Search Results (1,814)

With the rapid advancement of maritime sports, the integration of auxiliary unmanned surface vehicles (USVs) has emerged as a promising solution to enhance the efficiency and safety of maritime education, particularly in tasks such as buoy deployment and escort operations. This paper presents a novel high-precision trajectory tracking control algorithm designed to ensure stable navigation of the USVs along predefined competition boundaries, thereby facilitating the reliable execution of buoy placement and escort missions. First, the paper proposes an improved adaptive fractional-order nonsingular fast terminal sliding mode control (AFONFTSMC) algorithm to achieve precise trajectory tracking of the reference path. To address the challenges posed by unknown environmental disturbances and unmodeled dynamics in marine environments, a nonlinear lumped disturbance observer (NLDO) with exponential convergence properties is proposed, ensuring robust and continuous navigation performance. Additionally, an artificial potential field (APF) method is integrated to dynamically mitigate collision risks from both static and dynamic obstacles during trajectory tracking. The efficacy and practical applicability of the proposed control framework are rigorously validated through comprehensive numerical simulations. Experimental results demonstrate that the developed algorithm achieves superior trajectory tracking accuracy under complex sea conditions, thereby offering a reliable and efficient solution for maritime sports education and related applications. Full article

Background and Objective: Temperature management is key for reliable operation of permanent magnet synchronous motors (PMSMs). The lumped-parameter thermal network (LPTN) is fast and interpretable but struggles with nonlinear behavior under high power density. We propose OLTEM, a physics-informed deep model that combines LPTN with a thermal neural network (TNN) to improve prediction accuracy while keeping physical meaning. Methods: OLTEM embeds LPTN into a recurrent state-space formulation and learns three parameter sets: thermal conductance, inverse thermal capacitance, and power loss. Two additions are introduced: (i) a state-conditioned squeeze-and-excitation (SC-SE) attention that adapts feature weights using the current temperature state, and (ii) an enhanced power-loss sub-network that uses a deep MLP with SC-SE and non-negativity constraints. The model is trained and evaluated on the public Electric Motor Temperature dataset (Paderborn University/Kaggle). Performance is measured by mean squared error (MSE) and maximum absolute error across permanent-magnet, stator-yoke, stator-tooth, and stator-winding temperatures. Results: OLTEM tracks fast thermal transients and yields lower MSE than both the baseline TNN and a CNN–RNN model for all four components. On a held-out generalization set, MSE remains below 4.0 °C² and the maximum absolute error is about 4.3–8.2 °C. Ablation shows that removing either SC-SE or the enhanced power-loss module degrades accuracy, confirming their complementary roles. Conclusions: By combining physics with learned attention and loss modeling, OLTEM improves PMSM temperature prediction while preserving interpretability. This approach can support motor thermal design and control; future work will study transfer to other machines and further reduce short-term errors during abrupt operating changes. Full article

This paper proposes a novel hierarchical control architecture that combines Model Predictive Control (MPC) with Explicit Model-Following Control (EMFC) to enable accurate and efficient trajectory tracking for quadrotor electric Vertical Takeoff and Landing (eVTOL) aircraft operating in urban environments. The approach addresses the challenges of strong nonlinear dynamics, multi-axis coupling, and stringent safety constraints by separating the planning task from the fast-response control task. The MPC layer generates constrained velocity and yaw rate commands based on a simplified inertial prediction model, effectively reducing computational complexity while accounting for physical and operational limits. The EMFC layer then compensates for dynamic couplings and ensures the rapid execution of commands. A high-fidelity simulation model, incorporating rotor flapping dynamics, differential collective pitch control, and enhanced aerodynamic interference effects, is developed to validate the controller. Four representative ADS-33E-PRF tasks—Hover, Hovering Turn, Pirouette, and Vertical Maneuver—are simulated. Results demonstrate that the proposed controller achieves accurate trajectory tracking, stable flight performance, and full compliance with ADS-33E-PRF criteria, highlighting its potential for autonomous urban air mobility applications. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

The High-Pressure Common Rail System (HPCRS) is designed based on fundamental hydrodynamic principles, after which this paper formally defines the key control challenges. The proposed continuous sliding mode control strategy is developed based on a non-singular terminal sliding mode framework, integrated with an improved power reaching law. This design effectively eliminates chattering and achieves fast dynamic response with enhanced tracking precision. Subsequently, a bidirectional adaptive mechanism is integrated into the proposed control scheme to eliminate the necessity for a priori knowledge of unknown disturbances within the HPCRS. This mechanism enables real-time evaluation of the system’s state relative to a predefined detection region. To validate the effectiveness of the proposed strategy, experimental studies are conducted under three distinct operating conditions. The experimental results indicate that, compared with conventional rail pressure controllers, the proposed method achieves superior tracking accuracy, faster dynamic response, and improved disturbance rejection. Full article

To capture the complex metallic spatter and melt pool behavior during the rapid interaction between the laser and metal material, high-speed cameras are applied to record the laser powder bed fusion process and generate a large volume of image data. In this study, four deep learning algorithms are applied: YOLOv5, Fast R-CNN, RetinaNet, and EfficientDet. They are trained by the recorded videos to learn and extract information on spatter and melt pool behavior during the laser powder bed fusion process. The well-trained models achieved high accuracy and low loss, demonstrating strong capability in accurately detecting and tracking spatter and melt pool dynamics. A stability index is proposed and calculated based on the melt pool length change rate. Greater index value reflects a more stable melt pool. We found that more spatters were detected for the unstable melt pool, while fewer spatters were found for the stable melt pool. The spatter’s size can affect its initial ejection speed, and large spatters are ejected slowly while small spatters are ejected rapidly. In addition, more than 58% of detected spatters have their initial ejection angle in the range of 60–120$°$. These findings provide a better understanding of spatter and melt pool dynamics and behavior, uncover the influence of melt pool stability on spatter formation, and demonstrate the correlation between the spatter size and its initial ejection speed. This work will contribute to the extraction of important information from high-speed recorded videos for additive manufacturing to reduce waste, lower cost, enhance part quality, and increase process reliability. Full article

An electro-hydraulic servo loading system for a pavement mechanical properties test device was designed. The simulation analysis and test results showed that the PID control met the design requirements, but the output’s maximum error did not. Therefore, a fast terminal sliding mode control strategy with an extended state observer (ESO) was proposed. A tracking differentiator was constructed to obtain smooth differential signals from the input signals. The order of the system was reduced by considering the third and higher orders of the system as the total disturbance, and the states and the total disturbance of the system were estimated using the ESO. The fast terminal sliding mode control achieved fast convergence of the system within a limited time. The simulation results showed that the proposed control strategy improved the system accuracy and anti-disturbance ability, and system control performance was optimized. Full article

The continuously enhanced generative capabilities of artificial intelligence (AI) are challenging the existing patent system. There are still some issues, such as whether AI can be considered an inventor, whether technical solutions generated by AI are patentable, and how ownership should be allocated. AI-generated technical solutions fall under the category of patentable subject matter. Specifically, if they meet the requirements of the “three criteria,” they can become the subject of patent rights. Regarding the issue of AI’s eligibility as an inventor, a parallel technical generation registration system for AI should be established, with the current inventor system maintained in parallel. Concerning patent ownership issues, the assignable subjects of patent rights should be limited to the binary subjects of users and investors. Contractual agreements should take precedence to ensure contractual freedom, and ownership should generally be attributed to the user if no agreement exists. Additionally, a specialized fast-track review and authorization mechanism should be designed for AI-generated technical solutions, given the unique nature of AI-generated solutions. Moreover, their protection periods should be appropriately shortened to ensure a balance of interests. Furthermore, a disclosure system should be built across the entire lifecycle to prevent and mitigate risks that may arise during the machine generation of technical solutions, patent applications, patent authorizations, and dissemination stages. Full article

This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor strategy used in Oticon Medical devices. The study also explores how the SFE can be enhanced into a Temporal Fine Structure (TFS)-based strategy named Spectral Event Extraction (SEE), combining spectral sharpness with temporal cues. Background/Objectives: Many CI recipients understand speech in quiet settings but struggle with music and complex environments, increasing cognitive effort. De-smearing the power spectrum and extracting spectral peak features can reduce this load. The SFE targets feature extraction from spectral peaks, while the SEE enhances TFS-based coding by tracking these features across frames. Methods: The SFE strategy extracts spectral peaks and models them with synthetic pure tone spectra characterized by instantaneous frequency, phase, energy, and peak resemblance. This deblurs input peaks by estimating their center frequency. In SEE, synthetic peaks are tracked across frames to yield reliable temporal cues (e.g., zero-crossings) aligned with stimulation pulses. Strategy characteristics are analyzed using electrodograms. Results: A flexible Frequency Allocation Map (FAM) can be applied to both SFE and SEE strategies without being limited by FFT bandwidth constraints. Electrodograms of Crystalis and SFE strategies showed that SFE reduces spectral blurring and provides detailed temporal information of harmonics in speech and music. Conclusions: SFE and SEE are expected to enhance speech understanding, lower listening effort, and improve temporal feature coding. These strategies could benefit CI users, especially in challenging acoustic environments. Full article

To address the challenges of model uncertainty, strong nonlinearities, and controller tuning in high-precision trajectory tracking for hydraulic servo systems, this paper proposes a hierarchical GA-PID-MPC fusion strategy. The architecture integrates three functional layers: a Genetic Algorithm (GA) for online parameter optimization, a Model Predictive Controller (MPC) for future-oriented planning, and a Proportional–Integral–Derivative (PID) controller for fast feedback correction. These modules are dynamically coordinated through an adaptive cost-aware blending mechanism based on real-time performance evaluation. The MPC module operates on a linearized state–space model and performs receding-horizon control with weights and horizon length $\theta =[q,r,T_p]$ tuned by GA. In parallel, the PID controller is enhanced with online gain projection to mitigate nonlinear effects. The blending coefficient $\sigma \left(t\right)$ is adaptively updated to balance predictive accuracy and real-time responsiveness, forming a robust single-loop controller. Rigorous theoretical analysis establishes global input-to-state stability and $H_{\infty }$ performance under average dwell-time constraints. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

In recent years, 3D pixel sensors have been a topic of increasing interest within the High Energy Physics community. Due to their inherent radiation hardness, demonstrated up to a fluence of $3\times {10}^{16}$ 1 MeV equivalent neutrons per square centimeter, 3D pixel sensors have been used to equip the innermost tracking layers of the ATLAS and CMS detector upgrades at the High-Luminosity Large Hadron Collider. Additionally, the next generation of vertex detectors calls for precise measurement of charged particle timing at the pixel level. Owing to their fast response times, 3D sensors present themselves as a viable technology for these challenging applications. Nevertheless, both radiation hardness and fast timing require 3D sensors to be operated with high bias voltages on the order of ∼150 V and beyond. Special attention should therefore be devoted to avoiding problems that could cause premature electrical breakdown, which could limit sensor performance. In this paper, TCAD simulations are used to gain deep insight into the impact of surface isolation layers (i.e., p-stop and p-spray) used by different vendors on the high-voltage tolerance of small-pitch 3D sensors. Results relevant to different geometrical configurations and irradiation scenarios are presented. The advantages and disadvantages of the available technologies are discussed, offering guidance for design optimization. Experimentalmeasurements from existing samples based on both isolation techniques show good agreement with simulated breakdown voltages, thereby validating the simulation approach. Full article

This study develops a high-accuracy indoor positioning system using ultra-wideband (UWB) technology and the time-of-arrival (TOA) method. The system is built using Arduino Nano microcontrollers and DW1000 UWB chips to measure distances between anchor nodes and a mobile tag. Three positioning algorithms are tested: the triangle centroid algorithm (TCA), inner triangle centroid algorithm (ITCA), and the proposed intersection midpoint algorithm (IMA). Experiments conducted in a 732 × 488 × 220 cm indoor environment show that TCA performs well near the center but suffers from reduced accuracy at the edges. In contrast, IMA maintains stable and accurate positioning across all test points, achieving an average error of 12.87 cm. The system offers low power consumption, fast computation, and high positioning accuracy, making it suitable for real-time indoor applications such as hospital patient tracking and shopping malls where GPS is unavailable or unreliable. Full article

Considering the complexity and variability inherent in maritime environments, path planning algorithms for navigation have consistently been a subject of intense research interest. Nonetheless, single-algorithm approaches often exhibit inherent limitations. Consequently, this study introduces a path planning algorithm for autonomous surface vessels (ASVs) that integrates an improved fast marching method (FMM) with the dynamic window approach (DWA) for underactuated ASVs. The enhanced FMM improves the overall optimality and safety of the determined path in comparison to the conventional approach. Concurrently, it effectively merges the local planning strengths of the DWA algorithm, addressing the safety re-planning needs of the global path when encountering dynamic obstacles, thus augmenting path tracking accuracy and navigational stability. The efficient hybrid algorithm yields notable improvements in the path planning success rate, obstacle avoidance efficacy, and path smoothness compared with the isolated employment of either FMM or DWA, demonstrating superiority and practical applicability in maritime scenarios. Through a comprehensive analysis of its control output, the proposed integrated algorithm accomplishes efficient obstacle avoidance via agile control of angular velocity while preserving navigational stability and achieves path optimization through consistent acceleration adjustments, thereby asserting its superiority and practical worth in challenging maritime environments. Full article

The fixed-time trajectory tracking control problem of the uncertain nonlinear Euler–Lagrange system is studied. To ensure the fast, high-precision trajectory tracking performance of this system, a non-singular terminal sliding-mode controller based on Gaussian process regression is proposed. The control algorithm proposed in this paper is applicable to periodic motion scenarios, such as spacecraft autonomous orbital rendezvous and repetitive motions of robotic manipulators. Gaussian process regression is employed to establish an offline data-driven model, which is utilized for compensating parametric uncertainties and external disturbances. The non-singular terminal sliding-mode control strategy is used to avoid singularity and ensure fast convergence of tracking errors. In addition, under the Lyapunov framework, the fixed-time convergence stability of the closed-loop system is rigorously demonstrated. The effectiveness of the proposed control scheme is verified through simulations on a spacecraft rendezvous mission and periodic joint trajectory tracking for a robotic manipulator. Full article