Theoretical Algorithm Research Articles

Path planning of indoor mobile robot based on improved A* algorithm incorporating RRT and JPS

Indoor mobile robots are widely used in modern industry. Traditional motion control methods for robots suffer from discontinuous path curvature, low planning efficiency, and insufficient verification of theoretical algorithms. Therefore, a motion control system for an intelligent indoor robot was designed. By optimizing the radar map detecting and positioning, path planning, and chassis motion control, the performance of the system has been improved. First, a map of the warehouse environment is established, and the number of resampling particles interval is set for the Gmapping building process to improve the efficiency of map construction. Second, an improved A* algorithm is proposed, which converts the path solution with obstacles between two points into the path solution without obstacles between multiple points based on the Rapidly expanding Random Trees and Jump Point Search algorithms and further improves the pathfinding speed and efficiency of the A* algorithm by screening the necessary expansion nodes. The Dynamic Window Approach (DWA) algorithm based on the dynamic window is used to smooth the path, and the target velocity is reasonably assigned according to the kinematic model of the robot to ensure the smooth motion of the chassis. By establishing raster map models of different sizes, the traditional and improved A* pathfinding algorithms are compared and validated. The results illustrate that the improved pathfinding algorithm reduces the computing time by 67% and increases the pathfinding speed by 47% compared with the A* algorithm. Compared with the traditional method, the speed and effect are greatly improved, and the motion control system can meet the requirements of autonomous operation of mobile robots in indoor storage.

Cyber–physical system for fast prototyping of power electronic converters in EMI shaping context

Dynamic development of power systems, which utilize power electronic converters as interfaces between the renewable energy sources, as well as complex control and measuring arrangements, forces the cooperation of specialists representing various fields to assure electromagnetic compatibility (EMC) of the so-called smart industrial systems. Low voltage DC grid applications involving multiple power electronics devices may turn the grid susceptible to noise injection and impair the signal integrity and power quality due to the electromagnetic interference (EMI) emission and due to devices and grid interactions. The EMI propagation leads to misreading measurements and faulty conditions of equipments and in ICT (Information and Communications Technology) systems within the conducted emission frequency range, which impacts the hosting capacity of the grid. This paper proposes a cyber–physical system based on the NI-PXI platform for fast-prototyping and EMC assessment as pre-compliance tool of low-voltage DC grid in smart industrial systems. Thus, the LabVIEW-based design as well as the EMC-testbench system validation were performed with a DC/DC converter through the pulse-width modulation control algorithms. In addition, a case study has been investigated to evaluate the interferences between the converter and Power Line Communication. Hence, the theoretical control algorithms, hardware and software details, and experimental results are discussed, highlighting the paradigms of one flexible and collaborative cyber–physical system from the area of smart industrial systems.

Herty Medal goes to David Sherrill

The 2023 Charles H. Herty Medal goes to C. David Sherrill , Regents’ Professor in the Schools of Chemistry and Biochemistry and Computational Science and Engineering at Georgia Institute of Technology. This medal is awarded annually by the American Chemical Society Georgia Section to recognize the work and service of an outstanding chemist from the southeastern region of the US. The focus of Sherrill’s research is the development and application of theoretical methods and algorithms in computational quantum chemistry. He is a fellow of ACS, the American Association for the Advancement of Science, and the American Physical Society. Sherrill is an associate editor of the Journal of Chemical Physics and was recently elected to the board of the World Association of Theoretical and Computational Chemists. He is very active in ACS both nationally and in the Georgia Section. The gold Herty Medal will be presented to Sherrill at the 89th

Research on complex vector quasi-resonant control methods for permanent magnet synchronous motor

A complex vector quasi-resonant controller is proposed to solve the current controller’s dynamic coupling problem and static harmonics. Although current feedforward decoupling has been widely used, the calculation relies on the motor parameters. When the motor works at high speed and high torque load, the parameters change greatly, failing to decouple. In the static conditions, due to the inverter nonlinearity, the PMSM stator current contains lots of high harmonics, which causes the motor produces a specific frequency torque ripple. Therefore, complex vector quasi-resonance is proposed. The complex vector transfer function contains a virtual axis zero point, which varies with speed so that the zero point of the controller and the pole of the controlled object are eliminated. The quasi-resonant controllers are connected in parallel with the main controller and suppress harmonics at fixed frequencies of the system. The method is based on the resonant controller with infinite gain at the resonant frequency point, which can achieve zero steady-state error tracking, achieving the purpose of torque pulsation suppression. In this paper, through theoretical derivation and algorithm structure design, the algorithm’s effectiveness is verified in the simulation. The proposed algorithm has a fast response in the dynamic state and smaller current harmonics in the steady state.

Anti-diagonalization theory and algorithm of matrices—from skew-symmetric matrices to arbitrary matrices

In this paper, a novel algorithm for anti-diagonalization of skew symmetric matrices via using orthogonal similarity transformations has been introduced. The theory and algorithm about the anti-triangular factorization of skew-symmetric matrices are proved. In the case of skew-symmetric matrices, we prove that the anti-diagonal form is always obtained, resulting in developing a new factorization scheme. Moreover, a theoretical algorithm is given based on the theory of double eigenvector system, which provides all the information for the factorization of arbitrary matrices. Finally, the proposed algorithm is verified effective and efficient through the numerical experiments of anti-diagonalization of matrices over a general number field.

Convergence and stability of split-step θ methods for stochastic variable delay differential equations

In this paper, a split-step θ algorithm is proposed for nonlinear stochastic variable delay differential equations. It is proved that the numerical solution obtained by the algorithm converges to the exact solution under the local Lipschitz condition. Moreover, it is shown that the numerical algorithm can maintain the mean square stability of the analytical solution for linear scalar equations. Finally, numerical experiments demonstrate the correctness of the theoretical algorithm.

Event-Triggered formation control for time-delayed discrete-Time multi-Agent system applied to multi-UAV formation flying

This paper studies the formation control for a time-delayed discrete-time multi-agent system (MAS). An event-triggered controller is proposed to reduce the communication load of the system. Based on the designed event-triggered condition and properties of Schur stable matrix, the stability of formation for discrete-time MAS is proved. Utilizing the virtual simulation platform integrated Robot Operating System (ROS) and Gazebo, a virtual scene with unmanned aerial vehicles (UAVs) models is built and the verification for the theoretical algorithm is completed. Finally, an experimental platform with four practical UAVs is constructed and the result shows that the expected formation is achieved and controller proposed can solve the formation control problem for time-delayed discrete-time MASs. Besides, the effectiveness of the event-triggered mechanism on reducing communication frequency is comfirmed in practical scenarios.

АЛГОРИТМ ПРОВЕРКИ ПОДЛИННОСТИ ИСПОЛНИТЕЛЬНЫХ УСТРОЙСТВ В АВТОМАТИЗИРОВАННЫХ СИСТЕМАХ УПРАВЛЕНИЯ ТЕХНОЛОГИЧЕСКИМ ПРОЦЕССОМ

The paper proposes an algorithm for verifying the authenticity of automated process control system actuators based on the HART standard, which can act as the main or additional measure of protection against threats to the integrity of the system. The principle of operation of the HART standard is considered, a theoretical algorithm is given, additional technical solutions that increase its reliability are considered, as well as scenarios of possible attacks.

Wind Speed Prediction Model Based on Deep Learning

This article selects hourly wind speed data recorded by meteorological monitoring stations as the dataset, and conducts in-depth analysis on the preprocessing methods of wind speed data in response to the nonlinearity and instability of wind speed time series. At the same time, the algorithm principles and steps of empirical mode decomposition, comprehensive empirical mode decomposition, and complementary ensemble empirical mode decomposition were introduced, and the decomposition results of different methods were compared. In addition, in the selection of prediction algorithms for wind speed prediction models, the theoretical basis and algorithm steps of backpropagation neural networks, deep confidence networks, and long-term and short-term memory neural networks were studied, and a single model prediction performance comparison was conducted on three time series short-term prediction models. Compared with the LSTM model, the RMSE of the model established in this article decreased by 0.9422, MAE decreased by 0.6789, and MAPE decreased by 7.23%.

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty

Robust learning and optimization in distributionally robust stochastic variational inequalities under uncertainty is a crucial research area that addresses the challenge of making optimal decisions in the presence of distributional ambiguity. This research explores the development of methodologies and algorithms to handle uncertainty in variational inequalities, incorporating a distributionally robust framework that considers a range of possible distributions or uncertainty sets. By minimizing the worst-case expected performance across these distributions, the proposed approaches ensure robustness and optimality in decision-making under uncertainty. The research encompasses theoretical analysis, algorithm development, and empirical evaluations to demonstrate the effectiveness of the proposed methodologies in various domains, such as portfolio optimization and supply chain management. The outcomes of this research contribute to the advancement of robust optimization techniques, enabling decision-makers to make reliable and robust decisions in complex real-world systems

Random-telegraph noise mitigation and qubit decoherence in solid-state experiments

We investigate the recently proposed theoretical models and algorithms in Song et al. [1] for mitigating decoherence in solid-state qubit systems, where qubits are affected by charge (random telegraph) noises. The model includes a setup of a logical qubit (data qubit) and a spectator qubit, where the latter is used as a probe of the noise. The probe results can be used in correcting the phase error in order to improve the decoherence of the data qubit. In this work, we apply the proposed model with parameters extracted from recent solid-state qubit experiments. We extract parameters such as the noise switching rates, the qubit sensitivities to noise, and the measurement dead time. Using these parameters, we then numerically simulate the data qubit’s phase and the qubit decoherence. We also show that the proposed phase-correction technique using Bayesian estimation can improve the data qubit decoherence significantly.

Calibration free laser ablation molecular isotopic spectrometry (CF-LAMIS) for boron isotopic composition determination

A theoretical molecular spectra generator algorithm (MAHADEV), which is used to fit experimental spectra to calculate the isotopic composition of boron samples, is developed. This method doesn't require any standard samples.

Forest Canopy Height Extraction Method Based on ICESat-2/ATLAS Data

Ice, cloud, and land elevation satellite (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) multibeam micropulse photoncounting light detection and ranging (LiDAR) can be effectively applied to extract forest canopy height. However, the ICESat-2/ATLAS photon point cloud interfered with the signal-to-noise ratio (SNR), fraction vegetation coverage (FVC), and terrain slope. The main challenge of this research is to extract high-precision canopy heights. Therefore, this article improves the canopy height extraction method based on the ICESat-2/ATL08 theoretical algorithm. First, an adaptive filter, Threshold Segmentation based on Spatial Clustering and Bimodal Reconstruction (TS-SCABR), is proposed, which can adapt to different SNR scenarios. Then, combined with the gradient method, the discontinuous data are detrended in sections to eliminate the edge mutation problem of the detrended data. Based on the detrended data, the iterative filtering algorithm of the local terrain is employed to fit the ground curve, and the mutation detection and empirical mode decomposition (EMD)-digital smoothing polynomial (DISPO) filtering remove the pseudoground photons to identify the data of ground and nonground photons accurately. Finally, the percentile statistics method is utilized to extract the canopy-top photons from the nonground photons according to their elevation difference. The results indicate that, under different natural conditions, the improved algorithm has better adaptability than the previous algorithm. Compared with the original ATL08 ATBD algorithm, the canopy height accuracy is significantly improved, especially in low FVC and high slope scenarios. When the FVC is lower than 25%, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R_{2}$ </tex-math></inline-formula> increases by 50.3%, and the root mean square error (RMSE) is reduced by 2.175 m, and when the slope is higher than 45°, it increases by 41.7%, and the RMSE is reduced by 2.159 m. Therefore, the algorithm has apparent advantages in inverting the canopy height in a mountainous environment with lush forests.

Allergenic and Mas-Related G Protein-Coupled Receptor X2-Activating Properties of Drugs: Resolving the Two

Since the seminal description implicating occupation of the Mas-related G protein-coupled receptor X2 (MRGPRX2) in mast cell (MC) degranulation by drugs, many investigations have been undertaken into this potential new endotype of immediate drug hypersensitivity reaction. However, current evidence for this mechanism predominantly comes from (mutant) animal models or invitro studies, and irrefutable clinical evidence in humans is still missing. Moreover, translation of these preclinical findings into clinical relevance in humans is difficult and should be critically interpreted. Starting from our clinical priorities and experience with flow-assisted functional analyses of basophils and cultured human MCs, the objectives of this rostrum are to identify some of these difficulties, emphasize the obstacles that might hamper translation from preclinical observations into the clinics, and highlight differences between IgE- and MRPGRX2-mediated reactions. Inevitably, as with any subject still beset by many questions, alternative interpretations, hypotheses, or explanations expressed here may not find universal acceptance. Nevertheless, we believe that for the time being, many questions remain unanswered. Finally, a theoretical mechanistic algorithm is proposed that might advance discrimination between MC degranulation from MRGPRX2 activation and cross-linking of membrane-bound drug-reactive IgE antibodies.

Character data processing: from simple to complex

The article proposes multi-level materials for students of grades 7–8 to master the theme "Character data processing" in the Python programming language. The first level of complexity involves consideration of the minimum theoretical information and basic algorithms for processing character data, including algorithms for texts editing (replacing a character, deleting and inserting a character, searching for an occurrence of a given pattern); the material is aimed at students who master informatics at a basic level. The second (advanced) level of complexity implies not only actual knowledge of the material being studied, including string processing methods, but also the ability to apply it in new situations; the material can be offered to the basic school students who master informatics at an advanced level. The third (high) level of complexity involves the acquaintance of students with the Olympiad tasks; the material is aimed at the most prepared and motivated students. Working with string data, implemented at different levels of complexity, can become both an alternative and a supplement for tasks with a mathematical context, traditionally included in the content of the informatics course of the basic school.

Controlled interacting particle algorithms for simulation-based reinforcement learning

This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions. Particular attention is given to the linear quadratic (LQ) control problem. There is a growing interest in re-visiting this classical problem, in part due to the successes of reinforcement learning (RL). The main question of this body of research (and also of our paper) is to approximate the optimal control law without explicitly solving the Riccati equation. A novel simulation-based algorithm, namely a dual ensemble Kalman filter (EnKF), is introduced. The algorithm is used to obtain formulae for optimal control, expressed entirely in terms of the EnKF particles. An extension to the nonlinear case is also presented. The theoretical results and algorithms are illustrated with numerical experiments. • First controlled interacting particle system to solve the optimal control problem. • Suitably coupling the simulations solves the policy optimization problem. • Numerical demonstrations and comparison with state-of-the-art approaches for LQR problem. • For LQR, proposed algorithm is two orders of magnitude more computationally efficient.

A Non-Archimedean Interior Point Method and Its Application to the Lexicographic Multi-Objective Quadratic Programming

This work presents a generalized implementation of the infeasible primal-dual interior point method (IPM) achieved by the use of non-Archimedean values, i.e., infinite and infinitesimal numbers. The extended version, called here the non-Archimedean IPM (NA-IPM), is proved to converge in polynomial time to a global optimum and to be able to manage infeasibility and unboundedness transparently, i.e., without considering them as corner cases: by means of a mild embedding (addition of two variables and one constraint), the NA-IPM implicitly and transparently manages their possible presence. Moreover, the new algorithm is able to solve a wider variety of linear and quadratic optimization problems than its standard counterpart. Among them, the lexicographic multi-objective one deserves particular attention, since the NA-IPM overcomes the issues that standard techniques (such as scalarization or preemptive approach) have. To support the theoretical properties of the NA-IPM, the manuscript also shows four linear and quadratic non-Archimedean programming test cases where the effectiveness of the algorithm is verified. This also stresses that the NA-IPM is not just a mere symbolic or theoretical algorithm but actually a concrete numerical tool, paving the way for its use in real-world problems in the near future.

The public health exposome and pregnancy-related mortality in the United States: a high-dimensional computational analysis

BackgroundRacial inequities in maternal mortality in the U.S. continue to be stark.MethodsThe 2015–2018, 4-year total population, county-level, pregnancy-related mortality ratio (PRM; deaths per 100,000 live births; National Center for Health Statistics (NCHS), restricted use mortality file) was linked with the Public Health Exposome (PHE). Using data reduction techniques, 1591 variables were extracted from over 62,000 variables for use in this analysis, providing information on the relationships between PRM and the social, health and health care, natural, and built environments. Graph theoretical algorithms and Bayesian analysis were applied to PHE/PRM linked data to identify latent networks.ResultsPHE variables most strongly correlated with total population PRM were years of potential life lost and overall life expectancy. Population-level indicators of PRM were overall poverty, smoking, lack of exercise, heat, and lack of adequate access to food.ConclusionsIn this high-dimensional analysis, overall life expectancy, poverty indicators, and health behaviors were found to be the strongest predictors of pregnancy-related mortality. This provides strong evidence that maternal death is part of a broader constellation of both similar and unique health behaviors, social determinants and environmental exposures as other causes of death.

Generalized Energy-Conserving Dissipative Particle Dynamics with Mass Transfer. Part 1: Theoretical Foundation and Algorithm.

An extension of the generalized energy-conserving dissipative particle dynamics method (GenDPDE) that allows mass transfer between mesoparticles via a diffusion process is presented. By considering the concept of the mesoparticles as property carriers, the complexity and flexibility of the GenDPDE framework were enhanced to allow for interparticle mass transfer under isoenergetic conditions, notated here as GenDPDE-M. In the formulation, diffusion is described via the theory of mesoscale irreversible processes based on linear relationships between the fluxes and thermodynamic forces, where their fluctuations are described by Langevin-like equations. The mass exchange between mesoparticles is such that the mass of the mesoparticle remains unchanged after the transfer process and requires additional considerations regarding the coupling with other system properties such as the particle internal energy. The proof-of-concept work presented in this article is the first part of a two-part article series. In Part 1, the development of the GenDPDE-M theoretical framework and the derivation of the algorithm are presented in detail. Part 2 of this article series is targeted for practitioners, where applications, demonstrations, and practical considerations for implementing the GenDPDE-M method are presented and discussed.

A New Ai–Zhang Type Interior Point Algorithm for Sufficient Linear Complementarity Problems

In this paper, we propose a new long-step interior point method for solving sufficient linear complementarity problems. The new algorithm combines two important approaches from the literature: the main ideas of the long-step interior point algorithm introduced by Ai and Zhang and the algebraic equivalent transformation technique proposed by Darvay. Similar to the method of Ai and Zhang, our algorithm also works in a wide neighborhood of the central path and has the best known iteration complexity of short-step variants. However, due to the properties of the applied transforming function in Darvay’s technique, the wide neighborhood definition in the analysis depends on the value of the handicap. We implemented not only the theoretical algorithm but a greedy variant of the new method (working in a neighborhood independent of the handicap) in MATLAB and tested its efficiency on both sufficient and non-sufficient problem instances. In addition to presenting our numerical results, we also make some interesting observations regarding the analysis of Ai–Zhang type methods.