Acquisition Of Parameters Research Articles

Minimization of a ship's magnetic signature under external field conditions using a multi-dipole model

The paper addresses the innovative issue of minimizing the ship's magnetic signature under any external field conditions, i.e., for arbitrary values of ambient field modulus and magnetic inclination. Varying values of the external field, depending on the current geographical location, affect only the induced part of ship's magnetization. A practical problem in minimizing the ship signature is separating permanent magnetization from induced magnetization. When the ship position changes, a signature measurement has to be made under new magnetic field conditions to update the currents in the coils. This is impractical or even difficult to do (due to the need for a measuring ground), so there is a need to predict the ship's magnetization value in arbitrary geographical location conditions based on the reference signature determined on the measuring ground. In particular, the model predicting the signatures at a new geographical location must be able to separate the two types of magnetization, as permanent magnetization is independent of external conditions. In this paper, a FEM model of the vessel is first embedded in an external field and permanent magnetization is simulated using DC coils placed inside the model. Then, using the previously developed rules for data acquisition and determination of model parameters, a multi-dipole model is synthesized in which the induced and permanent parts are separated. The multi-dipole model thus developed has been successfully confronted with the initial model in FEM environment. The separation of permanent and induced magnetization allows the latter to be scaled according to new values of the external field. In the paper, the situation of determining a signature at one geographical position and its projection onto two other positions is analyzed. Having determined the signature with a high degree of accuracy anywhere in the world, it is possible to perform classical signature minimization by determining DC currents in coils placed inside the ship's hull. The paper also analyzes the effectiveness of ship's signature minimization and the influence of ship's course on the signature value. The advantage of the method presented in this paper is an integrated approach to the issue of scaling and minimization of ship magnetic signature, which has not been presented in the literature on such a scale before.

A Framework for Managing and Monitoring the Battery Health Parameters of Rental Electric Vehicles

The rapid adoption of electric vehicles (EVs) has paved the way for a sustainable transportation future. However, ensuring the safety and reliability of rental EVs, particularly regarding their battery health, presents unique challenges. This paper proposes a comprehensive framework for monitoring and managing rental EV batteries, encompassing real-time data acquisition, advanced analytics, and proactive maintenance strategies. By implementing such a framework, rental EV providers can optimize battery performance, extend lifespan, and guarantee the safety and convenience of their customers. The surging popularity of rental electric vehicles (EVs) poses unique challenges in ensuring their safety and reliability, particularly concerning battery health. This paper presents a comprehensive framework for proactive monitoring and management of rental EV batteries. The framework encompasses real-time data acquisition of key battery parameters, advanced analytics for predicting degradation and identifying influencing factors, and data-driven maintenance strategies like cell balancing and optimized charging. Implementing this framework empowers rental EV providers to maximize battery lifespan, enhance customer experience, and contribute to a sustainable transportation future. This leads to improving the factors and the process to monitor the battery health parameters of the rental electric vehicle. By adopting the various monitoring algorithms to measure and monitor the battery health parameters such as State of Charge, State of Health, battery voltage, balancing current, and temperature are monitored by the lifespan of the battery is extended and the safety and reliability of the battery is ensured by monitoring the temperature.

Influence of VF and SOR-Filtering Methods on Tree Height Inversion Using Unmanned Aerial Vehicle LiDAR Data

Forests, as the main body of the terrestrial ecosystem, have long been focal points for accurate structural parameter extraction. Among these parameters, tree height is a fundamental measurement factor that plays an important role in monitoring forest structure and biomass. The emergence of unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology has provided a strong guarantee of the acquisition of forest tree height parameters. However, UAV-LiDAR point cloud data have problems such as a large volume and data redundancy, and different point cloud data processing methods have different effects. Based on voxel filtering (VF) and statistical outlier removal (SOR)point cloud data processing experimental analysis, this study explored the influence of different filtering methods on the forest tree height inversion efficiency and accuracy. First, the point cloud data processed by VF is significantly better than that of SOR in terms of point cloud number, file size, running time, etc. The number of point clouds for VF decreased by an average of 96.91% compared with the original point clouds. Second, the VF tree height inversion accuracy was better than the tree height inversion data using SOR. The average accuracy of VF was 96.24%, while that of SOR was 94.17%. In summary, VF can effectively reduce data redundancy and improve tree height inversion accuracy.

Nonempirical fractal permeability model and experimental verification of hydrate-bearing sediments based on heterogeneous distribution of particles

The one of the factors limiting production of gas-water is affected by sediments' permeability. Evaluating the normalized permeability (Kn) of sediments containing hydrates is necessary for analyzing the saturation (decomposition level) and flow capacity. The mistakes in Kn prediction are frequently brought about by the assumption such as homogenous particles distribution and ignoring microscopic pore structure. To address the issue, a fractal-based nonempirical prediction model of Kn was developed by modeling, parameter acquisition using SEM and Analyzer, experiment validation, error comparison with published data, and sensitivity analysis for evolution, respectively. Assuming that sediment skeleton particles were misaligned spherical particles of equal diameter. The heterogeneity of sediments was described by the vertical and horizontal distance ratio (m) and offset angle (θ) of the particles. This led to a novel approach for determining average tortuosity (τav,0) and tortuosity fractal dimension (Dτ,0). The viability of the model was confirmed by permeability measurement experiments. Additionally, using the measuring function of SEM, the maximum pore diameter of sediments was obtained. Furthermore, a comparison with earlier data demonstrated the effect. Considering heterogeneity, the average accuracy of Geometric Mean Variance in Kn has increased to 9.66%. Finally, each parameter's impact on prediction was examined. At m = 1.357, Dτ,0 grows symmetrically. Kn,GC varies 15 times more with an increase in Dτ,0 than Kn,PF. All findings indicate that the new model is more likely to be forecasted Kn for a variety of systems, including clayey-silt, sand, and silty-sand system, and especially in reflecting the heterogeneity.

Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements

DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, which often suffer from computational complexity and low accuracy due to noisy arterial input function (AIF) measurements. Although some deep learning approaches have been proposed to tackle these challenges, most existing methods rely on supervised learning that requires paired input DCE-MRI and labeled pharmacokinetic parameter maps. This dependency on labeled data introduces significant time and resource constraints and potential noise in the labels, making supervised learning methods often impractical. To address these limitations, we present a novel unpaired deep learning method for estimating pharmacokinetic parameters and the AIF using a physics-driven CycleGAN approach. Our proposed CycleGAN framework is designed based on the underlying physics model, resulting in a simpler architecture with a single generator and discriminator pair. Crucially, our experimental results indicate that our method does not necessitate separate AIF measurements and produces more reliable pharmacokinetic parameters than other techniques.

Dynamic modeling and reliability analysis of satellite antenna deployment mechanism based on parameter uncertainty

AbstractThis study delves into the factors that affect the reliability of satellite antenna deployment mechanisms. It employs the Kolmogorov–Smirnov (K‐S) test to quantify uncertainty characteristics, leading to the acquisition of pertinent stochastic parameters. Subsequently, three‐dimensional models and finite element models of the satellite antenna deployment mechanism are established using SOLIDWORKS and ANSYS software. Modal distribution, harmonic response analysis, random vibration analysis, and deployment process simulation are conducted. Parameterized modeling of the deployment mechanism's dynamics simulation process is performed, taking into consideration parameter uncertainty. Finally, a Kriging surrogate model is utilized to formulate an approximate expression between the factors influencing the reliability of the antenna deployment mechanism and its dynamic response. Based on the general stress‐strength interference theory, a reliability analysis model is constructed. Combined with active learning algorithms, this approach achieves efficient and precise calculations of the reliability of the satellite antenna deployment mechanism.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

Flagellar beating forces of human spermatozoa with different motility behaviors

BackgroundOne of the causes of male infertility is associated with altered spermatozoa motility. These sperm features are frequently analyzed by image-based approaches, which, despite allowing the acquisition of crucial parameters to assess sperm motility, they are unable to provide details regarding the flagellar beating forces, which have been neglected until now.ResultsIn this work we exploit Fluidic Force Microscopy to investigate and quantify the forces associated with the flagellar beating frequencies of human spermatozoa. The analysis is performed on two groups divided according to the progressive motility of semen samples, as identified by standard clinical protocols. In the first group, 100% of the spermatozoa swim linearly (100% progressive motility), while, in the other, spermatozoa show both linear and circular motility (identified as 80 − 20% progressive motility). Significant differences in flagellar beating forces between spermatozoa from semen sample with different progressive motility are observed. Particularly, linear motile spermatozoa exhibit forces higher than those with a circular movement.ConclusionsThis research can increase our understanding of sperm motility and the role of mechanics in fertilization, which could help us unveil some of the causes of idiopathic male infertility.

Derivation of Creep Parameters for Surrounding Rock through Creep Tests and Deformation Monitoring Data: Assessing Tunnel Lining Safety

Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. The integration of a Mohr–Coulomb element with the Burgers model in series serves to characterize the yield creep behavior of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilizing randomized creep parameters and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Understanding and modeling the plastic deformation of 3D printed concrete based on viscoelastic creep behavior

3D printing concrete (3DPC) is susceptible to plastic deformation, for example, plastic shrinkage and plastic settlement, due to the absence of formwork. Monitoring and determining plastic deformation of 3DPC in large-scale construction remain challenging. In this study, we developed an analytical modeling method for predicting plastic deformation based on the creep behavior of 3DPC. A new creep model with high fitting accuracy was proposed for capturing creep characteristics over different resting time and applied stress level. Feasible acquisition of model parameters was achieved by correlating creep test results with UUCT (unconfined uniaxial compressive test) results. A multi-parameter modeling procedure based on superposition principle was then developed, and further validated by experimental results. This research may contribute to a better understanding and assessment of the plastic deformation of 3DPC in digital construction.

Back analysis of shear strength parameters of slope based on BP neural network and genetic algorithm

AbstractEfficient and accurate acquisition of slope shear strength parameters is the key to slope stability analysis and landslide prevention engineering design. This paper establishes a back analysis method based on uniform design, artificial neural network, and genetic algorithm. It can obtain the shear strength parameters of slopes based on information such as the radius and center coordinates of the slip surface obtained from on‐site investigations. This method has been applied to engineering practice. The research results indicate that the stability of the waste dump slope is most sensitive to the response of the internal friction angle of the loose body, followed by cohesion, and least sensitive to the response of the soil volume weight. This method can effectively reduce the number of network training samples and efficiently and quickly determine the initial weights of the BP (abbreviation for back‐propagation) neural network. This method can efficiently and quickly conduct back analysis to obtain the shear strength parameters of slopes. Using the obtained shear strength parameters for slope stability calculation, the most dangerous slip surface abscissa error, ordinate error, and slip surface radius error are only 3.59%, 0.95%, and 1.83%. It is recommended to promote the back analysis method of shear strength parameters in engineering practice in the future.

High Spatial Resolution Leaf Area Index Estimation for Woodland in Saihanba Forestry Center, China

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R2 of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions.

CURVA DE CRECIMIENTO DE CUYES MACHOS (Cavia porcellus) DE LA RAZA PERÚ MEDIANTE MODELOS NO LINEALES EN COLOMBIA

<p><strong>Background</strong>: Animal growth modeling is a tool that enables the acquisition of parameters to evaluate animal performance and predict outcomes for decision-making. <strong>Objective:</strong> To describe the growth of male guinea pigs of the Peruvian breed using the non-linear Brody, Gompertz, Logistic, and Von Bertalanffy models. <strong>Methodology</strong>: Twelve male guinea pigs with an initial weight of 393 ± 55 g were housed in metabolic cages with <em>ad libitum</em> feeding of a complete pellet diet. They were weighed every seven days for 13 weeks until reaching 1197 ± 84 g. Criteria used to assess the model's fitting ability included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R<sup>2</sup>), concordance correlation coefficient (CCC), and mean squared prediction error (CMEP). <strong>Results:</strong> The Logistic model consistently predicted maturity weight (1421g), initial weight (187g), age (65 days), and weight (711g) at the growth curve inflection point. Gompertz and Von Bertalanffy's models tended to overestimate adult weight (A) and exhibited the lowest maturity index (k). Among Gompertz, Logistic, and Von Bertalanffy models, AIC, BIC, R<sup>2</sup>, CCC, and CMEP fitting criteria showed no significant differences (p > 0.05). <strong>Implications:</strong> The Brody model was the only one with biologically inconsistent parameters. <strong>Conclusion</strong>: Considering the biological significance of parameters and residual analysis, the Logistic model is more suitable for describing the growth curve of male guinea pigs of the Peruvian breed.<strong><em></em></strong></p>

Fully Automated MV Cable Monitoring and Measurement System for Multi-Sample Acquisition of Artificial Aging Parameters

The Power quality of our networks is influenced by transients, short- and long-duration variations, voltage unbalance, waveform distortions, voltage fluctuations and power frequency variations. These impacts affect the aging rapidity of the electrical equipment and finally the entire electrical network. By partial discharge (PD) and tan(δ) analyses, it is possible to determine the electrical equipment’s condition. The significance of the results depends on the quality of the measurement system and of the data interpretation. Based on these results, a reliable maintenance and investment strategy could be made, and the reliability of the network could be improved. A specially designed aging system for the accelerated aging of MV cables has been developed to point out the most relevant aging parameters and their limits, and, in this way, to upgrade the accuracy of the MV cable diagnostics. In this paper, the system’s “intelligence”, which controls the aging process and acquires, evaluates and stores all necessary data is presented.

Research Progress on the Microfracture of Shale: Experimental Methods, Microfracture Propagation, Simulations, and Perspectives

The fracture network generated by hydraulic fracturing in unconventional shale reservoirs contains numerous microfractures that are connected to macroscopic fractures. These microfractures serve as crucial pathways for shale gas to flow out from micro- and nano-scale pores, playing a critical role in enhancing shale gas recovery. Currently, more attention is being given by academia and industry to the evolution of macroscopic fracture networks, while the understanding of the microfracture mechanisms and evolution is relatively limited. A significant number of microfractures are generated during the hydraulic fracturing process of shale. These microfractures subsequently propagate, merge, and interconnect to form macroscopic fractures. Therefore, studying the fracture process of rock masses from a microscale perspective holds important theoretical significance and engineering value. Based on the authors’ research experience and literature review, this paper provides a brief overview of current progress in shale microfracture research from five aspects: in situ observation experiments of microfractures in shale, formation and evolution processes of discontinuous microfractures, the impact of inhomogeneity on microfracture propagation, measurement methods for microscale mechanical parameters and deformation quantities in shale, and numerical simulation of shale microfractures. This paper also summarizes the main challenges and future research prospects in shale microfracture studies, including: (1) quantitative characterization of in situ observation experimental data on shale microfractures; (2) formation and evolution laws of macroscopic, mesoscopic, and microscopic multi-scale discontinuous fractures; (3) more in-depth and microscale characterization of shale heterogeneity and its deformation and fracture mechanisms; (4) acquisition of shale micro-mechanical parameters; (5) refinement and accuracy improvement of the numerical simulation of microfractures in shale. Addressing these research questions will not only contribute to the further development of microfracture theory in rocks but also provide insights for hydraulic fracturing in shale gas extraction.

Numerical Simulation of an Online Cotton Lint Sampling Device Using Coupled CFD–DEM Analysis

Cotton processing is the process of converting harvested seed cotton into lint by cleaning, ginning, and cleaning the lint. The real-time acquisition of lint parameters during processing is critical in improving cotton processing quality and efficiency. The existing online inspection system cannot realize quantitative sampling detection, resulting in large fluctuations in the detection of moisture rate, and the impurity content of lint can only be measured according to the number of impurity grains and the percentage of impurity areas. This research developed a quantitative sampling device for cotton lint processing that can collect the right number of cotton samples and obtain the weight of the samples, laying the foundation for the accurate detection of cotton lint dampness and impurity rates. This research aimed to develop an online quantitative sampling device with a sampling plate as its core. The quantitative sampling procedure, consisting of a gas–solid two-phase flow in a cotton pipeline, was numerically simulated and experimentally analyzed using computational fluid dynamics (CFD) and the discrete element method (DEM). According to the coupling results, the maximum pressure differential between the top and bottom regions of the sampling plate when conveying was 1024.45 Pa. This pressure is adequate to allow for cotton samples to accumulate on the sampling plate. Simultaneously, the steady conveying speed of lint is 59.31% of the unloaded conveying wind speed, providing a theoretical foundation for the sampling time of the quantitative sample device in the processing chain. The results from testing the prototype indicate that the quantitative sampling device in the cotton flow can effectively perform the quantitative sampling of cotton lint under uniform conditions, with a sampling pass rate of 84%.

Design and experiment of high-speed and precise positioning seeding control system for rice seedlings based on dual-position feedback adjustment (DPFA)

To improve the alignment accuracy of the mechanical drum-type rice bowl seedling raising precision seeding line under the high-speed sowing operation state between the seed outlet and the bowl hole. This paper proposes a high-speed and precise positioning seeding control system for rice seedlings based on dual position feedback adjustment (DPFA). Based on Siemens PLC controller, through sensor selection and installation, controller parameter acquisition and calculation, and human–computer interaction software development. We have completed the design of the high-speed and precise alignment seeding control system for rice pot seedlings. Realize the dual position detection of the mechanical drum-type seed wheel and the 406-hole tray. Applying the fuzzy PID controller optimized the dual-position feedback regulation rice pot seedling high-speed and precise alignment planting control system. The response time was shortened to 0.0432 s, the overshoot was 4.5 %, and the adjustment time was 0.1105 s. The sowing test was conducted with sowing speed and height as the main influencing factors and sowing uniformity, leakage rate, and sowing pass rate as the evaluation indexes. The test results show that the sowing speed is within 500 trays/h ∼ 600 trays/h, the sowing uniformity is higher than 65 %, the missed seeding rate is less than 1.5 %, and the qualified rate of sowing is above 94 %. Among them, the maximum sowing uniformity was 66.27 %, the minimum value of missing seeding was 0.89 %, and the sowing qualification rate was 97.06 %. The accuracy rate of material shortage alarms is 100 %. The DPFA-based high-speed and precise alignment sowing control system for rice pot seedlings can realize the exact alignment control function of the mechanical drum-type seedling raising and sowing line.

A review of quality monitoring for ultrasonic metal welding

Ultrasonic metal welding, an emerging solid state bonding method, has drawn extensive attention and been applied in various manufacturing scenarios in recent years. This paper reviews the quality monitoring system of ultrasonic metal welding from in-situ testing (online monitoring), non-in-situ testing and prediction of ultrasonic weld quality through machine learning methods. In-situ testing focuses on the acquisition of different process parameters and their relationship to weld quality, while non-in-situ testing focus on the monitoring indicators of the weld quality and the testing means. The exploration of machine learning methods concentrates on the influence of model selection and parameter input on prediction accuracy. Based on the analysis, the future development trend of ultrasonic weld quality monitoring is provided.

Compact VOC emission test chamber

The publication presents the test method and construction of a compact VOC emission test chamber. Volatile organic compounds (VOCs) are mainly occur in paint and construction materials containing chemical resins, bitumen, binders, adhesives, etc., as well as residues of unreacted monomers. VOC emission testing is carried out on product samples in environmental chambers in accordance with PN-EN ISO 16000-9. The developed chamber, together with a proprietary control and test parameter acquisition system, allows testing at a stabilized temperature and humidity of air washing over the sample at a specified rate. Qualitative identification and quantitative testing of compounds present in the air inside chambers is carried out using a chromatograph, and the test results are compared with regulations on permissible chemical pollutants in the air of buildings and in atmospheric air. The developed chamber with horizontal sample loading is another version of VOC chambers, developed in response to market demands, offered and implemented in research laboratories by the Łukasiewicz Institute for Sustainable Technologies in Radom.

Retracted: Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis

Retracted: Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis