Search Results (91)

In airport operations management, accurately estimating the service durations of ground support equipment such as Potable Water Trucks (PWTs) is essential for improving resource allocation efficiency and ensuring timely aircraft turnaround. Traditional estimation methods often use fixed averages or assume normal distributions, failing to capture real-world variability and extreme scenarios effectively. To address these limitations, this study performs a comprehensive statistical analysis of PWT service durations using operational data from Beijing Daxing International Airport (ZBAD) and Shanghai Pudong International Airport (ZSPD). Employing chi-square goodness-of-fit tests, twenty probability distributions—including several heavy-tailed candidates—were rigorously evaluated under segmented scenarios, such as peak versus non-peak periods, varying temperature conditions, and different aircraft sizes. Results reveal that heavy-tailed distributions offer context-dependent advantages: the stable distribution exhibits superior modeling performance during peak operational periods, whereas the Burr distribution excels under non-peak conditions. Interestingly, contrary to existing operational assumptions, service durations at extremely high and low temperatures showed no significant statistical differences, prompting a reconsideration of temperature-dependent planning practices. Additionally, analysis by aircraft category showed that the Burr distribution best described service durations for large aircraft, while stable and log-logistic distributions were optimal for medium-sized aircraft. Numerical simulations confirmed these findings, demonstrating that the proposed heavy-tailed probabilistic models significantly improved resource prediction accuracy, reducing estimation errors by 13% to 25% compared to conventional methods. This research uniquely demonstrates the practical effectiveness of employing context-sensitive heavy-tailed distributions, substantially enhancing resource efficiency and operational reliability in airport ground handling management. Full article

A methodology for detecting systematic errors in sets of equally accurate, uncorrelated, aggregate measurements is proposed and applied within the automatic real-time dispatch control system of a copper concentrator plant (CCP) to refine the technical and economic performance indicators (EPIs) computed by the system. This work addresses and solves the problem of selecting and obtaining reliable measurement data by exploiting the redundant measurements of process streams together with the balance equations linking those streams. This study formulates an approach for integrating cloud technologies, machine learning methods, and forecasting into information control systems (ICSs) via predictive analytics to optimize CCP production processes. A method for combining the hybrid cloud infrastructure with an LSTM-DNN neural network model has been developed, yielding a marked improvement in TEP for copper concentration operations. The forecasting accuracy for the key process parameters rose from 75% to 95%. Predictive control reduced energy consumption by 10% through more efficient resource use, while the copper losses to tailings fell by 15–20% thanks to optimized reagent dosing and the stabilization of the flotation process. Equipment failure prediction cut the amount of unplanned downtime by 30%. As a result, the control system became adaptive, automatically correcting the parameters in real time and lessening the reliance on operator decisions. The architectural model of an ICS for metallurgical production based on the hybrid cloud and the LSTM-DNN model was devised to enhance forecasting accuracy and optimize the EPIs of the CCP. The proposed model was experimentally evaluated against alternative neural network architectures (DNN, GRU, Transformer, and Hybrid_NN_TD_AIST). The results demonstrated the superiority of the LSTM-DNN in forecasting accuracy (92.4%), noise robustness (0.89), and a minimal root-mean-square error (RMSE = 0.079). The model shows a strong capability to handle multidimensional, non-stationary time series and to perform adaptive measurement correction in real time. Full article

In contrast to terrestrial networks, the rapid movement of low-earth-orbit (LEO) satellites causes frequent changes in the topology of intersatellite links (ISLs), resulting in dynamic shifts in transmission paths and fluctuations in multi-hop latency. Moreover, limited onboard resources such as buffer capacity and bandwidth competition contribute to the instability of these links. As a result, providing reliable quality of service (QoS) for time-sensitive flows (TSFs) in LEO satellite networks becomes a challenging task. Traditional terrestrial time-sensitive networking methods, which depend on fixed paths and static priority scheduling, are ill-equipped to handle the dynamic nature and resource constraints typical of satellite environments. This often leads to congestion, packet loss, and excessive latency, especially for high-priority TSFs. This study addresses the primary challenges faced by time-sensitive satellite networks and introduces a management framework based on software-defined networking (SDN) tailored for LEO satellites. An advanced queue management and scheduling system, influenced by terrestrial time-sensitive networking approaches, is developed. By incorporating differentiated forwarding strategies and priority-based classification, the proposed method improves the efficiency of transmitting time-sensitive traffic at multiple levels. To assess the scheme’s performance, simulations under various workloads are conducted, and the results reveal that it significantly boosts network throughput, reduces packet loss, and maintains low latency, thus optimizing the performance of time-sensitive traffic in LEO satellite networks. Full article

In the domain of intelligent inspection, the precise recognition of pointer meter readings is of paramount importance for monitoring equipment conditions. To address the challenges of insufficient robustness and diminished detection accuracy encountered in practical applications of existing methods for recognizing pointer meter readings based on object detection, we propose a novel approach that integrates YOLOv11-OBB rotating object detection with adaptive template matching techniques. Firstly, the YOLOv11 object detection algorithm is employed, incorporating a rotational bounding box (OBB) detection mechanism; This effectively enhances the feature extraction capabilities related to pointer rotation direction and dial center, thereby boosting detection robustness. Subsequently, an enhanced angle resolution algorithm is leveraged to develop a mapping model that establishes a relationship between pointer the deflection angle and the instrument range, facilitating precise reading calculation. Experimental findings demonstrate that the proposed method achieves a mean Average Precision (mAP) of 99.1% in a self-compiled pointer instrument dataset. The average relative error of readings is 0.41568%, with a maximum relative error of less than 1.1468%. Furthermore, the method exhibits robustness and reliability when handling low-quality meter images characterized by blur, darkness, overexposure, and tilt. The proposed approach provides a highly adaptable and reliable solution for pointer meter reading recognition in the intelligent industrial field, with significant practical value. Full article

Despite advances in technique and technology, the chainsaw is still the most widely used tool in forestry. For this reason, equipment manufacturers are developing new technical solutions to make working with a chainsaw as easy and efficient as possible. Some examples of this are the development of professional battery-powered chainsaws and the development of new types of saw chains by the leading industry manufacturers. The aim of this paper was to determine the efficiency of the Stihl MSA 300C battery-powered chainsaw equipped with two different types of professional saw chains (Stihl Rapid Super and Stihl Rapid Hexa) when sawing round wood. The efficiency was determined based on measurements of electricity consumption, sawing speed, sawn wood cross-section, and wood chips and dust mass produced during sawing. The second aim was to determine whether there is a difference in measured vibration magnitude between the two tested saw chains. Fresh-fallen European beech (Fagus sylvatica L.) log, approx. 25 cm diameter without pronounced ellipticity, was used for sampling. Results indicate that although the saw chain manufacturer claims the new type of saw chain (Stihl Rapid Hexa) enables greater efficiency of the chainsaw, this was not the case. Results point to a 37% increase in mean sawing time, as well as a 23% increase in energy consumption when using the Rapid Hexa chain, with statistically significant difference (p ≤ 0.05). It should be emphasized that the manual operation of the chainsaw does not allow for a reliable determination of differences in energy consumption caused by changes in saw chain geometry. The advantages of this saw chain are that it is easier to maintain (sharpen) and significantly less wood chips and dust are produced. The measured vibration magnitude shows a statistically significant difference (p ≤ 0.05), i.e., a lower vibration total value on the front handle when using the Stihl Rapid Hexa chain. Full article

Roll-to-roll (R2R) manufacturing processes demand precise control of web or yarn velocity and tension, alongside robust mechanisms for handling system failures. This paper presents an integrated approach combining high-performance control with reliable fault detection for an experimental R2R system. A model-based cascade control strategy is designed, incorporating system identification, radius compensation for varying roll diameters, and a Kalman filter to mitigate load sensor noise, ensuring accurate regulation of yarn velocity and tension under normal operating conditions. In parallel, a data-driven fault detection layer uses Gaussian Process Regression (GPR) models, trained offline on healthy operating data, to predict yarn tension and motor speeds. During operation, discrepancies between measured and GPR-predicted values that exceed predefined thresholds trigger an immediate shutdown of the system, preventing material loss and equipment damage. Experimental trials demonstrate tension regulation within ±0.02 N and velocity errors below ±5 rad/s across varying roll diameters, while yarn-break and motor-fault scenarios are detected within a single sampling interval (<100 milliseconds) with zero false alarms. This study validates the integrated system’s capability to enhance both the operational precision and resilience of R2R processes against critical failures. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. Full article

In view of the diversity and varying complexity of ship equipment, and the difficulty of existing state assessment methods in effectively handling the differences in the influence of different characteristic parameters on the equipment’s health state, leading to poor evaluation results, this paper proposes a ship equipment health state assessment method based on BP-RF and combined weighting. This method utilizes the BP-RF model to mine the implicit relationship between ship equipment feature parameters and state patterns, converting monitoring data into state pattern probability information. A trapezoidal membership function is used to determine the membership degree of each state pattern probability to different health state levels. The combined weighting method, which reflects a symmetric concept, balances expert experience and data information by integrating the subjective and objective weights of each state pattern probability, thus determining the equipment’s health state level. Through a case study of a specific type of ship’s gas turbine, the BP-RF model achieves a diagnostic accuracy of 98.3%, with $F_1$ scores improved by 5.1%, 5.0%, 8.6%, 9.9%, 6.7%, 3.4%, and 5.3% compared to the BP neural network, RF, Support Vector Machine (SVM), Convolutional Neural Network (CNN), BP-SVM, SVM-RF, and CNN-SVM models, respectively. Additionally, the evaluation results of this method exhibit clear boundaries for each state membership degree, effectively addressing the problem of unbalanced contributions from characteristic parameters and comprehensively reflecting the relative importance and correlation of each parameter. Overall, this method provides a more accurate and comprehensive assessment of ship equipment health compared to other methods, offering reliable support for ship equipment maintenance and assurance. Full article

Anchor–bolt support operations are lengthy and conducted under harsh conditions, restricting the efficiency and safety of roadway excavation. To address these challenges, we developed an integrated solution combining mechanical structure optimization with control algorithms. Specifically, we designed a novel automated drilling system equipped with a robotic manipulator and an anchor–bolt magazine to handle modular hollow self-drilling anchor bolts, enabling automated support operations. To achieve precise docking in unmanned conditions, we employed an inner-loop fractional-order proportional–integral–derivative (FOPID) controller optimized by an improved particle swarm optimization (ILPSO) algorithm. Additionally, robust control based on H∞ control theory was introduced to ensure reliable system performance under disturbances and model uncertainties. Simulation results indicate that the ILPSO-tuned FOPID controller significantly outperforms conventional controllers in dynamic response accuracy; frequency–domain analysis further confirms that the H∞ control approach enhances system stability. Collectively, these results provide a theoretical basis for advancing automated mining technologies. Full article

Human reliability is a key factor in long-term sustainability, especially for tasks that are critical to safety. It is also evident that human behavior is often the main or significant cause of system failures. Identifying human error is challenging, particularly when it comes to determining the exact moment when an error occurred that led to an accident, as errors develop over time. It is essential to understand the causes and mechanisms of human errors. This finding is not new; for over 30 years, it has been recognized that human operations in safety-critical systems are so important that they should be modeled as part of risk assessment in operation. This article discusses various HRA methodologies and argues that further research and development are necessary. An example of selected HRA techniques will be demonstrated through a case study on acetylene filling activities. When filling acetylene into pressure vessels or cylinders, it is critically important to analyze the reliability of the human factor, as this process involves handling a highly explosive gas. Irresponsibility, lack of training, or incorrect decision-making can lead to severe accidents. Any deficiencies in this process can result in not only equipment damage but also endanger the health and lives of people nearby. This case may also suggest potential improvements to existing guidelines, international standards, and regulations, which often require the consideration of a wider range of ergonomic factors in the risk assessment process. Full article

This paper introduces a groundbreaking framework for optimizing microgrid operations using the Quantum Approximate Optimization Algorithm (QAOA). The increasing integration of decentralized energy systems, characterized by their reliance on renewable energy sources, presents unique challenges, including the stochastic nature of energy supply-and-demand management. Our study leverages quantum computing to enhance the operational efficiency and resilience of microgrids, transcending the limitations of traditional computational methods. The proposed QAOA-based model formulates the microgrid scheduling problem as a Quadratic Unconstrained Binary Optimization (QUBO) problem, suitable for quantum computation. This approach not only accommodates complex operational constraints—such as energy conservation, peak load management, and cost efficiency—but also dynamically adapts to the variability inherent in renewable energy sources. By encoding these constraints into a quantum-friendly Hamiltonian, QAOA facilitates a parallel exploration of multiple potential solutions, enhancing the probability of reaching an optimal solution within a feasible time frame. We validate our model through a comprehensive simulation using real-world data from a microgrid equipped with photovoltaic systems, wind turbines, and energy storage units. The results demonstrate that QAOA outperforms conventional optimization techniques in terms of cost reduction, energy efficiency, and system reliability. Furthermore, our study explores the scalability of quantum algorithms in energy systems, providing insights into their potential to handle larger, more complex grid architectures as quantum technology advances. This research not only underscores the viability of quantum algorithms in real-world applications but also sets a precedent for future studies on the integration of quantum computing into energy management systems, paving the way for more sustainable, efficient, and resilient energy infrastructures. Full article

This paper presents a moving horizon estimator-based cooperative model predictive control strategy for a low-voltage distribution area equipped with symmetric distributed generators (DGs). First, DGs have their symmetries in the control structures that can be utilized for the control design. Then, a simplified model using feedback linearization theory for the symmetric DGs with hierarchical control reduces the high-order detailed models to low-order ones. To supplement the loss of accuracy and reliability in the proposed model, the controller introduces a moving horizon estimator to observe the unmeasured state variables under the poor communication condition of a low-voltage distribution network. Compared to the conventional method, the moving horizon estimator has advantages in handling uncertain disturbances, communication delays, constraints, etc. Furthermore, with all measured and observed state information, a cooperative distributed model predictive controller can be executed, and the stability and feasibility of controller are given. Finally, the effectiveness of the proposed control technique is verified through simulation based on Matlab/Simulink. Full article

(1) Background: The reliability of port equipment is of significant interest to industry stakeholders due to the economic and logistical factors governing the operation of maritime container terminals. Failures of key equipment like quay cranes can halt operations or cause economically significant delays. (2) Methods: The impact assessment of these disruptive events is conducted through terminal activity modeling and discrete-event simulation of internal processes. The system’s steady-state or transient condition, induced by disruptive events, is statistically assessed within a set of scenarios proposed by the authors. (3) Results: The Heidelberg–Welch and Geweke tests enabled the evaluation of steady-state and transient conditions within the modeled system, which was affected by the reduced reliability of container-handling equipment. (4) Conclusions: The research findings confirmed the usefulness of modeling and simulation in assessing the impact of equipment reliability on maritime container terminal operations. If the magnitude of the disruptive event exceeds the terminal’s absorption capacity, the system may become blocked or remain in a transient state without the ability to recover. This underscores the necessity of analyzing the reliability of critical handling equipment and implementing corrective maintenance actions when required. Full article

Accurately counting chickens in densely packed cages is a major challenge in large-scale poultry farms. Traditional manual counting methods are labor-intensive, costly, and prone to errors due to worker fatigue. Furthermore, current deep learning models often struggle with accuracy in caged environments because they are not well-equipped to handle occlusions. In response, we propose the You Only Look Once-Chicken Counting Algorithm (YOLO-CCA). YOLO-CCA improves the YOLOv8-small model by integrating the CoordAttention mechanism and the Reversible Column Networks backbone. This enhancement improved the YOLOv8-small model’s F1 score to 96.7% (+3%) and average precision_50:95 to 80.6% (+2.8%). Additionally, we developed a threshold-based continuous frame inspection method that records the maximum number of chickens per cage with corresponding timestamps. The data are stored in a cloud database for reliable tracking during robotic inspections. The experiments were conducted in an actual poultry farming environment, involving 80 cages with a total of 493 chickens, and showed that YOLO-CCA raised the chicken recognition rate to 90.9% (+13.2%). When deployed on a Jetson AGX Orin industrial computer using TensorRT, the detection speed increased to 90.9 FPS (+57.6 FPS), although the recognition rate slightly decreased to 93.2% (−2.9%). In summary, YOLO-CCA reduces labor costs, improves counting efficiency, and supports intelligent poultry farming transformation. Full article

Transformers are essential for voltage regulation and power distribution in electrical systems, and monitoring their top-oil temperature is crucial for detecting potential faults. High oil temperatures are directly linked to insulation degradation, a primary cause of transformer failures. Therefore, accurate oil temperature prediction is important for proactive maintenance and preventing failures. This paper proposes a hybrid time series forecasting model combining ARIMA, LSTM, and XGBoost to predict transformer oil temperature. ARIMA captures linear components of the data, while LSTM models complex nonlinear dependencies. XGBoost is used to predict the overall oil temperature by learning from the complete dataset, effectively handling complex patterns. The predictions of these three models are combined through a linear-regression stacking approach, improving accuracy and simplifying the model structure. This hybrid method outperforms traditional models, offering superior performance in predicting transformer oil temperature, which enhances fault detection and transformer reliability. Experimental results demonstrate the hybrid model’s superiority: In 5000-data-point prediction, it achieves an MSE = 0.9908 and MAPE = 1.9824%, outperforming standalone XGBoost (MSE = 3.2001) by 69.03% in error reduction and ARIMA-LSTM (MSE = 1.1268) by 12.08%, while surpassing naïve methods 1–2 (MSE = 1.7370–1.6716) by 42.94–40.74%. For 500-data-point scenarios, the hybrid model (MSE = 1.9174) maintains 22.40–35.53% lower errors than XGBoost (2.4710) and ARIMA-LSTM (3.6481) and outperforms naïve methods 1–2 (2.8611–2.9741) by 32.97–35.53%. These results validate the approach’s effectiveness across data scales. The proposed method contributes to more effective predictive maintenance and improved safety, ensuring the long-term performance of transformer equipment. Full article