Two-point Model Research Articles

Functional multiple-point simulation

We present a new paradigm, called functional multiple-point simulation, in which multiple-point geostatistical simulation can be performed when functions or curves are observed at each location of a random field. Multiple-point simulation is a non-parametric method used for conditional geostatistical simulation of complex spatial patterns by inferring multiple-point statistics from a training image, rather than from a two-point variogram or covariance model. When the observable at each spatial location is a functional random variable, such multiple-point simulation must take into account not only the spatial correlation among locations but also the similarity of functions or curves observed at each location. The data events to be compared in this case are now functional, in the sense that they consist of spatial arrangements of functions. Consequently, we propose four distances, inspired by the functional data analysis literature, for measuring similarities between functional data events and use these to extend the direct sampling method to perform multiple-function geostatistical simulation with functional fields. We coin the new method Functional Direct Sampling and carry out extensive qualitative and quantitative performance comparison between the four proposed distances using simulation techniques on two well-known applications of multiple-point simulation: simulating copies of a functional random field and gap-filling of locations in a functional random field. We apply the proposed method to a gap-filling task of simulated wind profiles spatial functions over the Arabian Peninsula.

Generalized two-point visual control model of human steering for accurate state estimation

Abstract We derive and validate a generalization of the two-point visual control model, an accepted cognitive science model for human steering behavior. The generalized model is needed as current steering models are either insufficiently accurate or too complex for online state estimation. We demonstrate that the generalized model replicates specific human steering behavior with high precision (85% reduction in modeling error) and integrate this model into a human-as-advisor framework where human steering inputs are used for state estimation. As a benchmark study, we use this framework to decipher ambiguous lane markings represented by biased lateral position measurements. We demonstrate that, with the generalized model, the state estimator can accurately estimate the true vehicle state, providing lateral state estimates with under 0.15 m error across participants. However, without the generalized model, the estimator cannot accurately estimate the vehicle's lateral state.

Reconstruction of Land Surface Temperature Derived from FY-4A AGRI Data Based on Two-Point Machine Learning Method

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution.

Brain-Inspired Modeling and Decision-Making for Human-Like Autonomous Driving in Mixed Traffic Environment

In this paper, a human-like driving system is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the human transportation systems and mitigate misunderstanding and conflicts when interacting with human-driven vehicles. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function with multi-constraint, a dynamic game algorithm is developed to model the interactions and decision making between AV and human-driven vehicles. Additionally, to guarantee the safety during lane-change of AVs, an artificial potential field model is built for collision risk assessment. Further, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with a two-point preview model. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments, and the results demonstrated the feasibility and effectiveness of the proposed method.

The reverse prediction of the ship principal dimensions based on the Kelvin ship waves

This study examines the impact of the ship’s principal dimensions on the steady waves generated by the ship, and practical approaches are subsequently proposed to reverse-predict the ship’s length, width, draught, and related parameters. The ship length Ls and the related Froude number Fr=Vs/gLs are found to relate with the quasi-period of the divergent wave amplitude function. The ship length can be predicted within an accuracy of ±5% overall based on the two-point wake model considered here. The effects of the ship width and the ship draught are different, and a practical method is proposed here to predict the ship width and the ship draught based on the simplification of the Hogner theory. A noteworthy discovery of this research is that the hull form details have a more pronounced impact on the trough of the divergent waves compared to the transverse waves and the peaks of the divergent waves.

Determination of the coherence interval of radar targets using a multipoint signal reflection model

The problem of determining the coherence interval of the target (CIT) is considered – the time interval during which the signal reflected from the target can be considered coherent. In this case, the criterion of signal coherence is the condition of limiting the random phase fluctuation of the reflected signal to a value. The basis of the method of determining the CIT is the use of a multipoint model of a target that performs a rectilinear motion at a constant speed. When describing the resulting signal, the complex envelope method is used. It is shown that for an arbitrary number of reflection points, the resulting signal can be represented as a product of two functions. The first function is deterministic and determines the dependence of the signal phase on the speed of movement of the target center. The second function determines the amplitude and phase fluctuations of the reflected signal and, due to the unknown number and location of the reflection points, as well as the angle of observation of the target, can be considered random. In the case of a two-point target model, an approximate ratio for the CIT is obtained, depending on the relative size of the target, its range and speed of movement. It is shown that for typical target sizes and ranges of the order of several tens of kilometers, the errors in calculating the CIT when using the obtained approximate ratio do not exceed 10% of the true value of the CIT. It is also shown that the frequently used approach of calculating the CIT based on the calculation of the backscattering diagram of the target does not take into account the target range and therefore can lead to large errors in calculating the CIT. At the same time, the calculated values of the CIT, depending on the size of the target and its range, may differ from the true values by an order of magnitude or more. When switching to the multipoint model of the target, differences in comparison with the two-point model in the calculation of the CIT are determined and estimates of the most rational number of reflection points are obtained, the choice of which can be limited to five or six points.

고각구동 프레임 구조물의 힌지 위치에 따른 최적설계 및 해석

In this paper, the design of a high-angle drive frame structure supporting a large load while considering the necessary variables such as hinge position and frame weight is presented; furthermore, the geometrical design variables are solved through geometric boundary conditions and optimal deflection equations. This study consists of two parts. First, the mathematical model of the deflection under the high load condition of the high-angle drive frame, boundary conditions, and design variables are selected, and the solution of the geometric deflection equation are then checked. The cross-section of the frame has a rectangular shape, and the deflection model is a two-point support simple beam model. The deflection equation and boundary condition equation of the high-angle drive frame are determined using the hinge position and frame length. Second, the validity of the deflection equation is verified through finite element analysis; further, it is verified whether the hinge position and frame length corresponding to the geometric solution satisfy the optimal deflection. The optimal geometrical design parameters for designing a high-angle driving frame structure that can withstand high loads are determined. Then the appropriateness of the optimal solution is demonstrated through structural analysis. Finally, the validity of the geometrical optimization method is verified.

Separatrix parameters and core performances across the WEST L-mode database

WEST database analysis shows a correlation of the recycled neutral source around the separatrix with core performances. This observation questions the causality chain between particle source and turbulent transport up to the core in L-mode, high recycling plasmas, an unavoidable phase of all scenarios. The best core performances correlate with the lowest values of the density at the separatrix, , similarly to ASDEX Upgrade (AUG) tokamak and Joint European Torus (JET) tokamak in H-mode (Verdoolaege et al 2021 Nucl. Fusion 61 076006). Reflectometry in the midplane provides , while the temperature at the separatrix, is inferred by the ‘two-point model’ using Langmuir probe data on divertor targets. Lower separatrix resistivity does not correlate with better core performances, unlike H-mode observations (Eich et al 2020 Nucl. Fusion 60 056016). As expected in the presence of an efficient neutral source due to recycling fluxes, correlates with the D recycled particle flux at the divertor measured by visible spectroscopy. Coherently, at a given controlled central line integrated density , lower correlates with a larger density gradient around the separatrix as well as a larger global density peaking, , measured by interferometry. The latter correlates as well with lower collisionality in the core, similarly to JET and AUG H-modes (Angioni et al 2007 Nucl. Fusion 47 1326). The correlations reported allow phrasing the subsequent causality question: what is the interplay chain between low neutral recycling at the divertor plates, low density at the separatrix, high density peaking at the separatrix, high global density peaking, higher central temperature and better core energy confinement quality? Understanding the causality chain is essential to prepare ITER operation and design DEMO scenarios where the ratio of the divertor leg to the ionization length will be larger and where the pumped flux with respect to the plasma volume will be lower than presently operating tokamaks.

Argon–seeded detachment during ELM control by RMPs in KSTAR

In this study, we demonstrate argon-seeded discharges that exhibited a detached divertor during the full suppression and mitigation of edge-localized modes (ELMs) by an International Thermonuclear Experimental Reactor-like, three-row resonant magnetic perturbation (RMP) configuration in KSTAR. During the ELM suppression phase, the peak heat flux on the divertor target was successfully reduced from 1.6 MW m−2–0.5 MW m−2 via argon seeding. Further, the ion saturation current densities corresponding to the particle fluxes on both targets were reduced by more than 50%. During the RMP grassy-ELM regime, a further reduction to 0.1 MW m−2 in the divertor heat load was successfully achieved. A highly localized radiation zone near the X-point was also observed during divertor detachment. The calculated degree of detachment based on the two-point model increased to levels of approximately 3 and 2.3 for the outer target and inner target cases, respectively. These results provide valuable information regarding the effect of mid-Z impurities on RMP-detachment-compatible discharges.

Reduced transport models for a tokamak flight simulator

In this work, a very fast integrated transport model involving every region that interacts directly with the plasma of a tokamak, has been developed. The confined region is modeled in 1.5D, while the scrape-off layer has a 0D structure. For the core region, a physics-based analytical regression based on a set of simulations with the transport model TGLF [Staebler 2005 Phys. Plasmas 12 102508] has been produced. For the H-mode regime, an average edge-localized-modes model is applied in the pedestal region. In the scrape-off layer a two-point model for electron temperature (exhaust) and a particle balance for the species density at the separatrix have been implemented. All the models have first been validated individually in a standalone setting. Finally, six fully integrated simulations of an L-mode discharge, and five H-mode discharges, have been performed in the Fenix flight simulator [Janky et al 2019 Fusion Eng. Des. 146 1926, Fable et al 2022 Plasma Phys. Control. Fusion 64 044002], including transients, matching the experimental trajectories of an ASDEX upgrade discharge during flat-top and ramp-down. A broader validation including more discharges and the ramp-up phase is planned for the near future.

A Guiding and Positioning Motion Strategy Based on a New Conical Virtual Fixture for Robot-Assisted Oral Surgery

In robot-assisted oral surgery, the surgical tool needs to be fed into the target position to perform surgery. However, unmodeled extraoral and complex intraoral environments bring difficulties to motion planning. Meanwhile, the motion is operated manually by the surgeon, causing relatively limited accuracy as well as the risk of misoperation. Moreover, the random movements of the patient’s head bring additional disturbance to the task. To achieve the task, a motion strategy based on a new conical virtual fixture (VF) was proposed. First, by preoperatively specifying a conical guiding cone as the VF, virtual repulsive forces were applied on the out-of-range end effector. Then, based on the two-point adjustment model and velocity conversion, the effect of VF was established to prevent the end-effector from exceeding the constraint region. Finally, a vision system corrects the guiding cone to compensate for the random movement of the patient’s head to feed to a dynamic target. As an auxiliary framework for surgical operation, the proposed strategy has the advantages of safety, accuracy, and dynamic adaptability. Both simulations and experiments are conducted, verifying the feasibility of the proposed strategy.

Driving with a Haptic Guidance System in Degraded Visibility Conditions: Behavioral Analysis and Identification of a Two-Point Steering Control Model

The objective of this study is to determine the ability of a two-point steering control model to account for the influence of a haptic guidance system in different visibility conditions. For this purpose, the lateral control of the vehicle was characterized in terms of driving performance as well as through the identification of anticipation and compensation parameters of the driver model. The hypothesis is that if the structure of the model is valid in the considered conditions, the value of the parameters will change in coherence with the observed behavior. The results of an experiment conducted on a driving simulator demonstrate that the identified model can account for the cumulative influence of the haptic guidance system and degraded visibility. The anticipatory gain is sensitive to changes in driving conditions that have a direct influence on the produced trajectory, and the compensatory gain is sensitive to a decrease in the variability of the lateral position. However, a model with only the steering wheel angle as output is not able to determine whether the change in lateral position variability is due to the driver’s lack of anticipation or to the assistance provided by the haptic guidance system.

A meta-heuristic algorithm based on Henry's law for the load-following of a two-point PWR model

Henry's law has become the basis of a new meta-heuristic algorithm called Henry gas solubility optimization (HGSO). The novelty of this research is based on the use of this new algorithm in an important issue in nuclear energy, that is, reactor dynamics. The results can signify the validity of HGSO optimization process by comparing with established methods such as particle swarm optimization along with many studies conducted in different sciences. The two-point model of a pressurized light-water nuclear reactor (PWR) is controlled by an HGSO-tuned PID controller based on the integral of the time-weighted absolute error performance index and the axial offset following strategy. A Lyapunov approach guarantees the stability of the tuned PID. Unlike conventional theoretical estimating methods, these algorithms require the least mathematical calculations with the highest accuracy and speed to achieve optimal responses. The results signify the efficiency of this method than an empirically tuned PID, with the least mathematical calculations.

Predicting sugarcane quality using a portable visible near infrared spectrometer and a benchtop near infrared spectrometer

Sugar quality (Brix and Pol) is the key index to evaluate the value of sugarcane. Hence, a rapid, accurate, and time-efficient method is needed to determine the sugar quality. This study develops a two-point sugarcane quality model that uses a benchtop near infrared (NIR) spectrometer and a portable visible–near infrared (Vis-NIR) spectrometer to measure the sugarcane juice and stalk spectra, respectively. GT two experiments for developing a two-point sugarcane quality model. In the first, a model to calibrate the sugar quality as measured by a polarimeter and refractometer, and also by the benchtop NIR spectrometer. In the second, we developed a model to calibrate the sugar quality predicted from the calibration model developed in the first experiment, by measuring the sugarcane stalk absorption spectra using a portable Vis-NIR spectrometer. The results of the first experiment showed that the standard normal variate (SNV) spectral pretreatment was the most effective method for Brix calibration, with a coefficient of determination of prediction ([Formula: see text]) of 0.99 and root mean square error of prediction (RMSEP) of 0.2%. In the case of Pol, second derivatives were the best spectral pretreatment for effective calibration (r2 = 0.99, RMSEP = 0.3%). The results of the second experiment showed that the multiple linear regression model developed using the stalk spectra with the second derivative was the best model for Brix calibration (r2 = 0.70, RMSEP = 1.4%). The second derivative with the SNV pretreatment was best for Pol calibration (r2 = 0.70, RMSEP = 1.4%). Our study showed that a sugar quality regression model can be developed for a portable Vis-NIR spectrometer using the data from the sugar quality predicted by a benchtop NIR spectrometer.

A New Magnetic Target Localization Method Based on Two-Point Magnetic Gradient Tensor

The existing magnetic target localization methods are greatly affected by the geomagnetic field and exist approximation errors. In this paper, a two-point magnetic gradient tensor localization model is established by using the spatial relation between the magnetic target and the observation points derived from magnetic gradient tensor and tensor invariants. Based on the model, the equations relating to the position vector of magnetic target are constructed. Solving the equations, a new magnetic target localization method using only a two-point magnetic gradient tensor and no approximation errors is achieved. To accurately evaluate the localization accuracy of the method, a circular trajectory that varies in all three directions is proposed. Simulation results show that the proposed method is almost error-free in the absence of noise. After adding noise, the maximum relative error percentage is reduced by 28.4% and 2.21% compared with the single-point method and the other two-point method, respectively. Furthermore, the proposed method is not affected by the variation in the distance between two observation points. At a detection distance of 20 m, the maximum localization error is 1.86 m. In addition, the experiments also verify that the new method can avoid the influence of the geomagnetic field and the variation in the distance, and achieve high localization accuracy. The average relative error percentage in the y-direction is as low as 3.78%.

Modelling of tungsten impurity edge transport and screening for different divertor conditions in EAST

Tungsten (W) transport and screening in the edge plasma are investigated for EAST high dissipative divertor conditions. By combining the 1D impurity fluid model (1DImpFM) and the two-point model formatting (2PMF), W screening is proved to be enhanced for high upstream plasma density conditions, mainly because the impurity temperature gradient velocity decreases with the increase of the upstream plasma density. Based on dedicated EAST density ramp-up experiments, 2D simulations of W erosion and transport are carried out for different levels of dissipative divertor conditions by using the SOLPS-DIVIMP code package, and the modeling results are benchmarked with the 1DImpFM analytic model. The prompt-redeposition, the divertor screening, and the main SOL screening are quantitatively analyzed. For detached divertor conditions, the increase in the W ionization length reduces the prompt redeposition rate, but both the divertor screening and SOL screening are reinforced. The 1DImpFM can interpret well the W leakage in the near separatrix region; however, the 2D simulations suggest that the impurity pressure gradient force which is neglected by the 1DImpFM plays an important role, especially in the far-SOL region. With the divertor condition varied from the high-recycling regime to the deep detachment regime, the W source moves from the near strike point region to the far SOL, and thus makes the W transport in the far SOL more important. Therefore, the impurity pressure gradient force cannot be neglected for edge W transport analysis, especially for the detached divertor conditions.

Modeling Human Steering Behavior in Haptic Shared Control of Autonomy-Enabled Unmanned Ground Vehicles.

A human steering model for teleoperated driving is extended to capture the human steering behavior in haptic shared control of autonomy-enabled Unmanned Ground Vehicles (UGVs). Prior studies presented human steering models for teleoperation of a passenger-sized Unmanned Ground Vehicle, where a human is fully in charge of driving. However, these models are not applicable when a human needs to interact with autonomy in haptic shared control of autonomy-enabled UGVs. How a human operator reacts to the presence of autonomy needs to be studied and mathematically encapsulated in a module to capture the collaboration between human and autonomy. Human subject tests are conducted to collect data in haptic shared control for model development and validation. The ACT-R architecture and two-point steering model used in the previous literature are adopted to predict the operator's desired steering angle. A torque conversion module is developed to convert the steering command from the ACT-R model to human torque input, thus enabling haptic shared control with autonomy. A parameterization strategy is described to find the set of model parameters that optimize the haptic shared control performance in terms of minimum average lane keeping error (ALKE). The model predicts the minimum ALKE human subjects achieve in shared control. The extended model can successfully predict the best haptic shared control performance as measured by ALKE. This model can be used in place of human operators, enabling fully simulation-based engineering, in the development and evaluation of haptic shared control technologies for autonomy-enabled UGVs, including control negotiation strategies and autonomy capabilities.

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks: an autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. The benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable of providing accurate detachment prediction (usually within a few per cent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing with the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilising this knowledge could open a new path for plasma control in magnetic fusion energy research.

Human–Machine Cooperative Steering Control Considering Mitigating Human–Machine Conflict Based on Driver Trust

To reduce the impact of human–machine conflict on vehicle safety, this study proposes a novel human–machine cooperative steering control approach from the perspective of driver trust in the machine. The relationship between driver trust in the machine and driving skill is analyzed by the chi-square test method, and an online cooperative algorithm is designed using fuzzy control for different conditions, which assigns control authority based on driver trust under safe conditions and gives most of the authority to the machine to ensure safety under dangerous conditions. The machine is designed using model predictive control as an alternative controller parallel to the driver. To implement the proposed approach, a simulation platform that includes drivers and a test vehicle is established. Based on the driving data of human drivers collected in field tests, a two-point visual driver model is established to simulate steering behaviors and reflect physical workload. The parameters of the driver model are identified by a particle swarm optimization method to represent different drivers. The effectiveness of the approach, such as guaranteeing vehicle safety and reducing physical workload and human–machine conflict, is verified by simulations under typical conditions and obstacle avoidance conditions based on veDYNA vehicle dynamics software.

Simulation of plasma transport in MPS-LD linear plasma device by using BOUT++

A linear plasma device (LPD) module based on the 2D transport module under the BOUT++ framework is developed in this paper to simulate plasma transport in the LPD. The LPD module includes three parts, i.e. magnetic field calculation, simulation mesh generation and plasma transport setup. The magnetic field is calculated with the circular current loop using the location and current information of each coil. The mesh generation code can produce a simulation mesh for LPD by employing the magnetic field. The plasma transport model is based on the reduced Braginskii equations, which consist of the continuity equation, momentum equation, and energy equation. The fluid neutral model is applied for neutral particle simulation. Deuterium (D) and helium (He) ions and atoms can be simulated by the model. The first attempt to simulate plasma transport in the new LPD, called multiple plasma simulation linear plasma device (MPS-LD), is presented using the developed model. The D plasma transport in the MPS-LD is simulated and benchmarked against the two-point model, showing the validation of the BOUT++ simulation. The effects of radial transport on the heat load to the target are studied, which illustrates the significant impact of the radial diffusivities and on the plasma. Moreover, the He impurity injection process during the discharge is studied with emphasis on plasma–impurity interactions. The simulation results show that He injection can reduce the plasma energy load to the target significantly, and the efficiency depends on the He source density and injection velocity. The present work provides an alternative and flexible simulation tool for plasma transport in LPDs.