Search Results (258)

Long-term station-keeping of stratospheric airships is challenged by strong time-varying wind fields, pronounced vertical stratification of wind speed and direction, and limited onboard energy. Existing reinforcement-learning approaches typically rely on instantaneous observations to make reactive decisions and therefore struggle to deliver foresighted control in dynamic environments. This paper proposes a Spatio-Temporal Foresight Reinforcement-Learning framework (STF-RL) that explicitly incorporates future wind information. A Transformer is introduced to model multi-step, multi-altitude forecast wind sequences, and a time–height dual positional encoding is designed to characterize both the temporal evolution and the vertical structure of the wind field. A task-conditioned attention pooling mechanism then extracts the future-wind features most relevant to the current state, which are concatenated with the airship state and fed into an actor–critic network to enable foresighted policy learning. A continuous action space supporting three-dimensional maneuvering is constructed, together with a multi-objective reward that jointly accounts for station-keeping performance, energy consumption and safety. Experimental results show that the proposed method outperforms baseline approaches in station-keeping performance, trajectory stability and energy-utilization efficiency, while exhibiting strong robustness across different wind-field conditions. Full article

With the large-scale integration of electric vehicles (EVs), charging demand exhibits significant spatiotemporal variability, further intensified by climatic factors, which makes it difficult for existing uncertainty models to capture underlying dependency structures. To address this issue, this paper proposes a Copula–Wasserstein-based distributionally robust optimization (C-WDRO) framework for the coordinated operation of distribution networks and charging stations. A climate-sensitive physical mapping model of electric vehicle energy consumption is first developed to establish a coupled climate–energy–load mechanism. Copula functions are then used to characterize dependencies among temperature, precipitation, and charging demand, and are incorporated into a bi-level optimization formulation. The model is solved using Karush–Kuhn–Tucker (KKT) conditions and a column-and-constraint generation (C&CG) algorithm. Case studies on the IEEE 33-bus system show that the proposed method reduces total operating cost by 4.26% compared with robust optimization (RO), while maintaining economic efficiency, and reduces the load shedding rate by 0.14 percentage points compared with Wasserstein distributionally robust optimization (WDRO), while keeping voltage security. These results demonstrate that explicitly modeling dependency structures can enhance operational efficiency and support more sustainable and reliable power–transportation system operation under uncertainty. Full article

Shipboard helicopter landing in the near-deck region requires stable attitude regulation and high-precision deck-relative motion control under substantial model uncertainty and environmental disturbances, conditions under which conventional model-based controllers may lose performance or become overly conservative. This paper proposes a task-oriented, learning-enhanced control algorithm for ship-relative near-deck station keeping and landing by integrating a model-based baseline controller with residual reinforcement learning in a deck-relative closed-loop framework. The algorithmic contribution is the deck-relative baseline–residual control architecture: a split-channel incremental nonlinear dynamic inversion (INDI) outer loop and a reduced-order dynamic inversion (DI) inner loop provide the nominal baseline pathway, while a bounded residual Proximal Policy Optimization (PPO) policy supplies compensation in the same physical outer-loop command channels to suppress unmodeled nonlinearities and time-varying disturbances. Simulation results show that Residual PPO improves hover robustness and landing performance relative to the baseline controller and Pure PPO. With approximately 20–30% residual authority, it achieved 90.0% Desired landing rates in both tested descent-and-landing scenes. Full article

Sustainable urban logistics requires infrastructure that can support routine low-carbon freight delivery while maintaining emergency supply capacity under disruptions. However, existing underground logistics system studies mainly focus on routine freight efficiency and network feasibility, whereas emergency logistics research is largely based on surface transport systems. Limited attention has been paid to the integrated design and operational validation of dual-use underground logistics networks under uncertain routine and emergency demand. To address this gap, this study proposes a dual-use underground logistics system (DULS) framework that combines robust layout optimization with dynamic simulation. A multi-echelon network consisting of supply centers, primary nodes, secondary nodes, and demand points is constructed. Candidate primary nodes are screened using an entropy-weighted TOPSIS method, and a Wasserstein-based distributionally robust optimization model is formulated to jointly determine node location, resource allocation, and freight paths under demand uncertainty. A hybrid heuristic is developed to solve the model, and an AnyLogic-based discrete-event simulation model is used to evaluate operational performance under different demand-generation patterns and train operation strategies. In the Nanjing case, the optimized DULS includes 19 primary nodes and 72 secondary nodes, achieves an emergency-demand fulfillment rate of 84.84%, and keeps the average end-to-end emergency supply time within 4 h. Cross-station operation performs better than the all-stop mode in both transport time and deprivation cost. An ex-post operational emission comparison further indicates that the DULS can reduce road-based freight emissions by 60.20% under routine operations. The proposed framework provides methodological support for planning sustainable dual-use underground logistics infrastructure serving both routine freight delivery and emergency supply. Full article

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller. Full article

This study presents a hydrodynamic assessment of a toroidal wave energy converter (WEC) operating under low-energy conditions of the west coast of Mexico. Performance analysis incorporates the coupling surge, heave, and pitch motions. To investigate mooring–device interaction, two mooring configurations were examined: (A) a single catenary system and (B) a catenary system with a surface-floating buoy. The WEC was evaluated under operational conditions, operational conditions with a constant surface current, and extreme seas. The results show that under operational conditions, the WEC-mooring B configuration achieves higher energy capture than the WEC-mooring A configuration, with performance peaks at 13 s and 11 s, respectively. The presence of a surface current does not significantly influence absorbed power. Under extreme conditions, mooring B reduces mooring-line stresses but causes greater horizontal foundation forces and increased floater drift compared to mooring A. When mooring effects are included, mooring A’s performance is advantageous because it shifts peak energy capture toward the dominant sea states at the study site. This maintains better station-keeping capability and achieves a maximum capture width ratio (CWR) of approximately 0.5. Full article

Seismic–volcanic monitoring networks often operate in remote areas over heterogeneous links (e.g., microwave radio and cellular). During event-driven seismic episodes, sustained multi-station waveform streams can stress both last-mile connectivity and data acquisition systems, yielding discontinuities in center-side archives even when stations keep recording locally. This paper presents the Python Recovery Tool (PRT), a lightweight command-line artifact that retrieves buffered waveform files after reconnection and rebuilds daily archives that can be ingested by the monitoring center without hardware upgrades. PRT detects archive gaps from daily (Julian day) file partitions and embedded timestamps, and reduces recovery traffic by selectively fetching only the files needed to backfill missing intervals. We evaluated PRT on five event-driven recovery cases using operational file-based evidence from station and center listings complemented with a simple bandwidth-based recovery-time model. Across the cases, PRT restored archive continuity while reducing download volume by 4.43–93.75% relative to naive bulk retrieval, with modeled catch-up times ranging from 0.79 to 207.59 min, depending on station-side packaging granularity and bottleneck link capacity. These results support a practical retrofit path to improve archive completeness under constrained links and heterogeneous deployments. Full article

This paper has the goal of exploring the potential of electromagnetic propulsion systems based on tethers to create artificial Sun-synchronous orbits for remote sensing satellites, as well as performing station-keeping maneuvers and de-orbiting of the satellite after the end of its useful life. To create artificial Sun-synchronous orbits, the force is applied to keep the longitude of the ascending node with the same angular velocity of the apparent motion of the Sun around the Earth, which is the definition of a Sun-synchronous orbit. These orbits are very important for remote sensing satellites, because in these orbits the satellite passes by a given point at the same time, helping in analyzing the data collected. The use of electrodynamic tethers can extend the regions of Sun-synchronous orbits, both in terms of inclination and semi-major axis. To perform the de-orbiting of the satellite, the same tether can apply a force in the opposite direction of the motion of the satellite, so reducing its energy and decreasing the semi-major axis until the satellite crashes into the atmosphere of the Earth. This is very important to avoid increasing the presence of space debris in space, a very serious problem nowadays. For the station-keeping maneuvers, we just need to use the appropriate control laws, from time to time, to correct any errors in the Keplerian elements. A significant advantage of employing an electrodynamic tether over traditional thrusters is that it does not require consumption of fuel. The study assumes that a current can flow in both directions through the tether, so interacting with the magnetic field of the Earth to create the Lorentz force. The possibility of using electrodynamic tethers with autonomous charge generation, to avoid dependence on plasma densities and other external factors, is considered. The results presented here help in space and planetary science, since they give more options for remote sensing satellites, which are a key element in planetary science. Full article

Modern-day urban warehouses face exploding large inventory and tight spaces requiring fast, accurate, and safe stocktaking in a narrow aisle in a GPS-denied environment. This paper proposes a complete UAV-enabled framework performing real-time QR code scanning with inventory synchronization through a safety-aware trajectory generation for obtaining collision-free motion. A novel hybrid workflow integrating MATLAB/Simulink R2024b and Unreal Engine is used for dynamics and photorealistic rendering, alongside a real-time warehouse setup using drone cameras and 3D LiDAR coupled with a ground control station and live dashboard. The system in this paper was evaluated by testing with single and multi-UAV models across high-fidelity simulations and experiments. Results demonstrate simulated QR accuracy of approximately 95 to 96%, with experimental validation achieving between 86 and 90.5% due to real-world environmental factors. In experimental and simulation analysis, mean end-to-end latency remained under half a second, trajectory error range between 8 and 10 cm, and safety margins were consistently maintained throughout the test. It was further observed that multi-UAV coordination halved mission time compared to single-drone tests while keeping duplicate reads negligible, indicating a scalable and safe pipeline for industry application. Full article

In the framework of Harmony, the 10th ESA Earth Explorer mission, this paper presents a general methodology to optimize the formation parameters relevant to the single-pass, cross-track interferometry (XTI) configuration. The proposed method considers the requested height sensitivity and the maximum allowable temporal lag and derives the formation parameters for an optimal coverage over different ranges of latitudes by leveraging the relative eccentricity and inclination vector formalism. Our approach addresses the problem of interferometric coherence through the wavenumber support alignment method which is able to take into account the specific geometry of XTI in Harmony, which is a long-baseline multistatic configuration with large squint angles. The analysis is completed by an estimate of the propellant budget, required to maintain the optimized formation, which can be used as a further trade-off parameter within the mission design process. The results indicate that the passively stable helix configuration (with relative eccentricity and inclination phase angles set to 90°) provides a robust solution at equatorial and mid-latitude regions with perpendicular baselines up to the order of 1 km and temporal lag below 10 ms. Conversely, for high-latitude and polar regions, two alternative strategies are identified, revealing a trade-off between enhanced interferometric performance and increased formation maintenance requirements. For polar regions, a first strategy adopts relative eccentric and phase angles of 10°, achieving satisfactory performance across most latitudes, whereas an alternative approach retains the value of 90° and optimizes the formation specifically for high latitudes. These two options result in distinct station-keeping demands since the former strategy requires a $\Delta \mathrm{V}$ budget about two orders of magnitude higher, while the latter remains within a $\Delta \mathrm{V}$ range that is typical for missions of the considered class. Full article

This paper addresses the multi-phase homing and docking missions of over-actuated autonomous underwater vehicles (AUVs), where switching among forward cruising, reverse braking, and hovering can induce actuator saturation, rate limit violations, and undesirable transients. We propose a unified framework that couples supervisory mode management with mode-driven constrained control allocation solved by a warm-started sequential quadratic programming (SQP) routine. The controllable wrench is modeled by a mode-dependent differentiable map constructed from the actuator models, and the allocator enforces amplitude bounds and per-cycle increment limits while trading off wrench tracking and actuator usage through mode-scheduled weights. To mitigate switching transients, a continuous transition factor is introduced to interpolate the desired wrench and dominant cost weights, and an integrator alignment reset is applied at switching instants to keep the outer-loop proportional–integral–derivative (PID) output continuous. The allocator is further warm-started by projecting the previous solution onto the post-switch constraint box. The framework is integrated into the Mission-Oriented Operating Suite–Interval Programming (MOOS-IvP) autonomy middleware with adaptive line-of-sight (ALOS) guidance and adaptive PID motion control and is validated on the TS-100 AUV in water tank experiments. Comparative results against a PID-only baseline without control allocation and a variant without bumpless switching show reduced roll transients during the reverse-to-hover transition and improved hover-mode depth station keeping while maintaining feasible actuator commands under constraints. Full article

Dynamic positioning (DP) systems are essential to maritime operations, as they ensure precise station keeping. Yet human error remains a major contributor to DP incidents, often interacting with technical failures and environmental conditions. This study proposes an adaptive probabilistic framework to characterise human-error contributions to DP risk and support targeted mitigation. We compare integrated Bayesian network (BN)/fuzzy analytic hierarchy process (AHP) and Bayesian network (BN)/Dempster–Shafer (D-S) theory to model causal relationships, aggregate uncertain expert judgements, and prioritise risk factors. Historical incident narratives, accident reports, and expert elicitation inform the model to analyse failure propagation and quantify factor contributions. In a representative DP case application, insufficient training, operator fatigue, and reduced situational awareness—together with software anomalies and adverse environmental loads—emerge as dominant contributors; BN backward analysis corroborates their diagnostic relevance. The approach yields actionable insights for risk reduction, including tailored training programmes, strengthened safety protocols, and integration of real-time monitoring. It provides an auditable, updateable basis for scenario-based training, software/maintenance assurance, and environment-aware operating envelopes, and is readily extendable as new evidence becomes available. Overall, the framework offers practical value for improving safety, operational continuity, and system resilience in DP operations. Full article

The large-scale adoption of electric vehicles (EVs) improves transport sustainability but creates severe peak-time stress on distribution grids. In PV-assisted charging networks, station operators must jointly decide retail charging prices and energy-storage dispatch under uncertain demand and generation conditions. This paper develops a distributed federated deep reinforcement learning framework for multi-station scheduling, where each station trains a local soft actor–critic (SAC) policy and only model parameters are exchanged with a global aggregator. To better adapt prices to local supply–demand conditions, we introduce a sales-factor-based correction mechanism that links the announced price to demand pressure and storage status. The objective combines station revenue, operating expenses, and user-discomfort-related penalties under operational constraints. Simulation results on a five-station setting show stable convergence and consistent gains over benchmark methods, with profit improvements of 3.90–39.00%. The framework keeps raw operational data local and supports collaborative optimization across stations. Full article

Electric vehicles (EVs) and renewable energy generation are widely regarded as key drivers of low-carbon transformation in the transportation and energy sectors due to their emission reduction potential and environmental benefits. However, the inherent intermittency and volatility of photovoltaic (PV) power, coupled with increasingly stochastic and disorderly EV charging demand, pose significant challenges to grid stability and local renewable energy utilization. To address these issues, this paper proposes a dynamic pricing optimization approach based on a Stackelberg game framework, in which the PV charging station operator acts as the leader and EV users as followers. Unlike conventional models, the proposed framework explicitly incorporates user psychological expectations and response deviations through a three-stage “dead-zone-linear-saturation” responsiveness structure, thereby capturing the uncertainty and partial rationality of EV charging behavior. The upper-level objective seeks to maximize operator profit and enhance PV self-consumption, while the lower-level objective minimizes user energy cost under price-responsive charging decisions. The bilevel optimization problem is solved via a differential evolution (DE) algorithm combined with YALMIP + CPLEX. Simulation results for a regional PV-EV charging station show that the proposed strategy increases PV self-consumption to about 90.5% and shifts the load peak from 18:00–20:00 to 10:00–15:00, effectively aligning charging demand with PV output. Compared with both flat and standard time-of-use (TOU) tariffs, the dynamic pricing scheme yields higher operator profit (about 7% improvement over flat pricing) while keeping total user energy expenditure essentially unchanged. In addition, the cumulative carbon reduction cost over the operating cycle is reduced by approximately 4.1% relative to flat pricing and 1.9% relative to TOU pricing, demonstrating simultaneous economic and environmental benefits of the proposed game-based dynamic pricing framework. Full article