Multipoint Model Research Articles

Multi-Point Deformation Prediction Model for Concrete Dams Based on Spatial Feature Vector

Deformation can effectively reflect the structural state of concrete dams and, thus, establishing na accurate concrete dam deformation prediction model is important for dam health monitoring and early warning strategies. To address the problem that the spatial coordinates introduced in the traditional multi-point deformation prediction model of dams not being able to accurately and efficiently reflect the spatial correlation of multiple-measuring points, a 2D-1D-CNN model is proposed which expresses the spatial correlation between each measuring point through spatial feature vectors, replacing the spatial coordinates in the traditional multi-point model. First, the spatial feature vector is extracted from the historical spatio-temporal panel series of deformation values of measuring points via a Two-Dimensional Convolutional Neural Network (2D-CNN); second, the vector is combined with the environmental impact factor of dam deformation to form the final input factor of fused spatial features; and, thirdly, this vector is combined with the environmental impact factors of dam deformation to form the final input factor of fused spatial features, and the non-linear linkage between the factors and the measured displacement values is constructed by the efficient feature processing capability of a One-Dimensional Convolutional Neural Network (1D-CNN) to obtain the prediction results. Finally, the actual monitoring data of a concrete dam in China are used as an example to verify the validity of the model. The results show that the proposed model outperforms the other models in most cases, respectively, which verifies the effectiveness of the proposed model in this paper.

Prediction model with multi-point relationship fusion via graph convolutional network: A case study on mining-induced surface subsidence.

Accurate prediction of surface subsidence is of significance for analyzing the pattern of mining-induced surface subsidence, and for mining under buildings, railways, and water bodies. To address the problem that the existing prediction models ignore the correlation between subsidence points, resulting in large prediction errors, a Multi-point Relationship Fusion prediction model based on Graph Convolutional Networks (MRF-GCN) for mining-induced subsidence was proposed. Taking the surface subsidence in 82/83 mining area of Yuandian No. 2 Mine in Anhui Province in eastern China as an example, the surface deformation data obtained from 250 InSAR images captured by Sentinel-1A satellite from 2018 to 2022, combined with GNSS observation data, were used for modeling. The deformation pattern of each single observation point was obtained by feeding their deformation observation data into the LSTM encoder, after that, the relationship graph was created based on the correlation between points in the observation network and MRF-GCN was established. Then the prediction results came out through a nonlinear activation function of neural network. The research shows that the R2R2 value of MRF-GCN model was 0.865 0, much larger than that of Long-Short Term Memory (LSTM) and other conventional models, while mean square error (MSE) of MRF-GCN model was 1.59 899, much smaller than that of LSTM and other conventional models. Therefore, the MRF-GCN model has better prediction accuracy than other models and can be applied to predicting surface subsidence in large areas.

Seismic response analysis of submerged floating tunnel considering non-uniform excitation and flexible-support boundary effect

In order to ensure the safety of submerged floating tunnel(SFT) during operation, it is necessary to carry out SFT response analysis under environmental loads. Given that SFTs are often constructed in valley areas situated in seismic zones, the aseismic performance of SFT needs to be investigated. However, there are important factors of non-uniform excitation and flexible-support boundary which have not been explored thoroughly. Non-uniform excitation affects the load input characteristics while flexible-support boundary affects the dynamic characteristics of structure itself. In this paper, we built a multi-point seismic wave synthesis model which fits well with the target power spectrum and a numerical response analysis model which reflects the dynamic characteristics of SFT structure well. The results based on the proposed model show that vertical non-uniform excitation can significantly increase the peak value of SFT tether tension and tube displacement with flexible-support boundary under whether artificial or natural seismic wave series input. While flexible-support boundary can availably produce vibration control effect on peak value of tether tension and tube response of whether displacement or stress indices under non-uniform wave series input. This work can provide reference for dynamic safety assessment and vibration control design of SFT to improve the development of ocean space.

Ultra-short-term Photovoltaic Power Prediction Based on Multi-head ProbSparse Self-attention and Long Short-term Memory

To provide accurate predictions of photovoltaic (PV) power generation, an MHPSA-LSTM ultra-short-term multipoint PV power prediction model combining Multi-head ProbSparse self-attention (MHPSA) and long short-term memory (LSTM) network is posited. The MHPSA is first used to capture information dependencies at a distance. Secondly, the LSTM is used to enhance the local correlation. At last, a pooling layer is added after LSTM to reduce the parameters of the fully-connected layer and alleviate overfitting, thus improving the prediction accuracy. The MHPSA-LSTM model is validated on a PV plant at the Desert Knowledge Australia Solar Centre as an example, and the RMSE, MAE, and R2 of MHPSA-LSTM are 0.527, 0.264, and 0.917, respectively. MHPSA-LSTM has higher prediction accuracy compared with BP, LSTM, GRU, and CNN-LSTM.

Modification technique for a space manipulator Joint's thermal model parameters

This study takes into account the multi-point thermal model's automatic adjustment mechanism. The combined heat balance test temperature is used as the genetic algorithm's optimization target value. To minimize time and resources when running simulations, a back propagation neural network-based surrogate model between temperature and parameters is used. The best parameter set is discovered when the thermal model is modified at high temperatures. The overall temperature distribution of the thermal model is significantly improved by the 50% decrease in the heat transfer coefficient X1 of the joint body and the multi-layer insulation assembly, among others. The model's root-mean-square error before and after the adjustment is decreased from 4.51 °C to 0.95 °C. The best parameter set has been effectively contrasted and validated by reintroducing it into the low temperature environment. When the model is updated, the root-mean-square error drops from 2.73 °C to 1.09 °C. Following the model update, the joint temperature distribution is more in accord with the experimental findings, and all errors are within 2 °C. Therefore, the finite element thermal model of a space manipulator joint can be altered using a combination of back propagation neural network and genetic algorithm.

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.

Quantifying prior model complexity for subsurface reservoir models

In Bayesian approaches to history matching for subsurface inference, the prior model specifies the uncertain model parameters and the joint probability of those parameters before incorporating production-related data. A good prior model is generally complex enough to capture the future reservoir behavior in the long term, realistic enough to be plausible, consistent with geologic knowledge, and simple enough to allow calibration for data matching. Model complexity is often associated with the number of model parameters, thus the focus on finding the sufficient number of parameters needed for history matching and quantifying future uncertainty.This work explores model choice based on concepts of complexity and informativeness of models for subsurface reservoir models. It focuses on the effect of the misspecification of prior models for assimilating flow data and their predictive accuracy. The concept of the effective number of parameters is used to investigate the suitability of various types of prior models with different levels of complexity, ranging from a highly simplified polynomial trend model to a more realistic multipoint statistical model(MPS) and a family of isotropic Gaussian models and explore the effect of level of model complexity on the robustness of forecasting. The numerical experiments were performed with different combinations of data type, prior informativeness, forecast type, and model type to compare the effect of different prior models on the robustness of the results. The effective number of parameters was computed for each prior model and their accuracy for predicting future reservoir behavior was analyzed.The results suggest that effective model dimension is a useful measure of model complexity for history matching problems, although it is not independent of the data used for model calibration and the number of effective model parameters is generally much smaller than the number of model parameters. In a data-rich problem, realism of a model is much less important than the complexity of a model, while for a problem with few data, realism was beneficial for reliable forecasts.

Dynamic Modeling and Stability Prediction of Robot Milling Considering the Influence of Force-Induced Deformation on Regenerative Effect and Process Damping

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping.

Application of multipoint heat source model to breast surface temperature map

This paper proposed a new method of the clinical analysis of breast tumors based on infrared thermal imaging. The theoretical foundation was constructed on the basis of the widely-used Pennes bio-heat transfer equation to find the relationship between the body surface temperature distribution and the inner heat source. Then, a multi-point heat source model was established. Benign and malignant lesions could be distinguished by analyzing the feature parameters of inner heat source. Moreover, without harm to and contact with human body, this method was suitable for breast cancer screening for its good sensitivity and rapid diagnosis process.

Modular fuzzy control for nonlinear multipoint based core model of TMI PWR under variable fuel cycle conditions

This paper provides design of modular fuzzy controller based on reactor power for multipoint kinetic model of TMI PWR, under transients and variable operational fuel cycle conditions. The validated four-point kinetic model is developed considering six groups of delayed neutron precursors, reactivity feedbacks and diffusion among selected nodes. Fuel burn-up effects and power variations are modeled using the variable thermal coefficients. The ability of adaptive multi-module fuzzy controller for reactor power is evaluated and compared at start and end of burn-up cycle. The power dependant nonlinearity of reactor core and time dependant variations of reactivity characteristics are handled with modular approach of TS fuzzy control. The performance monitoring and control improvements are achieved by online parametric identification of process model. The four modules are distributed on the power base line using the triangular mapping. Design of fuzzy controller is based on the optimized gain requirement at different power levels, error and error change. Control output is mapped as biased linear function of selected input variables using TS type fuzzy logic inference. Safety analysis is performed with Lyapunov function criteria for multipoint model. Transient analysis is performed for power tracking and accident handling ability of proposed control design and compared with the PID controller. The power variation among the adjacent nodes is reduced up to 5% under load following transients with the proposed model. Modular fuzzy approach found proficient in handling the changing operational conditions and variable dynamics of the system.

Path Tracking Control of Commercial Vehicle Considering Roll Stability Based on Fuzzy Linear Quadratic Theory

Commercial vehicles generally drive at a higher speed on structured expressways, and their higher center of mass leads to a lower rollover threshold and a greater rollover risk while steering. Therefore, the design of a lateral trajectory-tracking control strategy for commercial vehicles should not only consider the accuracy of trajectory tracking but also consider roll stability. Based on this control objective, a fuzzy linear quadratic controller was designed in this study to ensure rolling stability in the path-tracking control process and improve the adaptability of the strategy to the driving scenario. Firstly, a steering and braking cooperative control model based on the four-degree-of-freedom model and the multi-point preview model was established. Then, a path tracking controller considering roll stability was designed based on the linear quadratic theory. On this basis, a fuzzy linear quadratic controller was designed to realize the online optimization of cost function weights. Finally, the effectiveness of the control strategy was verified using co-simulation and hardware-in-loop experiments. The results show that the designed controller can effectively adjust the weight of path-tracking and stability according to the vehicle’s state. This effectively improves the vehicle’s control distribution problem.

Density Parameters of the Probability Distribution of Fluctuations of the Apparent Center of Radiation from the Helicopter Propeller Bushing during its Radar Observation

The properties of angular noise of radar reflections from the rotor head are considered. On the basis of a multipoint geometric model of the rotor head, analytical expressions are obtained that allow calculating the parameters of the probability distribution density of angular noise for the azimuth planes and the elevation of the rotor head of a single-bladed screw at an undirected angle of the control sleeve. Correlation obtained for one blade rotor head is generalized to thease of a rotor head with an arbitrary number of blades. It is shown that the rotor head angular noise is a random process with periodically changing parameters. The theoreticalresults are confirmed by mathematical modeling.

Multi-point detection method of dynamic deflection of super long-span bridge based on chain laser model

Real-time monitoring of bridge deflection is of great significance for ensuring bridge safety. Aiming at the problems of poor real-time performance and low precision of deflection measurement of continuous multi-span and super long-span bridges, this paper proposes a multi-point chain laser detection model and method for dynamic deflection of super long-span bridges. Firstly, we construct a multi-point deflection parameter detection model based on chain laser reference and image processing, and give the system error adjustment scheme of the model; Then, the hardware framework of the system is designed, and the real-time deflection parameter data acquisition, processing and solution methods are analyzed. In addition, the related factors affecting the measurement accuracy of the system are analyzed, and the system error is analyzed and corrected. Finally, the principle prototype is built, and the intelligent monitoring platform based on cloud data processing is designed. The system parameter calibration experiment is carried out and verified on the displacement platform built outdoors. The results show that the measurement accuracy of the system can reach 1 mm under the condition of considering the pitch angle of the monitoring equipment, which can realize the real-time, dynamic and high-precision monitoring of the deflection of the long-span bridge beam.

Multi-point discharge model: study on corona discharge of double-ended needle in large space

Corona discharge, as a common means to obtain non-equilibrium plasma, can generally obtain high-concentration plasma by increasing discharge points to meet production needs. However, the existing numerical simulation models used to study multi-point corona discharge are all calculations of small-scale space models, which cannot obtain the distribution characteristics of plasma in large space. Based on our previous research, this paper proposes a hybrid model for studying the distribution of multi-point discharge plasma in large-scale spaces, which divides the computational domain and computes separately with the hydrodynamic model and the ion mobility model. The simulation results are verified by a needle–ball electrode device. Firstly, the electric field distribution and plasma distribution of the needle electrodes with single tip and double tips are compared and discussed. Secondly, the plasma distribution of the needle electrode with the double tip at different voltages is investigated. Both computational and experimental results indicate that the charged particle concentration and current of the needle electrode with double tips are both twice as high as those of the needle electrode with a single tip. This model can extend the computational area of the multi-point corona discharge finite element model to the sub-meter (25 cm) or meter level, which provides an effective means to study the plasma distribution generated by multiple discharge points in large-scale space.

A MODEL OF REFLECTIONS OF PULSED RADIO SIGNALS FROM THE EARTH'S SURFACE AND ITS PRACTICAL USE IN ASSESSING THE ACCURACY OF AMPLITUDE DIRECTION FINDERS

The description of a mathematical model of reflections of pulse radio signals from the underlying Earth's surface and results of its practical use for estimation of accuracy of amplitude direction finders with horn antennas and antenna arrays of Vivaldi elements are presented. It is shown that the use of the proposed multipoint model of reflections from the ground surface allows an analysis of distortions of received pulse signals and estimation of errors of direction finding of radio emission sources by a monopulse amplitude direction finder on surface traces of radio wave propagation of different lengths.

A Multi-Point Contact Model Considering Rough Surface for Linear Ultrasonic Motors: Validation and Simulation.

The performance and wear life of linear ultrasonic motors are directly determined by the stator-mover frictional contact behaviors. A complete contact model is important to clearly understand the stator-mover contact mechanism and accurately estimate the motor performance. In this paper, a multi-point frictional contact model considering the roughness of contact interfaces is presented based on a finite model of the stator and an analytical model of the mover. The static/dynamic contact behaviors and output performance of the motor can be simulated efficiently. A quantitative measuring methodology for the dynamic contact forces between the stator and mover is developed. The effectiveness of the contact model for simulating the stator-mover contact forces is first evaluated by experiment. Based on the developed model, several dynamic characteristics of a linear ultrasonic motor are discussed: (a) the static force transferred between contact interfaces under pre-pressure; (b) the transient forces and energy exchange between contact interfaces; (c) the steady-state output performance of motor under different electric excitation parameters; (d) the effects of micro-topography parameters on the output performance of the motor and the force transmission of the contact interface.

A closed form solution for the internal stability of landfills considering spatial variability of the input seismic motion

The scope of this paper is to develop a calculation method of a closed form solution for the internal stability of the landfill considering spatial variability of the input seismic motion based on log-spiral combined sliding surface. A calculation method for multi-point seismic load of the log-spiral combined sliding surface landfill was proposed, which used to establish the multi-point seismic stability calculation model of the log-spiral combined sliding surface landfill. The calculation results for a typically shaped landfill of this method showed a lower result comparing with the method considering pseudo-static seismic action and translational slip surface. Thus, the coupling effect of the internal sliding of the landfill and multi-point seismic action should not be ignored in the seismic design of landfills.

Двухточечная модель вращающихся лопастей

In this paper, the angular noises of the rotating blades of a radar object are considered. A multipoint geometric model of rotating blades of a single propeller is considered. On its basis, analytical expressions are obtained that make it possible to calculate the angular coordinates of the apparent center of radiation of a propeller with rotating blades. Also, on the basis of a multi-point geometric model, relations were obtained that make it possible to synthesize a two-point geometric model of rotating propeller blades. Theoretical results are confirmed by mathematical mode¬ling for two configurations of a single-rotor small-sized aircraft: with one rotating blade and with two rotating blades.

A multipoint prediction model for nonlinear displacement of concrete dam

AbstractMeasurement points are installed in concrete dams to monitor nonlinear displacement behavior of dam systems working in complex and aggressive environment. Most modeling works for predicting displacements of these measurement points are limited to single output in point‐by‐point modeling way, with little attention paid to partition‐based multipoint combined modeling. Therefore, a novel multipoint prediction model (MPM) for concrete dam displacement is proposed whose novelty lies in, first, a partition method for measurement points of dam engineering that considers waveform used to identify dam deformation trend and chaos used to describe overall stability and inherent stochasticity in nonlinear displacement series of dam system, and second, a multi‐output support vector machine optimized by an improved grey wolf optimizer with better hierarchy and information transmission. The monitoring data of a super‐high concrete arch dam are used to verify the proposed model. Results indicate that the MPM shows feasibility and generalization capability regarding dam engineering and reaches the performance level of single‐point benchmark models. Robustness, sensitivity, and reasonability of the model are also discussed by random noise, sliding window, and perturbations.

Low-Profile Dual-Resonant Wideband Backfire Antenna for Vehicle-to-Everything Applications

A novel design approach to a low-profile dual- resonant wideband backfire antenna for Vehicle-to-Everything (V2X) applications is proposed. The antenna is composed of a semicircular principal radiator, a 152° sectorial reflector and a 112° sectorial director all installed above a metallic planar ground. At first, a 2.0-wavelength prototype magnetic dipole is introduced to lay down the flared angle, radius and the usable resonant TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</sub> and TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3,1</sub> modes of the principal radiator. In these ways, a wideband backfire radiation characteristic can be generated. Then, the multi-point equivalent source model is developed and used to predict the initial values of the sectorial reflector and director, e.g., the flared angles, radii, and the separations between the principal radiator and director/reflector. Prototype antennas are in final designed and fabricated to experimentally validate the dual-resonant wideband property on the 2 mm-thick air substrate, thereby exhibiting an impedance bandwidth of 27.6%. As also numerically and experimentally validated, the backfire gain can be further enhanced to 4.2 dBi. It is evidently validated that the proposed approach can be employed to effectively enhance the backfire radiation of low-profile, vehicular antennas.