Path Computation Research Articles

Resilience enhancement for interdependent power systems by AI-assisted disaster response

The failure of urban Electrical Distribution Systems (EDSs) can lead to direct or indirect consequences for their interdependent critical infrastructures, for example, the hospitals, the fire department, etc., negatively affecting the well-being of citizens in the city. To improve the resilience of the coupled power network, the Operational Interdependence Simulator (OIS) is proposed to help prioritize the queue of failure recovery and Minimum Spanning Tree and Shortest Path Tree topology reconfiguration to minimize critical load downtime. Additionally, AI agents are introduced to assist decision-making processes. By utilizing machine learning and training in thousands of offline scenarios, the system can be used for real-time calculation of the optimal recovery paths when given the number and location of electrical faults. The IEEE 70-node distribution system case validated the high prediction accuracy and post-disaster topology reconfiguration while considering the interdependencies.

Analysis and evaluation of suitability of high-pressure dynamic constitutive model for concrete under blast and impact loading

Concrete demonstrates complex dynamic mechanical behaviors under the influence of blast or impact loads, and there are inherent limitations in experimental and theoretical methods when dealing with highly nonlinear problems. As computational technologies and mechanics continue to evolve, it is possible to conduct high-fidelity simulations of the transient response of concrete structures subjected to intense dynamic loads. Such simulations play a crucial role in revealing the propagation laws of stress waves, the progression of damage, the mechanisms of structural failure, and in conducting protective engineering design. An accurate concrete material model is essential for conducting numerical studies. This article reviews the development of high-pressure dynamic constitutive models for concrete in recent years from both experimental research and theoretical modeling perspectives, focusing on the analysis and evaluation of the modeling methods and main shortcomings of the equation of state(EOS), strength model, and damage model. Using single-element numerical simulations under a single loading path and numerical calculations of engineering cases under complex loading paths, a systematic analysis and comparison were conducted on the predictive capabilities of the HJC, RHT, KCC, and CSC models, as well as the newly developed Kong-Fang and Yan-Chen models. It pointed out the impact of high-pressure mechanical behavior of concrete and the cumulative effect of damage under hydrostatic pressure on the calculation results. Finally, a discussion was conducted on the inherent flaws, applicability, and research difficulties of local constitutive models of concrete in the finite element method. This provides a reference for the selection and research of constitutive models when conducting numerical analysis of the blast and impact resistance of concrete structures.

Probabilistic Path Computation and Frequency Assignment to Mitigate Spectral Fragmentation in Elastic Optical Networks

Probabilistic Path Computation and Frequency Assignment to Mitigate Spectral Fragmentation in Elastic Optical Networks

Autonomous trajectory planning method for aircraft based on ripple diffusion algorithm

Aiming at the problem that route optimization in dynamic continuous airspace (non-discrete road network) is difficult to ensure optimality and has low computational efficiency, a non-circular ripple diffusion algorithm is proposed. Firstly, considering the influence of time-varying airflow and obstacle area in dynamic airspace, the airspace wind speed model, route network and dynamic network weight model are established, and 6 airspace environments and 4 road network structures with the influence of time-varying airflow and obstacle area are constructed. Then, the effects of traditional dynamic path optimization algorithm, co-evolutionary path optimization algorithm and non-circular ripple diffusion algorithm on route optimization are compared in 1000 experiments, taking the optimized path length, flight time and calculation time as indicators. The simulation results show that the traditional dynamic path optimization algorithm has the worst effect under the three indicators, and the calculation time of non-circular ripple diffusion algorithm is significantly less than that of the other two algorithms, which effectively improves the engineering calculation efficiency.

Stretching vibration driven adiabatic transfer kinetics for photoexcited hole transfer from semiconductor to adsorbate

Interfacial hole transfer from a photoexcited semiconductor to surface adsorbates is pivotal for initiating solar-to-chemical energy conversion, yet the atomic-level transfer kinetics remains elusive. Using the methoxy/TiO2(110) system as an archetype, here we elucidate the hole transfer mechanism from hole-trapping lattice oxygen to the methoxy adsorbate at gas/solid and liquid/solid interfaces through molecular dynamics simulations and static minimum energy path calculations. Instead of direct nonadiabatic hopping, we uncover an adiabatic migration pathway adapted to local substrate relaxation, driven by a bond-stretching mechanism supported by stronger Ti-O stretching vibrations. Notably, this mechanism persists at the aqueous methoxy/TiO2(110) interface, albeit hindered by interfacial water and coadsorbates. Surprisingly, the hole transfer barriers across various photoexcited adsorbate/TiO2 interfaces correlate more closely with the vertical excitation energies of the adsorbates rather than their redox potentials, indicating an early-type transition-state nature. These insights deepen our understanding of elementary hole transfer kinetics in surface photochemistry.

Low-temperature oxidation of methane mediated by Al-doped ZnO cluster and nanowire: a first-principles investigation.

First-principles calculations are performed to investigate the catalytic oxidation of methane by using N2O as an oxidizing agent over aluminum (Al)-doped Zn12O12 cluster and (Zn12O12)2 nanowire. The impact of Al impurity on the geometry, electronic structure, and surface reactivity of Zn12O12 and (Zn12O12)2 is thoroughly studied. Our study demonstrates that Al-doped ZnO systems have a better adsorption ability than the corresponding pristine counterparts. It is found that N2O molecule is initially decomposed on the Al site to provide the N2 molecule, and an Al-O intermediate which is an active species for the CH4 oxidation. The conversion of CH4 into CH3OH over AlZn11O12 and (AlZn11O12)2 requires an activation energy of 0.45 and 0.29eV, respectively, indicating it can be easily performed at normal temperatures. Besides, the overoxidation of methanol into formaldehyde cannot take place over the AlZn11O12 and (AlZn11O12)2, due to the high energy barrier needed to dissociate C-H bond of the CH3O intermediate. Dispersion-corrected density functional theory calculations were performed through GGA-PBE exchange-correlation functional combined with a numerical double-ζ plus polarization (DNP) basis set as implemented in DMol3. To include the relativistic effects of core electrons of Zn atoms, DFT-semicore pseudopotentials were adopted. The DFT + D scheme proposed by Grimme was used to involve weak dispersion interactions within the DFT calculations. The reaction energy paths were generated by the minimum energy path calculations using the NEB method.

Shortest path counting in complex networks based on powers of the adjacency matrix.

Complex networks describe a broad range of systems in nature and society. As a fundamental concept of graph theory, the path connecting nodes and edges plays a crucial role in network science, where the computation of shortest path lengths and numbers has garnered substantial focus. It is well known that powers of the adjacency matrix can calculate the number of walks, specifying their corresponding lengths. However, developing methodologies to quantify both the number and length of shortest paths through the adjacency matrix remains a challenge. Here, we extend powers of the adjacency matrix from walks to shortest paths. We address the all-pairs shortest path count problem and propose a fast algorithm based on powers of the adjacency matrix that counts both the number and the length of all shortest paths. Numerous experiments on synthetic and real-world networks demonstrate that our algorithm is significantly faster than the classical algorithms across various network types and sizes. Moreover, we verified that the time complexity of our proposed algorithm significantly surpasses that of the current state-of-the-art algorithms. The superior property of the algorithm allows for rapid calculation of all shortest paths within large-scale networks, offering significant potential applications in traffic flow optimization and social network analysis.

A technique to improve the fixed-sphere decoding algorithm for Spectrally Efficient Frequency Division Multiplexing systems

Spectral Efficient Frequency Division Multiplexing (SEFDM) enhances spectral efficiency by narrowing subcarrier bandwidth, however it introduces severe inter-carrier interference (ICI). Currently, the most effective solution is the Fixed Sphere Detection (FSD) algorithm. In previous studies, we found that in order to better reduce the computational complexity of FSD, it is always through adding preprocessing and other pre-algorithms. Although this approach can effectively fix the search radius g in FSD, the complexity of the FSD algorithm itself has not been reduced. Since FSD determines the exploration path by fixing the tree width (Tw), and the selection of exploration paths can directly affect the final search for the optimal candidate values. In this paper we analyze the traditional algorithm from three angles - computational path, ICI generation, and computational complexity, and propose an Optimization Fixed Sphere Detection (OFSD) algorithm. It reduces the number of calculations required when initially selecting exploration paths by narrowing the search range. This allows us to avoid most unnecessary calculations during path selection. In this way, we can greatly reduce the computational complexity. Based on the simulation results, the proposed algorithm in the paper achieves a significant reduction in computational complexity compared to the FSD algorithm (up to 25% at maximum), while the performance drops by at most 0.12 dB when BCF is 0.8 and drops by at most 0.09 dB when BCF is 0.9.

A Software Tool for the True Height Analysis of Ionograms Using the Iterative Gradient Correction (IGC) Method

AbstractDeriving the precise true height electron density profile from the measured ionosonde virtual heights is quite a challenging problem. Recently, Ankita and Tulasi Ram (2023, https://doi.org/10.1029/2023RS007808) presented a new method, Iterative Gradient Correction (IGC) method, for true height analysis that uses HF radio wave propagation path computations to reconstruct the true height profile. Through iterative corrections on electron density gradients between successive points, the IGC method minimizes errors below a specified tolerance at each point and reconstructs a complete electron density profile. The derived profiles from the IGC method are found to be accurate when compared with Incoherent Scatter Radar and Global Navigation Satellite System—Radio Occultation observations. To facilitate true height analysis by IGC method for a wider user community, a MATLAB‐based software has been developed and is outlined in this report. The software can be installed on any Windows platform and is designed with a user‐friendly interface for easy and efficient application by the users. It can analyze multiple scaled ionograms in a single run and outputs the real height profiles in ASCII format. Further, the software also captures important ionospheric parameters such as the base altitudes and peak frequencies of E‐ and F‐layers (e.g., hE, hF, foE, and foF2) etc., from the computed true height profiles and tabulates in a separate output file for the ready use. The software also provides the option for extrapolation of true height profile into top‐side ionosphere up to a user‐specified height and reconstructs the complete vertical electron density profile.

Construction and experimental verification of wave velocity model for source location in goaf overlying rock strata

The geological structure of the goaf overlying rock is complex, a consequence of coal mining that has modified the original stratified structure of the sedimentary strata. To enhance the accuracy of microseismic source location in such intricate geological formations, a wave velocity model for the “three zones” goaf was constructed based on natural divisions within the strata using Snell's law and assuming a homogeneous medium. The model took into account the effects of rock deformation and fracture development, enabling the derivation of formulas for microseismic wave propagation path and travel time calculation. Additionally, the concept of equivalent wave velocity was defined. An indoor simulation test using similar materials was conducted to establish a geological model of the goaf. By comparing the errors between the theoretical and measured values of equivalent wave velocity, assessing the locating effects before and after implementing the wave velocity model of the goaf, and verifying the feasibility of the model, it was demonstrated that establishing a wave velocity model based on the characteristics of the strata structure was crucial for improving the accuracy of the microseismic source location. Notably, as the propagation path of microseismic waves in the goaf increased, the equivalent wave velocity decreased. The wave velocity structure in the goaf exhibited nonuniformity, with the relative error between the theoretical and measured values of equivalent wave velocity being limited to 10 %. The incorporation of this established wave velocity model into the location method resulted in a substantial 58.57 % increase in locating accuracy.

Multi-layer resource scheduling architecture and algorithm for a service-oriented optical network based on a fine grain OTN

With the development of service, there are demands for flexible connection scheduling based on customer-side services, fast connection response, and efficient scheduling of line-side resources in an optical transport network (OTN). This article proposes a service-oriented multi-layer resource scheduling architecture and algorithm based on the introduction of OTN fine grain technology. Specifically, we propose a service awareness scheme that enables OTNs to better match service requirements. Based on a centralized distributed collaborative architecture, we extend a path computation element communication protocol (PCEP) to achieve more efficient connection distribution. We further design a resource scheduling strategy and an algorithm based on multi-layer collaboration to perform differentiated scheduling for services with different priorities. Through the mechanisms and algorithms proposed, it is technically feasible to achieve precise matching of customer-side services, flexible scheduling of connections and resources, and efficient service response, and promote the evolution of the OTN service supply from traditional fixed point-to-point connections to flexible service-oriented optical network (SOON) connections.

A-Star (A*) with Map Processing for the Global Path Planning of Autonomous Underwater and Surface Vehicles Operating in Large Areas

The global path planning system is one of the basic systems ensuring the autonomous operation of unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs) in a complex aquatic environment. The A* path planning algorithm is one of the most well-known algorithms used to obtain an almost optimal path, avoiding obstacles even in a complex environment containing objects with specific shapes and non-uniform arrangements. The main disadvantage of this algorithm is the computational cost of path calculation. This article presents a new approach based on the image processing of the map before determining the path using A*. The results of numerical research based on a large-sized map expressing the port area confirm the proposed method’s effectiveness, which reduces the calculation time by over 500 times with a slight increase in the path length compared to the basic version of the A* algorithm. Based on the obtained results, the proposed approach also increases the path’s safety by designating narrow and risky areas as closed to vehicle movement. For this reason, the method seems suitable for use in global path planning for autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) operating in large areas.

Physical layer-aware circuit reallocation to eons using make-before-break and push-pull techniques

This paper proposes a new circuit reallocation algorithm with make-before-break and push-pull techniques that considers the effects of physical layers in transparent elastic optical networks called Just One Circuit Reallocation (JOC). The JOC algorithm reallocates just one already established circuit to avoid blocking new circuit requests due to impairments in the physical layer. The results of the JOC algorithm were compared to three other algorithms: Circuit Reallocation Strategy - Physical Layer (CRS-PL), Circuit Reallocation for Block Reduction related to the QoT of the circuits (R-RQoT), and Make-Before- Break based defragmentation algorithm (MBBr). The reallocation algorithms are evaluated under the Bandwidth Blocking Probability (BBP), Circuit Blocking Probability (CBP), and the Number of reallocated circuits (N), external fragmentation, and percerntual of circuit by modulation for USA and EON topologies. Moreover, we also evaluated the performance of reallocation algorithms using complete sharing, K-shortest path computation, modified Dijkstra paths computation, and K-shortest path with reduction of QoTO to routing and spectrum assignment. Simulation results show that the proposed algorithm performs better than the CRS-PL, R-RQoT and MBBr algorithms regarding BBP, PBC, and N. Regarding BBP our algorithm presented minimum reductions of approximately 65.36% e 55.6% for the USA and EON topology, respectively.

Robot Path Planning and Tracking with the Flower Pollination SearchOptimization Algorithm

Robot path planning and tracking involve two critical aspects of autonomous navigation systems: determining an optimal route for the robot to follow and ensuring that it accurately adheres to this path in real-time. Path planning focuses on generating a feasible and efficient trajectory from a starting point to a destination while considering obstacles, dynamic environments, and constraints. This paper investigates the effectiveness of the Flower Pollination Search Optimization (FPSO) algorithm for robot path planning and compares its performance with traditional algorithms such as A* Algorithm, Dijkstra, and Rapidlyexploring Random Tree (RRT). The FPSO algorithm was evaluated across three distinct scenarios, demonstrating superior performance in terms of path length, computation time, and path smoothness. In Scenario 1, FPSO achieved an optimized path length of 10.5 meters with a computation time of 3.2 seconds, a path smoothness score of 8.9, and a path efficiency of 95%. In Scenario 2, FPSO resulted in a path length of 12.3 meters and a computation time of 3.5 seconds, with a smoothness score of 8.7 and an efficiency of 93%. Scenario 3 showed FPSO's best performance, with a path length of 9.8 meters, computation time of 3.0 seconds, a smoothness score of 9.0, and a path efficiency of 96%. Comparatively, the A* Algorithm, Dijkstra, and RRT exhibited longer path lengths and higher computation times, with lower smoothness scores and efficiencies.

A Multi-Objective Direction of Arrival Estimation Technique MinimizingEnergy Consumption in Wireless Sensor Network

Wireless Sensor Network (WSN) demand for secure communication is challenging due to its limited resource constraints. Hence, this research developed an effective distributed DOA estimation model through Direction Optimization Integrated Ranking Voting (DOIRV) to improve the performance of WSNs. The developed model comprises the multi-objective optimization model with the Whale technique for the WSN. The constructed DOIRV model uses objective function estimation with branch-and-bound for effective data transmission. The developed DOIRV model uses the Artificial Intelligence-based machine learning model for the DOA routing path computation and estimation. Finally, the DOIRV estimation is evaluated for the performance analysis with the consideration of the Cramer-Rao analysis for the data transmission in the WSN model. Simulation results stated that the proposed weighted technique significantly improves the network performance such as packet delivery ratio, throughput, and network delay. Analysis of the technique expressed that the proposed DOIRV technique exhibits effective performance rather than the conventional technique in terms of network delay, throughput, and packet delivery ratio. The comparative analysis stated that the performance of the proposed DOIRV model is ~13% higher than the conventional technique in terms of packet delivery ratio and ~18% higher for the throughput.

GPU-accelerated parallel all-pair shortest path routing within stochastic road networks

All-pair shortest path routing within stochastic road networks is often more complicated and computationally challenging than routing in deterministic networks because uncertainties in travel time are introduced. We develop and test a GPU-enabled approach to resolve this computational challenge. This approach is based on a simulation framework that includes four steps: simulation of road networks impedances, computation of candidate paths using an all-pair shortest path algorithm, travel time estimation across candidate paths, and all-pair optimal path calculations. We developed GPU algorithms to accelerate the second and third steps, which are computationally demanding for large road networks. We used the road network of Wuhan, China as a case study. Our experiments indicate that the GPU approach leads to thousands of times in improvement of acceleration, which makes all-pair shortest path routing within stochastic road networks computationally feasible.

A Design of Three-Dimensional Spatial Path Planning Algorithm Based on Vector Field Histogram.

In this paper, we present a novel three-dimensional spatial path planning algorithm based on the Vector Field Histogram* (VFH*) approach, specifically tailored for underwater robotics applications. Our method leverages the strengths of VFH* in obstacle avoidance while enhancing its capability to handle complex three-dimensional environments. Through extensive simulations, we demonstrate the superior performance of our algorithm compared to traditional methods, such as RS-RRT algorithm. Our results show significant improvements in terms of computational efficiency and path optimality, making it a viable solution for real-time path planning in dynamic underwater environments.

Research, Analysis, and Improvement of Unmanned Aerial Vehicle Path Planning Algorithms in Urban Ultra-Low Altitude Airspace

Urban ultra-low altitude airspace (ULAA) presents unique challenges for unmanned aerial vehicle (UAV) path planning due to high building density and regulatory constraints. This study analyzes and improves classical path planning algorithms for UAVs in ULAA. Experiments were conducted using A*, RRT, RRT*, and artificial potential field (APF) methods in a simulated environment based on building data from Chengdu City, China. Results show that traditional algorithms struggle in dense obstacle environments, particularly APF due to local minima issues. Enhancements were proposed: a density-aware heuristic for A*, random perturbation for APF, and a hybrid optimization strategy for RRT*. These modifications improved computation time, path length, and obstacle avoidance. The study provides insights into the limitations of classical algorithms and suggests enhancements for more effective UAV path planning in urban environments.

Flat-Bottom Elastic Network Model for Generating Improved Plausible Reaction Paths.

Rapid generation of a plausible reaction path connecting a given reactant and product in advance is crucial for the efficient computation of precise reaction paths or transition states. We propose a computationally efficient potential energy based on the molecular structure to generate such paths. This potential energy has a flat bottom consisting of structures without atomic collisions while preserving nonreactive chemical bonds, bond angles, and partial planar structures. By combining this potential energy with the direct MaxFlux method, a recently developed reaction-path/transition-state search method, we can find the shortest plausible path passing within the bottom. Numerical results show that this combination yields lower energy paths compared to the paths obtained by the well-known image-dependent pair potential. We also theoretically investigate the differences between these two potential energies. The proposed potential energy and path generation routine are implemented in our Python version of the direct MaxFlux method, available on GitHub.

Efficient Path Planning Algorithm Based on Laser SLAM and an Optimized Visibility Graph for Robots

Mobile robots’ efficient path planning has long been a challenging task due to the complexity and dynamism of environments. If an occupancy grid map is used in path planning, the number of grids is determined by grid resolution and the size of the actual environment. Excessively high resolution increases the number of traversed grid nodes and thus prolongs path planning time. To address this challenge, this paper proposes an efficient path planning algorithm based on laser SLAM and an optimized visibility graph for mobile robots, which achieves faster computation of the shortest path using the optimized visibility graph. Firstly, the laser SLAM algorithm is used to acquire the undistorted LiDAR point cloud data, which are converted into a visibility graph. Secondly, a bidirectional A* path search algorithm is combined with the Minimal Construct algorithm, enabling the robot to only compute heuristic paths to the target node during path planning in order to reduce search time. Thirdly, a filtering method based on edge length and the number of vertices of obstacles is proposed to reduce redundant vertices and edges in the visibility graph. Additionally, the bidirectional A* search method is implemented for pathfinding in the efficient path planning algorithm proposed in this paper to reduce unnecessary space searches. Finally, simulation and field tests are conducted to validate the algorithm and compare its performance with classic algorithms. The test results indicate that the method proposed in this paper exhibits superior performance in terms of path search time, navigation time, and distance compared to D* Lite, FAR, and FPS algorithms.