Search Results (24,918)

Orbit determination for non-cooperative targets represents a significant focus of research within the domain of space situational awareness. In contrast to cooperative targets, non-cooperative targets do not provide their orbital parameters, necessitating the use of observation data for accurate orbit determination. The increasing prevalence of low-cost, low-thrust spacecraft has heightened the demand for advancements in real-time orbit determination and parameter estimation for low-thrust maneuvers. This paper presents a novel dual-layer filter approach designed to facilitate real-time acceleration estimation for non-cooperative targets. Initially, the method employs a square-root cubature Kalman filter (SRCKF) to handle the nonlinearity of the system and a Jerk model to address the challenges in acceleration modeling, thereby yielding a preliminary estimation of the acceleration produced by the thruster of the non-cooperative target. Subsequently, a specialized filtering structure is established for the estimated acceleration, and two filtering frameworks are integrated into a dual-layer filter model via the cubature transform, significantly enhancing the estimation accuracy of acceleration parameters. Finally, to adapt to the potential on/off states of the thrusters, the Interacting Multiple Model (IMM) algorithm is employed to bolster the robustness of the proposed solution. Simulation results validate the effectiveness of the proposed method in achieving real-time orbit determination and acceleration estimation. Full article

To address the safety and efficiency challenges in planning emergency evacuation routes for personnel in complex environments, this study proposes an integrated and improved ant colony optimization (ACO) with a genetic algorithm (GA). First, an emergency evacuation route planning model for subway incidents is constructed by optimizing evacuation time, route risk, and the passenger panic index. Then, the ant colony algorithm is enhanced by assigning pheromones to each objective and optimizing the state transition probabilities, which helps avoid premature convergence on local optima. Simultaneously, a GA is employed to conduct a global search and generate an initial population, which serves as the initial pheromone for the ACO. This approach achieves the integration of ACO and GA, enabling them to synergistically leverage the advantages of global and local search. Finally, an evacuation simulation was conducted using a specific subway station as an example, and the results were compared with those of traditional algorithms. The results indicate that the proposed algorithm can find the optimal solution for all evacuation routes and significantly improve convergence speed and global search capabilities. In simulations across different hazard development stages, the proposed integrated method outperforms basic ACO and SSA by accounting for evacuation time, safety, and crowd panic to yield optimal routes. Full article

To address the issue of traditional Particle Swarm Optimization (PSO) being prone to local optima and insufficient global search capability in sparse phased array optimization, a hybrid optimization algorithm integrating PSO with a Genetic Algorithm (GA) is proposed. Within the PSO framework, the proposed algorithm incorporates the adaptive crossover and mutation operations of the GA to enhance population diversity. It combines an adaptive weighting factor and a constriction factor to balance global exploration and local exploitation capabilities. Furthermore, a density-weighted method is employed to generate a high-quality initial population, thereby accelerating convergence. The proposed algorithm is applied to an 8 × 8 planar sparse array. On the E-plane (φ = 0°) and H-plane (φ = 90°), simulation results indicate that the achieved normalized maximum sidelobe level is −23.14 dB, which is significantly superior to those obtained by standalone PSO and GA. Based on these simulation results, microstrip patch antennas are introduced for array constitution and analysis. Full-wave electromagnetic simulation proves that the proposed sparse array has the ability of wide-angle scanning and low sidelobe. Our work demonstrates that the PSO-GA hybrid algorithm effectively enhances search capability and convergence performance, providing a reliable solution for sparse array design. Full article

This study addresses the challenges of real-time performance, safety, and trajectory smoothness in robot navigation by proposing an innovative variable-horizon model predictive control (MPC) scheme that utilizes evolutionary algorithms. To effectively adapt to the complex and dynamic conditions during navigation, a constrained multi-objective evolutionary algorithm is used to tune the control parameters precisely. The optimized parameters are then used to dynamically adjust the MPC’s prediction horizon online. To further enhance the system’s real-time performance, warm start and multiple shooting techniques are introduced, significantly improving the computational efficiency of the MPC. Finally, simulation and real-world experiments are conducted to validate the effectiveness of the proposed method. Experimental results demonstrate that the proposed control scheme exhibits excellent navigation performance in differential-drive robot models, offering a novel solution for intelligent mobile robot navigation. Full article

We introduce a novel Bayesian approach for feature (variable) selection using Gaussian process regression, which is crucial for enhancing interpretability and model regularization. Our method employs nearest neighbor Gaussian processes as scalable approximations to classical Gaussian processes. Feature selection is performed by conditioning the process mean and covariance function on a random set representing the indices of relevant variables. A priori beliefs regarding this set control the feature selection, while reference priors are assigned to the remaining model parameters, ensuring numerical robustness in the process covariance matrix. For model inference, we propose a Metropolis-within-Gibbs algorithm. The effectiveness of the proposed feature selection approach is demonstrated through evaluation on simulated data, a computer experiment approximation, and two real-world data sets. Full article

This study explores the application of multiple predictive algorithms under general versus route-specialized modeling strategies to estimate passenger boarding demand in public bus transportation systems. Accurate estimation of boarding patterns is essential for optimizing service planning, improving passenger comfort, and enhancing operational efficiency. This research evaluates a range of predictive models to identify the most effective techniques for forecasting demand across different routes and times. Two modeling strategies were implemented: a generalistic approach and a specialized one. The latter was designed to capture route-specific characteristics and variability. A real-world case study from a medium-sized metropolitan region in Brazil was used to assess model performance. Results indicate that ensemble-tree-based models, particularly XGBoost, achieved the highest accuracy and robustness in handling nonlinear relationships and complex interactions within the data. Compared to the generalistic approach, the specialized approach demonstrated superior adaptability and precision, making it especially suitable for long-term and strategic planning applications. It reduced the average RMSE by 19.46% (from 13.84 to 11.15) and the MAE by 17.36% (from 9.60 to 7.93), while increasing the average R² from 0.289 to 0.344. However, these gains came with higher computational demands and mean Forecast Bias (from 0.002 to 0.560), indicating a need for bias correction before operational deployment. The findings highlight the practical value of predictive modeling for transit authorities, enabling data-driven decision making in fleet allocation, route planning, and service frequency adjustment. Moreover, accurate demand forecasting contributes to cost reduction, improved passenger satisfaction, and environmental sustainability through optimized operations. Full article

Single-axis solar tracking maximizes photovoltaic energy production under clear-sky conditions; however, its effectiveness decreases under cloudy and overcast skies, where diffuse irradiance dominates and the optimal module orientation changes. Conventional tracking algorithms either neglect sky conditions or rely on simplified diffuse-response strategies that may trigger frequent tracker repositioning under variable cloud cover, leading to increased mechanical wear with marginal energy gains. This work proposes an enhanced diffuse-response tracking algorithm that explicitly accounts for both the intensity and temporal persistence of cloudiness. By requiring overcast conditions to persist for a minimum duration before reorienting the tracker to a diffuse-stow position, the proposed approach reduces unnecessary movements while preserving the benefits of diffuse-response operation. The algorithm is evaluated through numerical simulations based on historical meteorological data and validated using field measurements on monofacial and bifacial photovoltaic strings. The results show that the proposed strategy reduces excess tracker movement from 114% to 0.16% while maintaining nearly the same energy yield. Compared to a conventional diffuse-response algorithm, the associated energy reduction is minimal (≈0.17%) relative to the ≈0.37% yield gain observed at the studied location. These findings demonstrate that incorporating cloudiness duration enables a practical compromise between energy performance and tracker durability, particularly for monofacial photovoltaic systems. Full article

To address the challenges of limited detection accuracy and the difficulty of deployment on edge devices caused by dust obstruction, low illumination, and complex background interference in coal mine conveyor belt foreign object detection, this paper proposes an improved algorithm model, RTA-YOLOv11, based on the YOLOv11 framework. First, a Receptive Field Enhancement Module (RFEM) is utilized to expand the field of view by fusing multi-scale perception paths, strengthening the network’s semantic capture capability for subtle targets. Second, a Triplet Attention mechanism is introduced to suppress environmental noise and enhance the saliency of low-contrast foreign objects through cross-dimensional joint modeling of spatial and channel information. Finally, a lightweight detection head based on MBConv is designed, utilizing inverted bottleneck structures and re-parameterization strategies to compress redundant parameters and improve deployment efficiency on edge devices. Experimental results indicate that the mAP@0.5 of the improved RTA-YOLOv11 model is 4.0 percentage points higher than that of the original YOLOv11, with an inference speed of 79 FPS and a reduction in parameters of approximately 22%. Compared with algorithms such as Faster R-CNN, SSD, and YOLOv8, this model demonstrates a superior balance between accuracy and speed, providing an efficient and practical solution for intelligent mine visual perception systems. Full article

Distributed Interactive Simulation (DIS) systems are highly sensitive to temporal delays. Conventional Dead Reckoning (DR) algorithms suffer from limited prediction accuracy and are often inadequate in mitigating simulation latency. To address these issues, a heuristic hybrid prediction model based on Polar Lights Optimization (PLO) is proposed. First, the Transformer architecture is modified by removing the decoder attention layer, and its temporal constraints are optimized to adapt to the one-way dependency of DR time series prediction. Then, a hybrid model integrating the modified Transformer and LSTM is designed, where Transformer captures global motion dependencies, and LSTM models local temporal details. Finally, the PLO algorithm is introduced to optimize the hyperparameters, which enhance global search capability and avoid premature convergence in PSO/GA. Furthermore, a closed-loop mechanism integrating error feedback and parameter updating is established to enhance adaptability. Experimental results for complex aerial target maneuvering scenarios show that the proposed model achieves a trajectory prediction $R^2$ value exceeding 0.95, reduces the Mean Squared Error (MSE) by 42% compared with the results for the traditional Extended Kalman Filter (EKF) model, and decreases the state synchronization frequency among simulation nodes by 67%. This model significantly enhances the prediction accuracy of DR and minimizes simulation latency, providing a new technical solution for improving the temporal consistency of DIS. Full article

This study presents an integrated decision support system (DSS) designed to optimize real-time reservoir operation during typhoons by balancing flood control and water supply. The system combines ensemble quantitative precipitation forecasts (QPF) from WRF/MM5 models, a physically based rainfall–runoff model (KW-GIUH), and a three-stage optimization algorithm for reservoir release decisions. Eighteen ensemble rainfall members are processed to generate 6 h inflow forecasts, which serve as inputs for determining adaptive outflow strategies that consider both storage requirements and downstream flood risks. The DSS was tested using historical typhoon events—Talim, Saola, Trami, and Kong-rey—at the Tseng-Wen Reservoir in Taiwan. Results show that the KW-GIUH model effectively reproduces hydrograph characteristics, with a coefficient of efficiency around 0.80, while the optimization algorithm successfully maintains reservoir levels near target storage, even under imperfect rainfall forecasts. The mean deviation of reservoir water levels from the recorded to the target values is less than 0.18 m. The system enhances operational flexibility by adjusting release rates according to the proposed outflow index and flood-stage classification. During major storms, the DSS effectively allocates storage space for incoming floods while maximizing water retention during recession periods. Overall, the integrated framework demonstrates strong potential to support real-time reservoir management during extreme weather conditions, thereby improving both flood mitigation and water-supply reliability. Full article

With the rapid development of the shipping industry, the collision risk among ships in open waters has been steadily increasing, making effective multi-ship collision avoidance decision-making a critical issue for ensuring navigational safety. This paper proposes a multi-ship collision avoidance decision-making method based on the COLREGs. First, a fuzzy comprehensive evaluation method is used to construct a collision risk index model. Then, considering navigational safety, COLREG compliance, turning amplitude, and path economy, an objective function for ship collision avoidance is formulated. Next, the AVOA is improved by incorporating SA to simulate the foraging and navigation behavior of vultures. The Metropolis acceptance criterion is applied to help the algorithm escape local optima and enhance global search capabilities. Experiments conducted in the VSC simulation environment show that the proposed method significantly improves decision-making performance in multi-ship encounter scenarios compared to the standard AVOA. Full article

This paper presents an optimization framework for Multi-Robot Task Allocation (MRTA) for a heterogeneous robot fleet operating in dynamic, failure-prone environments. In contrast to traditional MRTA approaches that handle only the initial allocation, our system extends functionality by integrating real-time crisis response and intelligent task recovery from failure points. The framework combines island model genetic algorithm-based initial optimization with an event-driven architecture for handling robot failures during mission execution. Our key contribution is the integration of crisis-aware capabilities with the island model paradigm, enabling task resumption from failure points and dynamic reoptimization, while preserving the diversity benefits of multi-population evolution. When a robot fails, the system intelligently substitutes replacement robots and resumes interrupted tasks from their exact failure point, rather than restarting from the beginning. This significantly improves mission efficiency and resilience. We introduce a temporal scheduling mechanism that tracks actual task execution states and calculates remaining work upon failure, enabling true task continuation. Experimental validation across 57 diverse scenarios with 2,340 independent runs demonstrates that the island model achieves higher fitness scores, maintains greater population diversity, exhibits more consistent performance, and recovers faster from crisis events compared to the standard single-population genetic algorithm. Full article

Desert–Gobi–wasteland regions possess abundant wind resources and are strategic areas for future renewable energy development and meteorological monitoring. However, existing studies have limited capability in addressing the highly complex and dynamic environmental characteristics of these regions. In particular, few modeling approaches can jointly represent terrain variability, solar radiation distribution, and wind-field characteristics within a unified framework. Moreover, conventional deep reinforcement learning methods often suffer from learning instability and coordination difficulties when applied to multi-agent layout optimization tasks. To address these challenges, this study constructs a multidimensional environmental simulation model that integrates terrain, solar radiation, and wind speed, enabling a quantitative and controllable representation of the meteorological monitoring network layout problem. Based on this environment, an Environment-Aware Proximal Policy Optimization (EA-PPO) algorithm is proposed. EA-PPO adopts a compact environment-related state representation and a utility-guided reward mechanism to improve learning stability under decentralized decision-making. Furthermore, a Global Layout Optimization Algorithm based on EA-PPO (GLOAE) is developed to enable coordinated optimization among multiple monitoring nodes through shared utility feedback. Simulation results demonstrate that the proposed methods achieve superior layout quality and convergence performance compared with conventional approaches, while exhibiting enhanced robustness under dynamic environmental conditions. These results indicate that the proposed framework provides a practical and effective solution for intelligent layout optimization of meteorological monitoring networks in desert–Gobi–wasteland regions. Full article

An optimal topographical arrangement of wind turbines (WTs) is essential for increasing the total power production of a wind farm (WF). This work introduces PSO-GA, a newly formulated algorithm based on the hybrid of Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA) method, to provide the best possible and reliable WF layout (WFL) for enhanced output power. Because GA improves on PSO-found solutions while PSO investigates several regions; therefore, hybrid PSO-GA can effectively handle issues involving multiple local optima. In the first phase of the framework, PSO improves the original variables; in the second phase, the variables are changed for improved fitness. The goal function takes into account both the power production of the WF and the cost per power while analyzing the wake loss using the Jenson wake model. To evaluate the robustness of this strategy, three case studies are analyzed. The algorithm identifies the best possible position of turbines and strictly complies with industry-standard separation distances to prevent extreme wake interference. This comparative study on the past layout improvement process models demonstrates that the proposed hybrid algorithm enhanced performance with a significant power improvement of 0.03–0.04% and a 24–27.3% reduction in wake loss. The above findings indicate that the proposed PSO-GA can be better than the other innovative methods, especially in the aspects of quality and consistency of the solution. Full article

This study aims to optimize the voltage profile of the grid by obtaining an optimum level of reactive power support from photovoltaic (PV) plants, thereby enhancing the efficiency of PV systems in power distribution networks and ensuring grid stability. Initially, voltage profiles in the sector, together with the structure and operating principles of PV plants, were considered in detail. Subsequently, the limits of reactive power support that can be provided by PV plants were determined. Then, the optimum levels of reactive power from the plants were determined using particle swarm optimization, genetic algorithm, Jaya algorithm, and firefly algorithm separately. The algorithms were tested through simulations conducted on a power distribution system operator in Türkiye. Additionally, a Modbus-based communication application was developed and tested, as a feasibility demonstration, to verify PV inverter accessibility and the capability of remotely writing reactive power reference setpoints. The quantitative optimization results reported in this manuscript are obtained from DIgSILENT PowerFactory simulations using the actual feeder model and time-series profiles. The results have revealed that PV plants can be effectively utilized as reactive power compensators to contribute to the operation of the grid under more ideal voltage profile conditions. In Türkiye, there is no regulatory or market mechanism to support reactive power provision from PV plants. Therefore, this study is novel in the Turkish market. The experimental results confirm that power generation from renewable energy can provide reactive support effectively when needed, which reveals that this approach is both technically feasible and practically relevant. Full article