A three-degree-of-freedom air-floating simulation platform is commonly used for attitude maneuver simulation and control system design. To reduce the impact of gravity on the air-floating platform, adjustments must be made to the platform's center of mass so that it coincides as closely as possible with the center of rotation (CR). For larger three-degree-of-freedom platforms, it is often different to easily obtain the moment of inertia, which presents challenges for automatic leveling. In response to this issue, an automatic leveling method was proposed in this study. This method utilizes attitude and angular velocity information, and during the leveling process, only a linear motion mechanism is required to drive a mass block for adjustment. An analysis of the uncertainties present in the model was conducted, and the uncertainties in the system were processed separately. Adaptive control techniques were then applied to design the control method. The stability of the system was demonstrated through the Lyapunov stability theorem. Finally, the algorithm was tested on a three-degree-of-freedom air-floating platform. The experimental results showed that the proposed method can achieve rapid and effective leveling of the platform.
A novel approach for road crack detection and segmentation was proposed, incorporating Einstein operators within an Adaptive Neuro-Fuzzy Inference System (ANFIS). This methodology leverages advanced fuzzy aggregation techniques and adaptive mechanisms, combined with dynamic Einstein sum and product operators, to enhance the identification of cracks. The model was designed to effectively manage varying crack intensities, geometries, and noise levels, thereby ensuring high sensitivity and accuracy in real-world road conditions. In the preprocessing stage, robust fuzzification was applied using Gaussian membership functions alongside Einstein operators, which significantly improved feature extraction. The segmentation framework based on ANFIS ensured precise detection and delineation of cracks. The performance of the proposed model was demonstrated through a comparative analysis, showing superior accuracy (95.2%), precision (94.1%), recall (96.4%), and F1-score (95.2%) when compared to state-of-the-art models. Statistical validation was conducted, with p-values < 0.01 for all performance metrics, confirming the reliability and statistical significance of the results. Advanced post-processing techniques, including fuzzy morphological refinement and adjacency matrix-based connectivity analysis, were employed to accurately identify even faint or disconnected cracks. The proposed method exhibits exceptional resilience to environmental variations, offering a reliable and adaptive solution for road maintenance and monitoring. This work highlights the potential of fuzzy logic, statistical validation, and adaptive mechanisms in addressing real-world challenges in road crack detection.
The growing demand for energy, driven by urbanization and environmental concerns, has highlighted the need for innovative solutions in power management, particularly within residential and small business settings. This study presents a comprehensive smart home automation system, which is based on Internet of Things (IoT), designed to address these challenges. By integrating a smartphone application with Arduino-based hardware, the proposed system enables real-time remote operation, scheduling, and monitoring of electrical appliances. Bluetooth connectivity, coupled with advanced coding techniques, was employed to accurately measure power consumption and compute associated costs. The system's user interface was evaluated for its ease of use, responsiveness, and high accuracy, providing users with the ability to track energy usage trends, optimize appliance operation, and make informed decisions regarding energy consumption and cost management. Furthermore, the solution promotes sustainable energy practices by facilitating the reduction of unnecessary energy consumption. This scalable, cost-effective approach is poised to support the broader adoption of energy-efficient technologies. Future enhancements, such as integration with voice assistants and the addition of Wi-Fi connectivity, are expected to further expand the system's capabilities. The findings demonstrate the significant potential of IoT technologies to transform energy management and foster environmentally conscious behavior in smart homes.
Accurate monitoring of turbine speed is essential for ensuring operational stability and efficiency in power generation systems, particularly within the context of low-carbon and renewable energy integration. This study evaluates the performance of three Variable Reluctance Sensors (VRSs)—VRS1, VRS2, and VRS3—used for real-time speed monitoring of the Steam Turbine Generator (STG) 1.0 at the Tambak Lorok Combined Cycle Power Plant (CCPP). The evaluation was conducted using statistical methods, including Root Mean Square Error (RMSE), standard deviation, and two-factor Analysis of Variance (ANOVA) without replication, to assess the accuracy and consistency of the sensors under varying operational conditions. The operational conditions were simulated through a motor controlled by a Variable Speed Drive (VSD), which allows for precise control over speed variations. The results indicate that the VRSs exhibit high accuracy and reliability, with RMSE values ranging from 0.08% to 0.28%. Among the three sensors, VRS3 demonstrated the highest performance, achieving minimal variability, with a standard deviation of 0.000 at a frequency of 50.00 Hz. ANOVA revealed no significant differences in performance between the three sensors (P-value = 1.000), suggesting uniformity in their measurement capabilities. These findings substantiate the suitability of VRSs for turbine speed monitoring in power plants, ensuring operational stability and supporting the integration of renewable energy technologies. The results reinforce the potential of VRSs as a reliable tool for improving the efficiency of sustainable energy systems
The ability of neural network-based control systems for trajectory tracking in wheeled mobile vehicles was evaluated in this study. A significant challenge often encountered is the deviation from the desired trajectory, particularly in high-speed motion. A robust control scheme, designed using the Nonlinear Auto-Regressive Moving Average-Level 2 (NARMA-L2) approach, was employed to enhance the tracking performance under dynamic conditions. The NARMA-L2 controller, a well-established technique for nonlinear systems, was utilized to improve the accuracy and robustness of trajectory tracking in the presence of external disturbances and noise. In heavy-duty mobile vehicles, such as agricultural machines, maintaining straight-line motion at high speeds is particularly susceptible to external load effects and system noise. The proposed control strategy integrates several stages, including system modeling, controller design, and the training of the neural network. To optimize the parameters of a proportional-integral-derivative (PID) controller, the Particle Swarm Optimization (PSO) algorithm was applied, ensuring precise regulation of the vehicle’s speed. The controller generates a reference velocity, which is fed as a signal to control the motion of the left and right wheels, enabling effective steering and trajectory adherence. Simulation results demonstrate the effectiveness of the proposed controller in mitigating the impact of disturbances and load effects. The optimization of control parameters successfully minimizes the discrepancy between the left and right wheel positions, bringing them closer to zero. The robust parameter optimization approach, which was employed to counteract the influence of external loads, can significantly improve system performance under varying conditions.
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