Path Prediction Research Articles

Navigation Path Prediction for Farmland Road Intersections Based on Improved Context Guided Network

Navigation Path Prediction for Farmland Road Intersections Based on Improved Context Guided Network

Psychological Empowerment as a Mediator of Effort-Reward Imbalance Effects on Burnout, Anxiety and Depression Among Nursing Home Professionals: A Cross-Sectional Study.

Few studies have explored empowerment as a predictor of mental health outcomes in geriatric healthcare professionals. This research addresses this gap by using the 'effort-reward imbalance' theory of work-related stress to develop a comprehensive model, examining the role of psychological empowerment in the psychological outcomes of nursing home professionals. This cross-sectional exploratory study used structural equation modelling (SEM) to test a model on the mediating role of psychological empowerment in the relationship between effort-reward ratio and burnout, anxiety and depression. From 2021 to 2023, we used convenience sampling to enrol 280 physicians, nurses and other healthcare workers from 13 nursing homes in France. Self-administered measurements included Psychological Empowerments Scale, Effort-Reward Imbalance Questionnaire, Maslach burnout inventory-Human Services Survey and Hospital Anxiety and Depression Scale. Of the 280 healthcare professionals included, 245 had complete data. Findings confirmed direct predictive paths from the effort-reward ratio to burnout, anxiety and depression. The negative direct path between effort-reward ratio and psychological empowerment was significant, as was burnout and depression prediction by psychological empowerment. Anxiety was not explained by psychological empowerment. Mediation effects were confirmed. Model fit indices indicated a good fit, supporting the role of psychological empowerment in reducing the negative impact of effort-reward imbalance on psychological outcomes, except for anxiety. Psychological empowerment appears to be a promising way to enhance and protect psychological work-related health of nursing home healthcare professionals. Understanding the impact of psychological empowerment on healthcare is a first step towards implementing interventions for healthcare professionals to enhance the quality of care and work conditions.

Continuously Optimizing Radar Placement with Model Predictive Path Integrals

Continuously Optimizing Radar Placement with Model Predictive Path Integrals

Attention mechanism fusion neural network for typhoon path prediction

Attention mechanism fusion neural network for typhoon path prediction

Epic motifs of the Mongol-Oirat epic story of Parchen and their parallels in the national versions of Dzhangar epic

The article is devoted to the consideration of epic motifs of the Mongol-Oirat epic of the epicteller Parchen. The relevance of the study is due to the fact that, despite the wide coverage of the range of problems of the epic of the Mongolian peoples, the issues of studying the motifs of the Mongol-Oirat epic tradition still remain insufficiently studied. The novelty of the study lies in the consideration of the epic motifs of the Mongol-Oirat heroic epic in a comparative aspect with the national versions of the Dzhangar epic. The purpose of this article is to consider the epic motifs of the heroic poems of the Mongol-Oirat epic of the epicteller Parchen, which were recorded, translated into Russian and published by Boris Vladimirtsov. The objectives of the study are to characterize the epic repertoire of the epicteller Parchen, to identify epic motifs in the Mongol-Oirat heroic epic and to consider them in a comparative aspect in the Xinjiang-Oirat and Kalmyk traditions of the Dzhangar epic. The work uses descriptive, comparative and contrastive methods, and textual analysis methods are used in the study of epic texts. The material of the study is the texts of the Oirat epics: Bum-Erdeni, Daini-Kuryul, Kigiin-Kiytyun-Keke-Temur-Zeve, Egil-Mergen, Ergil-Tyurgul, Shara-Bodon, the texts of the epic songs of the Xinjiang Oirat version of Dzhangar, published in China in 1986–2000, translated into the modern Kalmyk language by the orientalist Bulyash Todaeva, as well as the texts of the Kalmyk version of Dzhangar. The examination of the epic motifs of the Mongol-Oirat epics showed that in the process of historical development of peoples from the patriarchal-clan to the feudal system, archaic motifs (the motif of the first creation, miraculous birth, naming, the destined bride, the “external soul”, “difficult tasks”, three competitions, etc.) were transformed and inherited by the heroic Dzhangar epic. The motif of the first creation, present in the Mongol-Oirat epic Egil-Mergen in the Xinjiang-Oirat version of Dzhangar depicts the mythological early time, which passes to a hierarchically higher level, receiving the status of the original water chaos. The image of this chaos, taking an intermediate form, opens the next phase of the existence of the universe. The motif of naming, being an important component of the epic biography of the hero, is accompanied by a ritual in the Turkic-Mongolian epic, and in the national versions of Dzhangar – by a yoryal-good wish, which is a magical blessing and a prediction of the future heroic path of the hero. The miraculously born hero of the Oirat epics, performing heroic deeds in the interests of his family, in the national versions of Dzhangar is transformed into the heir of the epic ruler, a member of the family of heroes, the first among equals (Dzhangar-bogdo) in selfless service to the fatherland – the Bombay state. The study of the epic of the Mongolian peoples in this direction seems promising for further research into the problems of epictelling, motifs, poetics and style of folklore text.

Quadcopter Trajectory Tracking Based on Model Predictive Path Integral Control and Neural Network

This paper aims to address the trajectory tracking problem of quadrotors under complex dynamic environments and significant fluctuations in system states. An adaptive trajectory tracking control method is proposed based on an improved Model Predictive Path Integral (MPPI) controller and a Multilayer Perceptron (MLP) neural network. The technique enhances control accuracy and robustness by adjusting control inputs in real time. The Multilayer Perceptron neural network can learn the dynamics of a quadrotor by its state parameter and then the Multilayer Perceptron sends the model to the Model Predictive Path Integral controller. The Model Predictive Path Integral controller uses the model to control the quadcopter following the desired trajectory. Experimental data show that the improved Model Predictive Path Integral–Multilayer Perceptron method reduces the trajectory tracking error by 10.3%, 9.8%, and 5.7% compared to the traditional Model Predictive Path Integral, MPC with MLP, and a two-layer network, respectively. These results demonstrate the potential application of the method in complex environments.

An Exploration of Spiral of Silence in Online News Comments: Characteristics of Four Flow Patterns of Users’ Comments

Abstract To address prior methodological, contextual, and conceptual gaps in research on the spiral of silence (SOS), we investigated whether SOS manifests in online news comments where public opinions naturally emerge and examined the impact of these comments on public opinion. To analyze the progression of comments, we adopted a novel approach to time-series analysis—previously confined to social media—by implementing the dynamic time warp (DTW) technique. Data were collected from 47,845 comments on 281 online news articles related to three controversial topics published on a Korean portal site. Using DTW, we categorized comment patterns into four clusters across all topics: Contentious, Parallel, Overwhelming, and Gradual gap. A key observation was that minority opinions did not consistently follow a predictable path but rather displayed varied patterns. This indicates that SOS is selectively relevant in online discussions, with various factors influencing how minority opinions are expressed and shape public opinion. However, over a longer timeframe, the Contentious type, where minority views remained strong, significantly decreased in later discussions compared with earlier ones. Moreover, the current study corroborates previous research on the influence of initial comments. The pattern of comment flows is also influenced by factors such as click speech, neutral comments, and age.

A Study on the Analysis and Prediction of the Evolution Path of China’s Electric Vehicle Industry Policy Based on Text Mining

A Study on the Analysis and Prediction of the Evolution Path of China’s Electric Vehicle Industry Policy Based on Text Mining

A Hybrid Neural Network–Based Approach to Predict Crack Propagation Paths

ABSTRACTA data‐driven method based on a hybrid neural network (HNet) model is proposed to predict the crack propagation path. Using images as input enables the HNet model to predict crack propagation paths for different structures and defect types. To validate the effectiveness of this method, crack propagation paths on holed plates are investigated. The HNet model is trained to approximate the nonlinear relationship between the structural geometric parameters and the crack propagation paths. The feasibility of this method is verified by comparing the prediction results of the HNet model with the finite element calculation results. Furthermore, explainable artificial intelligence enhances the transparency of the HNet model, increasing its credibility. The challenge of data acquisition is effectively addressed by active learning, reducing the required training data volume. This method provides a fresh insight into the path prediction of crack growth problems.

Deep Learning Based Traffic Management in Knowledge Defined Network

In recent Artificial Intelligence developments, large datasets as knowledge are a prime requirement for analysis and prediction. To manage the knowledge of the network, the Data Center Network (DCN) has been considered a global data storage facility on edge servers and cloud servers. In recent research trends, knowledge-defined networking (KDN) architecture is considered, where the management plane works as the knowledge plane. The major network management task in the DCN is to control traffic congestion. To improve network management, i.e., optimized resource management, enhanced Quality of Service (QoS), we propose a path prediction technique by combining the convolution layer with the RNN deep learning model, i.e., Convolution-Long short-term memory network as Convolution-LSTM and the bi-directional long short-term memory (BiLSTM) network as Convolution-BiLSTM. The experimental results demonstrate that, in terms of many metrics, i.e., network latency, packet loss ratio, network throughput, and overhead, our proposed methodologies perform better than the existing works, i.e., OSPF, FlowDCN, modified discrete PSO, ANN, CNN, and LSTM-based routing approaches. The proposed approach improves the network throughput by approximately 30% and 12% as compared to existing CNN and LSTM-based routing approaches, respectively.

Triple point path prediction for height of burst explosion in high-altitude environment

Triple point path prediction for height of burst explosion in high-altitude environment

Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning

Robotic machining has been increasingly applied in intelligent manufacturing production lines. Compared with the traditional machine tools, commissioning for robotic machining system (RMS) is particularly important due to the low accuracy of industrial robots (IRs). Traditional site commissioning has large workload and is difficult to handle the multi-source errors. Since digital twin (DT) provides strategies for staying synchronized with the physical entities in whole lifecycle, a DT-driven virtual commissioning (VC) system for RMS is developed in this study to improve machining accuracy and reduce the difficulty of commissioning. Firstly, the framework of DT-driven VC system is designed including several function modules such as interaction, data pre-processing, DT model of RMS (RMSDT), and optimization service. Since RMSDT is the kernel of precise VC, a machine learning-enhanced RMSDT oriented to actual machining path prediction is then constructed based on a proposed joint error equivalent strategy, which can fully consider the coupled multi-source errors of machining robot. After that, a practical consistency retention method for RMSDT is proposed based on a stepwise updating strategy, where the model performance can be maintained with low updating costs. Finally, a visual VC system is developed for the experimental 6-degree of freedom robotic milling platform to verify the feasibility and effectiveness of the VC framework. Multiple experiments are also performed to test the performance of RMSDT and contour error compensation. This study has useful reference for the enterprises engaged in RMS and has positive significance for promoting the robotic machining.

Vehicle Motion Control for Overactuated Vehicles to Enhance Controllability and Path Tracking

The motion control of vehicles poses distinct challenges for both vehicle stability and path tracking, especially under critical environmental and driving conditions. Overactuated vehicles can effectively utilize the available tyre–road friction potential by leveraging additional actuators, thus enhancing their stability and controllability even in challenging scenarios. This paper introduces a novel modular upstream control architecture for overactuated vehicles, integrating a fast and robust linear time-varying model predictive path and speed tracking controller with a model following approach and nonlinear control allocation to form a holistic vehicle motion controller. The architecture decouples the path and speed tracking task from the actuator allocation, where torque vectoring and rear-wheel steering are applied to achieve linear understeer reference vehicle behavior. It allows for the use of a simpler path tracking controller, enabling long preview horizons and enhanced computational efficiency. Nonlinearities, such as the mutual influence of lateral and longitudinal tyre forces, are accounted for within the control allocation. The simulation results demonstrate that the proposed control architecture and overactuation improve vehicle stability in critical driving conditions and reduce path tracking errors compared to a dual-motor vehicle.

Intelligent vehicle path tracking coordinated optimization based on dual-steering cooperative game with fault-tolerant function

Since that a single controller for intelligent vehicle path tracking is difficult to adapt to complex working conditions and achieve good system control performance, inspired by the concept of control redundancy, an adaptive weight based dual-steering control cooperative game coordinated optimization with fault-tolerant control method for intelligent vehicle path tracking is proposed in this paper. Firstly, the three-degree-of-freedom of lateral, yaw and longitudinal directions is considered to establish the vehicle single-track dynamics model, and two model predictive path tracking steering controllers with different control objective emphases in tracking accuracy and yaw stability are designed. Then, the cooperative game theory is introduced to model the steering interaction of dual controllers, and its Pareto equilibrium optimal solution is solved by the distributed model predictive control framework. According to the influence trend of two game players’ relative participation weights on the path tracking results, an adaptive weight adjustment strategy based on predicted output deviation is designed, to achieve the coordinated optimization of dual steering control, and the steering fault-tolerant control is also realized by cooperative game in some cases of steering partial failure. Carsim/Simulink co-simulation and hardware-in-loop test under the variable curvature path and control failure conditions prove that by the proposed method intelligent vehicle path tracking dual steering control can be coordinated optimized and optimal path tracking performance is obtained, and demonstrate the effectiveness of the adaptive weight adjustment method which has better fault-tolerant control compared to fixed weight allocation under different degrees of steering output failures.

Carbon Peak Prediction and Carbon Reduction Path Simulation under the Dual Carbon Target: A Case Study of Jiangsu Province

To build a low-carbon and harmonious society that supports sustainable development, China has set forth the significant strategic goal of achieving carbon peaking by 2030 and carbon neutrality by 2060, commonly referred to as the dual carbon target. The key to achieving this target lies in scientifically and accurately predicting the timeline for carbon peaking, followed by developing effective carbon reduction paths. Using Jiangsu Province as an example, this study utilizes energy and economic data from 2010 to 2020 and introduces a penalized Lasso-Kaya model to forecast carbon emissions. The applicability of deep learning models, such as LSTM, in carbon peak prediction is also validated. Based on this, three levels of carbon reduction targets were established: baseline, steady progress, and ambitious. Through parameter simulation, the study derives an optimal carbon reduction path for Jiangsu Province through 2060. Consequently, this paper proposes a quantitative assurance mechanism for carbon reduction that maximizes economic growth, providing a valuable reference for future policy intervention.

Multi-span optical power spectrum prediction using cascaded learning with one-shot end-to-end measurement

Scalable methods for optical transmission performance prediction using machine learning (ML) are studied in metro reconfigurable optical add-drop multiplexer (ROADM) networks. A cascaded learning framework is introduced to encompass the use of cascaded component models for end-to-end (E2E) optical path prediction augmented with different combinations of E2E performance data and models. Additional E2E optical path data and models are used to reduce the prediction error accumulation in the cascade. Off-line training (pre-trained prior to deployment) and transfer learning are used for component-level erbium-doped fiber amplifier (EDFA) gain models to ensure scalability. Considering channel power prediction, we show that the data collection process of the pre-trained EDFA model can be reduced to only 5% of the original training set using transfer learning. We evaluate the proposed method under three different topologies with field deployed fibers and achieve a mean absolute error of 0.16 dB with a single (one-shot) E2E measurement on the deployed 6-span system with 12 EDFAs.

A Novel WTG Method for Predicting Ship Trajectories in the Fujian Inshore Area Based on AIS Data

The increasing congestion in major global maritime routes poses significant threats to international maritime safety, exacerbated by the proliferation of large, high-speed vessels. To improve the detection of abnormal ship behavior, this research employed automatic identification system (AIS) data for ship trajectory forecasting. Traditional methods primarily focus on spatial and temporal correlations but often lack accuracy and reliability. In this study, ship path predictions were enhanced using the WTG model, which combines wavelet transform, temporal convolutional networks (TCN), and gated recurrent units (GRU). Initially, wavelet decomposition was applied to deconstruct the input trajectory time series. The TCN and GRU modules then extracted features from both the time series and the decomposed data. The predicted elements were reassembled using a multi-head attention mechanism and a pooling layer to produce the final predictions. Comparative experiments demonstrated that the WTG model surpasses other models in the accuracy of ship trajectory prediction. The model proposed in this study proves to be reliable for forecasting ship paths, which is crucial for marine traffic management and ensuring safe navigation.

Experimental and numerical studies on the propagation paths of gear root cracks

Gear bending fatigue failure has a significant impact on the operational performance of advanced machinery, such as electric vehicles and wind turbines, potentially leading to failures and catastrophic consequences for transmission systems. Several methods are currently used to predict gear crack propagation; however, a comprehensive comparison of their accuracy and efficiency in predicting crack paths is lacking. In this study, the propagation path of root cracks in commonly used automotive gears was investigated using finite element (FE) analysis and experimental testing. Three mixed crack path prediction criteria were integrated to predict the gear root crack propagation using the commercial software ABAQUS by user-defined Python script. The impacts of gear crack parameters, including the gear initial crack length, on gear root crack propagation behaviour were analysed. The simulations were verified by the gear bending fatigue test using a single tooth bending test device. The results indicate that the simulation outcomes align with the experimental tests, demonstrating that all three criteria are effective in predicting gear root crack propagation behaviours.

Efficient Nonlinear Model Predictive Path Tracking Control for Autonomous Vehicle: Investigating the Effects of Vehicle Dynamics Stiffness

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.

An energy efficient prediction based protocol for target tracking in wireless sensor networks

Target tracking is one of the most attractive applications of wireless sensor networks, used for estimating the moving target's position accurately. A primary challenge in this domain is achieving precise target path estimation while conserving energy resources. This paper introduces an energy-efficient target tracking protocol in wireless sensor networks, considering both accuracy and reduced energy consumption. The protocol uses the Kalman filter to estimate the target's position and predict the subsequent step of its path. In each step of target tracking, a selected sensor, named the ‘leader’ performs computations for position estimation and path prediction, while two other sensors, known as ‘assistants’ help the leader in the tracking process. Leader selection within the protocol is performed in two phases: an initial phase occurring upon the target's entry to the network and a subsequent phase named the forced handoff phase. The forced handoff phase performs the selection of a new leader when either the target exits the sensing range of the current leader or the leader's energy decreases significantly. Although the proposed protocol is a new work, it can be considered as an improvement of the PPCP protocol by adding several changes and also replacing the binary variational filter with the Kalman filter. The efficiency of the proposed protocol is evaluated through simulations in Matlab. Results demonstrate the protocol's ability to achieve high-precision target tracking while maintaining low energy consumption. Comparative analysis shows its energy efficiency, which significantly increases the network lifetime.