Search Results (4,943)

This paper proposes a novel image tone-mapping framework that incorporates meta-learning, a psychophysical model, Bayesian optimization, and light-field virtual diffraction. First, we formalize the virtual diffraction process as a mathematical operator defined in the frequency domain to reconstruct high-dynamic-range (HDR) images through phase modulation, enabling the precise control of image details and contrast. In parallel, we apply the Stevens power law to simulate the nonlinear luminance perception of the human visual system, thereby adjusting the overall brightness distribution of the HDR image and improving the visual experience. Unlike existing methods that primarily emphasize structural fidelity, the proposed method strikes a balance between perceptual fidelity and visual naturalness. Secondly, an adaptive parameter tuning system based on Bayesian optimization is developed to conduct optimization of the Tone Mapping Quality Index (TMQI), quantifying uncertainty using probabilistic models to approximate the global optimum with fewer evaluations. Furthermore, we propose a task-distribution-oriented meta-learning framework: a meta-feature space based on image statistics is constructed, and task clustering is combined with a gated meta-learner to rapidly predict initial parameters. This approach significantly enhances the robustness of the algorithm in generalizing to diverse HDR content and effectively mitigates the cold-start problem in the early stage of Bayesian optimization, thereby accelerating the convergence of the overall optimization process. Experimental results demonstrate that the proposed method substantially outperforms state-of-the-art tone-mapping algorithms across multiple benchmark datasets, with an average improvement of up to 27% in naturalness. Furthermore, the meta-learning-guided Bayesian optimization achieves two- to five-fold faster convergence. In the trade-off between computational time and performance, the proposed method consistently dominates the Pareto frontier, achieving high-quality results and efficient convergence with a low computational cost. Full article

Multi-robot ensembles comprising several manipulators are commonly used in industrial settings to process non-deterministic flows of items loaded by an upstream source onto a shared transportation system. After the execution of a given task, the robots regularly deposit the items on a common output flow, which conveys the semi-finished material towards the downstream portion of the plant for further processing. The productivity and reliability of the entire process, which is affected by the plant layout, by the quality of the adopted scheduling and task assignment algorithms, and by the proper balancing of the input and output flows, may be degraded by random disturbances and transient conditions of the input flow. In this paper, a highly accurate event-based simulator of this kind of system is used in conjunction with a rollout algorithm to optimize the performance of the plant in all operating scenarios. The proposed method relies on a simulation of the plant that comprehensively considers the dynamic performance of the manipulators, their actual motion planning algorithms, the adopted scheduling and task assignment methods, and the regulation of the material flows. The simulation environment is built upon computationally efficient maps able to predict the execution time of the tasks assigned to the robots, considering all the determining factors, and on a representation of the manipulators themselves as finite state automata. The proposed formalization of the line balancing problem as a Markov Decision Process and the resulting rollout optimization method are shown to substantially improve the performance of the plant, even in challenging situations, and to be well suited to real-time implementation even on commodity hardware. Full article

To address the challenges of strong nonlinearity, stringent terminal constraints, and the trade-off between computational efficiency and accuracy in the midcourse guidance trajectory optimization problem of aerodynamically controlled interceptors, this paper proposes a variable-discrete-scale sequential convex programming (SCP) method. Firstly, a dynamic model is established by introducing the range domain to replace the traditional time domain, thereby reducing the approximation error of the planned trajectory. Second, to overcome the critical issues of solution space restriction and trajectory divergence caused by terminal equality constraints, a terminal error-proportional relaxation approach is proposed. Subsequently, an improved second-order cone programming (SOCP) formulation is developed through systematic integration of three key techniques: terminal error-proportional relaxation, variable trust region, and path normalization. Finally, an initial trajectory generation algorithm is proposed, upon which a variable-discrete-scale optimization framework is constructed. This framework incorporates a residual-driven discrete-scale adaptation mechanism, which balances discretization errors and computational load. Numerical simulation results indicate that under large discretization scales, the computation time required by the improved SOCP is only about 5.4% of that of GPOPS-II. For small-discretization-scale optimization, the SCP method with the variable discretization framework demonstrates high efficiency, achieving comparable accuracy to GPOPS-II while reducing the computation time to approximately 7.4% of that required by GPOPS-II. Full article

Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments. Full article

Dynamic technological changes and the variability of market requirements pose significant challenges for modern manufacturing companies in the effective development and implementation of new technological solutions. The aim of the research was to develop an integrated approach covering all key stages of implementation—from formulating technological solutions, through selecting and evaluating variants, to preparing and managing production processes—under the conditions of a medium-sized manufacturing company specializing in the batch production of steel constructions. The analysis was based on an interdisciplinary approach, combining methods of creative design of new technological solutions, including Blue Ocean Strategy, value proposition design, and QFD methodology, with analytical approaches that include multi-criteria evaluation of solution variants, technical preparation of production, as well as the organization and management of production processes in modified organizational conditions. This approach enabled a comprehensive assessment of the developed solutions, taking into account both their operational potential and practical feasibility in realistic implementation conditions, through the use of case studies and simulations to validate the results. The results of the research indicate that integrating methods for creating new solutions with analytical assessment and simulation tools leads to a more precise and data-driven approach to process design, enabling better decision-making based on thorough analysis and predictive modeling. Furthermore, this approach allows for a significant reduction in the risk of implementation failure through early identification of potential problems. The conclusion of the study confirms that a comprehensive and interdisciplinary approach to the implementation of new technologies ensures better alignment with customer demands, reduces production downtime, and enhances product optimization and resource utilization, which are critical factors in building a sustainable competitive advantage for manufacturing companies. The proposed approach enables more deliberate design and organization of manufacturing processes, supporting their flexible adaptation to changing market and technological conditions. Full article

Federated learning (FL) enables privacy-preserving adaptation of large language models (LLMs) across distributed clients. However, deploying FL in edge environments remains challenging because of the high communication overhead of full-model updates. Recent advances in parameter-efficient fine-tuning (PEFT), particularly low-rank adaptation (LoRA), have substantially reduced update sizes by injecting lightweight trainable matrices into pretrained transformers, thereby making FL with LLMs more feasible. In this paper, we propose LoRaC-GA, a communication-aware optimization framework that dynamically determines the optimal number of clients to participate in each round under a fixed bandwidth constraint. We formulated a max-min objective to jointly maximize the model accuracy and communication efficiency and solved the resulting non-convex problem using a genetic algorithm (GA). To further reduce the overhead, we integrated a structured peer-to-peer collaboration protocol with ${log}_{2}K$ complexity, enabling scalable communication without full connectivity. The simulation results demonstrate that LoRaC-GA adaptively selects the optimal client count, achieving competitive accuracy while significantly reducing the communication cost. The proposed framework is well-suited for bandwidth-constrained edge deployments involving large-scale LLMs. Full article

Aiming at the problem of model parameter perturbation in vehicle trajectory tracking control, an improved robust model predictive control (RMPC) method is proposed. Based on the two-degree-of-freedom vehicle model and Serret Frenet error model, a multi-cell hypercube vertex modeling is adopted to map the disturbance range of parameters such as vehicle speed and lateral stiffness to a set of vertices, and dynamic linear combination is achieved through normalized weights. The algorithm design mainly focuses on the dual-layer optimization of the switching mechanism, decomposing the infinite time domain problem into finite time domain optimization and terminal constraints. At the same time, it dynamically updates the vertex parameters to match time-varying uncertainties and then combines Lyapunov theory to design a control invariant set. The results show that in complex road conditions and vehicle state transitions, RMPC can reduce the peak lateral deviation from 1.0 m to 0.2 m, converge the heading deviation to within 2 deg, and significantly reduce the mean and root mean square values of control errors compared to traditional MPC, under the influence of vehicle model parameter perturbations. RMPC has good robustness and real-time performance. Full article

Forest ecosystems are vital to sustainable development, contributing to economic, environmental and social well-being. However, the increasing frequency and severity of wildfires threaten these ecosystems, demanding more effective and integrated fire management (IFM) strategies. Current suppression efforts face limitations due to high resource demands and the need for timely, informed decision-making under uncertain conditions. This paper presents the SILVANUS project’s approach to developing an advanced Decision Support System (DSS) designed to assist incident commanders in optimizing resource allocation during wildfire events. Leveraging Geographic Information Systems (GIS), real-time data collection, AI-enhanced analytics and multicriteria optimization algorithms, the SILVANUS DSS component integrates diverse data sources to support dynamic, risk-informed decisions. The system operates within a cloud-edge infrastructure to ensure scalability, interoperability and secure data management. We detail the formalization of the resource allocation problem, describe the implementation of the DSS within the SILVANUS platform, and evaluate its performance in both controlled simulations and real-world pilot scenarios. The results demonstrate the system’s potential to enhance situational awareness and improve the effectiveness of wildfire response operations. Full article

Gantry cranes play a key role in modern industrial logistics. However, the traditional dynamic model based on the assumption of cable rigidity faces difficulty in accurately describing the complex swing characteristics of flexible cables, resulting in low load positioning accuracy and limited operation efficiency. To address this problem, this paper proposes a cable modeling method that considers the flexible deformation and nonlinear dynamic characteristics of the cable. Based on the theory of continuum mechanics, a flexible cable dynamic model that can accurately describe the flexible deformation and distributed mass characteristics of the cable is established. In order to solve the transportation time optimization and full-state constraint problems, a velocity trajectory optimization algorithm based on a discretization framework is proposed. Through inverse kinematics analysis and numerical integration technology, a reverse angle enumeration reasoning (RAER) method is proposed to suppress the swing of the load. Under the same constraints of distance, velocity, acceleration, cable swing angle, and residual swing angle, RAER requires a longer transportation time but achieves smaller peak swing and residual swing, making it the only algorithm that satisfies full-state constraints. Under the energy criterion, the proposed algorithm also requires the least amount of energy. Comprehensive comparisons through simulations and experiments show that the predicted swing angles of the flexible cable are highly consistent with the experimental results. Full article

Public transit subsidization often suffers from a double (or bilateral) moral hazard problem, where both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts. Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge. This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators are active co-producers of service quality whose own unobservable efforts—distinct from the subsidy itself—are critical to outcomes.The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery. Full article

The cooling system is a core component for a vehicle’s powertrains to operate smoothly and maintain a satisfying noise, vibration, and harshness (NVH) performance. However, advances in new energy vehicles bring with them complex requirements for the cooling fan design due to new issues such as increased heat load, dynamic variations, and high-speed vibrations, which demand the optimization of fan dynamics over a wide range of parameters. In this paper, by thoroughly checking the effect of rigid–flexible coupling and the geometrically complex elastic frame of the fan, we propose a combined modeling approach to reduce the computational time of broad-range parameter variation analysis and examine the vibration problem in the cooling fans under various external excitations. First, the complicated frame of the fan is simplified through virtual prototyping based on an experiment. Then, modal transition is applied, reducing the complex kinetic expression, and a time-invariant system model is derived with multi-blade coordinate transformation. Stability and bifurcation analysis are performed regarding different excitation couplings from the rotor, powertrain, and road. The results of the simulation and experiment illustrate that the proposed methodology achieves a substantial reduction in computational time, and all degrees of freedom (DOFs) are divided into two groups including symmetrical and asymmetrical types. The results also imply the great potential for the optimization and control of the high-speed fan’s vibration for new energy cars. Full article

Nonlinear robotic systems often operate under widely varying conditions that challenge traditional linear control approaches. The Linear Parameter-Varying (LPV) paradigm overcomes these limitations and offers a unifying framework by representing the system’s time-varying dynamics as a convex blend of linear models. This enables both controller and observer synthesis through convex optimization, while considering nonlinearities and time-dependent behavior. This paper presents a linear matrix inequality (LMI)-based methodology for simultaneous stabilization control and state estimation in robotic application within the LPV/polytopic setting. Parallel to controller design, the full-state estimation challenge posed by limited sensors in robotics is addressed. An LPV observer architecture, based on the Luemberger observer, is proposed. The simultaneous observer/controller gains synthesis is then reduced to an LMI feasibility problem. The efficacy of our approach is then demonstrated and illustrated through simulations. Full article

Offshore vessels are nowadays equipped with dynamic positioning systems, meaning they have additional thrusters dedicated to the station keeping of the unit. However, there is no rational criterion on the placement of these devices to increment station keeping capabilities. This is true both in case of a vessel retrofitting or for the design of a new unit. The present work proposes investigating a methodology for the optimal placement of thrusters along the hull of an offshore unit. This implies the adoption of a suitable optimisation algorithm capable of handling all the constraints of the optimisation problem. As the target is the optimal capability, the optimisation should handle multiple dynamic positioning capability calculations, meaning (in a quasi-static approach) that it is capable of solving multiple thrust allocation problems at each optimisation step. As thruster allocation is another optimisation problem, the process should handle two nested optimisations. Here, the global location problem is solved with a differential evolution algorithm, while the thrust allocation employs non-linear programming. As the capability calculations imply the adoption of a specific wind–wave correlation, the present work compares the effect of different correlations on the optimised location of the thrusters. The results presented on a reference Pipe Lay Crane Vessel highlight the differences in the final optimum as a function of the environmental modelling. Full article

In response to the issues of premature convergence and insufficient parameter control in Particle Swarm Optimization (PSO) for high-dimensional complex optimization problems, this paper proposes a Multi-Strategy Topological Particle Swarm Optimization algorithm (MSTPSO). The method builds upon a reinforcement learning-driven topological switching framework, where Q-learning dynamically selects among fully informed topology, small-world topology, and exemplar-set topology to achieve an adaptive balance between global exploration and local exploitation. Furthermore, the algorithm integrates differential evolution perturbations and a global optimal restart strategy based on stagnation detection, together with a dual-layer experience replay mechanism to enhance population diversity at multiple levels and strengthen the ability to escape local optima. Experimental results on 29 CEC2017 benchmark functions, compared against various PSO variants and other advanced evolutionary algorithms, show that MSTPSO achieves superior fitness performance and exhibits stronger stability on high-dimensional and complex functions. Ablation studies further validate the critical contribution of the Q-learning-based multi-topology control and stagnation detection mechanisms to performance improvement. Overall, MSTPSO demonstrates significant advantages in convergence accuracy and global search capability. Full article

This study proposes a semi-analytic harmonic modeling method that significantly improves the accuracy and efficiency of complex magnetic field modeling by integrating numerical and analytical approaches. Compared to traditional methods such as the equivalent charge method and finite element method, this approach optimizes the distribution of surface and body charges in the magnetic dipole model and introduces a finite and variable permeability model to accommodate material non-uniformity. Through harmonic expansion and analytical optimization, the method more accurately reflects the characteristics of real magnets, providing an efficient and precise solution for complex magnetic field problems, particularly in the design of high-performance magnets such as Halbach arrays. In this study, the effectiveness of the new modeling method is verified through the combination of simulation and experiment: the magnetic field distribution of the new Halbach array is accurately simulated, and the applicability of the model in the description of complex magnetic fields is analyzed. The dynamic response ability of the optimized model is verified by modeling and simulating the variation of the permeability under actual conditions. The distribution of scalar potential energy with permeability was simulated to evaluate the adaptability of the model to the real physical field. Through the comparative analysis of simulation and experimental results, the advantages of the new method in modeling accuracy and efficiency are clearly pointed out, and the effectiveness of the semi-analytic harmonic modeling method and its wide application potential in the design of new magnetic fields are proved. In this study, a semi-analytic harmonic modeling method is proposed by combining numerical and analytical methods, which breaks through the efficiency bottleneck of traditional modeling methods, and achieves the unity of high precision and high efficiency in the magnetic field modeling of the new Halbach array, providing a new solution for the study of complex magnetic field problems. Full article