Search Results (6,563)

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Data centers have become essential infrastructure for digital services, while their rapidly growing electricity demand makes coordinated workload and power management an important optimization problem. This paper studies the multi-data-center operation problem under time-of-use electricity pricing and formulates it as a multi-data-center mixed-integer nonlinear programming model (MDC-MINLP). The model jointly represents binary task scheduling decisions, including temporal workload shifting and spatial task migration, and continuous power-side variables, including device-level utilization, IT and auxiliary power consumption, energy storage dynamics, grid power procurement, and quality-of-service constraints. The objective is to minimize the total operating cost by integrating electricity purchasing cost, IT operation loss, storage degradation cost, and migration cost. To solve the resulting large-scale discrete–continuous coupled problem, an Adaptive Large Neighborhood Search algorithm with Hierarchical Collaboration (HC-ALNS) is proposed. HC-ALNS reconstructs feasible task action sets, employs a surrogate objective for fast candidate screening, performs accurate power-layer evaluation for selected solutions, and adaptively adjusts search intensity according to convergence behavior. Numerical results show that HC-ALNS reduces the total operating cost by 3.67% and achieves better convergence and solution quality than NSGA-II and PSO. These findings demonstrate that the proposed MDC-MINLP and HC-ALNS provide an effective mathematical optimization framework for coordinated computation–power scheduling. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

In the context of the “dual-carbon” target, the large-scale integration of renewable energy sources leads to an increased risk of transient voltage instability at the high voltage direct current (HVDC) transmission receiving end. The HVDC transmission system possesses fast and accurate power regulation capability. After a fault occurs near the inverter station, reducing the DC current enables the reactive power from the compensation devices to be released and injected into the receiving-end power grid, thereby providing emergency voltage support for the receiving-end grid. To reduce control costs, an optimization model constrained by transient voltage violation is established, and the DC current modulation is acquired via an online solution. To maintain system stability and meet the requirements of online applications, it is crucial to rapidly solve the optimization model based on the grid operating mode and contingency information to update the emergency control strategy table in the special protection system (SPS). Conventional global orthogonal collocation (GOC) and adaptive orthogonal collocation (AOC)-based solution methods transform the optimization model in the continuous time domain into a nonlinear programming (NLP) problem for solution, which addresses the low efficiency of traditional rolling optimization. However, the GOC- and AOC-based solution methods improve the discretization accuracy of the model by pursuing global uniform densification of collocation points, making it difficult to balance solution accuracy and solution efficiency. To this end, this paper proposes an efficient interval partition dynamic adaptive orthogonal collocation (IP-DAOC)-based solution method. Firstly, the overall optimization time window is interval-partitioned into multiple initial intervals, and an interval-partitioned transient voltage stability emergency control optimization model is established. Furthermore, the interval length and the number of collocation points are dynamically adjusted according to the curvature of interpolation polynomials at collocation points in different intervals. Finally, after interval adjustment, the dynamic equations discretized in adjacent intervals are made continuous by reconstructing the differential matrix. This solution method reduces the total number of collocation points, thereby decreasing the scale of the NLP problem and narrowing the search space, significantly improving solution efficiency while ensuring solution accuracy. To verify the effectiveness of the proposed solution method, simulations are carried out on a modified IEEE 14-bus system. The results are compared with those of the traditional GOC- and AOC-based solution methods, which further demonstrate the superiority of the proposed solution method. Full article

Simultaneous multi-ion detection is important for interpreting leaching, corrosion, hydration, and solidification processes in cement-based materials, because these processes are controlled by coupled ion migration, binding, and precipitation–dissolution reactions. Conventional methods such as pore-solution extraction, ion chromatography, inductively coupled plasma optical emission spectroscopy, and single-ion potentiometric measurements provide useful chemical information, but they generally rely on discrete sampling or isolated ion channels and therefore have limited ability to capture time-aligned multi-ion evolution. In this study, an IoT-based in situ multi-ion detection system was developed by integrating ion-selective electrodes for Cl⁻, Ca²⁺, F⁻, and H⁺ with an ADS1115 analog-to-digital converter, an ESP32 microcontroller, and a voltage amplification module. The system achieved minimum resolvable concentrations of 10⁻⁵ M for Cl⁻ and F⁻ and 10⁻⁴ M for Ca²⁺, while maintaining pH measurement over the range of 2–12. Ten consecutive measurements at 0.01 M showed relative standard deviations below 0.12%, indicating good short-term repeatability under laboratory calibration conditions. Interference and temperature tests showed that Br⁻ and NO₃⁻ affected the chloride channel at high concentrations, Ca²⁺ reduced free F⁻ activity through Ca–F precipitation equilibrium, and the temperature drift of Cl⁻ and F⁻ electrodes changed direction with concentration, whereas the Ca²⁺ response decreased monotonically with increasing temperature. When applied to phosphogypsum–cement hardened pastes, the system captured rapid Ca²⁺ release, low-level F⁻ fluctuation controlled by Ca–F interaction, non-monotonic Cl⁻ release, and alkaline pH evolution on the same time axis. Compared with existing single-ion or offline methods, the proposed system provides synchronized in situ evidence for interpreting coupled ion leaching in cement-based solid-waste systems.: Full article

Blast furnace (BF) ironmaking is a complex multiphase process involving gas–solid flow, heat transfer, chemical reactions, burden movement, and phase transformation under high-temperature conditions. Since many internal states of the blast furnace cannot be directly observed during operation, numerical simulation and mathematical modeling have become important tools for understanding furnace behavior and optimizing operational parameters. This paper reviews recent advances in blast furnace numerical simulation and internal state reconstruction methods. Existing approaches, including packed-bed flow models, cohesive zone reconstruction methods, burden distribution models, and temperature field prediction methods, are summarized and discussed. In addition, the evolution of blast furnace mathematical models from early one-dimensional steady-state formulations to modern three-dimensional multifluid and hybrid simulation approaches is reviewed. Recent developments in computational fluid dynamics (CFD), the discrete element method (DEM), digital twin, and data-driven modeling are also discussed. Compared with traditional simplified models, modern multidimensional and hybrid approaches show improved capability in describing asymmetric furnace inner states, multiphase transport behavior, and operational parameter effects under industrial conditions. However, challenges still remain in achieving computational efficiency, parameter calibration, multiphase coupling, and real-time industrial application. Future studies are expected to focus on the integration of mechanism-based simulation and intelligent data-driven methods to improve prediction accuracy, operational adaptability, and intelligent control capability in blast furnace ironmaking. Full article

As a prominent high-efficiency metal cutting process, plunge milling has found increasing applications in the rough machining of integral impellers. However, challenges arise due to the time-consuming process of avoiding interference-induced overcutting at the blade-root fillet, leading to excessive residual material. Consequently, the full potential of plunge milling’s high-efficiency advantages is constrained. To address these issues, a method to avoid overcutting caused by cutter interference at the blade-root fillet in integral impeller plunge milling is proposed. First, a parameterized model of the blade-root fillet is established using a rolling ball model. Second, a semi-analytical model for identifying cutter interference at the blade-root fillet is established through micro-element discretization. Lastly, the cutter position is adjusted along the direction of the vertical cutter axis vector to avoid overcutting. The modeling error of the blade-root fillet remains within 0.1%, ensuring high accuracy in overcut detection. Furthermore, the identification process is completed in less than 1s, demonstrating its computational efficiency. Compared with the conventional depth-reduction method, the proposed interference elimination strategy reduces the excessive residual material volume by 66% while avoiding overcutting, with only a 26% increase in plunge roughing time. Simulation and experimental validation on an 820 mm diameter impeller confirm the method’s effectiveness in balancing interference avoidance and material removal efficiency. Full article

The electrification of public transportation requires reliable and efficient technologies for energy transfer. Trolleybus systems represent a promising solution, as they combine high energy efficiency with reduced battery requirements. However, a central technical challenge is the precise and automatic positioning of the flexible current collector poles that connect to the overhead line. During positioning through motor actuation, the current collector shoe is caused to oscillate by external disturbances and the movement itself. To reduce oscillations, the current collectors need to be damped actively by respective actuation. This task critically depends on accurate and fast motor speed estimation for real-time control of the actuating motors. Since motor speed is not measured directly in the system, it has to be estimated from the encoder-based motor position, which introduces sensitivity to measurement noise and requires filtering. This work investigates four practical estimation approaches in the context of trolleybus applications. These include discrete-time numerical differentiation combined with FIR and IIR filtering and a modern algebraic differentiation approach. These estimation methods are evaluated under identical experimental conditions and predefined filter specifications focusing on noise suppression and time delay characteristics. The most promising approaches are further validated in closed-loop operation with respect to measurement noise-induced variations in the control input and motor speed tracking accuracy. The results demonstrate that algebraic differentiation achieves a favorable balance between noise suppression, latency, and filter order for the considered current collector system. It therefore provides a suitable basis for real-time deployment in the investigated current collector positioning control and for future active oscillation damping strategies. Full article

Traditional white canes offer a limited perception range, whereas end-to-end visual models face challenges in real-time deployment on edge devices. To address these limitations, this paper proposes a lightweight real-time assistive system that integrates geometric topology reconstruction with state-adaptive warning logic. The system utilizes YOLOv9 to extract discrete semantic primitives of tactile paving. It constructs a dual-branch perception framework based on Median Absolute Deviation and the Minimum Spanning Tree algorithm to analyze the topological structure of tactile paving. For complex intersections characterized by warning indicators, a one-dimensional connectivity clustering algorithm based on longitudinal topology is proposed. It generates accurate macroscopic feasible directional prompts under field-of-view boundary constraints. Additionally, a hierarchical scheduling framework dynamically orchestrates scenario-specific finite state machines to enable continuous dynamic interaction across typical high-risk scenarios. Evaluated on a custom real-world dataset, the system achieves a 95.21% frame-level comprehensive accuracy for straight-path deviation correction and intersection directional prompting. Dynamic temporal stress tests confirm the temporal stability and logical coherence of state transitions. Furthermore, latency evaluations demonstrate the logic layer’s minimal computational overhead, proving its theoretical feasibility for real-time edge deployment. This approach provides an effective, low-latency solution for delivering directional prompts and hazard warnings to visually impaired users. Full article

Efficient task assignment for mobile robots is a crucial challenge in modern intralogistics. This paper presents an integrated cyber-physical framework combining predictive tree search on switching max-plus linear systems with a physical IoT-based dispatch interface. The scheduling problem is modelled as a discrete event system, where standard max-plus algebra captures robot synchronization, and a switching mechanism represents alternative resource assignments. To address real-world operational disturbances, the predictive model is enhanced with a fault-tolerant control (FTC) mechanism that dynamically estimates and adapts to non-stationary transport delays. The resulting decision space, which grows exponentially with the prediction horizon, is explored via a predictive tree search algorithm utilizing a quadratic cost function to penalize excessive and uneven transport times. The physical dispatch layer is realized using KIS.BOX IoT devices acting as operator-controlled stations, communicating with the central controller via a WebSocket/STOMP event stream and a lightweight REST API. Simulation results obtained in a Blender 3D environment demonstrate that the proposed FTC predictive strategy significantly reduces the variance of task completion times under fault conditions compared to a baseline First-In-First-Out approach. Furthermore, the IoT integration successfully simulates and validates the feasibility of human-in-the-loop task injection within a realistic, stochastic scenario. Full article

To address the parameter coordination and system integration issues in nonlinear discrete-time cascade control systems, this paper proposes, for the first time, a novel collaborative PI-P controller design method. First, an augmented state-space model for the PI-P controller is introduced, where the integral term is embedded into the state variables. Then, stability conditions are derived using Lyapunov stability theory, which are formulated as linear matrix inequality (LMI) constraints, enabling the simultaneous design of both primary and secondary controllers. The method is validated through simulations on a steam temperature control system. The results demonstrate that the proposed method outperforms conventional methods, achieving a faster response, reduced overshoot, and enhanced robustness. Moreover, the proposed method shows strong disturbance rejection capability and improved overall dynamic performance, further confirming its effectiveness and potential for application in nonlinear discrete-time cascade control systems. Full article

This paper introduces a random activation framework for cure rate modeling that provides a novel latent mechanistic interpretation of the standard mixture cure model, utilizing a Waring-distributed number of latent causes. The proposed approach represents unobserved heterogeneity through a discrete latent variable interpreted as the number of potential risk factors, providing a flexible and biologically interpretable characterization of individual susceptibility. In contrast to classical competing risks models based on extremal operators or deterministic activation schemes, the event time is assumed to arise from a stochastic selection among latent causes. This random activation mechanism defines a unified probabilistic framework in which the cure fraction emerges naturally as the probability of having zero latent causes. The Waring distribution is adopted to model the latent count structure due to its hierarchical formulation, which accommodates overdispersion and heavy-tailed behavior strictly within the latent parametrization of individual risk factors. Under this framework, while the population survival function mathematically reduces to the classical mixture cure representation, the model provides an alternative structure where covariates directly impact the expected latent burden. Parameter estimation for the identifiable regression structure is performed via maximum likelihood, and the finite-sample performance of the estimators is assessed through Monte Carlo simulations, showing accurate parameter recovery and stable inferential properties. An application to real survival data illustrates the practical relevance and epidemiological interpretability of the proposed framework. Overall, this work extends the understanding of existing cure rate models by integrating latent count structures and stochastic activation within a coherent setting, providing a powerful interpretation tool for heterogeneous survival data with long-term survivors. Full article

Time-fractional diffusion equations (TFDEs) are essential for modeling anomalous transport in heterogeneous media, but high-fidelity long-time simulations face two bottlenecks: the $O\left(N^2\right)$ complexity of non-local fractional derivatives, and the spatial truncation error of polynomial-based finite element methods (FEMs) when resolving oscillatory plumes or singular sources. We propose a framework combining a C-Bézier FEM for spatial approximation with a fast L1 temporal discretization. By coupling the shape parameter of the C-Bézier basis to the mesh size ($\mu =\pi h$), the scheme reproduces trigonometric profiles of the corresponding frequency exactly; for solutions whose spatial part lies in the C-Bézier space this eliminates the spatial truncation error and drives the associated error constant to near zero. A sum-of-exponentials (SOE) approximation reduces the temporal complexity from $O\left(N^2\right)$ to $O\left(N\right)$ and storage to $O\left(1\right)$, enabling scalable 3D simulation. We prove the optimal $O({\tau }^{2-\alpha }+h^{k+1})$ convergence, and numerical experiments confirm these rates. For profiles matched by the basis, the method yields substantially smaller errors than Lagrange FEM; for a general solution outside the C-Bézier space, the two methods share the same order and comparable error magnitudes, so the gains are specific to fields reproduced by the basis. We further examine low-regularity scenarios, including discontinuous interfaces and Dirac-delta injections. Full article

This paper presents a comparative analysis of three workspace discretization strategies, Cartesian, cylindrical and spherical, integrated into a graph-based path planning application developed in Python and connected to RoboDK. The study starts from the observation that the workspace of an articulated industrial robot is not naturally aligned with a uniform Cartesian partitioning, and this aspect can influence the internal structure of the graph and the planning effort. For the initial analysis, the three discretizations were tested for the same start-goal pair and for resolutions ranging from 1500 mm to 600 mm. All three variants led to the same validated route, with a length of 3292.215 mm, which shows that the main differences did not occur at the level of the final geometric solution, but at the level of the internal structure of the graph. On average, the spherical discretization generated the most compact graph, with 101.7 nodes and 256.4 edges, compared to 277.3 nodes and 724.9 edges for the Cartesian discretization. The average planning time was also shorter for the spherical discretization, 0.0069 s, compared to 0.0150 s for the Cartesian discretization and 0.0127 s for the cylindrical discretization. At the 600 mm resolution, the spherical discretization used approximately 63% fewer nodes and 66% fewer edges than the Cartesian discretization, while retaining a larger number of candidate routes. The evaluation was then extended by 180 additional trials, performed on two scenarios and on several start-goal pairs. Of these, 151 led to valid routes, corresponding to an overall success rate of 83.9%. The results show that the spatial representation influences the graph size, connectivity, planning time and length of validated routes. However, additional tests also show that these effects depend on the scenario and the criterion analyzed. The spherical discretization produced the most compact graphs, but did not lead in all cases to the shortest routes or the highest success rate. Therefore, the contribution of the paper consists in a controlled comparative evaluation of the influence of the spatial representation on a graph-based planning pipeline, not in demonstrating the universal superiority of a single discretization. Full article

Variable-amplitude vibrating screens are widely adopted for screening frozen sea buckthorn berry particles. Investigating their motion characteristics and particle behaviors on the screen surface is essential for optimizing the screening process and improving equipment performance and screening efficiency. In this work, a variable-amplitude vibrating screen is taken as the research subject. Its structural composition and working principle are elaborated, and kinematic simulations are conducted via RecurDyn. The results reveal that the vertical amplitude and velocity of the screen surface increase gradually from the feed end to the discharge end, which facilitates rapid particle penetration. Meanwhile, the horizontal velocity remains stable across all sections of the screen. Specifically, crank length governs the screen amplitude, while crank rotational speed determines the vibration frequency. A dynamic model of particles and the screen surface is established by combining EDEM 2024 and RecurDyn V9R4, and two-way coupling of the discrete element model is realized. Coupled simulation results indicate that the dynamic screening efficiency rises with increasing crank length and rotational speed, reaching the maximum at a crank length of 20 mm and a rotational speed of 208 r/min. Crank parameters exert remarkable effects on the thickness of the particle layer and the quantity of penetrated particles: a thicker particle layer leads to a longer residence time of materials on the screen. Field tests are carried out to verify the model accuracy. It turns out that the simulation results are basically consistent with experimental data. In conclusion, crank length and rotational speed are critical influencing factors for variable-amplitude vibrating screens. Research on the screen’s motion characteristics and particle behaviors can provide a theoretical reference for its efficient operation and optimal design. Full article