Search Results (1,577)

Accurate docking of Autonomous Guided Vehicles (AGVs) is a critical requirement for efficient automated production systems in Industry 4.0, particularly for collaborative tasks with robotic arms that have a limited working range. This paper introduces a cost-effective software-upgrade solution to enhance the precision of the final docking phase without requiring new hardware. Our approach is based on a two-stage strategy: first, a switch from a global dead reckoning system to a local navigation scheme, is triggered near the docking station; second, a dedicated Takagi-Sugeno Fuzzy Logic Controller (FLC), guides the AGV to its final position with high accuracy. The core novelty of our FLC is its implementation as a gain-scheduling lookup table (LUT), which synthesizes critical state variables—heading error and distance error—from limited proximity sensor data, to robustly handle positional uncertainty and environmental variations. This method directly addresses the inadequacies of traditional odometry, whose cumulative errors become unacceptable at the critical docking point. For experimental validation, we assume the global navigation delivers the AGV to a general switching point, near the assembly station with an unknown true pose. We detail the design of the fuzzy controller and present experimental results that demonstrate a significant improvement, achieving repeatable docking accuracy within industrially acceptable tolerances. Full article

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system. Full article

This work aims towards the automatic detection of faults in industrial air-cooling equipment used in a production line for staple fibers and ultimately provides maintenance scheduling recommendations to ensure seamless operation. In this context, various deep learning models are tested to ultimately define the most effective one for the intended scope. In the examined system, four vibration and temperature sensors are used, each positioned radially on the motor body near the rolling bearing of the motor shaft—a typical setup in many industrial environments. Thus, by collecting and using data from the latter sources, this work exhaustively investigates the feasibility of accurately diagnosing faults in staple fiber cooling fans. The dataset is acquired and constructed under real production conditions, including variations in rotational speed, motor load, and three fault priorities, depending on the model detection accuracy, product specification, and maintenance requirements. Fault identification for training purposes involves analyzing and evaluating daily maintenance logs for this equipment. Experimental evaluation on real production data demonstrated that the proposed ResNet50-1D model achieved the highest overall classification accuracy of 97.77%, while effectively resolving the persistent misclassification of the faulty impeller observed in all the other models. Complementary evaluation confirmed its robustness, cross-machine generalization, and suitability for practical deployment, while the integration of predictions with maintenance logs enables a severity-based prioritization strategy that supports actionable maintenance planning.deep learning; fault classification; industrial air-cooling; industrial automation; maintenance scheduling; vibration analysis Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

Rural electrification in isolated communities requires reliable and affordable renewable solutions. This paper analyses a hybrid microgrid case study in a rural area integrating PV–biomass–BESS using mathematical models and simulations in MATLAB/Simulink Version 2025a, characterizing local resources (climate and biomass), and evaluating irradiance, temperature, and demand profiles. On typical days, the system meets demand with overall efficiencies of 93–103%; solar energy contributes 6.8–8.9 kWh/day (37–42%), biomass 9.5–13.2 kWh/day (54–62%), and BESS ≈ 0.6 kWh/day (≈3%), operating at 60–90% SoC. Between March and June, photovoltaic generation increased from 7.2 to 8.9 kWh/day (+23.6%), raising overall efficiency from 97% to 103%; in October, the contribution was 40% PV, 57% biomass, and 3% BESS. Coordinated operation—prioritizing solar and scheduling biomass at peaks—is robust and replicable. It is recommended to increase photovoltaic collection by ~20% and add ≥2.5 kWh of storage to reduce biomass dependence by 15–20% and improve nighttime autonomy. This integrated approach to solar generation, biomass management, and storage for efficient and sustainable supply is applied and validated in a theoretical case study developed in the rural area of Argelia-Viotá, Cundinamarca, Colombia. Full article

The increasing penetration of renewable energy presents substantial challenges to frequency stability, particularly in low-inertia microgrids. This study introduces an agent-based microgrid model that integrates generators, loads, an energy storage system (ESS), and renewable sources, mathematically formalized through the discrete-event system specification (DEVS) to ensure both structural clarity and extensibility. To dynamically simulate power system behavior, the model incorporates multiple control strategies—including ESS scheduling, automatic generation control (AGC), predictive AGC, and grid-forming (GFM) inverter control—each posed as an mathematically defined control problem. Simulations on the IEEE 13-bus system demonstrates that the coordinated operation of ESS, GFM, and the proposed strategies markedly enhances frequency stability, reducing frequency peaks by 1.14, 1.14, and 0.72 Hz, and shortening the average recovery time by 9.05, 0.15, and 2.58 min, respectively. Collectively, the model provides a systematic representation of grid behavior and frequency regulation mechanisms under high renewable penetration, and establishes a rigorous mathematical framework for advancing microgrid research. Full article

The purpose of this study was to examine the role of traditional fire management practices in the general mitigation of wildfire risk in Greece. Major emphasis was placed on assessing people’s opinions about the perceived effectiveness of traditional fire management strategies that were historically and culturally employed by local communities—such as weather condition monitoring, prescribed burning, proper land use planning, and mosaic burning—in the general mitigation of wildfire risks. An online questionnaire was used to collect data from 397 environmental experts in Greece. The study shows that traditional fire control methods reduce wildfire risk. First, weather monitoring was found to be crucial to wildfire forecasting and prevention. The results showed that early warning, successful firefighting, and fire prevention depend on meteorological data. Additionally, prescribed burning was revealed to have reduced wildfire risk. Respondents accepted that they could reduce unprescribed fires, protect natural ecosystems, remove wildfire-prone areas, and regulate flame intensity. This suggests that scheduled burning in Greece may reduce wildfire damage. The study underlines the importance of including conventional fire management in the wildfire mitigation strategy of Greece. The aforementioned activities may help the environment and civilization progress by safeguarding ecosystems and reducing wildfire damage. These techniques, combined with community engagement and improved early warning systems, may help manage climate change-induced wildfires. Overall, the study contributes to wildfire management in Greece and other Mediterranean countries. The study emphasizes the need to incorporate traditional fire practices into Greece’s wildfire risk reduction strategies. Taking into account the success rates of these practices in other areas, as well as Greece’s old tradition of conducting fire, this paper stresses that further studies and policy developments be made in order to reinstate these practices in today’s wildfire management. Full article

With the rapid development of renewable energy and the continuous pursuit of efficient energy utilization, distributed photovoltaic power generation has been widely used in village-level microgrids. As a key platform connecting distributed photovoltaics with users, energy storage systems play an important role in alleviating the imbalance between supply and demand in VMG. However, current energy storage systems rely heavily on lithium batteries, and their frequent charging and discharging processes lead to rapid lifespan decay. To solve this problem, this study proposes a hybrid energy storage system combining supercapacitors and lithium batteries for VMG, and designs a hybrid energy storage scheduling strategy to coordinate the “source–load–storage” resources in the microgrid, effectively cope with power supply fluctuations and slow down the life degradation of lithium batteries. In order to give full play to the active support ability of supercapacitors in suppressing grid voltage and frequency fluctuations, the scheduling optimization goal is set to maximize the sum of the virtual inertia time constants of the supercapacitor. In addition, in order to efficiently solve the high-complexity model, the reason for choosing the snow goose algorithm is that compared with the traditional mathematical programming methods, which are difficult to deal with large-scale uncertain systems, particle swarm optimization, and other meta-heuristic algorithms have insufficient convergence stability in complex nonlinear problems, SGA can balance global exploration and local development capabilities by simulating the migration behavior of snow geese. By improving the convergence effect of SGA and constructing a multi-objective SGA, the effectiveness of the new algorithm, strategy and model is finally verified through three cases, and the loss is reduced by 58.09%, VMG carbon emissions are reduced by 45.56%, and the loss of lithium battery is reduced by 40.49% after active support optimization, and the virtual energy inertia obtained by VMG from supercapacitors during the scheduling cycle reaches a total of 0.1931 s. Full article

The continuous evolution of advanced wireless IoT systems necessitates novel network protocols capable of enhancing resource efficiency and performance to support increasingly demanding applications. Full-duplex (FD) communication emerges as a key advanced wireless technology to address these needs by doubling spectral efficiency. However, unlocking this potential is non-trivial, as it introduces complex interference scenarios and requires sophisticated management of heterogeneous Quality of Service (QoS) demands, presenting a significant challenge for existing MAC protocols. To overcome these limitations through protocol optimization, this paper proposes IDA-FDMAC, a novel MAC architecture tailored for FD-enabled IoT networks. At its core, IDA-FDMAC employs a dynamic priority scheduling mechanism that concurrently manages interference and provisions for diverse QoS requirements. A comprehensive theoretical model is developed and validated through extensive simulations, demonstrating that our proposed architecture significantly boosts system throughput and ensures QoS guarantees. This work thus contributes a robust, high-performance solution aligned with the development of next-generation wireless IoT systems. Full article

The increasing demand for low-latency, computationally intensive vehicular applications, such as autonomous navigation and real-time perception, has led to the adoption of cloud–edge–vehicle infrastructures. These applications are often modeled as Directed Acyclic Graphs (DAGs) with interdependent subtasks, where precedence constraints enforce causal ordering while allowing concurrency. We propose a task offloading framework that decomposes applications into precedence-constrained subtasks and formulates the joint scheduling and offloading problem as a Markov Decision Process (MDP) to capture the latency–energy trade-off. The system state incorporates vehicle positions, wireless link quality, server load, and task-buffer status. To address the high dimensionality and sequential nature of scheduling, we introduce DepSchedPPO, a dependency-aware sequence-to-sequence policy that processes subtasks in topological order and generates placement decisions using action masking to ensure partial-order feasibility. This policy is trained using Proximal Policy Optimization (PPO) with clipped surrogates, ensuring stable and sample-efficient learning under dynamic task dependencies. Extensive simulations show that our approach consistently reduces task latency, energy consumption and QOS compared to conventional heuristic and DRL-based methods. The proposed solution demonstrates strong applicability to real-time vehicular scenarios such as autonomous navigation, cooperative sensing, and edge-based perception. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

Objectives: Despite the widespread adoption of clear aligner therapy (CAT), its effectiveness in managing rotations remains debated. This systematic review aims to evaluate rotational accuracy in adults and the influence of treatment variables—such as attachments, interproximal reduction (IPR), and staging. Methods: Following PRISMA guidelines, seven databases and two grey literature sources were searched up to July 2025. Eligible studies assessed rotational accuracy in patients treated exclusively with clear aligners, using 3D digital model superimposition. Primary outcomes included percent accuracy, lack of correction (LC), or mean absolute error (MAE). Risk of bias (RoB 2, ROBINS-I) and certainty of evidence (GRADE) were assessed. Results: Twelve studies (one RCT, eleven non-randomized) were included, showing wide heterogeneity in aligner systems, tooth types, outcome measures, and adjunctive strategies. Reported accuracy ranged from 36% to 85%, averaging around 65%. LC values varied from 0.7° to 4.5°, and mean MAE was about 2.3°. Incisors and molars showed higher predictability, whereas maxillary canines and premolars remained the least reliable. Attachments and IPR were widely used, but their effectiveness was inconsistent. Staging protocols were generally set at 2°/aligner and most studies adopted 7–14-day wear schedules. Nearly all investigations showed moderate-to-serious risk of bias, and certainty of evidence was rated low to moderate. Conclusions: CAT shows limited yet improving predictability in rotational movements, with performance strongly influenced by tooth morphology and staging. Attachments, IPR, and overcorrections may contribute but lack consistent validation. Given the low certainty and high risk of bias of current evidence, these findings should be interpreted cautiously. Well-designed RCTs with standardized protocols are required to develop reliable clinical guidelines. Full article

Electronic Health Records (EHRs) have enhanced access to medical information but have also introduced challenges for healthcare providers, such as increased documentation workload and reduced face-to-face interaction with patients. To mitigate these issues, we propose RAGMed, a Retrieval-Augmented Generation (RAG)-based AI assistant designed to deliver automated and clinically grounded responses to frequently asked patient questions. This system combines a vector database for semantic retrieval with the generative capabilities of a large language model to provide accurate, reliable answers without requiring direct physician involvement. In addition to patient-facing support, the assistant facilitates appointment scheduling and assists clinicians by summarizing clinical notes, thereby streamlining healthcare workflows. Additionally, to evaluate the influence of retrieval quality on overall system performance, we compare two embedding models, gte-large and all-MiniLM-L6-v2, using real-world medical queries. The models are assessed within the RAG-Triad Framework, focusing on context relevance, answer relevance, and factual groundedness. The results indicate that gte-large, owing to its higher-dimensional embeddings, retrieves more informative context, resulting in more accurate and trustworthy responses. These findings underscore the importance of not only the potential of incorporating RAG-based systems to alleviate physician workload and enhance the efficiency and accessibility of healthcare delivery but also the dimensionality of models used to generate embeddings, as this directly influences the relevance, accuracy, and contextual understanding of retrieved information. This prototype is intended for the retrieval-augmented answering of medical FAQs and general informational queries, and is not designed for diagnostic use or treatment recommendations without professional validation. Full article