Precise Calculation Research Articles

Automated Vehicle Regulation Needs to Speak to Code, not to Humans: Keeping Safety and Ethics in the Public Domain

Abstract In anticipation of the market introduction of highly and fully automated vehicles, regulations for their behavior in public road traffic are emerging in various countries and regions. Yet, as we show using the example of EU and German regulations, these rules are both incomplete and exceptionally vague. In this paper we introduce three traffic scenarios highlighting conflicting ethical, legal, and utility-related claims, and perform a legal analysis with regards to the expected behavior of AVs in these scenarios. We show that the existing regulatory framework disregards the realities of algorithmic decision-making in automated vehicles, such as the incomplete and imprecise perception of their environment and the probabilistic nature of their predictions. Importantly, the current regulations are written in abstract language addressing human interpreters rather than the precise logical-numerical computer code. We argue that the required interpretation and translation of the abstract legal language into the logical-numerical domain is so ambiguous that the regulations as they stand fail to guide or limit automated vehicle behavior in any meaningful way. This comes with significant ethical implications, as the interpretation and translation is unavoidable and, if not provided by regulatory bodies, will have to be performed by manufacturers. We argue that ethical decisions with significant impact on public safety must not be delegated to private companies, and thus, regulatory frameworks need significant improvements.

Comprehensive Comparative Analysis and Innovative Exploration of Green View Index Calculation Methods

Despite the widespread use of street view imagery for Green View Index (GVI) analyses, variations in sampling methodologies across studies and the potential impact of these differences on the results, including associated errors, remain largely unexplored. This study aims to investigate the effectiveness of various GVI calculation methods, with a focus on analyzing the impact of sampling point selection and coverage angles on GVI results. Through a systematic review of the extensive relevant literature, we synthesized six predominant sampling methods: the four-quadrant view method, six-quadrant view method, eighteen-quadrant view method, panoramic view method, fisheye view method and pedestrian view method. We further evaluated the strengths and weaknesses of each approach, along with their applicability across different research domains. In addition, to address the limitations of existing methods in specific contexts, we developed a novel sampling technique based on three 120° street view images and experimentally validated its feasibility and accuracy. The results demonstrate the method’s high reliability, making it a valuable tool for acquiring and analyzing street view images. Our findings demonstrate that the choice of sampling method significantly influences GVI calculations, underscoring the necessity for researchers to select the optimal approach based on a specific research context. To mitigate errors arising from initial sampling angles, this study introduces a novel concept, the “Green View Circle”, which enhances the precision and applicability of calculations through the meticulous segmentation of observational angles, particularly in complex urban environments.

On the Effectiveness of a Common Attack to Chebyshev Chaotic Encryption Scheme

Chebyshev polynomials define a rather canonical chaotic cryptosystem and some strong attacks have been designed to that cryptosystem. We report the numerical experiments performed with multiple precision arithmetic using conventional software as gmp and mpfr to test the Chebyshev cryptosystem and Bergamo’s attack. As a conclusion, we point out the relevance in the cryptosystem robustness of the number of significant digits (length) of plaintexts and the number of correct digits (precision) of the arithmetical calculations. Furthermore, after the observed experimental results, we use techniques of Numerical Analysis to explain the occurrences of the observed results and to reinforce the importance of the above-mentioned parameters ℓ and m.

Ultra Low-Latency Block Cipher uLBC

In recent years, there has been a growing interest in low-latency ciphers. Since the first low-latency block cipher PRINCE was proposed at ASIACRYPT 2012, many low-latency primitives sprung up, such as Midori, MANTIS, QARMA and SPEEDY. Some ciphers, like SPEEDY and Orthros, introduce bit permutations to achieve reduced delay. However, this approach poses a challenge in evaluating the resistance against some cryptanalysis, especially differential and linear attacks. SPEEDY-7-192, was fully broken by Boura et.al. using differential attack, for example. In this paper, we manage to propose a novel low-latency block cipher, which guarantees security against differential and linear attacks. Revisiting the permutation technique used in Orthros, we investigate the selection of nibble permutations and propose a method for selecting them systematically rather than relying on random search. Our new nibble permutation method ensures the existence of impossible differential and differential trails for up to 8 rounds, while the nibble permutations for both branches of Orthros may lead to a 9-round impossible differential trail. Furthermore, we introduce a new approach for constructing low-latency coordinate functions for 4-bit S-boxes, which involves a more precise delay computation compared to traditional methods based solely on circuit depth. The new low-latency primitive uLBC we propose, is a family of 128-bit block ciphers, with three different versions of key length, respectively 128-bit and 256-bit key, as well as a 384-bit tweakey version with variable-length key. According to the key length, named uLBC-128, uLBC-256 and uLBC-384t. Our analysis shows that uLBC-128 exhibits lower latency and area requirements compared to ciphers such as QARMA9-128 and Midori128. On performance, uLBC-128 has excellent AT performance, the best performance except SPEEDY-6, and even the best performance in UMC 55nm in our experiments.

Higher partial wave contamination in finite-volume 1-to-2 transitions

In their seminal work, Lellouch and Lüscher derived a conversion factor relating a finite-volume matrix element, calculable using numerical lattice QCD, with the infinite-volume decay amplitude for K → ππ. The conversion factor depends on the ππ → ππ scattering amplitude with the same total isospin as the decay channel (either zero or two). Although an infinite tower of ππ → ππ partial-wave components affect the conversion factor, the S-wave (ℓ = 0) component is expected to dominate, and only this contribution is included in the well-known Lellouch-Lüscher factor, with other ππ → ππ partial-wave amplitudes formally set to zero. However, as the precision of lattice calculations increases, it may become important to assess the systematic uncertainty arising from this approximation. With this motivation, we compare the S-wave-only results with those truncated at the next contaminating partial wave: the G-wave (ℓ = 4) for zero total momentum in the finite-volume frame and the D-wave (ℓ = 2) otherwise. Using the general framework for 1→J2 transitions derived in ref. [1], we quantify the effect of higher partial waves for systems with zero and non-zero total momentum as well as with anti-periodic boundary conditions, presenting both generic numerical examples and results for realistic ππ amplitudes taken from chiral perturbation theory and dispersive analysis. We also consider the accidental degeneracy occurring in the 8th excited state of the zero-momentum system. This exhibits qualitatively new features at ℓ = 4, not seen in the ℓ = 0 truncation.

Development Status and Future Prospects of Strapdown Inertial Navigation Technology

Background: Inertial navigation is a comprehensive technology involving precision machinery, computer technology, microelectronics, optics, automatic control, materials, and other disciplines and fields. Strapdown inertial navigation systems have gradually developed into the mainstream and direction of inertial navigation systems because of their small size, low cost, simple structure, and high reliability. The angular rate and acceleration information of the carrier relative to the inertial space are measured by the inertial measurement unit (IMU), and the instantaneous velocity and position information of the carrier are automatically calculated by Newton's law of motion. It has the characteristics of not relying on external information, not radiating energy to the outside world, not being disturbed, and good concealment. Therefore, it is widely used in aerospace, aviation, and navigation, especially in the military field. Objective: This paper describes the development history of the Strapdown Inertia Navigation System summarizes the research status of the Strapdown Inertia Navigation System, and looks forward to its future development direction. Method: The key technologies of strapdown inertial navigation systems are investigated, and the related research on strapdown inertial navigation technology is understood in detail. The hardware composition and related algorithms of the strapdown inertial navigation system are analyzed in detail. Results: Nowadays, strapdown inertial navigation technology is the mainstream of inertial navigation technology. Compared with the gimbaled inertial navigation system technology, the strapdown inertial navigation system has higher precision, smaller volume, and lower cost. At present, the initial alignment technology, error compensation technology, navigation algorithm technology, and inertial device technology of strapdown inertial navigation technology have made full development. Conclusion: With the development of initial alignment technology, error compensation technology, navigation algorithm technology, and inertial device technology, the accuracy, response speed and anti-interference ability of strapdown inertial navigation technology have been significantly improved. The development of strapdown inertial navigation is closely related to the gyroscope technology which belongs to inertial device technology. The strapdown inertial navigation system based on optical gyroscopes such as laser gyroscopes and fibre optic gyroscopes is mature, and the MEMS strapdown inertial navigation system has a wide application prospect compared with the optical gyroscope strapdown inertial navigation system.

A new three-dimensional microscopic modeling strategy in studying coupled electro-mechanical properties for piezoelectric composites

In this study, an innovative micromechanical modeling framework based on the three-dimensional finite-volume direct averaging micromechanics theory is presented to investigate mechanical properties of 0–3 type piezoelectric composites subjected to a coupled electro-mechanical environment. To this end, the electro-mechanical coupling constitutive relation is presented. Moreover, the parametric elements are further constructed to improve the calculation precision. In order to verify the proposed micromechanical model, the numerical results of local stress and electric field distribution are also obtained by finite element method. The convergence and calculation accuracy of the proposed model are demonstrated by excellent consistency of the numerical results. On this basis, The elastic modulus, piezoelectric coupling coefficient and dielectric permittivity coefficient are obtained by three-dimensional FVDAM model at different particle volume fractions. By applying different loading conditions, the local stress and electric field intensity distributions on the interface between particle and matrix are investigated. It can be concluded that the proposed micromechanical model offers a useful tool in studying the effective properties and local responses of the piezoelectric composites.

An interpretable unsteady flow model for membrane structure shear-valve type magnetorheological damper

The study introduces an innovative, interpretable, nonlinear damping hysteresis model for membrane structure shear-valve magnetorheological dampers anchored in non-Newtonian fluid mechanics. In addition to the fluid mechanism, the model takes into account the hysteresis phenomenon of magnetic fluids. Drawing upon the parallel plate model—predominantly utilized in steady-state analyses—the Laplace transform is employed to solve the transient fluid constitutive equation in the [Formula: see text]-domain, drawing parallels with Newtonian fluids and introducing robust prior conditions to minimize the problem-solving scale. A fast inverse Laplace transformation algorithm is applied to achieve the numerical solution in the time domain. This model can be quickly built from the damper’s design parameters, laying the foundation for swift, and proficient damper control. Furthermore, a novel membrane-structured shear-valve-type MR damper is designed and fabricated, which exhibits minimal passive damping and accurately reflects the genuine fluid dynamic characteristics, optimizes the methodology for calculating the damper’s magnetic field and acquires damping characteristic curves to authenticate the proposed unsteady flow model. Experimental outcomes demonstrate that our proposed model dispenses the need for a priori damping characteristic data, provides robust computational speed and precision, and maintains interpretability.

Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing

Automated String Art Creation: Integrated Advanced Computational Techniques and Precision Art Designing

Machine Learning-Driven Analysis of Heat Transfer in Chemically Reactive Fluid Flow Considering Soret-Dufour Effects

ObjectiveThe study investigates tangent hyperbolic nanofluid flow across an extended media, emphasizing magnetohydrodynamic influences, mixed convection, and nanoparticle transport with the help of an innovative and advanced technique. MethodologyIt employs a sophisticated Gaussian Neural Network and Hybrid Cuckoo Search to mimic complex fluid dynamics accurately, ensuring efficient computation and solution precision. By transforming the governing system of nonlinear partial differential equations into ordinary differential equations via similarity transformation, the methodology attains computational efficiency and solution precision. Core FindingsStatistical measures confirm the model's resilience, indicating absolute error variations from E−05 to E−12 and mean squared errors continuously between E−02 − E−07, highlighting the reliability of the model, while the Error in Nash-Sutcliffe Efficiency values fall within the interval E−05 − E−15. The Theil's Inequality Coefficient values lie in the range of E−01 − 10−07. Future Direction and ApplicationsThe findings underscore the model's capacity to improve thermal management, energy efficiency, and process dependability in sectors like chemical processing, environmental engineering, and sustainable energy production. This study positions the Gaussian Neural Networks framework as a robust computational instrument, providing a foundation for subsequent investigations into intricate geometries, multi-physics phenomena, and magnetohydrodynamic systems for novel technology applications.

CosIn: A statistical-based algorithm for computation of space-speed time delay in pedestrian motion

Precise assessment of Space-speed time delay (TD) is critical for distinguishing between anticipation and reaction behaviors within pedestrian motion. Besides, the TD scale is instrumental in the evaluation of potential collision tendency of the crowd, thereby providing essential quantitative metrics for assessing risk. In this consideration, this paper introduced the CosIn algorithm for evaluating TD during pedestrian motion, which includes both the CosIn-1 and CosIn-2 algorithms. CosIn-1 algorithm analytically calculates TD, replacing the numerical method of discrete cross-correlation, whereas the CosIn-2 algorithm estimates the TD from a statistical perspective. Specifically, the CosIn-1 algorithm addresses the precise computation of TD for individual pedestrians, while the CosIn-2 algorithm is employed for assessing TD at the crowd scale, concurrently addressing the imperative of real-time evaluation. Efficacy analyses of the CosIn-1 and CosIn-2 algorithms are conducted with data from single-file pedestrian experiments and crowd-crossing experiments, respectively. During this process, the discrete cross-correlation method was employed as a baseline to evaluate the performance of both algorithms, which demonstrated notable accuracy. This algorithm facilitate the precise evaluation of behavior patterns and collision tendency within crowds, thereby enabling us to understand the crowds dynamics from a new perspective.

A Thorough Review of Vehicle Detection and Distance Estimation Using Deep Learning in Autonomous Cars

Autonomous vehicle technologies are rapidly advancing, and one key factor contributing to this progress is the enhanced precision in vehicle detection and distance calculation. Deep Learning Networks (DLNs) have emerged as powerful tools to address this challenge, offering remarkable capabilities in accurately detecting and estimating vehicle positions. This study comprehensively reviews DLN applications for vehicle detection and distance estimation. It examines prominent DLN models such as YOLO, R-CNN, and SSD, evaluating their performance on widely used datasets such as KITTI, PASCAL VOC, and COCO. Analysis results indicate that YOLOv5, developed by Farid et al. achieves the highest accuracy level with a mAP (mean Average Precision) of 99.92%. Yang et al. showcased that YOLOv5 performs exceptionally in detection and distance estimation tasks, with a mAP of 96.4% and a low mean relative error (MRE) of 10.81% for distance estimation. These achievements highlight the potential of DLNs to enhance the accuracy and reliability of vehicle detection systems in autonomous vehicles. The study also emphasizes the importance of backbone architectures like DarkNet 53 and ResNet in determining model efficiency. The choice of the appropriate model depends on the specific task requirements, with some models prioritizing real-time detection and others prioritizing accuracy. In conclusion, developing DLN-based methods is crucial in advancing autonomous vehicle technology. Research and development remain crucial in ensuring road safety and efficiency as autonomous vehicles become more common in transportation systems.

MLody—Deep Learning–generated Polarized Synchrotron Coefficients

Abstract Polarized synchrotron emission is a fundamental process in high-energy astrophysics, particularly in the environments around black holes and pulsars. Accurate modeling of this emission requires precise computation of the emission, absorption, rotation, and conversion coefficients, which are critical for radiative transfer simulations. Traditionally, these coefficients are derived using fit functions based on precomputed ground truth values. However, these fit functions often lack accuracy, particularly in specific plasma conditions not well represented in the data sets used to generate them. In this work, we introduce MLody, a deep neural network designed to compute polarized synchrotron coefficients with high accuracy across a wide range of plasma parameters. We demonstrate MLody's capabilities by integrating it with a radiative transfer code to generate synthetic polarized synchrotron images for an accreting black hole simulation. Our results reveal significant differences, up to a factor of 2, in both linear and circular polarization compared to traditional methods. These differences could have important implications for parameter estimation in Event Horizon Telescope observations, suggesting that MLody could enhance the accuracy of future astrophysical analyses.

Enhancing Melanoma Diagnostic: Harnessing the Synergy of AI and CNNs for Groundbreaking Advances in Early Melanoma Detection and Treatment Strategies

ABSTRACTSkin cancer is one of the most prevalent and deadly neoplasms globally. Although melanoma constitutes a minor percentage of all skin cancer types, it presently stands as the primary cause of skin cancer‐related deaths. Previous studies indicate that deep learning algorithms may identify subtle patterns and detailed features in medical images for melanoma detection, however, challenges remain due to the scarcity of annotated images and the intricacy of cancer images. The need for early skin cancer detection, particularly melanoma, is an urgent concern due to its potential for high mortality when not identified and treated promptly. This paper introduces a comprehensive method for melanoma detection in medical images through the incorporation of data augmentation approaches. We employed a CNN model for the categorization of melanoma images with data augmentation techniques such as random horizontal flips, random cropping, grayscale conversion, Gaussian blur, and random perspective transformations. Experiments demonstrate that the suggested method surpasses the existing peak performance in melanoma identification within medical imaging. The results indicate the potential of data augmentation techniques in alleviating the issue of insufficient medical images and improving melanoma detection. We attained an overall accuracy of 93.43%, a sensitivity of 99.74%, and a specificity of 88.53% in melanoma detection, surpassing state‐of‐the‐art approaches with the HAM10000 dataset. Our model is beneficial in clinical settings to aid dermatologists in precisely identifying patients, facilitating early intervention, and potentially preserving lives. In the future, we intend to test our algorithm on more skin cancer datasets that may enhance the accuracy of melanoma diagnosis. A crucial component of the design is the ablation study, which seeks to discover and improve the most significant model parameters for computational efficiency and diagnostic precision. The HAM10000 dataset is utilized for ablation tests to assess and validate the efficacy of the suggested method.

Aerodynamic interaction of rain and wind turbine blades: the significance of droplet slowdown and deformation for leading-edge erosion

Abstract. Leading-edge rain erosion is a severe problem in the wind energy community since it leads to blade damage and a reduction in annual energy production by up to a few percent. The impact speed of rain droplets is a key driver of the erosion rate; therefore, its precise computation is essential. This study investigates the aerodynamic interaction of rain droplets and wind turbine blades. Based on findings from the literature and an analysis of the relevant parameter space, it is found that the aerodynamic interaction leads to a reduction in the impact speed. Additionally, the rain droplets deform and break up as they approach the wind turbine blade. An existing Lagrangian particle model, developed for research in aircraft icing, is adapted, extended, and validated for leading-edge rain erosion to study the process in more detail. Results show that the droplet slowdown reduces predicted damage toward the tip of the blade by over 50 %. The model indicates that the aerodynamic blade interaction affects small droplets significantly more than large droplets. Due to this drop-size dependency, the damage accumulation is shifted toward higher-rain-intensity events. Additionally, the droplet impact speed is sensitive to the aerodynamic nose radius of the airfoil. Due to this sensitivity and its drop-size dependency, the slowdown effect provides interesting levers for erosion mitigation via blade design or operational adjustments. To conclude, the aerodynamic interaction between droplet and blade is non-negligible and needs to be taken into account in erosion lifetime models.

Deep learning-based prediction approaches of binary star parameters

The precise computation of binary star parameters is crucial for understanding their formation, evolution, and dynamics. However, large datasets of available astronomical measurements require substantial effort for computing using classic astronomical methods. Deep learning (DL) is a promising approach that can provide a proper solution for estimating the parameters and reducing the burden of the lengthy procedures of astronomical computations. This study proposes two DL-based models for estimating binary star parameters. The first is the well-known multi-layer perceptron (MLP) model, whereas the second is based on long short-term memory (LSTM). We rely on databases, such as large sky multi-object fiber spectroscopic telescope area (LAMOST), to train the proposed models. In addition, we verify the training ratio showing that the performance of both models at a low training ratio of 30%, based on the mean square error (MSE), results in acceptable performance. Furthermore, the LSTM-based DL model outperforms the conventional MLP for different training ratios. Eventually, the two models have superiority compared to the benchmark methods.

Advanced hybrid algorithm for estimation of circularity deviation in coordinate metrology

Abstract In the field of precision mechanics such as automobiles or aeronautics, the estimation of defects in the shape of mechanical parts is a very common task, and the declaration of conformity is mainly based on the precision of this value.
This research introduces an innovative hybrid optimization methodology that enhances the precision of estimation of circularity defects. The method proposed combins the Genetic Algorithm (GA) and the Interior Point Method (IPM), this approach overcomes the limitations of traditional methods like Least Squares (LS) and Orthogonal Distance Regression (ODR), which rely on initial estimates parameters to ensure convergence. The GA-IPM hybrid eliminates the need for such initial guesses, leading to faster convergence and more accurate results.
To control a mechanical part, a cloud of points is preleved by using a Coordinate Measuring Machines (CMMs), after tratement of those datasets the estimation of circularity defects is calculated using the proposed algortihm GA-IPM. To validate our approach a comparative study is carried out according to the standard ISO 10360-6, which consists of comparing the results (substitute geometry parameters) obtained with those of National Institute of Standards and Technology (NIST) considered as reference values. Several examples have been processed, circle totally or partially measured in order to verify the robustness of the algorithm in terms of convergence and calculation precision, the circularity defect is subsequently calculated on the basis of these parameters. The comparison shows that our method estimates the parameters and the circularity defect well without needing a vector of initial estimates parameters.
Given the results obtained, this hybrid approach represents a contribution to the estimation of the circularity deviation, thus offering industries a reliable, precise method to ensure the declaration of conformity of mechanical parts.

Integral identities and universal relations for solitons

We show that any nonlinear field theory giving rise to static solutions with finite energy like, e.g., topological solitons, allows us to derive an infinite number of integral identities which any such solution has to obey. These integral identities can always be understood as being generated by field transformations and their related Noether currents. We also explain why all integral identities generated by coordinate transformations become trivial for Bogomolnyi-Prasad-Sommerfield (BPS) solitons, i.e., topological solitons which saturate a topological energy bound. Finally, we consider applications of these identities to a broad class of nonlinear scalar theories, including the Skyrme model. More concretely, we find nontrivial integral identities that can be seen as model-independent relations between certain physical properties of the solitons in such theories, and we comment on the possible connection between these new relations and those already found in the context of astrophysical compact objects. We also demonstrate the usefulness of said identities to estimate the precision of the numerical calculation of soliton observables. Published by the American Physical Society 2024

Third-Kind Chebyshev Spectral Collocation Method for Solving Models of Two Interacting Biological Species

This paper develops numerical methods for solving a system of two nonlinear integro-differential equations that arise in biological modeling. A spectral collocation method utilizing third-kind Chebyshev polynomials forms the basis of the solution methodology, which efficiently converts the integro-differential system into a collection of nonlinear algebraic equations. To guarantee precise and effective calculation, these algebraic equations are subsequently numerically solved using Newton’s method. In comparison to current methods, the suggested approach offers notable gains in computational efficiency and precision. The spectral collocation method’s accuracy is confirmed by contrasting the outcomes with those derived from other numerical methods that are published in the literature. To further illustrate the applicability, dependability, and computational efficiency of the suggested approach in resolving complicated biological systems, a number of illustrative instances are provided. The ability of spectral collocation techniques based on third-kind Chebyshev polynomials to solve integro-differential equations in a variety of scientific and engineering applications is highlighted by this work.

A 1T1M-based efficient probability storage and computing cell for the VAE-SBN hybrid model

Abstract Bayesian network are crucial components for uncertainty reasoning and knowledge representation in probabilistic graphical models. As model complexity increases, traditional digital methods face challenges in efficiency and resource utilization. In this work, a novel Bayesian network optimization method based on one-transistor one-memristor (1T1M) units for storing and accessing probability values was proposed. This method leverages the sigmoid-like characteristics of 1T1M for computational optimization, integrates probability storage into sigmoid belief network (SBN), and extends to the variational autoencoder (VAE) framework. By storing and manipulating probabilities at hardware level, this approach enhances storage efficiency and access speed while enhancing computational precision and model expressiveness through the sigmoid-like space of 1T1M. Validation experiments using the MNIST digit generation task demonstrate that this method exhibits unique advantages in SBN structures, particularly in handling complex conditional probabilities and inference tasks. Compared to traditional digital implementations, the proposed method exhibits potential advantages in inference speed and hardware utilization.