Search Results (2,930)

Sweeping jet actuators (SJAs) are promising for active flow control in aerospace systems, but integrating actuator-resolved unsteady CFD into full-configuration simulations is often impractical due to small geometric scales and $\mathcal{O}({10}^2)$ Hz oscillations that demand fine grids and small time steps. This work develops a reduced-order modeling (ROM) framework to generate time-resolved boundary conditions at the actuator exit from SJA flow data. Dynamic mode decomposition (DMD) is particularly attractive for this purpose because it provides a linear, data-driven input–output representation of the actuator effect, even though it does not explicitly model the underlying nonlinear switching mechanism. We introduce an eigenvalue-sorted dynamic mode decomposition (ES-DMD) method that performs stability-aware mode ranking based on the discrete-time DMD eigenvalues, prioritizing modes with $\Re (\lambda )$ closest to unity to retain near-neutrally stable oscillatory dynamics, improving robustness relative to conventional amplitude-based selections for high-frequency oscillatory flows. The method is evaluated across multiple operating conditions, with detailed analysis performed for the highest mass-flow case ($\dot{m}=0.01$ lb/s), representing the most dynamically demanding condition considered. Across multiple operating conditions, ES-DMD yields consistent reconstructions of the dominant switching dynamics. For one-dimensional exit-plane profiles, combining ES-DMD with time-delay embedding enables accurate reconstruction and multi-period prediction using only 20 modes (7.6% of the full system rank). The proposed approach provides a practical pathway to incorporate unsteady SJA effects into large-scale aerospace CFD through compact, predictive boundary-condition models. Full article

The COVID-19 pandemic exposed significant vulnerabilities in public-sector administrative capacity, particularly in the implementation of crisis-related state aid schemes. Under conditions of extreme workload, time pressure, and legal constraints, administrative processes became critical determinants of policy effectiveness rather than routine implementation mechanisms. This study examines how such processes perform under crisis conditions and whether process modeling and simulation can identify efficiency gains without undermining procedural control. Using a case study of a COVID-19 state aid scheme administered by the Ministry of Transport of the Slovak Republic, the study combines Business Process Model and Notation (BPMN)-based process modeling, discrete-event simulation, and Monte Carlo analysis, and can identify efficiency gains in crisis-related state aid administration. The methodological approach integrates BPMN-based process modeling, discrete-event simulation, and scenario-based (“what-if”) sensitivity analysis to evaluate process performance under crisis-induced demand surges. Key performance indicators, including processing time, labor costs, and resource utilization, are analyzed using simulation outputs and dashboard-based visualization. Data analysis is conducted through simulation-based evaluation of key performance indicators, including processing time, labor costs, queue length, and resource utilization, under both baseline (AS-IS) and redesigned (TO-BE) process configurations. Scenario-based (“what-if”) and sensitivity analyses are applied to assess the effects of crisis-induced demand surges and capacity constraints on administrative performance. The results show that increased application volume during the crisis led to disproportionate growth in processing times due to queue accumulation and resource contention. Simulation-based process redesign reduced the average process cycle time by up to 12.8% and labor costs per application by up to 8.4% compared to the AS-IS configuration. However, efficiency gains diminished as resource utilization approached capacity limits, indicating structural constraints inherent to public administration. These findings demonstrate that process-oriented simulation provides a robust analytical tool for understanding administrative behavior under crisis conditions and for designing more efficient and resilient state aid mechanisms. The study contributes to public administration research by offering a micro-level, process-based perspective on crisis governance that complements the existing macro-level policy evaluations. Full article

This paper addresses the question of how many autonomous aerial vehicles (UAVs or drones) can safely operate within a bounded three-dimensional airspace. First, we derive the absolute mathematical limits on drone density using geometric arguments from sphere packing and covering theory. Then, we verify these limits empirically by simulating a swarm controlled via model predictive control. We incrementally increase the number of drones until motion becomes impossible. Each drone is modeled as a double-integrator system with a bounded speed and acceleration and is surrounded by a radius spherical safety zone $r>0$. The drones are controlled via model predictive control with hard separation constraints. We formalize complete blockage as the loss of any feasible non-trivial trajectory set, either due to geometric crowding or dynamic limitations. Using tools from discrete geometry, we establish absolute upper bounds on a safe population via sphere-packing results and sufficient conditions for total immobilization via sphere-covering arguments. We extend these static bounds by incorporating dynamics through stopping-distance analysis, leading to an inflated exclusion radius that captures the effect of finite control authority. In addition, we prove min-cut style flow-capacity bounds that limit feasible throughput across bottlenecks and derive horizon-dependent conflict-graph conditions that capture MPC infeasibility at high densities. These results provide a rigorous theoretical framework for determining the transition from feasible multi-drone operation to inevitable gridlock, offering explicit quantitative thresholds that can inform airspace design, drone density regulation, and the tuning of predictive controllers. We evaluate our theoretical findings with a simulation environment. Full article

Topology optimization using density-based approaches often requires high-resolution meshes to achieve reliable compliance evaluation and robustness against mesh dependency. However, increasing the problem sizes—especially in 3D—results in prohibitively expensive computation times. Coarse-mesh approaches significantly accelerate runtimes; however, they also introduce discretization errors that can guide the optimizer towards incorrect topology families if left unregulated. To address this issue, a multifidelity framework with acceptance control was developed that enables runtime verification and explicitly manages the optimizer state. The main idea is to use coarse discretizations to generate new design proposals and transfer candidate designs to fine discretizations at periodic intervals for verification. Proposals are then accepted or rejected using a best-referenced criterion; if verification fails, the optimizer reverts to the best verified state. The proposed framework balances fine-discretization accountability with coarse-discretization efficiency through configurable verification schedules and a cleanup phase. The framework is evaluated on standard 2D and 3D structural benchmark problems with deterministic load perturbations, and performance is assessed in terms of final verified compliance, wall-clock runtime, acceptance rate, and gray fraction. Full article

This paper addresses the transient voltage stability problem in power systems with high penetration of renewable energy by proposing a hierarchical risk-warning method that integrates Koopman-theory-based transient voltage trajectory prediction and stability margin quantification. First, an online Koopman-theory-based transient voltage trajectory prediction model is constructed through the adaptive optimization of basis functions, a dynamic operator update mechanism, and multistage error correction, significantly enhancing prediction accuracy and generalization capability. Second, a piecewise-weighted quantitative index for transient voltage stability margins is proposed, achieving refined stability assessments ranging from individual nodes to the entire system. Finally, a risk-mapping function based on utility theory is established to convert continuous margin indices to discrete risk levels, forming a complete hierarchical warning system for the transient voltage risk. Simulation results demonstrate that the proposed method achieves precise voltage trajectory prediction and stable-state judgment across various scenarios, effectively identifies critical system weaknesses, and provides reliable technical support for the safety prevention and control of the power system’s transient voltage. Full article

In our longitudinal study, we examined self-reported or explicit basic emotions, i.e., happiness, sadness, anxiety, and anger, in depressed patients during acute illness and inpatient treatment. For exploratory purposes, we also assessed implicit emotions. We analyzed how changes in emotional clarity relate to changes in emotions and depressive symptoms. A sample of depressed inpatients (n = 52) was examined at admission and on average after seven weeks of multimodal psychiatric treatment. A healthy control group (n = 52) was tested at the same time interval. Basic emotions were measured via the Differential Emotions Scale and a discrete-emotions variant of the Implicit Positive and Negative Affect Test. Emotional clarity was measured with the WEFG scales. Patients reported lower explicit happiness and heightened explicit sadness, anxiety, and anger compared to healthy controls, regardless of time of measurement. Across groups and time points, implicit happiness was greater than implicit sadness, anxiety, and anger, with no group differences. Patients’ emotional clarity improved and correlated with improvements in depressive symptoms, explicit happiness, sadness, and implicit anger. In summary, depressed patients experience heightened anxiety and anger, suggesting broader alterations of negative emotions beyond sadness. Increased emotional clarity during treatment was found to be correlated with changes in explicit and implicit affectivity. Full article

The convergence of Lean manufacturing and Industry 4.0 requires digital infrastructures capable of transforming high-frequency telemetry into actionable insights. However, architectures that integrate near real-time data with closed-loop process control remain scarce, particularly in the food-processing industry. This study proposes a “Lean 4.0” framework based on a six-layer Digital Twin (DT) architecture to digitise waste detection and optimise a medium-scale bakery. The methodology integrates a heterogeneous Industrial Internet of Things (IIoT) network comprising 17 ESP32 (Espressif Systems, Shanghai, China)-based monitoring nodes. Data collection is managed via an edge-centric MQTT–InfluxDB (version 2.7, InfluxData, San Francisco, CA, USA) data pipeline. Furthermore, the analytics layer employs discrete-event simulation in Siemens Plant Simulation (version 2302, Siemens Digital Industries Software, Plano, TX, USA), constraint programming with Google OR-Tools (version 9.8, Google LLC, Mountain View, CA, USA), and machine learning models (Isolation Forest and SARIMA). Multi-month validation in a brownfield bakery, including a 60-day continuous monitoring test, demonstrated that the proposed architecture reduced production cycle time by 24.4% and inter-operational waiting time by 51.2%. Moreover, manual planning time decreased by 87.4% through the use of low-code scheduling interfaces. In addition, state-based control of critical ovens reduced energy consumption by 23.06%. These findings indicate that combining deterministic simulation and combinatorial optimisation with data-driven analytics provides a scalable blueprint for implementing cyber-physical systems in food-processing SMEs. This approach effectively bridges the gap between traditional Lean principles and data-driven smart manufacturing. Full article

The rapid growth of remote sensing data offers unprecedented opportunities for global environmental monitoring and resource assessment, yet poses significant challenges for efficient selection of large-scale image datasets. Traditional conditional retrieval methods often return extensive sets with substantial spatial redundancy, imposing heavy selection burdens on users. Existing automated selection methods struggle to balance coverage accuracy, redundancy control, and computational efficiency in large-scale scenarios, making efficient and accurate image selection a critical challenge for large-scale applications. To address this, we propose SCOPES (Spatially-Constrained Optimization for Efficient Image Selection), a novel spatial constraint optimization framework. SCOPES operates directly on actual image footprints in continuous space, thereby circumventing the limitations of traditional discretization-based modeling. We design a unit area cost function aimed at balancing image quality with spatial contribution. To ensure computational efficiency and solution optimization, SCOPES adopts a three-stage “preliminary selection-structural optimization-supplementary selection” strategy: employing lazy greedy for efficient initial selection, spatial Boolean overlay for redundancy control, and supplementary selection for coverage gap repair. Experiments conducted in four regions of different scales demonstrate that compared to baseline methods, SCOPES minimizes the number of selected images and maximizes coverage while achieving a near-universally minimal redundancy ratio. Meanwhile, the introduction of the lazy greedy algorithm significantly improves computational efficiency, achieving up to a 229-fold speedup in the large-scale East Asia region. Overall, SCOPES provides an efficient, accurate, and scalable solution for remote sensing data selection, substantially reducing the manual selection workload for platform users. Full article

To improve the safety of planned paths and the accuracy of tracking control for intelligent tracked vehicles, this paper investigates the application of a CILQR-based motion-planning and tracking-control framework to intelligent tracked vehicles. Firstly, based on an improved discrete-point quadratic smoothing algorithm and the adapted CILQR, collision-free multi-objective optimal path generation in dynamic environment is achieved. Secondly, based on the discretization error model of the intelligent tracked vehicle, an LQR-MPC hybrid control method is proposed based on switching strategy. Finally, an experimental platform is formed, and real-vehicle tests are carried out. Experimental results demonstrate the efficiency and accuracy of the proposed framework. The adapted CILQR algorithm significantly reduces computation time to approximately 1.5 ms per iteration, ensuring real-time performance. Furthermore, field tests confirm that the hierarchical LQR-MPC controller achieves robust tracking with an average lateral error of only 5.7 cm at a speed of 0.5 m/s, effectively validating the system’s capability in obstacle avoidance and precise trajectory tracking. Full article

Understanding how gate-opening variations affect the upstream water level is essential for quantitative water allocation and automation in irrigation canals. Using an indoor recirculating rectangular open-channel facility equipped with a standard flat sluice gate, we deployed five upstream water-level gauges (Points 1#D–5#H) and conducted step response tests and pseudo-random binary sequence (PRBS) tests under four representative operating conditions (Q ≈ 30–85 m³/h). For step tests, the upstream water-level dynamics were well approximated by a first-order plus dead-time (FOPDT) model. Under low flow (Condition A, Q ≈ 29.5 m³/h) with a 1.5 → 2.0 cm opening step, the identified parameters were K ≈ −15.4 mm/mm, L ≈ 4.5–5.7 s, and T ≈ 71 s, and the five points exhibited strong spatial consistency. Under higher flow (Condition B, Q ≈ 72.5 m³/h) with a 3.0 → 3.5 cm step, the gain magnitude decreased (K ≈ −10.6 mm/mm), the dead time increased moderately (L ≈ 8.0–10.3 s), and the time constant became smaller (T ≈ 41–43 s), indicating a faster response but weaker sensitivity to gate-opening changes. For PRBS tests, a discrete-time ARX (2,2,1) model was identified between gate opening and the upstream level deviation at Point 3#F. The identified ARX models achieved R² of 0.992 (Condition C) and 0.946 (Condition D), with MAE and RMSE within 0.65–1.85 mm, and residual diagnostics supported the adequacy of the selected model structure. Finally, steady-state gains derived from dynamic identification were consistent with static water-level–flow–opening relations obtained from quasi-steady experiments, providing a physical basis for the models. The proposed simplified models offer a unified and engineering-friendly plant description for designing and comparing controllers such as PID, fuzzy control, and reinforcement learning-based approaches. Full article

The growing penetration of Distributed Energy Resources (DERs)—such as photovoltaic generation, battery energy storage, electric vehicles, hydrogen technologies and flexible loads—requires advanced Energy Management Systems (EMS) capable of coordinating their operation and leveraging controllability to optimize microgrid performance and enable flexibility provision to the grid. When the physical, electrical, and economic system model is properly defined, the main sources of performance degradation typically arise from forecast uncertainty and temporal discretization effects, which propagate into sub-optimal schedules and infeasible setpoints. This paper proposes and tests a two-stage deterministic EMS architecture featuring rolling-horizon planning at an upper layer and fast local setpoint adaptation at a lower layer, jointly to reduce the impact of forecast errors and other uncertainties on the objective function. The first stage can be deployed either on the edge or in the cloud, depending on computational requirements, whereas the second stage is executed locally, close to the physical assets, to ensure timely corrective action. In the simulated cloud-executed planning case, moving from hourly to 15 min granularity improves the objective value from −49.39€ to −72.12€, corresponding to an approximate 46% reduction in operating cost. In our case study, the proposed second-stage local adaptation can reduce the mean absolute error (MAE) of the EMS performance loss by approximately 50% compared with applying the first-stage schedule without local correction. Results show that this two-stage hierarchical EMS effectively limits objective-function degradation while preserving operational efficiency and robustness. Full article

In this paper, frequency control of shipboard microgrids is achieved in the presence of measurement noise, dynamic uncertainty, and actuator faults. Measurement noise arises from incorrect signal processing, electromagnetic interference, converter switching dynamics, mechanical vibrations from propulsion and generators, and transients caused by sudden changes in load or generation. Actuator faults are caused by intense mechanical vibrations, temperature-induced stress, degradation of power electronic devices, communication latency, and wear or saturation in fuel injection and governor components. To regulate the frequency deviation under these challenges, a cross-entropy-based fault-tolerant model predictive control method, utilizing a discrete-time linear Kalman filter, is developed. Firstly, the discrete-time linear Kalman filter ensures that uncertain states of the shipboard microgrids are measurable in a noisy environment. Afterward, the model predictive control scheme is employed to obtain an optimal control input based on the measurable states. This controller ensures the frequency regulation of shipboard microgrids in the presence of measurement noise. Furthermore, a fault-tolerant control technique that utilizes the concept of cross-entropy is extended to provide a robust controller that verifies the frequency regulation of shipboard microgrids with actuator faults. To demonstrate the stability of the closed-loop system of the shipboard microgrids based on the proposed controller, considering the effects of measurement noise, state uncertainty, and actuator faults, the Lyapunov stability concept is employed. Finally, simulation results in MATLAB/Simulink R2025b are provided to show that the proposed control method for frequency regulation in renewable shipboard microgrids is both effective and practicable. Full article

Timing skew is a critical bottleneck in high-speed Time-Interleaved (TI) Analog-to-Digital Converters (ADCs) that severely degrades dynamic range. This paper presents a mathematically rigorous, data-driven synchronization framework for calibrating effective sampling timing in TI-ADCs based on the Kuramoto oscillator model. Conventional clock-alignment methods often fail to capture signal-path mismatches, such as sampling switch aperture delay, while correlation-based techniques suffer from signal-dependent “blind-spot” regions. Overcoming this fundamental limitation without analog complexity is achieved via a fully digital feedback loop where each sub-ADC channel is modeled as a coupled oscillator following discrete-time Kuramoto dynamics. Unlike traditional approaches that rely on auxiliary analog phase detectors, the proposed scheme utilizes the ADC outputs to estimate and correct the effective sampling instants directly. A Lyapunov-based stability analysis proves that global phase synchronization is guaranteed when the adaptive coupling strength exceeds a critical value $K_c$. Theoretical results show that the system ensures exponential convergence of phase alignment, driving the total inter-channel timing error toward zero without relying on input-signal statistics. Behavioral MATLAB R2025a simulations of a 12-bit, 4-channel, 10 GS/s TI ADC confirm the analytical predictions. The proposed Kuramoto-based calibration achieves a residual skew reduction of over 99% and an SFDR improvement of 55.12 dB compared to correlation-based methods, even at blind-spot input frequencies, while adaptively reducing digital control power through dynamic coupling adjustment. The study demonstrates that data-driven, synchronization-based calibration provides an input-independent, energy-efficient, and mathematically verifiable solution for system-level timing correction in TI ADCs. Full article

Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders where early detection is vital. However, the need for long-term monitoring is incompatible with data-scarce settings, and methods trained on one subject often fail on another due to cross-subject variability. To address these limitations, this study proposes a cross-subject, single-channel electroencephalography (EEG)-based method that uses Multi-Entropy Feature Concatenation (MEFC) to classify AD and FTD. First, single-channel EEG is processed through the Discrete Wavelet Transform (DWT) to extract five rhythms: delta, theta, alpha, beta, and gamma. Subsequently, Permutation Entropy (PE), Singular Spectrum Entropy (SSE), and Sample Entropy (SE) are calculated for each rhythm and concatenated to form a combined MEFC to characterize the non-linear dynamic properties of EEG. Lastly, Dynamic Time Warping (DTW), Pearson Correlation Coefficient (PCC), Wavelet Coherence (WC), and Hilbert Transform Correlation (HTC) are employed to measure the similarity between unknown rhythmic MEFC and those from AD, FTD, and Healthy Control (HC) groups, performing a data-driven classification via similarity measurement. Experimental results on 88 subjects in the AHEPA dataset demonstrate that the beta-rhythm with PCC yields a three-class accuracy of 76.14% using single-channel FP2. In another dataset, the Florida-Based dataset, involving 48 subjects, theta-rhythm with WC achieves a two-class accuracy of 83.33% using FP2. Furthermore, a MATLAB R2023b-based toolbox is developed using the proposed method. Such outcomes are impressive, given the limited data per individual (data-efficient), reliable performance across new subjects (cross-subject), and compatibility with wearable devices (single-channel), providing a novel entropy-based approach for EEG-based applications in biomedical engineering. Full article

In complex, semi-structured orchard environments, mobile inspection robots often suffer from excessive turning points, low search efficiency, limited trajectory-tracking accuracy, and poor adaptability to dynamic obstacles. To address these issues, this study proposes an integrated autonomous navigation method that employs an improved A* algorithm for global path planning, a Fuzzy-Weighted Dynamic Window Approach (FW-DWA) for local path optimization, and a model predictive control (MPC)-based trajectory-tracking controller. First, a dynamic heuristic-weight adjustment strategy is introduced into the conventional A* algorithm, in which a correction factor adaptively tunes the heuristic weight; a two-stage node optimization procedure then removes hazardous and redundant nodes to improve path smoothness and safety. Second, the FW-DWA, grounded in fuzzy control theory, uses goal distance and obstacle distance to update the weights of the heading, clearance, and velocity evaluation functions in real time, thereby enhancing obstacle avoidance in dynamic environments. Finally, a discrete kinematic model is established to design the MPC Controller, which achieves high-precision tracking through receding-horizon optimization and feedback correction. Experiments conducted in real orchards demonstrate that the proposed method reduces path length by 5.79%, shortens planning time by 3.64%, and increases the minimum safety distance by 50%. Comparative results further show that the MPC Controller attains a mean position error of 0.032 m and a mean heading error of 3.14°, clearly outperforming a conventional Proportional–Integral–Derivative (PID) controller. These findings provide an effective solution for reliable autonomous navigation of orchard inspection robots and offer a valuable reference for smart agricultural robotics applications. Full article