Potential Field Research Articles

Coronal Models and Detection of the Open Magnetic Field

A plethora of coronal models, from empirical to more complex magnetohydrodynamic (MHD) ones, are being used for reconstructing the coronal magnetic field topology and estimating the open magnetic flux. However, no individual solution fully agrees with coronal hole observations and in situ measurements of open flux at 1 au, as there is a strong deficit between the model and observations contributing to the known problem of the missing open flux. In this paper, we investigate the possible origin of the discrepancy between modeled and observed magnetic field topology by assessing the effect on the simulation output by the choice of the input boundary conditions and the simulation setup, including the choice of numerical schemes and the parameter initialization. In the frame of this work, we considered four potential field source surface-based models and one fully MHD model, different types of global magnetic field maps, and model initiation parameters. After assessing the model outputs using a variety of metrics, we conclude that they are highly comparable regardless of the differences set at initiation. When comparing all models to coronal hole boundaries extracted by extreme-ultraviolet filtergrams, we find that they do not compare well. This mismatch between observed and modeled regions of the open field is a candidate contributing to the open flux problem.

Multitask learning of a biophysically-detailed neuron model.

The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

Integration of improved APF and RRT algorithms for enhanced path planning in mobile robotics

In the process of path planning for mobile robots, situations occur such as local optimal solutions and failure to reach the target point when the traditional Artificial Potential Field (APF) method is used independently for path search; When the traditional Rapidly-exploring Random Tree method (RRT) is used independently for path search, the generated random path tree has multiple branching branches, thereby resulting in high time and distance costs. In response to the above issues, it’s attempted that the two methods were integrated and compensated for each other’s shortcomings. Firstly, the mathematical model of the traditional APF algorithm was improved to solve the problems of unbalanced force and unreachable targets. Secondly, the improved APF was established in the RRT algorithm space by the guide of geometric probability model, and APF contributed to enhancing the target directionality of RRT. Simulation experiments had shown that the fusion algorithm had significant advantages in path length and runtime.

The Influence of Relative Position on Ground Potential and Electric Field Based on Interfacial Charge Accumulation in HVDC

AbstractWith the development of high‐voltage direct current (HVDC) engineering around the world, there has been increasing attention to the issue of selecting site for direct current (DC) grounding electrodes since the pollution coming from ground potential and electric field. As previous study result, the distance is closer to the grounding electrode, the influence is greater for ground potential and electric field, as well as vice versa. Through proposing the analysis approach of equivalent interfacial charge accumulating, the article goes beyond this previous general‐conclusion and extracts the relative position conception from the mono‐distance for further studying on weakening their impacts on the environment. In comparison with the previous results, the research finding shows that the movement of relative position is able to achieve the best distribution of ground potential or electric field corresponding to different soil‐layered structure, furthermore, the specific mathematic formula about the best relative position has been derived by the accumulated charge model proposed. Finally, the results have been demonstrated by simulation calculation of the CDEGS software for a practical HVDC project in China, moreover, regarding soil resistivity variation related to the different seasons, the optimized relative position about 13 ~ 24 km has been adopted in the real HVDC grounding project as a suggested site. It reveals that the underlying relative position behind the mono‐distance could be applied to selecting the site of grounding electrode as reference in the future. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Spiking Laguerre Volterra networks—predicting neuronal activity from local field potentials

Objective. Understanding the generative mechanism between local field potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. Approach. Here, we fill this gap by proposing novel spiking Laguerre–Volterra network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. Main results. The proposed networks were applied on extracellular microelectrode recordings of Parkinson’s Disease patients during deep brain stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. Significance. These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV Swarms

In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions.

MPC-based motion control of AGV with improved A* and artificial potential field

Nowadays, automatic guided vehicles (AGV) are extensively utilized for transportation and inspection tasks in workshops. The A* and artificial potential field (APF) are classic algorithms employed for path planning of AGVs. However, these algorithms still fail to meet the actual production needs and cannot avoid stuttering while encountering obstacles, leading to excessive energy consumption and unnecessary pause. In the paper, an improved A* algorithm is proposed to reduce route length and improving efficiency. On this basis, an integrated fusion strategy consisting of improved APF and nonlinear model predictive control (NMPC) is designed for collision avoidance and path tracking control. The proposed algorithm is tested both on simulation and a laser-guided real automatic guided vehicle experimental platform. Experimental results prove that the proposed algorithm has a great tracking performance under complex workplace.

Key indicators of caprock sealing assessment with consideration of faults in potential CO2 geological storage sites in Subei Basin, China

Good caprock sealing is crucial for ensuring the long-term safety and security of carbon dioxide (CO2) geological storage. However, concealed faults within caprocks and reservoirs can impact the transport and accumulation of CO2 plumes. Due to the uncertainties associated with the geometric structure, physical properties, and combined relationships among reservoir, caprock and fault, accurately evaluating caprock sealing is challenging. To address the challenge of identifying key indicators for caprock sealing considering faults, this study focuses on the one potential CO2 geological storage field in the Subei Basin, China. First, this study establishes a hydraulic-mechanical (HM) coupled finite element model considering a combination of caprock, reservoir and fault. Then, 22 research indicators, including fault offset, fault dip, caprock thickness, burial depth, permeability and mechanical properties, are analysed to evaluate their influence on caprock sealing via the tornado analysis and response surface methods. Finally, key indicators are determined based on evaluation criteria, such as pore pressure increment (ΔPP) and Coulomb failure stress (CFS), in the fault plane. The research results indicate that fault dip, fault offset, fault permeability, burial depth, and reservoir permeability are key indicators. The caprock thickness, caprock permeability, and caprock Young's modulus significantly influence the caprock sealing capacity. The ΔPP and CFS increase with increasing fault dip and offset. When the fault dip exceeds 45°, the ΔPP and CFS reach maximum values at the interface between the reservoir and caprock. Fault permeability has a greater impact than reservoir permeability and caprock permeability. The research results may provide guidance for the evaluation of caprock sealing capacity for CO2 geological storage.

Potential field mechanisms and distributed learning for enhancing the navigation of micro/nanorobot in biomedical environments

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.

Refinement of Global Coronal and Interplanetary Magnetic Field Extrapolations Constrained by Remote-sensing and In Situ Observations at the Solar Minimum

Solar magnetic fields are closely related to various physical phenomena on the Sun, which can be extrapolated with different models from photospheric magnetograms. However, the open flux problem (OFP), the underestimation of the magnetic field derived from the extrapolated model, is still unsolved. To minimize the impact of the OFP, we propose three evaluation parameters to quantitatively evaluate magnetic field models and determine the optimal free parameters in the models by constraining the coronal magnetic fields and the interplanetary magnetic fields (IMFs) with real observations. Although the OFP still exists, we find that magnetic field lines traced from the coronal models effectively capture the intricate topological configurations observed in the corona, including streamers and plumes. The OFP is lessened by using the Helioseismic and Magnetic Imager synoptic map instead of the Global Oscillation Network Group daily synoptic maps, and the potential field source surface + potential field current sheet (PFSS+PFCS) model instead of the current sheet source surface (CSSS) model. For Carrington Rotation 2231 at the solar minimum, we suggest that the optimal parameters for the PFSS+PFCS model are R ss = 2.2–2.5 R ☉ and R scs = 10.5–14.0 R ☉, as well as for the CSSS model, are R cs = 2.0–2.4 R ☉, R ss = 11.0–14.7 R ☉, and a = 1.0 R ☉. Despite the IMFs at 1 au being consistent with the measurements by artificially increasing the polar magnetic fields, the IMFs near the Sun are still underestimated. The OFP might be advanced by improving the accuracy of both the weak magnetic fields and polar magnetic fields, especially considering magnetic activities arising from interplanetary physical processes.

Adaptive Factor Fuzzy Controller for Keeping Multi-UAV Formation While Avoiding Dynamic Obstacles

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.

Gravity and magnetic joint imaging based on Gramian constraints

Gravity and magnetic imaging produce numerical images proportional to the density and magnetization source distribution through the upward continuation of potential field data. However, the inherent ambiguity and nonuniqueness in gravity or magnetic imaging restrict the reliability of the imaging model. We develop a joint iterative imaging framework for gravity and magnetic data based on Gramian constraints. The imaging fields of gravity and magnetic fields are initially calculated by the depth from extreme points imaging method. With the recovered magnetization and density distribution, the gradient directions of the Gramian function for these model parameters are calculated and used to regularize the joint imaging directions for gravity and magnetic fields. The introduction of Gramian constraints achieves mutual complementarity of the gravity and magnetic data and generates density and magnetization models with more distinct boundaries and improved structural coherence. Our approach is tested with two synthetic examples and applied to field data from the Galinge iron deposit in Qinghai, China. The real case results are verified by information from drillholes and physical properties measurements of ore and rock samples. Joint imaging provides an alternative to joint inversion with improved resolution and reliability of imaging models compared with separate imaging through the complementarity of gravity and magnetic data.

Research on multi-vehicle formation control based on improved artificial potential field method

Multi-vehicle formation can perform various special tasks in unstructured environment. How to take into account the safety of vehicles in avoiding obstacles and the ability to maintain formation has a certain research value. In this paper, the four-circle model of vehicle is established first, and the circle radius is adjusted according to the state of vehicle, so as to describe the safety boundary of vehicle. The improved RRT algorithm is used for the whole route planning, and the discrete path points are used as vehicle guidance. Then the artificial potential field is constructed, and the formation coordination potential field is proposed, so that the vehicles can cooperate with other vehicles to keep the preset formation as far as possible when avoiding obstacles. Then the control quantity of the vehicle is calculated according to the force condition of the vehicle in the potential field by the double exponential sliding mode control method. Finally, the effectiveness of the method is verified by the simulation experiments of triangle formation and circular formation under different working conditions, and the formation error is reduced by about 20%.

A fusion framework to characterize and evaluate air traffic clusters based on potential field theory

The dramatic increase in flight volume and density has inevitably led to the small-scale clustering of aircraft, presenting significant challenges for air traffic controllers due to the complex interrelationships involved. In order to better characterize the clustering phenomenon and its complexity with massive real-time data, this paper introduces a data-driven fusion framework. This framework incorporates an interdependent network and a novel indicator system based on potential field theory to characterize the interaction and superposition effects within these clusters, encompassing aircraft, airspace waypoints, and air traffic controllers. To further explore the complexity of clusters according to the characteristic indicator system, a deep learning model is employed to identify these cluster scenarios. Training results on these scenarios indicate that the deep learning model, using the GRU-attention model, excels in capturing the complex features of clusters. In practical scenario experiments, the proposed fusion framework integrates the interdependent network, characteristic indicator system, and the GRU-attention model. It has proven effective in characterizing clusters and evaluating their complexities. This not only provides real-time alerts but also supports decision-making for air traffic controllers in challenging situations.

The surface defect of Fe2Ge (001) induces a synergistic spin effect and regulates the surface magnetism

The defect structure leads to the fracture and recombination of chemical bonds, lattice distortion, electron localization, etc., which has a significant improvement on the physical properties of materials, and give them new functions. The unique structure of Fe2Ge forms the surface structure of Ge end face and Fe end face in the direction of (001), but the surface is easy to form defect structure, and affecting its properties. It is found that the spin direction of Ge atom is opposite to the spin direction of Fe atom in perfect Fe2Ge (001) surface structure. When there is VGe defect, the magnetic moments of the perfect Fe2Ge (001) surface and Fe atoms are enhanced. When there are VFe, VFe-Ge and VGe-Fe defects, the magnetic moments of defects Fe2Ge (001) surface and Fe atoms are weakened. The reason is that the spin state electrons of Fe atoms induce Ge atoms to produce spin electrons, which forms a synergistic spin effect. The Hirshfeld Charge distribution and charge spin of Fe and Ge atoms are affected by the local potential field, Ge atoms are excited with spin direction of Fe atoms with VFe and VFe-Ge, and Ge atoms induce magnetic moments with spin direction of Fe atoms with VGe and VGe-Fe. The effects mechanism is that the five orbitals dzz, dxy, dzy, dzx and dxx-yy of Fe atom are greatly affected by the structure of the defect under the action of crystal field, while the s, px, py and pz orbitals are less affected. In VGe and VFe-Ge defects, the spin splitting of the d orbitals is larger to form a larger magnetic moment. In VFe and VGe-Fe defects, the d orbital spin splitting is smaller, and resulting in a smaller magnetic moment. The s, px, py and pz of Ge atoms are greatly affected, the spin splitting of pz is larger than that of px and py orbitals, and indicating that the spin of Ge atoms mainly comes from pz orbitals.

Experimental Assessment of a Vision-Based Obstacle Avoidance Strategy for Robot Manipulators: Off-line Trajectory Planning and On-line Motion Control

Human-Robot Interaction is an increasingly important topic in both research and industry fields. Since human safety must be always guaranteed and accidental contact with the operator avoided, it is necessary to investigate real-time obstacle avoidance strategies. The transfer from simulation environments, where algorithms are tested, to the real world is challenging from different points of view, e.g., the continuous tracking of the obstacle and the configuration of different manipulators. In this paper, the authors describe the implementation of a collision avoidance strategy based on the potential field method for off-line trajectory planning and on-line motion control, paired with the Motion Capture system Optitrack PrimeX 22 for obstacle tracking. Several experiments show the performance of the proposed strategy in the case of a fixed and dynamic obstacle, disturbing the robot’s trajectory from multiple directions. Two different avoidance modalities are adapted and tested for both standard and redundant robot manipulators. The results show the possibility of safely implementing the proposed avoidance strategy on real systems.

Network resonance and the auditory steady state response

The auditory steady state response (ASSR) arises when periodic sounds evoke stable responses in auditory networks that reflect the acoustic characteristics of the stimuli, such as the amplitude of the sound envelope. Larger for some stimulus rates than others, the ASSR in the human electroencephalogram (EEG) is notably maximal for sounds modulated in amplitude at 40 Hz. To investigate the local circuit underpinnings of the large ASSR to 40 Hz amplitude-modulated (AM) sounds, we acquired skull EEG and local field potential (LFP) recordings from primary auditory cortex (A1) in the rat during the presentation of 20, 30, 40, 50, and 80 Hz AM tones. 40 Hz AM tones elicited the largest ASSR from the EEG acquired above auditory cortex and the LFP acquired from each cortical layer in A1. The large ASSR in the EEG to 40 Hz AM tones was not due to larger instantaneous amplitude of the signals or to greater phase alignment of the LFP across the cortical layers. Instead, it resulted from decreased latency variability (or enhanced temporal consistency) of the 40 Hz response. Statistical models indicate the EEG signal was best predicted by LFPs in either the most superficial or deep cortical layers, suggesting deep layer coordinators of the ASSR. Overall, our results indicate that the recruitment of non-uniform but more temporally consistent responses across A1 layers underlie the larger ASSR to amplitude-modulated tones at 40 Hz.

Algorithms for Route Planning and Navigation of Unmanned Aerial Vehicles

A brief overview of the results of recent research published in open sources in the field of route planning and navigation algorithms for unmanned aerial vehicles (UAV) is presented. Works devoted to global and local planning of trajectories taking into account known and detected obstacles in flight, as well as issues of navigation of groups of drones, are considered. Various approaches are analyzed, including graph algorithms (A*, Dijkstra, Rapidly-exploring Random Trees), methods of data mining in real time, potential fields. Special attention is paid to work on the use of neural networks and machine learning, SLAM and multi-agent technologies for planning UAV routes. The advantages and disadvantages of the main groups of algorithms are considered. A conclusion is drawn about the prospects for using hybrid methods, as well as machine learning technologies, to build intelligent UAV traffic control systems.

Safe and Efficient Exploration Path Planning for Unmanned Aerial Vehicle in Forest Environments

This study presents an enhanced exploration path planning for unmanned aerial vehicles. The primary goal is to increase the chances of survival of missing people in forest environments. Exploration path planning is an essential methodology for exploring unknown three-dimensional spaces. However, previous studies have mainly focused on underground environments, not forest environments. The existing path planning methods for underground environments are not directly applicable to forest environments. The reason is that multiple open spaces exist with various obstacles, such as trees, foliage, undergrowth, and rocks. This study mainly focused on improving the safety and efficiency to be suitable for forests rather than underground environments. Paths closer to obstacles are penalized to enhance safety, encouraging exploration at a safer distance from obstacles. A potential field function is applied based on explored space to minimize overlapping between existing and new paths to increase efficiency. The proposed exploration path planning method was validated through an extensive simulation analysis and comparison with state-of-the-art sampling-based path planning. Finally, a flight experiment was conducted to verify further the feasibility of the proposed method using onboard real hardware implementation in a cluttered and complex forest environment.

Molecular and eco-physiological responses of soil-borne lead (Pb2+)-resistant bacteria for bioremediation and plant growth promotion under lead stress

Lead (Pb) is the 2nd known portentous hazardous substance after arsenic (As). Being highly noxious, widespread, non-biodegradable, prolonged environmental presence, and increasing accumulation, particularly in arable land, Pb pollution has become a serious global health concern requiring urgent remediation. Soil-borne, indigenous microbes from Pb-polluted sites have evolved diverse resistance strategies, involving biosorption, bioprecipitation, biomineralization, biotransformation, and efflux mechanisms, under continuous exposure to Pb in human-impacted surroundings. These strategies employ a wide range of functional bioligands to capture Pb and render it inaccessible for leaching. Recent breakthroughs in molecular technology and understanding of lead resistance mechanisms offer the potential for utilizing microbes as biological tools in environmental risk assessment. Leveraging the specific affinity and sensitivity of bacterial regulators to Pb2+ ions, numerous lead biosensors have been designed and deployed worldwide to monitor Pb bioavailability in contaminated sites, even at trace levels. Besides, the ongoing degradation of croplands due to Pb pollution poses a significant challenge to meet the escalating global food demands. The accumulation of Pb in plant tissues jeopardizes both food safety and security while severely impacting plant growth. Exploring Pb-resistant plant growth-promoting rhizobacteria (PGPR) presents a promising sustainable approach to agricultural practices. The active associations of PGPR with host plants have shown enhancements in plant biomass and stress alleviation under Pb influence. They thus serve a dual purpose for plants grown in Pb-contaminated areas. This review aims to offer a comprehensive understanding of the role played by Pb-resistant soil-borne indigenous bacteria in expediting bioremediation and improving the growth of Pb-challenged plants essential for potential field application, thus broadening prospects for future research and development.