Precise Calculation Research Articles

Comment on “Modified expression for unsaturated hydraulic conductivity according to Mualem–van Genuchten to allow proper computations at low pressure heads” by M. Heinen

AbstractThis comment concerns evaluation of the Mualem–van Genuchten (MvG) unsaturated soil hydraulic functions at very low (negative) pressure heads as described recently in an important study by Heinen. He showed that below some critical pressure head, the unsaturated hydraulic conductivity should be approximated by a power function, even when evaluated using double precision computations. We show that a more precise approximation is possible when the approximation is formulated in terms of effective fluid saturation (Se) rather than the pressure head, h. More general constraints are also provided when the approximated hydraulic conductivity equation should be used. The alternative formulation and constraints were implemented earlier in the SOHYP and RETC software packages.

Multi-swarm surrogate model assisted PSO algorithm to minimize distribution network energy losses

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables.

Empirically contrasting urine drug screening-based opioid use disorder treatment outcome definitions.

A lack of consensus on the optimal outcome measures to assess opioid use disorder (OUD) treatment efficacy and their precise definition and computation has hampered the pooling of research data for evidence synthesis and meta-analyses. This study aimed to empirically contrast multiple clinical trial definitions of treatment success by applying them to the same dataset. Data analysis used a suite of functions, developed as a software package for the R language, to operationalize 61 treatment outcome definitions based on urine drug screening (UDS) results. Outcome definitions were derived from clinical trials that are among the most influential in the OUD treatment field. Outcome functions were applied to a harmonized dataset from three large-scale National Drug Abuse Treatment Clinical Trials Network (CTN) studies, which tested various medication for OUD (MOUD) options (n = 2492). Hierarchical clustering was employed to empirically contrast outcome definitions. The optimal number of clusters identified was three. Cluster 1, comprising eight definitions focused on detecting opioid-positive UDS, did not include missing UDS in outcome calculations, potentially resulting in inflated rates of treatment success. Cluster 2, with the highest variability, included 10 definitions characterized by strict criteria for treatment success, relying heavily on UDS results from either a brief period or a single study visit. The 43 definitions in Cluster 3 represented a diverse range of outcomes, conceptualized as measuring abstinence, use reduction and relapse. These definitions potentially offer more balanced measures of treatment success or failure, as they avoid the extreme methodologies characteristic of Clusters 1 and 2. Clinical trials using urine drug screening (UDS) for objective substance use assessment in outcome definitions should consider (1) incorporating missing UDS data in outcome computation and (2) avoiding over-reliance on UDS data confined to a short time frame or the occurrence of a single positive urine test following a period of abstinence.

On the autonomous validation and comparison of particle models for a Newtonian laminar flow mixing model using PEPT

A methodology for the validation of a Computational Fluid Dynamics (CFD) mixing model using Positron Emission Particle Tracking (PEPT) was applied to a horizontal laminar stirred tank containing Newtonian glycerol. Particle trajectories recorded using PEPT were compared to three one-way particle-coupled CFD models (CFD-DEM, and CFD-Lagrangian, and CFD-Massless) through time-averaging and binning into a grid of 3-D voxels. A cell-by-cell comparison of particle velocities between the computational models and PEPT highlighted the accuracy of the CFD-DEM and CFD-Massless models over the Lagrangian model. Furthermore, using PEPT as a validation dataset the Pearson r-squared was used as a cost function to guide an autonomous calibration of a 5-dimensional DEM parameter space. A novel statistic, the simulation quality coefficient Qsim, was applied to assess accuracy between trajectory datasets and revealed the extent to which a model can be considered calibrated. Considering the balance between computational cost and precision, the CFD-massless model was the most effective particle model for this validation workflow. Further investigations will apply this methodology to calibrate non-Newtonian laminar CFD models.

Sparse SAR Imaging Algorithm in Marine Environments Based on Memory-Augmented Deep Unfolding Network

Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes.

Sparse subnetwork inference for neural network epistemic uncertainty estimation with improved Hessian approximation

Despite significant advances in deep neural networks across diverse domains, challenges persist in safety-critical contexts, including domain shift sensitivity and unreliable uncertainty estimation. To address these issues, this study investigates Bayesian learning for uncertainty handling in modern neural networks. However, the high-dimensional, non-convex nature of the posterior distribution poses practical limitations for epistemic uncertainty estimation. The Laplace approximation, as a cost-efficient Bayesian method, offers a practical solution by approximating the posterior as a multivariate normal distribution but faces computational bottlenecks in precise covariance matrix computation and storage. This research employs subnetwork inference, utilizing only a subset of the parameter space for Bayesian inference. In addition, a Kronecker-factored and low-rank representation is explored to reduce space complexity and computational costs. Several corrections are introduced to converge the approximated curvature to the exact Hessian matrix. Numerical results demonstrate the effectiveness and competitiveness of this method, whereas qualitative experiments highlight the impact of Hessian approximation granularity and parameter space utilization in Bayesian inference on mitigating overconfidence in predictions and obtaining high-quality uncertainty estimates.

Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model

The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

A modified weakly compressible smoothed particle hydrodynamics mixture model for accurate simulation of wave and porous structure interaction

In this study, a modified weakly compressible smoothed particle hydrodynamics (WCSPH) mixture model was developed to more accurately simulate the interaction between waves and porous structures. In this model, we enhanced the governing equations of the traditional WCSPH mixture model by introducing Darcy velocity, apparent density, and an adjustable smoothing length. This refinement ensures that the modified model effectively maintains the conservation of fluid volume in seepage simulations. Additionally, this paper proposes a permeable interface treatment technique that replaces traditional smoothed particle hydrodynamics interpolation with finite element shape function interpolation, significantly enhancing computational efficiency. At the same time, we also introduced and revised a particle shifting technique, which further increases the computational precision of the model. The modified WCSPH mixture model was then applied to simulate several physical experiments, including the dam-break wave propagation in a permeable dam, the attenuation of solitary waves on a permeable riverbed, the propagation of the solitary wave on a submerged porous structure, and the breaking process of waves passing through permeable breakwaters. Through comparison with the experimental data and other numerical results, the current model was comprehensively verified from various aspects, such as fluid volume conservation, wave evolution in and around the porous structure, and pressure distribution characteristics. The results confirm the excellent performance of the current model in simulating the interaction between waves and porous structures.

2018年浙江古田山24公顷亚热带常绿阔叶林动态监测样地林冠结构和地形数据集

The canopy structure and topographic variables jointly influence the spatiotemporal variation of habitats for important species. However, there remains a considerable scarcity of precise forest canopy structure and topographic data obtained from large-scale forest dynamic plots. LiDAR data, acquired via near-surface remote sensing platforms, facilitates precise computation of both canopy structure and topographic variables. This study selected a 24-hectare dynamic plot in the subtropical evergreen broadleaf forest, located in the core conservation area of Qianjiangyuan National Park, as its focal point for survey. We conducted an analysis and quality control process on the point cloud data in 2018 using a near-surface remote sensing platform for this area, aiming to acquire corresponding canopy structure and topography data. Ground data collected through Real-Time Kinematic (RTK) surveying were used to verify and validate the accuracy of the digital elevation model (DEM). The analysis revealed a mean square error of elevation of 0.07 meters, suggesting a high level of accuracy of the dataset. The 24-hectare subtropical evergreen broad-leaved forest dynamic plot represents a typical low-altitude evergreen broad-leaved forest in the central subtropical region. This dataset can provide essential data support for the monitoring and research of biodiversity in subtropical broadleaved evergreen forests.

Shape-aware speckle matching network for cross-domain 3D reconstruction

The learning-based binocular 3D reconstruction method outperforms traditional vision algorithms in terms of precision and efficiency. However, in some cross-domain scenes, the precision of the well-trained network drastically decreases when handling samples in diverse contexts. This paper proposes an end-to-end shape-aware speckle matching network (SSMNet) that combines shape-mask information to achieve improved precision and completeness of disparity calculation in cross-domain applications. The cascade attention mechanism is inserted in the feature extraction stage to concentrate on valuable regions. The shape-aware module is designed to learn additional shape contour information, and multiscale features are integrated simultaneously to construct the cost-volume for the subsequent lightweight 3D aggregation. In addition, instance normalization is adopted to guarantee style migration, and a hybrid loss function is used to supervise the learning process. Furthermore, a high-precision binocular speckle dataset is built, including training and testing sets in different distributions. Extensive quantitative and qualitative experiments demonstrate that SSMNet enhances cross-domain capability and achieves state-of-the-art performance. Measurement precision evaluation illustrates that the proposed method can realize the desired highly precise 3D shape measurement in a real industrial scenario.

Distributed sliding mode control for reactive power sharing in an islanded microgrid

This paper presents a novel microgrid secondary control method based on distributed sliding mode control, addressing the issue of reactive power sharing in islanded microgrids. Acknowledging the complications posed by feeder impedance mismatch, the traditional droop control mechanism fails to facilitate efficient reactive power sharing. Utilizing a distributed framework, this paper initially introduces a reactive power ratio observer, enabling the observation of global average reactive power without the need for precise reactive power calculation as required in a conventional centralized control setup. Subsequently, employing the devised reactive power ratio observer, sliding mode control is integrated to achieve effective reactive power sharing. Furthermore, this paper broadens its scope to encompass both reactive power sharing and voltage restoration, resulting in the design of a comprehensive controller. Notably, the proposed distributed sliding mode controller not only achieves accurate reactive power sharing but also exhibits significant resilience in the face of load disturbances. The simulation results conducted under various scenarios effectively validate the efficacy of the proposed distributed sliding mode control strategy.

Encrypted Image Classification with Low Memory Footprint Using Fully Homomorphic Encryption.

Classifying images has become a straightforward and accessible task, thanks to the advent of Deep Neural Networks. Nevertheless, not much attention is given to the privacy concerns associated with sensitive data contained in images. In this study, we propose a solution to this issue by exploring an intersection between Machine Learning and cryptography. In particular, Fully Homomorphic Encryption (FHE) emerges as a promising solution, as it enables computations to be performed on encrypted data. We therefore propose a Residual Network implementation based on FHE which allows the classification of encrypted images, ensuring that only the user can see the result. We suggest a circuit which reduces the memory requirements by more than [Formula: see text] compared to the most recent works, while maintaining a high level of accuracy and a short computational time. We implement the circuit using the well-known Cheon-Kim-Kim-Song (CKKS) scheme, which enables approximate encrypted computations. We evaluate the results from three perspectives: memory requirements, computational time and calculations precision. We demonstrate that it is possible to evaluate an encrypted ResNet20 in less than five minutes on a laptop using approximately 15[Formula: see text]GB of memory, achieving an accuracy of 91.67% on the CIFAR-10 dataset, which is almost equivalent to the accuracy of the plain model (92.60%).

Compositional Simulation for Carbon Storage in Porous Media Using an Electrolyte Association Equation of State

Summary We present a new algorithm based on automatic differentiation that enables precise computation of the derivatives of the Z-factor, facilitating the utilization of Newton’s method or coupling with a robust flow solver. Leveraging a free open-source code [MATLAB Reservoir Simulation Toolbox (MRST)], we develop an electrolyte cubic plus association (e-CPA) equation of state (EoS) model to accurately represent the injection of carbon dioxide (CO2) in brine. By integrating flow and thermodynamics, we construct an advanced compositional simulator using MRST’s object-oriented, automatic differentiation framework and the newly developed e-CPA EoS model. This simulator offers flexibility through both overall-composition and natural-variable formulations, achieved by selecting different primary variables. The Péneloux volume translation technique is employed to modify the EoS model’s volume, ensuring accurate density calculation for the mixture. Additionally, we introduce a viscosity model, e-CPA-FV, which accurately predicts the viscosity of carbon capture and storage (CCS) fluids, surpassing the accuracy of the traditional Lohrenz-Bray-Clark (LBC) model. Our simulator demonstrates superior performance in predicting CO2-brine systems compared with the standard formulation based on the Peng-Robinson (PR) EoS and can handle brine with various salts. The self-contained source code necessary to reproduce all examples is available on the open-access Zenodo digital repository (doi: 10.5281/zenodo.10691505).

Super-resolution techniques for biomedical applications and challenges.

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

Sensitivity analysis of profile navigation command of underwater gliders to the initial heading error for improving trajectory accuracy

With the widespread application of underwater gliders (UGs) in marine research, there is an increasing demand for improved trajectory control accuracy. However, the initial heading error is unavoidable in internally actuated UGs, making it challenging to rectify the trajectory error generated in the heading error correction process. To effectively mitigate the impact of this error on trajectory accuracy, this study analyzes the sensitivity of profile navigation command parameters to the initial heading error through dynamic modeling of Petrel-L UG. Firstly, a simulator is developed to model the behaviors of Petrel-L, mainly involving dynamic modeling, underwater navigation, low-level control, and environment model. Subsequently, the sensitivity of profile navigation parameters in various commands to the initial heading error is analyzed. Surrogate model and Monte Carlo simulation are integrated to improve the computational speed of the analysis while preserving calculation precision. The average maximum single-profile horizontal error is 661 m, and the average maximum single-profile course error is 6.47°. Among profile navigation parameters, the target pitch during diving is proved to be the primary factor affecting the maximum trajectory error, and the increase of this parameter can enhance trajectory accuracy. This research also provides valuable reference for path following and station-keeping control of other UGs.

The Impact of Computational Accuracy on the Quality of Direct Drive Control

One of the main aspects of the control system development process for direct drives is the selection of the required computational accuracy while establishing its impact on the quality of the control. Understanding this relationship allows designers to consciously determine the system structure at the early stages of controller development, which enables the optimal usage of hardware resources. This paper analyzes the results of experimental research on the influence of computational accuracy on the quality of control of direct drives. During the carried-out research, several vector controllers with different computational precision were implemented using Field-Programmable Gate Arrays (FPGAs). The experiments were carried out on a dedicated research stand for testing direct drives. Test scenarios included position and trajectory monitoring under various torque loads. To assess the control quality, the measures based on the deviations from the value set by the controller were proposed. In this paper, the results of experiments have been presented in the form of values of the measures in relation to the computational accuracy. The obtained results proved that satisfactory drive operation parameters can be obtained despite the relatively low accuracy of calculations in the control algorithm.

Evaluation of Reaction Rate Distribution Measurement in the Prototype Fast Reactor Monju

In the prototype fast breeder reactor Monju, reaction rate distributions of the fission reaction rates of 239Pu, 235U, and 238U and the capture reaction rate of 238U were measured using activation foils during its system startup test. The measurements in the core and radial blanket regions were evaluated in detail, and their reliability and usefulness as the validation data for fast reactor neutronics design methodologies were examined through a comparison with calculations. The reaction rate data measured in Monju were confirmed all reliable and useful as the validation data. The fission reactions of 239Pu, 235U, and 238U can be validated with an accuracy of a few percent in the core and blanket regions. The capture reaction of 238U in the core region also can be validated with a similar accuracy, whereas a precise calculation of the foil cross section is necessary to consider the resonance shielding effects of the surrounding fuel pins and a foil.

Thenovel numerical solutions for conformable fractional Kuramoto-Sivashinsky equations by using Cq-HATM and CHPETM

The present study used the novel conformable fractional methods to examine the nonlinear Kuramoto-Sivashinsky equations of the conformable fractional derivative. The Kuramoto-Sivashinsky equation is a mathematical model employed to elucidate a diverse range of chemical and physical phenomena, encompassing chemical reaction-diffusion processes, plasma instabilities, viscous flow issues, flame front propagation, and magnetized plasmas. The present inquiry provides an examination of convergence and error for the future scheme. The conformable q-homotopy analysis transform method (Cq-HATM) provides h-curves that illustrate the convergence range of the series solution achieved. In order to validate the effectiveness and suitability of the Cq-HATM, we examine separate instances. In this study, we introduce an application that demonstrates the potential benefits and effectiveness of the proposed approach. Furthermore, an error analysis is performed in order to verify the accuracy of the scheme. Numerical simulations are conducted to validate the precision of the forthcoming approach. The findings obtained from the numerical and graphical analyses are presented in this study. The method provided in this study showcases a notable degree of computational precision and ease in examining and resolving intricate phenomena related to conformable fractional nonlinear partial differential equations within the domains of science and technology.

Calculation of product service systems in single and small batch production

AbstractSingle and small batch production is characterized by complex value-added processes and products. The transparent calculation of new offers and change requests is therefore a particular challenge. At the same time, the rising spread of product service systems (PSS) increases the complexity of costing, as additional intangible services have to be calculated precisely. In addition to the challenges posed by such precise calculation of intangible services, companies have to master another complexity driver in the form of PSS. Innovative information and communication technologies (ICT) offer new potential for effective and efficient design of the costing process for the entire life cycle. The rising availability of data along the entire product life cycle significantly increases transparency and, thanks to intelligent analysis algorithms, allows the identification of clear cause-and-effect relationships and forecasting options. The aim of the presented paper is thus to develop a model for calculation of PPS in single and small batch production that exploits the new potential of ICT.

Tensor-Based Forward–Backward Algorithms in the Physics-Informed Coupled Hidden Markov Model

Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on physical Coupled Hidden Markov Model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The above disadvantages lead to lower prediction accuracy of CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then the corresponding forward-backward algorithms are designed for resolving the evaluation problem of CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further selfcomparison experiments verify that our algorithms are effective and practical.