Computational Burden Research Articles

A comprehensive estimation method for vehicle motion states based on model constraints

The motion state estimation is indispensable for unmanned ground vehicles. Most traditional methods estimate individual states and utilize high-dimensional dynamics models that necessitate extensive prior information to improve accuracy. However, these approaches are beset with numerous challenges in the accuracy, complexity, and computational burden. In this paper, a novel method is proposed to address these problems with two novelties. Firstly, it possesses the capability to simultaneously estimate 10 fundamental states, including vehicle velocities, wheel slip ratios, and wheel sideslip velocities. The estimation accuracy is augmented since the coupling relationships among states are considered. Secondly, the method avoids the utilization of the complete vehicle model, instead employing the constraints derived from the models, thereby the requisite prior information and computational burden can be reduced. The accuracy of the proposed method was demonstrated through simulation experiments and field tests, as well as its robustness in challenging situations, such as the wheel slip.

Adaptive fault-tolerant containment control for heterogeneous uncertain nonlinear multi-agent systems under actuator faults

As modern control systems involve a growing number of actuators, the consequent number of faults occurrence, which may deteriorate the closed-loop system performance, is increasing. Focusing on the containment control problem of generic nonlinear high-order heterogeneous uncertain Multi-Agent Systems, this study proposes a novel distributed adaptive fault-tolerant Proportional-Integral-Derivative control protocol to improve the reliability and the resilience of the whole networked systems against actuator failures. The Lyapunov theory, combined with the Barbalat lemma, provides the design of suitable adaptive mechanisms that adjust the PID control actions to ensure the asymptotic convergence of the closed-loop containment errors for the overall network, hence implying the convergence of each agent state toward the convex region shaped by the multiple leaders, despite the occurrence of actuators faults. The fulfillment of the fault-tolerant containment objective is ensured by requiring a lower computational burden with respect to alternative solutions, hence resulting in more attractive for wider practical engineering applications. Numerical simulation results, also including a comparison analysis with a state-of-the-art controller, corroborate the efficiency and the advantages of the proposed distributed PID controller.

PV-YOLO: A lightweight pedestrian and vehicle detection model based on improved YOLOv8

With the frequent occurrence of urban traffic accidents, fast and accurate detection of pedestrian and vehicle targets has become one of the key technologies for intelligent assisted driving systems. To meet the efficiency and lightweight requirements of smart devices, this paper proposes a lightweight pedestrian and vehicle detection model based on the YOLOv8n model, named PV-YOLO. In the proposed model, receptive-field attention convolution (RFAConv) serves as the backbone network because of its target feature extraction ability, and the neck utilizes the bidirectional feature pyramid network (BiFPN) instead of the original path aggregation network (PANet) to simplify the feature fusion process. Moreover, a lightweight detection head is introduced to reduce the computational burden and improve the overall detection accuracy. In addition, a small target detection layer is designed to improve the accuracy for small distant targets. Finally, to reduce the computational burden further, the lightweight C2f module is utilized to compress the model. The experimental results on the BDD100K and KITTI datasets demonstrate that the proposed PV-YOLO can achieve higher detection accuracy than YOLOv8n and other baseline methods with less model complexity.

Cooperative Object Transport Via Non-Contact Prehensile Pushing by Magnetic Forces

Abstract Cooperative robot systems are an essential candidate for object transportation solutions. They offer cost-efficient and flexible operation for various types of robotic tasks. The benefits of cooperative robot systems have triggered the improvement of the object transportation field. In this study, a new way of transporting objects by cooperative robots is presented. The proposed method is performed by the pushing action of the magnetic forces of the robots. The permanent magnets mounted on the mobile robots and the cart create this repelling force. The rectangular object carrier cart equipped with passive caster wheels can be manipulated on flat terrains easily and be assigned to carry different shapes of objects. Using a carrier cart has the advantage of eliminating the vertical loads on the robots. Controlling a non-contact pushing method offers a low computational burden since simple velocity and position updates are adequate for operation management. Compared with the other methods of object transportation systems, the non-contact pushing method provides a faster operation with less sensitivity to control errors. Both simulations and real-world experiments are conducted and the performances are given comparatively with a generalized frictional contact object-pushing method. The results show that the proposed method provides 10.48% faster and 20.03% more accurate object transportation compared to the frictional contact method. It is envisioned that the presented method can be a promising candidate for object transportation tasks in the industry.

Non-inertial Computational Framework for Long-distance Shock-driven Object Dynamics

In the realm of dynamic separation problems, the motion of a body triggered by shock interactions is a common phenomenon. This is particularly important in terms of the safe separation of two-stage-to-orbit vehicles, where the motion must remain stable despite long-distance disturbances from shock waves. The flow field in these cases is complex, marked by interactions between hypersonic shock waves and a moving boundary. This leads to significant unsteady effects due to the body's translation and rotation over extended distances. Existing simulation techniques fall short in rapidly and accurately predicting the aerodynamic force and thermal properties for these problems, largely due to the overwhelming computational demands that result from oversize computational domains and the necessity of grid deformation. This paper presents a novel non-deforming grid method to address these challenges. The central concept is to anchor the reference frame to the moving object itself and to approach the problem from a non-inertial frame perspective. This accounts for the motion of the object solely via the inertial source term, circumventing the complexities of mesh manipulation typically required to link flow and motion equations. The moving shock boundary is designed to be closely compatible with selected shock-captured schemes, which reduces non-physical oscillations compared to the traditional method of direct assembly with theoretical shock relations. Other boundary conditions and the solution process are also refined to specifically target the unsteady, shock-dominated flow. These modifications significantly alleviate the computational burden. The effectiveness of the proposed method is demonstrated through several test cases. To showcase the method's practical application, a scenario is simulated wherein an ellipse is dislodged from a wedge by an incident shock wave, covering a long distance. These tests confirm the method's feasibility in aerospace engineering problems.

Efficient simultaneous migration of primary and free-surface related multiples using reformulated two-way wave-equation depth extrapolation scheme

The migration of free-surface related multiples can enhance subsurface illumination and improve overall imaging quality. However, this process encounters two main challenges: crosstalk artefacts resulting from the cross-correlation of non-reflection-related wavefields, and the increased computational burden of imaging different orders of multiples. We propose a novel method that simultaneously and efficiently migrates both primary and multiple reflections while mitigating crosstalk artefacts. The method employs a reformulated two-way wave-equation depth extrapolation scheme that simplifies up/down wavefield separation through straightforward summation and subtraction operations at each depth step. Two innovative algorithms are integrated into this scheme: a generalized up/down separation algorithm, and a simultaneous migration algorithm of primary and free-surface-related multiples. The up/down separation algorithm efficiently separates the up- and down-going wavefields into primary wavefield and multiple reflections of various orders at the measurement surface. The simultaneous migration algorithm then pairs these components as two-way quantities, allowing for efficient depth extrapolation using a unified propagator, followed by effective decomposition into corresponding one-way components for imaging. Numerical experiments conducted on synthetic models, including a two-dimensional two-layer model and the Sigsbee 2B model, as well as on real seismic data from a gas hydrates bearing zone, demonstrate that the proposed method simultaneously migrate both primary and multiple reflections with reduced crosstalk artefacts and limited computational overhead.

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

Response prediction is a fundamental yet challenging task in aeronautical engineering, requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction (FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation, we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods, the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime (3.514 s).

One-Equation Amplification-Factor-Transport Transition Model for High-Speed Flows

A one-equation transition model is proposed for high-speed transitional flows. The model is based on the amplification-factor-transport framework and involves only one transport equation for the amplification factor of streamwise instabilities. A new algebraic intermittency factor is established and applied to a turbulence model to predict transition, and the model is tested by applying it to several cases over a wide speed range. Comparisons with linear stability theory and available experimental data indicate that the model handles first- and second-mode-induced transitions in supersonic and hypersonic flows and reasonably captures the effects of Mach number, wall-to-edge temperature ratio, and nose bluntness. The new one-equation model offers reduced computational burden and simpler implementation while retaining a reasonable prediction accuracy.

Deep learning-based geological parameterization for history matching CO2 plume migration in complex aquifers

History matching is crucial for reliable numerical simulation of geological carbon storage (GCS) in deep subsurface aquifers. This study focuses on inferring highly complex aquifer permeability fields with multi- and intra-facies heterogeneity to improve the characterization of CO2 plume migration. We propose a deep learning (DL)-based parameterization strategy combined with the ensemble smoother with multiple data assimilation (ESMDA) algorithm to formulate an integrated inverse framework. The DL model is employed to parameterize non-Gaussian permeability fields using low-dimensional latent variables in a Gaussian distribution, thereby mitigating the non-Gaussianity issue faced by the ensemble-based ESMDA inverse method and simultaneously alleviating the computational burden of high-dimensional inversion. The efficacy of the integrated DL-ESMDA inverse framework is demonstrated using a 3-D GCS model, where it estimates the non-Gaussian permeability field characterized by multi- and intra-facies heterogeneity. Results show that the DL model is able to represent the highly complex and high-dimensional permeability fields using low-dimensional latent vectors. The DL-ESMDA framework sequentially updates these low-dimensional latent vectors instead of the original high-dimensional permeability field to obtain posterior estimations of the permeability field. The resulting CO2 plume migration closely matches historical measurements, suggesting a significantly improved model reliability after history matching. Additionally, a substantial reduction in uncertainty for future plume migration predictions beyond the history matching period is observed. The proposed framework provides an effective approach for reliable characterization of CO2 plume migration in highly heterogeneous aquifers, enhancing GCS project operation and risk analysis.

DeployFusion: A Deployable Monocular 3D Object Detection with Multi-Sensor Information Fusion in BEV for Edge Devices.

To address the challenges of suboptimal remote detection and significant computational burden in existing multi-sensor information fusion 3D object detection methods, a novel approach based on Bird's-Eye View (BEV) is proposed. This method utilizes an enhanced lightweight EdgeNeXt feature extraction network, incorporating residual branches to address network degradation caused by the excessive depth of STDA encoding blocks. Meantime, deformable convolution is used to expand the receptive field and reduce computational complexity. The feature fusion module constructs a two-stage fusion network to optimize the fusion and alignment of multi-sensor features. This network aligns image features to supplement environmental information with point cloud features, thereby obtaining the final BEV features. Additionally, a Transformer decoder that emphasizes global spatial cues is employed to process the BEV feature sequence, enabling precise detection of distant small objects. Experimental results demonstrate that this method surpasses the baseline network, with improvements of 4.5% in the NuScenes detection score and 5.5% in average precision for detection objects. Finally, the model is converted and accelerated using TensorRT tools for deployment on mobile devices, achieving an inference time of 138 ms per frame on the Jetson Orin NX embedded platform, thus enabling real-time 3D object detection.

Random-Coupled Neural Network

Improving the efficiency of current neural networks and modeling them on biological neural systems have become prominent research directions in recent years. The pulse-coupled neural network (PCNN) is widely used to mimic the computational characteristics of the human brain in computer vision and neural network fields. However, PCNN faces limitations such as limited neural connections, high computational costs, and a lack of stochastic properties. This study proposes a random-coupled neural network (RCNN) to address these limitations. RCNN employs a stochastic inactivation process, selectively inactivating neural connections using a random inactivation weight matrix. This method reduces the computational burden and allows for extensive neural connections. RCNN encodes constant stimuli as periodic spike trains and periodic stimuli as chaotic spike trains, reflecting the information encoding characteristics of biological neural systems. Our experiments applied RCNN to image segmentation and fusion tasks, demonstrating its robustness, efficiency, and high noise resistance. Results indicate that RCNN surpasses traditional methods in performance across these applications.

Mетод визначення зміни просторового положення об'єкта орбітального сервісу з невідомою формою

This paper presents a method for determining the pose of an on-orbit service object (target) with an unknown surface shape based on processing target surface point clouds. It is assumed that a point cloud is obtained using a sensor in the form of a lidar-based system onboard the service spacecraft. The algorithm of the method operates with a simplified description of the cloud; it uses only the coordinates of the cloud points without identifying any surface features. The idea of the method is as follows. From all cloud points obtained at a certain time instant, select points that are close to a certain degree to a given set of planes arranged in space in a certain way. From the point cloud corresponding to the next time instant, select points that are close to the same set of planes, but moved in space in a known way. By convention, let the arrangement of the projections of the selected cloud points onto the given planes be called a pattern of the intersection of the point clouds with the planes. By varying the displacement of the set of planes, maximize the coincidence of the intersection patterns of the first and second clouds of points. The degree of coincidence of the intersection patterns is characterized by a specified objective function whose arguments are target pose parameters. The target displacement parameters are found as the arguments of the objective function that maximize it. A variant of the objective function is proposed. A test example of the application of the proposed method is considered. A global optimization method known as the direct search method was used to find the arguments of the objective function. Test calculations were performed, and they confirmed the workability of the algorithm and determined its scope of applicability. The computational burden was not considered: the goal was to verify the workability of the method in principle. The advantage of the proposed method is its ability to determine a change in the pose of an object with an unknown surface shape. The proposed method may be considered as a source of observation data for a filtering procedure when estimating the parameters of the relative motion of a target with an unknown surface shape.

Very short-term wind power forecasting considering static data: An improved transformer model

The randomness and fluctuations in wind power generation present significant challenges for grid and wind farm dispatching. Accurate very short-term wind power forecasting (WPF) is therefore essential for the efficient operation of modern power systems. Data-driven models, such as Transformers, have demonstrated their effectiveness in WPF due to their ability to efficiently capture global features in long sequences. However, limited research has examined the impact of incorporating static data into WPF, which may limit forecasting accuracy. This paper proposes a Temporal Fusion Transformer forecasting model to address this challenge. This approach employs static data as the input features for the model. The model includes feature selection through a variable selection network and employs a specialized temporal fusion decoder to learn effectively from these static features. The case results show that the results of the proposed model are more accurate than the state-of-the-art methods, reducing MAPE by at least 1.32%, RMSE by 0.0091, and improving R2 by 0.035 in case studies. Additionally, the model maintains a manageable computational burden, underscoring its practical applicability.

Estimating the Sampling Distribution of Posterior Decision Summaries in Bayesian Clinical Trials.

Bayesian inference and the use of posterior or posterior predictive probabilities for decision making have become increasingly popular in clinical trials. The current practice in Bayesian clinical trials relies on a hybrid Bayesian-frequentist approach where the design and decision criteria are assessed with respect to frequentist operating characteristics such as power and type I error rate conditioning on a given set of parameters. These operating characteristics are commonly obtained via simulation studies. The utility of Bayesian measures, such as "assurance," that incorporate uncertainty about model parameters in estimating the probabilities of various decisions in trials has been demonstrated. However, the computational burden remains an obstacle toward wider use of such criteria. In this article, we propose methodology which utilizes large sample theory of the posterior distribution to define parametric models for the sampling distribution of the posterior summaries used for decision making. The parameters of these models are estimated using a small number of simulation scenarios, thereby refining these models to capture the sampling distribution for small to moderate sample size. The proposed approach toward the assessment of conditional and marginal operating characteristics and sample size determination can be considered as simulation-assisted rather than simulation-based. It enables formal incorporation of uncertainty about the trial assumptions via a design prior and significantly reduces the computational burden for the design of Bayesian trials ingeneral.

Toward Efficient Modeling of Nonradiative Decay in Extended INVEST: Overcoming Computational Challenges in Quantum Dynamics Simulations.

In recent years, an increasing number of fully organic molecules capable of thermally activated delayed fluorescence (TADF) have been reported, often with very small or even inverted singlet-triplet (INVEST) energy gaps. These molecules typically exhibit complex photophysics due to the close energy levels of multiple singlet and triplet states, which create various transition pathways toward emission. A predictive model for the rates of these transitions is thus essential for assessing the suitability of new materials for light-emitting devices. Quantum Dynamics (QD) calculations are ideal for this purpose, as they include quantum effects, without the limitations of first-order perturbative approaches, also allowing taking into account more than two electronic states at once. However, the huge computational demands of QD methodologies, especially for large molecules, currently limit their use as a standard tool. To address this problem, we here employ a strategy that allows us to include almost the whole set of the vibrational coordinates by selecting the key elements of the Hilbert space that significantly impact dynamics, thereby hugely reducing the computational burden. Application of this protocol to two relatively large INVEST molecules reveals that internal conversion in these systems is very fast, making indirect emissive pathways a possible channel for the population of the S1 state. More importantly, this study demonstrates that the dynamics can be accurately described even with a significantly reduced vibrational space, thus allowing quantum dynamics calculations that yield accurate transition rates in a few minutes of computational time.

A Comprehensive Method for Example-Based Color Transfer with Holistic–Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality.

A Hybrid Multiobjective Control Strategy Based on Combination of Modulated Model Predictive Control and Phase‐Shifted Modulation for a Unidirectional Five‐Level Rectifier

ABSTRACTThe unidirectional multilevel rectifier has immense potential in high‐power medium‐voltage industrial applications. This paper proposes a multiobjective control method based on the combination of optimal‐vector‐pair modulated model predictive control (OVP‐M2PC) and a phase‐shifted modulation strategy for a unidirectional five‐level rectifier. This strategy takes both of the inductor current tracking and capacitor voltage balancing as the control objectives. First, an optimal vector pair is determined based on the AC‐side current variation rate, and then, through combining the calculated action duration of OVP with carrier phase‐shifted modulation, inductor current tracking is achieved. Second, capacitor voltage balancing control is carried out by compensating for the duty cycles which also decouples the input current tracking and output capacitor voltage balance control. Compared with traditional FCS‐MPC, under the proposed control strategy, complex cost function calculation and weighting factor tuning are not required, which much reduced computational burden. Multiobjective control is achieved with better performance in both steady‐state and dynamic conditions. The superiority of the proposed strategy over traditional FCS‐MPC are validated through simulations and hardware experiments.

Design and Analysis of a Triple-Input Three-Level PV Inverter with Minimized Number of MPPT Controllers

Photovoltaic (PV) energy has been a preferable choice with the rise in global energy demand, as it is a sustainable, efficient, and cost-effective source of energy. Optimizing the power generation is necessary to fully utilize the PV system. Harvesting more power uses cascading of impedance source converters taking input from low-voltage PV arrays which requires multiple maximum power point tracking (MPPT) controllers. To solve this problem, a three-level inverter topology with a proposed PV arrangement, offering higher voltage boosting and a smaller size with a lower cost suitable for low-voltage panels, is designed in this article. The design criteria for parameters are discussed with the help of the small signal analysis. In this paper, three PV arrays are used to harvest maximum energy, which require only one MPPT controller and employ an extended perturb and observe (P&O) algorithm, being faster, highly efficient, and reducing the computational burden of the controller. Moreover, a three maximum power points tracker algorithm, which perturbs one parameter and observes six variables, is designed for the selected converter topology. Finally, the designed 1.1 kVA grid-connected PV system was simulated in MATLAB (R2023a) which shows that the MPPT algorithm offers better dynamics and is highly efficient with a conversion efficiency of 99.2% during uniform irradiance and 97% efficiency during variable irradiance conditions.

Neighborhood entropy guided by a decision attribute and its applications in multi-source information fusion and attribute selection

The existing methods for information fusion and attribute selection typically require a computationally intensive grid search to determine the optimal parameter. This grid search can significantly impact the efficiency of these processes. To tackle this issue, this paper introduces the concept of neighborhood entropy, which incorporates both the neighborhood and decision classes, and delves into its applications in multi-source information fusion and attribute selection. First, the concept of a δ-neighborhood based on a predefined distance is introduced. Then the neighborhood entropy based on the δ-neighborhood is defined. The optimal parameters for information fusion and attribute selection are determined by minimizing the neighborhood entropy. Furthermore, an information fusion method and an attribute selection algorithm based on the neighborhood entropy, conditional information entropy (CIE), and belief functions are developed. Finally, we demonstrate the effectiveness of our approach through experiments and Wilcoxon tests on 12 datasets, including a large-scale gene dataset. The experimental results show that the optimal parameter determined by the neighborhood entropy aligns with that obtained through grid search and the neighborhood entropy reduces the computational burden of information fusion and attribute selection compared to grid search. When compared to other state-of-the-art algorithms, our approach based on the neighborhood entropy and CIE demonstrates superior classification performance and time efficiency. These findings demonstrate the efficiency and effectiveness of neighborhood entropy and pave the way for applying granular-computing-based information fusion methods to large-scale datasets.

Online adaptive critic designs with tensor product B-splines and incremental model techniques

As an effective optimal control scheme in the field of reinforcement learning, adaptive dynamic programming (ADP) has attracted extensive attention in recent decades. Neural networks (NNs) are commonly employed in ADP algorithms to realize nonlinear function approximation. However, the learning process with NN approximation typically requires a substantial amount of training data and places a heavy computational burden. To improve learning efficiency and alleviate computational load, this paper develops a novel model-free ADP approach based on multivariate splines and incremental control techniques, aimed at achieving online learning of nonlinear control. By utilizing input and output data, an incremental linear approximation model is identified online without any prior knowledge of system dynamics. To improve nonlinear approximation capabilities and lessen computational demands, tensor product B-splines, instead of NNs, are integrated into the critic module to approximate the value function, and the recursive least squares temporal difference (RLS-TD) algorithm is employed to update the weights of spline bases. Convergence analysis of the proposed control scheme is conducted based on the dual-timescale stochastic approximation theory. To illustrate the effectiveness and feasibility of the proposed control scheme, numerical simulations are performed in solving the online nonlinear control problem within the context of the inverted pendulum system.