Theoretical Algorithm Research Articles

A novel framework for predicting 3D scene infrared radiation characteristics through AI-enhanced thermodynamic modeling

In the evolving landscape of modern warfare, virtual battlefield construction technology significantly impacts fields such as weapon development and tactical formulation. Currently, 3D infrared scene simulation face challenges like high computational resource demands, difficulty in depicting interaction mechanisms between dynamic targets and environments, and a lack of reliable validation for algorithms. In this paper, we propose a three-dimensional infrared characteristics prediction framework (3DICPF) that integrates data-driven and theoretical algorithms, capable of rapidly constructing realistic 3D infrared scenes based on limited input information. Considering the extremely high cost of data acquisition, a step-by-step construction approach is adopted to reduce dependence on data. For the aligned image inputs, a 3D shape reconstruction network is utilized to obtain the mesh calculation domain of the target. Subsequently, 3DICPF employs two temperature field reconstruction networks to obtain the temperature distribution of the target. Finally, by using Monte Carlo ray tracing techniques, 3DICPF calculates the radiative interactions between the target and the background, achieving real-time infrared scene rendering. Moreover, confirmatory experiments with a shape-similar tank replica were conducted, demonstrating that 3DICPF could simulate infrared scene images with an average similarity above 88.13%. Overall, our research presents a significant advancement in virtual warfare technology, offering an efficient tool for enhancing strategic military planning and decision-making.

Study on the feasibility of distinguishing ventricular and pre-excited arrhythmia rhythms by a new algorithm

BackgroundThe differentiation and diagnosis of ventricular tachycardia (VT) and pre-excited tachycardia (PXT) remains a challenging task, especially when typical AV dissociation is not present. The purpose of this article is to study the feasibility of a new theoretical algorithm for identifying ventricular arrhythmias (VA) and pre-excited arrhythmias (PA) rhythms (which can be used to distinguish VT from PXT, etc.). MethodThis study involved the deduction of a new algorithm by combining knowledge of cardiac anatomy, vectorcardiography, and cardiac electrophysiology. The new algorithm evaluated the diagnostic value through intracardiac electrophysiology in 205 cases of VA and PA. The new algorithm diagnoses VA based on the following 4-step process:1.The QRS complex in leads II, III, and aVF shows a unidirectional R wave, and lead aVR shows a QS pattern.2.S waves are predominant in two or more of leads I, aVF, and V6.3.Lead V2 shows ≥3 phase waves or returning branch notching (note: returning branch refers to the band of QRS complexes returning to the baseline).4.Lead V5 shows a negative wave in the initial portion or returning branch notching.If none of these criteria are met, the diagnosis is PA. The diagnostic value of the new algorithm is compared with the Steurer algorithm and the Vereckei algorithm (diagnosed based on the QRS waveform characteristics of the two algorithms during electrophysiological verification, excluding the diagnosis of atrioventricular dissociation). ResultsThe new algorithm showed significant advantages in terms of AUC value (0.83 vs. 0.61 vs. 0.57), sensitivity (83.6 % vs. 23.3 % vs. 24.8 %), and accuracy (82.9 % vs. 48.3 % vs. 46.3 %) compared to the Steurer algorithm and Vereckei algorithm based on QRS waveform characteristics for diagnosing VA (137 cases) and PA (68 cases). This indicates that the new algorithm is more accurate in identifying idiopathic VA. While there was a significant difference in specificity between the New algorithm and Steurer algorithm (82.3 % vs. 98.5 %, p < 0.05), the difference with Vereckei algorithm (82.3 % vs. 89.7 %) was not significant.In the New algorithm, the sensitivity and specificity for each step are as follows:-Step 1: Sensitivity 34.3 %, Specificity 94.1 %.-Step 2: Sensitivity 24.1 %, Specificity 98.5 %.-Step 3: Sensitivity 18.3 %, Specificity 100 %.-Step 4: Sensitivity 6.6 %, Specificity 89.7 %.Step 1 had the highest AUC value, indicating the best overall diagnostic performance among all steps. Step 2 and Step 3 also performed well, while Step 4 had relatively poorer diagnostic performance. ConclusionThe new algorithm is suitable for identifying the origin of VA and PA rhythms.

Spectral Galerkin Approximation and Error Analysis Based on a Mixed Scheme for Fourth‐Order Problems in Complex Regions

ABSTRACTIn this article, an efficient spectral Galerkin method, which is based on a mixed scheme, is proposed and studied for solving fourth‐order problems in complex regions. The fundamental idea behind this approach is to transform the initial problem into an equivalent form in cylindrical coordinates and to reshape the computational domain into a product‐type rectangular one, which facilitates the utilization of spectral methods. However, when considering the equivalent fourth‐order form directly in cylindrical coordinates, it introduces intricate pole conditions and variable coefficients, posing challenges to both theoretical analysis and algorithm implementation. To address this, we employ the orthogonality of Fourier series to further decompose it into a sequence of decoupled two‐dimensional fourth‐order eigenvalue problems. For each such problem, we introduce an auxiliary function to transform it into an equivalent second‐order coupled system. Building on this, we formulate a mixed variational formulation and discrete scheme, and prove the error estimates for eigenvalue and eigenfunction approximations. Furthermore, we extend this algorithm to the two‐dimensional complex domains. Finally, a series of numerical examples are presented, and the numerical results validate the effectiveness of the algorithm and the correctness of the theoretical results.

Revisiting RFID Missing Tag Identification: Theoretical Foundation and Algorithm Design

Revisiting RFID Missing Tag Identification: Theoretical Foundation and Algorithm Design

Research on the high-performance computing method for the neutron diffusion spatiotemporal kinetics equation based on the convolutional neural network

Due to the uncertainty of computational results and the lack of interpretability of models in solving physical field equations in current deep learning, this paper designs a convolutional neural network that can be used to solve the neutron diffusion spatiotemporal kinetics equation in polar and cylindrical coordinate systems. This algorithm directly utilizes the macroscopic cross-section of the material without using the lattice homogenization method, replaces the finite volume method with the extended matrices, and solves the extended matrices using the convolutional kernels instead of the iterative algorithms. Taking the simplified Tsinghua High Flux Reactor (THFR) as an example, the feasibility of the algorithm is verified on the PyTorch platform and compared with the calculation results of the source iteration method running on the GPU. The calculation results show that when the number of grids in the radial and axial sections of the simplified THFR model is 804,600 and 3,576,000, respectively, and the algorithm is iterated 3000 times, the normalized power of the convolutional neural network and the source iteration method converges to 10−10, and the maximum point by point error of the neutron flux density of the above two algorithms converges to 10−5. The computational time consumed by the convolutional neural network is approximately 880.64 s and 3729.62 s, which reduces the computational time by 4.66% and 5.05% compared to the GPU parallel accelerated source iteration method, and the former consumes 43.75% less memory compared to the latter. The convolutional neural network is mainly used as the virtual physics engine for the THFR digital twin system, in addition to solving the neutron diffusion spatiotemporal kinetics equation and further improving computational speed. The algorithm directly utilizes the neutron macroscopic cross-section of the material to calculate the neutron flux density distribution without using the lattice homogenization, providing theoretical guidance and algorithm support for developing the high-precision multi-physical field coupling model.

Estimation of abnormal states in shunt capacitor banks using transient disturbance feature extraction

Shunt capacitor banks are essential for reactive power compensation, ensuring voltage stability, and reducing system losses. These banks consist of multiple units with components in series and parallel. A few component failures do not immediately affect the safe operation of the capacitor bank, but component breakdown can lead to voltage redistribution. Under combined factors such as system overvoltage and equipment aging, and others can trigger an avalanche effect causing capacitor breakdown, resulting in significant safety accident risks. Practical operation experience shows that partial component breakdown generates many transient disturbance signals. Quantitative analysis of these signals can detect capacitor bank anomalies early. This paper proposes the quantitative extraction of transient disturbance characteristics using the Prony algorithm and estimates the phase and number of capacitors that break down to judge capacitor anomalies. The simulation part verifies the theoretical analysis and detection algorithm’s correctness through numerical simulations and PSCAD (Power Systems Computer Aided Design) electromagnetic transient simulations. The numerical simulations consider different signal lengths, noise levels, attenuation coefficients, and oscillation frequencies. In the PSCAD simulation environment, verification models are built under varying sampling frequencies, numbers of breakdown components, signal lengths, and signal-to-noise ratios. These simulation results verify the accuracy of the detection algorithm under different conditions.

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS.

MCMC Methods for Parameter Estimation in ODE Systems for CAR-T Cell Cancer Therapy.

Chimeric antigen receptor (CAR)-T cell therapy represents a breakthrough in treating resistant hematologic cancers. It is based on genetically modifying T cells transferred from the patient or a donor. Although its implementation has increased over the last few years, CAR-T has many challenges to be addressed, for instance, the associated severe toxicities, such as cytokine release syndrome. To model CAR-T cell dynamics, focusing on their proliferation and cytotoxic activity, we developed a mathematical framework using ordinary differential equations (ODEs) with Bayesian parameter estimation. Bayesian statistics were used to estimate model parameters through Monte Carlo integration, Bayesian inference, and Markov chain Monte Carlo (MCMC) methods. This paper explores MCMC methods, including the Metropolis-Hastings algorithm and DEMetropolis and DEMetropolisZ algorithms, which integrate differential evolution to enhance convergence rates. The theoretical findings and algorithms were validated using Python and Jupyter Notebooks. A real medical dataset of CAR-T cell therapy was analyzed, employing optimization algorithms to fit the mathematical model to the data, with the PyMC library facilitating Bayesian analysis. The results demonstrated that our model accurately captured the key dynamics of CAR-T cell therapy. This conclusion underscores the potential of parameter estimation to improve the understanding and effectiveness of CAR-T cell therapy in clinical settings.

Does the Novel Dual Source Heat Pump (DSHP) Out-Perform the Ideal DSHP? Experimentation Versus Simulation of DSHP Based Thermodynamic Irreversibility of the Throttle Valve

Purpose: The air-source heat pumps are associated with continuous frosting problems. To ensure a more energy efficiency technology, the novel dual-source heat pump (DSHP) was developed. This article aims to confirm and compare the energy performance of the novel dual-source heat pump (DSHP) with the performance of an ideal heat pump. Methods: We applied an experimental analysis from an installed DSHP system with converters and other components to obtain the energy performance of the DSHP, while the performance of an ideal pump was obtained from simulation based on the theoretical algorithm of the basic principle of the thermodynamic irreversibility of the throttle valve. Results: We found that the Ideal (simulated) DSHP has a higher coefficient of performance (COP) than the installed (experimented) DSHP. The Ideal DSHP’s COP of 4.52 is higher than the Novel DSHP’s COP, which range from 1.78 (day 3) to 2.01 (day 1). The DSHP are power efficient, which reduces carbon emission from whatever power source that is been used. Implications: Energy efficiency in buildings has severe implications for global warmings and the need for sustainability. Because the modern ventilation system utilise energy from waste and heat recovery to power heating and cooling systems, the DSHP remains promising for future generations of building ventilation design. Originality/Value: The value of the study is because it completes an experiment that analyse the performance of the DSHP, comparing the outcomes with results from a simulation based on an Ideal system.

Research on single-phase grounding fault location technology in distribution networks based on impedance method

This article mainly introduces the impedance based single-phase grounding fault location method for distribution networks, including its theoretical basis, algorithm steps, and simulation verification. First, starting from the impedance analysis of the transmission line model, the method of accurately measuring the location of the fault point through phase domain analysis is explained. Next, the process of impedance analysis for single-phase grounding faults was described in detail, that is, how to solve the impedance of the grounding fault points by calculating the voltage and current signals. Then, the specific process of the impedance based grounding fault location algorithm was introduced, including the calculation of equivalent load impedance, the calculation of starting voltage and current, the calculation of grounding current, and the solution of fault point location. Finally, simulation verification was conducted using an IEEE 34 node distribution system example in a MATLAB/Simulink environment, and the results showed that the algorithm has high positioning accuracy, with a maximum error of within 3%.

Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing

Harnessing the Power of Sample Abundance: Theoretical Guarantees and Algorithms for Accelerated One-Bit Sensing

Understanding Higher-Order Interactions in Information Space.

Methods used in topological data analysis naturally capture higher-order interactions in point cloud data embedded in a metric space. This methodology was recently extended to data living in an information space, by which we mean a space measured with an information theoretical distance. One such setting is a finite collection of discrete probability distributions embedded in the probability simplex measured with the relative entropy (Kullback-Leibler divergence). More generally, one can work with a Bregman divergence parameterized by a different notion of entropy. While theoretical algorithms exist for this setup, there is a paucity of implementations for exploring and comparing geometric-topological properties of various information spaces. The interest of this work is therefore twofold. First, we propose the first robust algorithms and software for geometric and topological data analysis in information space. Perhaps surprisingly, despite working with Bregman divergences, our design reuses robust libraries for the Euclidean case. Second, using the new software, we take the first steps towards understanding the geometric-topological structure of these spaces. In particular, we compare them with the more familiar spaces equipped with the Euclidean and Fisher metrics.

Research on the Optimal Design of Retaining Piles of a Wide Metro Tunnel Foundation Pit Based on Deformation Control

Engineers pay more and more attention to the economic benefits of foundation pit engineering. At present, the optimal design of the foundation pit supporting structure mainly focuses on strength and functional design, and there is no mature theoretical design method for deformation control. In this paper, a method for calculating the overall deformation of a foundation pit supporting structure based on the principle of minimum potential energy is proposed. Based on this method, the optimal design of the foundation pit of Guangzhou Baiyun District Comprehensive Transportation Hub Metro Station is realized. The deformation calculation results and optimization design scheme are validated by finite element numerical simulation and field monitoring data. The results show that the proposed theoretical algorithm predicts the pile deformation curves better than the finite element method, suggesting the proposed theoretical method is reasonable and the optimization scheme of the retaining pile is feasible. In the optimized design, the deformation of the foundation pit retaining pile is controlled by its push-back effect. The proposed deformation calculation method can realize the overall deformation calculation of the foundation pit supporting structure.

Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

Multi-USV cooperative oil spill source seeking via extremum seeking combined with information fusion

This paper addresses the issue of cooperative source seeking for oil spill pollution by multiple unmanned surface vehicles (USVs), where the oil spill plume propagation is modeled by a linear advection–diffusion equation in two-dimensional space. The main objective is to construct a cooperative source seeking scheme for a group of USVs in the framework of extremum seeking combined with information fusion to solve such cooperative source seeking problem. To do this, both a gradient-based control law and a formation control law are included in the cooperative source seeking scheme. The gradient-based control law that is constructed by extremum-seeking control combined with information fusion drives the multiple USVs to approach the oil spill source, where the gradient is estimated online via the extremum-seeking control and the fused concentration value that is derived via a linear H∞ filter from the local concentration measurements at the USV’s current position. In the source seeking process, the USVs driven by the formation control law based on the virtual structure method maintain a preset circular formation. The convergence performance of the cooperative source seeking scheme is analyzed rigorously by the Lyapunov method. Finally, extensive numerical simulations are included to validate the theoretical algorithm.

Research on the Support Performance of Internal Feedback Hydrostatic Thrust and Journal Bearing Considering Load Effect

This study aims to analyze the impact of uniform and eccentric load conditions on the performance of internal feedback hydrostatic thrust and journal bearing. Two distinct models are established: a three-degrees-of-freedom uniform load model and a five-degrees-of-freedom eccentric load model. The support stiffness, overturning stiffness, and flow rate for both thrust and journal bearings are calculated. Additionally, numerical analysis is conducted to examine the influence of oil film thickness, inlet pressure, and restrictor size on the operational characteristics of the bearings, revealing the interplay between an eccentric load and journal bearing speed. The validity of the theoretical algorithm is verified through finite element simulation. The research outcomes hold significant guiding implications for the design and application of internal feedback hydrostatic bearings.

On the Practical Power of Automata in Pattern Matching

Many papers in the intersection of theoretical and applied algorithms show that the simple, asymptotically less efficient algorithm, performs better than the bestcomplex theoretical algorithms on random data or in specialized “real world” applications. This paper considers the Knuth–Morris–Pratt automaton, and shows a counter-intuitive practical result. The classical pattern matching paradigm is that of seeking occurrences of one string—the pattern, in another—the text, where both strings are drawn from an alphabet set Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document}. Assuming the text length is n and the pattern length is m, this problem can naively be solved in time O(nm). In Knuth, Morris and Pratt’s seminal paper of 1977, an automaton, was developed that allows solving this problem in time O(n) for any alphabet. This automaton, which we will refer to as the KMP-automaton, has proven useful in solving many other problems. A notable example is the parameterized pattern matching model. In this model, a consistent renaming of symbols from Σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Sigma$$\\end{document} is allowed in a match. The parameterized matching paradigm has proven useful in problems in software engineering, computer vision, and other applications. It has long been believed that for texts where the symbols are uniformly random, the naive algorithm will perform as well as the KMP algorithm. In this paper, we examine the practical efficiency of the KMP algorithm versus the naive algorithm on a randomly generated text. We analyze the time under various parameters, such as alphabet size, pattern length, and the distribution of pattern occurrences in the text. We do this for both the original exact matching problem and parameterized matching. While the folklore wisdom is vindicated by these findings for the exact matching case, surprisingly, the KMP algorithm always works significantly faster than the naive in the parameterized matching case. We check this hypothesis for DNA texts and image data and observe a similar behavior as in the random text. We also show a very structured exact matching case where the automaton is much more efficient.

Calculation of Lateral Stability of Tank Trucks and its Relationship with Filling Rate

To investigate a theoretical algorithm model of the lateral stability of tank trucks and its relationship with the liquid filling rate, the shifting of the liquid centroid during non-full loading is considered. The deformation of the suspension and tires under load is assumed to exhibit a linear relationship. Using the principle of moment balance, a calculation model for the maximum stable roll angle and rollover critical acceleration of tank trucks based on the “liquid–tank–vehicle” tri-coupling is established, and the relationship between the maximum stable roll angle and rollover critical acceleration of tank trucks is analyzed and compared. Based on the result and a specific tank truck model, the effects of the liquid filling rate on the maximum stable roll angle, rollover critical acceleration, and tank tilt angle of tank trucks are investigated using numerical analysis methods. A comparison with the lateral stability of ordinary trucks loaded with solid goods is performed to further examine the effect of the liquid filling rate on the lateral stability of tank trucks. The results show that the maximum stable roll angle and rollover critical acceleration are consistent indicators for evaluating the lateral stability of tank trucks; the lateral stability of tank trucks decreases owing to the shift in the liquid centroid inside the tank; as the liquid filling rate increases, the lateral stability of the tank trucks deteriorates slightly.

Parallel Empirical Evaluations: Resilience despite Concurrency

Computational evaluations are crucial in modern problem-solving when we surpass theoretical algorithms or bounds. These experiments frequently take much work, and the sheer amount of needed resources makes it impossible to execute them on a single personal computer or laptop. Cluster schedulers allow for automatizing these tasks and scale to many computers. But, when we evaluate implementations of combinatorial algorithms, we depend on stable runtime results. Common approaches either limit parallelism or suffer from unstable runtime measurements due to interference among jobs on modern hardware. The former is inefficient and not sustainable. The latter results in unreplicable experiments. In this work, we address this issue and offer an acceptable balance between efficiency, software, hardware complexity, reliability, and replicability. We investigate effects towards replicability stability and illustrate how to efficiently use widely employed cluster resources for parallel evaluations. Furthermore, we present solutions which mitigate issues that emerge from the concurrent execution of benchmark jobs. Our experimental evaluation shows that – despite parallel execution – our approach reduces the runtime instability on the majority of instances to one second.

Intelligent interference cancellation and ambient backscatter signal extraction for wireless-powered UAV IoT network

Unmanned aerial vehicles (UAVs) offer a new approach to wireless communication, leveraging their high mobility, flexibility, and visual communication capabilities. Ambient backscatter communication enables Internet of Things devices to transmit data by reflecting and modulating ambient radio waves, eliminating the need for additional wireless channels, and reducing energy consumption and cost for sensors. However, passive ambient backscatter communication has limitations such as limited range and poor communication quality. By utilizing UAVs as communication nodes, these limitations can be overcome, expanding the communication range and improving the quality of communication. Although some research has been conducted on combining UAVs and ambient backscatter, several challenges remain. One key challenge is the strong direct link interference in ambient backscatter under UAV conditions, which significantly affects communication quality. In this paper, we propose an intelligent backward and forward straight link interference cancellation algorithm based on NOMA decoding technique to enhance ambient backscatter communication quality under UAV conditions and extract more ambient energy for UAV energy supply. The paper includes theoretical derivation, algorithm description, and simulation analysis to validate the error bit rate of labeled information bits. The results demonstrate that the forward algorithm reduces the error bit rate by approximately 20% under low signal-to-noise ratio (SNR) conditions, while the backward algorithm reduces the error bit rate under high SNR conditions. The combination of the forward and backward algorithms reduces the error bit rate under both high and low SNR conditions. The proposed method contributes to improving the quality of ambient backscatter communication in UAV settings.