Search Results (3,114)

As unmanned aerial vehicles (UAVs) are increasingly utilized in urban inspection and emergency rescue missions, path planning under strong wind conditions persists as a critical challenge. Traditional algorithms frequently exhibit deficiencies in environmental adaptability or encounter difficulties in balancing exploration and exploitation. This paper presents a dynamic-proportion Bat–Cuckoo Search (BA-CS) Hybrid Algorithm enhanced with wind field perception to tackle the challenges of UAV path planning in urban environments with strong winds, specifically addressing the issues of insufficient environmental adaptation and the exploration–exploitation imbalance. The algorithm integrates a dual-feedback mechanism that dynamically modifies the ratio of the BA/CS subpopulations in accordance with real-time iteration progress and population diversity. By incorporating wind field perception into population initialization, interpopulation information exchange, and wind resistance perturbation strategies, it attains efficient path optimization under multiple constraints. Experimental results under strong winds with speeds ranging from 10.8 to 13.8 m/s indicate that the proposed algorithm generates paths that are smooth, continuous, and entirely collision-free. It achieves a superior average wind resistance cost of 0.92, which is 9.8%, 17.1%, and 52.6% lower than those of the A*, RRT, and PSO algorithms, respectively. With a planning time of 3.95 s, it satisfies the path wind resistance stability requirements stipulated in the GB/T 38930-2020 standard, providing an effective solution for UAV inspection and emergency rescue operations in urban wind scenarios. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Unmanned aerial vehicles open new possibilities for developing technologies that support more sustainable and efficient agriculture. This paper presents a non-invasive method for repelling rodents from crop fields using ultrasound. The proposed system is implemented as a spherical-cap ultrasound loudspeaker array consisting of eight transducers, mounted on a drone that overflies the field while emitting sound in the 20–70 kHz range. The hardware design includes both the loudspeaker array and a custom printed circuit board hosting power amplifiers and a signal generator tailored to drive multiple ultrasonic transducers. In parallel, a genetic algorithm is used to compute flight paths that maximize coverage and increase the probability of driving rodents away from the protected area. As part of the validation phase, artificial intelligence models for rodent detection using a thermal camera are developed to provide quantitative feedback on system performance. The complete prototype is evaluated through a series of experiments conducted both in controlled laboratory conditions and in the field. Field trials highlight which parts of the concept are already effective and identify open challenges that need to be addressed in future work to move from a research prototype toward a deployable product. Full article

Path planning for multiple four-way shuttles in high-density warehousing is frequently hampered by efficiency-degrading conflicts, particularly head-on deadlocks. To address this challenge, this paper proposes a multi-agent reinforcement learning (MARL) framework based on Proximal Policy Optimization (PPO). The core of our approach is a novel Cooperative Avoidance Reward Mechanism (CARM), which employs a dual-component reward structure. This structure integrates a distance-guided reward to ensure efficient navigation towards targets and a cooperative avoidance reward that uses both immediate and delayed returns to incentivize implicit collaboration. This design effectively resolves conflicts and mitigates the policy instability often caused by traditional collision penalties. Experiments in a 20 × 20 grid simulation environment demonstrated that, compared to a rule-based A* and Conflict-Based Search (CBS) algorithms, the proposed method reduced the average travel distance and total time by 35.8% and 31.5%, respectively, while increasing system throughput by 49.7% and maintaining a task success rate of over 95%. Ablation studies further confirmed the critical role of CARM in achieving stable multi-agent collaboration. This work offers a scalable and efficient data-driven solution for real-time path planning in complex automated warehousing systems. Full article

One of the most critical challenges in gas turbine design is preventing the ingestion of hot mainstream gases into the disk space between the stator and rotor disks. Rim seals and superposed sealant flows are commonly used to mitigate the risk of component overheating. However, leakage paths inevitably form between the mating interfaces of adjacent components due to the complex architecture of the engine. Therefore, the interaction between the different flows present within the disk space complicates the accurate determination of the optimal sealing flow quantity. For this reason, this study experimentally investigates fluid dynamics inside a stator–rotor cavity, with a particular focus on leakage flows. In particular, this work examines the impact of multiple parameters, including injection radius position, number of leakage holes, and injection angle, on the sealing effectiveness values measured on the stator side of the cavity through CO${}_2$ gas sampling measurements. By comparing the effectiveness values with the swirl measurements derived from static and total pressure readings, the development of flow structures and the impact of leakage injection on sealing performance were finally evaluated. The results indicate that leakage injection has a minimal effect on the sealing effectiveness above the injection point, but significantly improves the performance at a lower radius. Moreover, it was observed that for a given mass flow rate, using a lower number of holes results in worse sealing performance due to a higher jet momentum, which causes the leakage flow to penetrate through the cavity toward the rotor side. In the end, employing two distinct injection angles—both aligned with the rotor’s direction of rotation—showed no substantial impact on sealing effectiveness. Full article

This paper presents a two-layer consistency control framework for the collaborative assembly of multiple aircraft in complex environments, comprising a low-level control layer and a high-level guidance layer. The control layer develops a robust anti-interference law by integrating an extended state observer (ESO) with Backstepping for attitude control and employing constrained Backstepping for velocity regulation. The guidance layer ensures safe and coordinated assembly. A time-varying communication topology is adopted to guarantee collision-free maneuvers. An assembly trajectory is generated for each aircraft based on a position allocation strategy and the Dubins path planning method. To achieve time-coordinated arrival, a speed consensus protocol is designed, guiding the aircraft into a sparse formation. Subsequently, consensus-based control laws for both attitude and velocity are implemented to transition into a tight formation. The effectiveness of the proposed framework is validated through aircraft six-degree-of-freedom (6-DoF) simulations, which confirm that it significantly improves the safety and robustness of the multi-aircraft assembly process. Full article

We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detection of rare extreme events, to support the reliable operation of renewable-rich power systems. The model combines a spatio-temporal transformer encoder with three convolutional neural network (CNN)/recurrent neural network (RNN) branches (CNN → long short-term memory (LSTM), LSTM → gated recurrent unit (GRU), CNN → GRU) and a dense pathway for tabular meteorological and calendar features. A multitask output head simultaneously performs the regression of PV power and binary classification of extremes defined above the 95th percentile. We evaluate HST–MB–CREH on the publicly available Renewable Power Generation and Weather Conditions dataset with hourly resolutions from 2017 to 2022, using a 5-fold TimeSeriesSplit protocol to avoid temporal leakage and to cover multiple seasons. Compared with tree ensembles (RandomForest, XGBoost), recurrent baselines (Stacked GRU, LSTM), and advanced hybrid/transformer models (Hybrid Multi-Branch CNN–LSTM/GRU with Dense Path and Extreme-Event Head (HMB–CLED) and Spatio-Temporal Multitask Transformer with Extreme-Event Head (STM–EEH)), the proposed architecture achieves the best overall trade-off between accuracy and rare-event sensitivity, with normalized performance of RMSE_z = 0.2159 ± 0.0167, MAE_z = 0.1100 ± 0.0085, mean absolute percentage error (MAPE) = 9.17 ± 0.45%, R² = 0.9534 ± 0.0072, and AUC_ext = 0.9851 ± 0.0051 across folds. Knowledge extraction is supported via attention-based analysis and permutation feature importance, which highlight the dominant role of global horizontal irradiance, diurnal harmonics, and solar geometry features. The results indicate that hybrid spatio-temporal multitask architectures can substantially improve both the forecast accuracy and robustness to extremes, making HST–MB–CREH a promising building block for intelligent decision-support tools in smart grids with a high share of PV generation. Full article

In this paper, a flower-shaped spoof surface plasmon polaritons (SSPPs) unit with strong slow-wave effect is proposed to construct bandpass filters (BPFs). Benefiting from extended current path induced by addition of rotated stubs around rectangular unit, the proposed SSPPs unit exhibits reduced asymptotic frequency. Following this, a single-band filter boasting multiple transmission zeros (TZs) in its upper stopband is developed by embedding the unit into half-mode substrate integrated waveguide (HMSIW). To improve suppression of the lower stopband, a pair of open circuited stubs are loaded to produce TZs and enhance its frequency selectivity. Consequently, the single-band BPF realizes an impressive roll-off rate of 0.116 dB/MHz. Subsequently, geometric dimensions of the open-circuited stubs are modified to dispose the TZs into passband and acquire dual-band operation. In addition, defected ground structures (DGSs) are loaded to broaden the bandwidth of notch between two passbands. Finally, a dual-band filter with a wide suppression band of 0.50 GHz is developed. With roll-off rates of 0.096 and 0.119 dB/MHz, the filter demonstrates good selectivity as well. Full article

The construction cost of offshore wind farms (OWFs) is heavily influenced by vessel scheduling and meteorological uncertainties. To address these challenges, this paper proposes a constraint-driven hierarchical optimization framework for the coordinated scheduling of installation vessels (IVs) and transport vessels (TVs). First, a Mixed-Integer Linear Programming (MILP) model is established to describe the operational constraints, which is then decomposed into two interrelated sub-problems: vessel path planning and scheduling optimization. For path planning, the problem is modeled as a Multiple Traveling Salesman Problem (MTSP) to ensure balanced fleet workloads. This stage is solved via a tailored three-stage heuristic combining balanced sweep clustering and penalized local search. For scheduling optimization, a hybrid Earliest Deadline First (EDF)-Simulated Annealing (SA) strategy is employed, where EDF generates a strictly feasible baseline to warm-start the SA optimization. Furthermore, a stochastic optimization approach integrates historical meteorological data to ensure schedule robustness against weather uncertainty. The validity of the framework is supported by two real-world OWF cases, which demonstrate total cost reductions of 15.44% and 13.20%, respectively, under stochastic weather conditions. These results demonstrate its effectiveness in solving high-constraint offshore engineering problems. Full article

Within the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation. Recently, several studies have explored the use of optical neural networks represented by the diffractive deep neural network (D²NN) for GANs. However, most of these focus on applications of the generative network, and there is currently no well-established D²NN architecture that simultaneously implements generative adversarial functionality. Here, we propose a novel implementation scheme for generative adversarial networks based on all-optical diffraction layers, demonstrating a complete all-optical adversarial architecture that simultaneously realizes both the generative network and the adversarial network (D²NN-GAN). We validated this method on the MNIST handwritten digit dataset, achieving Nash equilibrium convergence with the discriminator accuracy stabilizing around 50%. Concurrently, the average SSIM parameter of generated images reached 0.9573, indicating that the generated samples possess high quality and closely resemble real samples. Furthermore, we extended the framework to the KTH human action dataset, successfully reconstructing the “running” action with a discriminator accuracy of approximately 75%. The D²NN-GAN architecture introduces a fully optical generative adversarial model, providing a practical path for future optical modeling methods, such as image generation and video synthesis. Full article

In the field of energy transformation, the application of batteries is widening. To address the challenge of health state estimation of energy storage batteries with multiple operating conditions, this study analyzes the aging cycle operation data of lithium-ion batteries and develops a scheme to extract a number of raw features and their corresponding health status labels. Multidimensional candidate feature sets that capture aging information under different conditions are constructed. Subsequently, a three-stage feature selection strategy, including Pearson and Spearman correlation analysis, hierarchical redundancy elimination, and minimum redundancy maximum relevance, was applied to screen the candidate feature set of each condition, resulting in customized feature sets with condition adaptability. By analyzing the occurrence frequency and mean absolute correlation coefficient of each feature within the custom feature set, a comprehensive feature set with multi-condition adaptability was screened and determined. On this basis, by integrating temporal sequence information and operating condition information, a dual-path fusion estimation model with attention mechanism and condition modulation was established. The validation results of the lithium-ion battery multi-condition cycling aging dataset demonstrate that the model achieves accurate health state estimation, with mean absolute error and root mean square error of 0.8281% and 0.9835%, respectively. Finally, comparisons with other methods were conducted in terms of feature selection strategies and model estimation performance. The results demonstrate that the proposed approach achieves superior estimation accuracy and enhanced interpretability. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

Tomatoes are an important economic crop in China, and crop diseases often lead to a decline in their yield. Deep learning-based visual recognition methods have become an approach for disease identification; however, challenges remain due to complex background interference in the field and the diversity of disease manifestations. To address these issues, this paper proposes the SXA-YOLO (an improvement based on YOLO, where S stands for the SAAPAN architecture, X represents the XIoU loss function, and A denotes the AsDDet module) symmetric perception recognition model. First, a comprehensive symmetry architecture system is established. The backbone network creates a hierarchical feature foundation through C3k2 (Cross-stage Partial Concatenated Bottleneck Convolution with Dual-kernel Design) and SPPF (the Fast Pyramid Pooling module) modules; the neck employs a SAAPAN (Symmetry-Aware Adaptive Path Aggregation Architecture) bidirectional feature pyramid architecture, utilizing multiple modules to achieve equal fusion of multi-scale features; and the detection head is based on the AsDDet (Adaptive Symmetry-aware Decoupled Detection Head) module for functional decoupling, combining dynamic label assignment and the XIoU (Extended Intersection over Union) loss function to collaboratively optimize classification, regression, and confidence prediction. Ultimately, a complete recognition framework is formed through triple symmetric optimization of “feature hierarchy, fusion path, and task functionality.” Experimental results indicate that this method effectively enhances the model’s recognition performance, achieving a P (Precision) value of 0.992 and an mAP50 (mean Average Precision at 50% IoU threshold) of 0.993. Furthermore, for ten categories of diseases, the SXA-YOLO symmetric perception recognition model outperforms other comparative models in both p value and mAP50. The improved algorithm enhances the recognition of foliar diseases in tomatoes, achieving a high level of accuracy. Full article

In this work, we discuss a method for the QR-factorization of $N\times N$ matrices where $N\ge 3$ which is based on transformations which are called discrete signal-induced heap transformations (DsiHTs). These transformations are generated by given signals and can be composed by elementary rotations. The data processing order, or the path of the transformations, is an important characteristic of it, and the correct choice of such paths can lead to a significant reduction in the operation when calculating the factorization for large matrices. Such paths are called fast paths of the $N$-point DsiHTs, and they define sparse matrices with more zero coefficients than when calculating QR-factorization in the traditional path, that is, when processing data in the natural order $x_0,x_1,x_2,\dots$. For example, in the first stage of the factorization of a 512 $\times$ 512 matrix, a matrix is used with 257,024 zero coefficients out of a total of 262,144 coefficients when using the fast paths. For comparison, the calculations in the natural order require a 512 $\times$ 512 matrix with only 130,305 zero coefficients at this stage. The Householder reflection matrix has no zero coefficients. The number of multiplication operations for the QR-factorization by the fast DsiHTs is more than 40 times smaller than when using the Householder reflections and 20 times smaller when using DsiHTs with the natural paths. Examples with the 4 × 4, 5 × 5, and 8 × 8 matrices are described in detail. The concept of complex DsiHT with fast paths is also described and applied in the QR-factorization of complex square matrices. An example of the QR-factorization of a 256 $\times$ 256 complex matrix is also described and compared with the method of Householder reflections which is used in programming language MATLAB R2024b. Full article

Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m² and an $R^2$ value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article