictive maintenance purposes.

Rational partitioning of spectral feature space for effective clustering of massive spectral image data

We have successfully proposed and demonstrated a clustering method that overcomes the “needle-in-a-haystack problem” (finding minuscule important regions from massive spectral image data sets). The needle-in-a-haystack problem is of central importance in the characterization of materials since in bulk materials, the properties of a very tiny region often dominate the entire function. To solve this problem, we propose that rational partitioning of the spectral feature space in which spectra are distributed, or defining of the decision boundaries for clustering, can be performed by focusing on the discrimination limit defined by the measurement noise and partitioning the space at intervals of this limit. We verified the proposed method, applied it to actual measurement data, and succeeded in detecting tiny (~ 0.5%) important regions that were difficult for human researchers and other machine learning methods to detect in discovering unknown phases. The ability to detect these crucial regions helps in understanding materials and designing more functional materials.

Evaluating the Harmonic Effects on the Thermal Performance of a Power Transformer

Harmonics in the power grid contribute to increased power losses in both the core and windings of power transformers. These losses lead to abnormal rises in temperature causing overheating and reduce the efficiency of the transformer. If the losses and temperature exceed the values set during the design stage for linear load conditions, it can damage the transformer’s insulating materials and shorten its lifespan. To assess the thermal impact of power system harmonics on transformers under steady-state and transient conditions, the rated losses and harmonic losses of the transformer are calculated. These losses are then inputted into a developed thermal 3D finite element method (FEM) performance model to determine the temperature distribution of transformer components. The numerical results from the thermal model will be compared with data from a Hyundai test report and real measurements from Egypt’s Kureimat power plant, specifically a 750 MW combined cycle power plant. The thermal modeling is focused on a step-up (16.5/240 kV), 240 ± 4 × 2.5%, 180/240/300 MVA power transformer operating in ONAN, ONAF1, and ONAF2 modes. This paper shows that the developed model aligns closely with actual measurements and the HYUNDAI test report. The loss calculations reveal that the discrepancy in total losses, with and without accounting for harmonics, becomes more pronounced as the load increases. Using this model, the presence of grid harmonics results in a higher temperature distribution across transformer components, leading to an increase in the hot spot temperature.

Reducing Measurement Costs of Thermal Power: An Advanced MISM (Mamba with Improved SSM Embedding in MLP) Regression Model for Accurate CO2 Emission Accounting.

Current calculation methods for the carbon content as received (Car) of coal rely on multiple instruments, leading to high costs for enterprises. There is a need for a cost-effective model that maintains accuracy in CO2 emission accounting. This study introduces an MISM model using key parameters identified through correlation and ablation analyses. An Improved State-Space Model (ISSM) and an IS-Mamba module are integrated into a Multi-Layer Perceptron (MLP) framework, enhancing information flow and regression accuracy. The MISM model demonstrates superior performance over traditional methods, reducing the Root Mean Square Error (RMSE) by 22.36% compared to MLP, and by 9.65% compared to Mamba. Using only six selected parameters, the MISM model achieves a precision of 0.27% for the discrepancy between the calculated CO2 emissions and the actual measurements. An ablation analysis confirms the importance of certain parameters and the effectiveness of the IS-Mamba module at improving model performance. This paper offers an innovative solution for accurate and cost-effective carbon accounting in the thermal power sector, supporting China's carbon peaking and carbon neutrality goals.

Long-term tracking for high-frequency surface wave radar based on heading constraint filtering combined ELM

Abstract Aiming at the problem of low measurement resolution and poor tracking accuracy in High-frequency surface wave radar (HFSWR) tracking field, a tracking method that combines the heading constraint filter and Extreme Learning Machine (ELM) is proposed. Compared with the existing research, the innovation of this study lies in proposing a two-stage tracking method based on the short-term stable state of the target, and similarity of long-term trajectory. Firstly, the heading constraint information is introduced into the estimator and improve the estimation accuracy. In multi-target tracking scenarios, many tracklets can be obtained. Then, the ELM learns robust features of tracklets to achieve the trajectory segment classification from the same target. This method can be widely applied to long-term tracking of cargo or passenger ships with fixed destinations, significantly improving tracking performance by utilizing implicit domain knowledge without additional information. The actual data of this paper comes from HFSWR on Bohai Bay. Both the simulation and actual measurement experiments show that the proposed cascaded tracking method achieves long-term continuous tracking, and generates more complete trajectories. Moreover, the proposed Extended Kalman filter based on pseudo-measurement and intercept parameters (IPM-EKF) exhibits better tracking stability and accuracy in limited scan steps. This study provides new ideas and methods for improving the tracking performance of special targets in the HFSWR field by utilizing motion features.

Polygenic scores for complex traits are associated with changes in concentration of circulating lipid species.

Understanding perturbations in circulating lipid levels that often occur years or decades before clinical symptoms may enhance our understanding of disease mechanisms and provide novel intervention opportunities. Here, we assessed if polygenic scores (PGSs) for complex traits could detect lipid dysfunctions related to the traits and provide new biological insights. We constructed genome-wide PGSs (approximately 1 million genetic variants) for 50 complex traits in 7,169 Finnish individuals with routine clinical lipid profiles and lipidomics measurements (179 lipid species). We identified 678 associations (P < 9.0 × 10-5) involving 26 traits and 142 lipids. Most of these associations were also validated with the actual phenotype measurements where available (89.5% of 181 associations where the trait was available), suggesting that these associations represent early signs of physiological changes of the traits. We detected many known relationships (e.g., PGS for body mass index (BMI) and lysophospholipids, PGS for type 2 diabetes and triacyglycerols) and those that suggested potential target for prevention strategies (e.g., PGS for venous thromboembolism and arachidonic acid). We also found association of PGS for favorable adiposity with increased sphingomyelins levels, suggesting a probable role of sphingomyelins in increased risk for certain disease, e.g., venous thromboembolism as reported previously, in favorable adiposity despite its favorable metabolic effect. Altogether, our study provides a comprehensive characterization of lipidomic alterations in genetic predisposition for a wide range of complex traits. The study also demonstrates potential of PGSs for complex traits to capture early, presymptomatic lipid alterations, highlighting its utility in understanding disease mechanisms and early disease detection.

Study on the predictions of acoustic characteristics of microspeakers based on the specific airflow resistance of ventilation materials: Mesh type

In this study is presented a method for predicting the acoustic characteristics of microspeakers with mesh-type ventilation materials that combines specific airflow resistance with equivalent circuit model. The specific airflow resistances of eight types of ventilation materials were quantified using a self-made measurement system. These materials were then used to simulate the acoustic properties of 40 φ and 20 φ microspeakers, respectively. The results were compared with simulation results obtained using Dr. Maa’s porous plate acoustic impedance and actual measurements. After applying a stable correction factor, the self-made measurement system accurately determined specific airflow resistance values with high reproducibility and stability. A regression curve based on scanning electron microscope obtained porosity and measured specific airflow resistance effectively predicted the specific airflow resistance of materials with known porosity. Applying these values to acoustic simulations, the proposed method significantly improved accuracy, outperforming Dr. Maa’s method by a factor of six, and closely matched actual measurements. This innovative approach is versatile and applicable to various conditions and types of ventilation materials, as well as enhances predictions of their impact on electro-acoustic product performance.