Search Results (754)

Reliable individual detection under dense and cluttered conditions is a prerequisite for automated monitoring in modern poultry systems. We propose CF-DETR, an end-to-end detector that builds on RT-DETR and is tailored to chicken face detection in production-like environments. CF-DETR advances three technical directions: Dynamic Inception Depthwise Convolution (DIDC) expands directional and multi-scale receptive fields while remaining lightweight, Polar Embedded Multi-Scale Encoder (PEMD) restores global context and fuses multi-scale information to compensate for lost high-frequency details, and a Matchability Aware Loss (MAL) aligns predicted confidence with localization quality to accelerate convergence and improve discrimination. On a comprehensive broiler dataset, CF-DETR achieves a mean average precision at IoU 0.50 of 96.9% and a mean average precision (IoU 0.50–0.95) of 62.8%. Compared to the RT-DETR baseline, CF-DETR reduces trainable parameters by 33.2% and lowers FLOPs by 23.0% while achieving 81.4 frames per second. Ablation studies confirm that each module contributes to performance gains and that the combined design materially enhances robustness to occlusion and background clutter. Owing to its lightweight design, CF-DETR is well-suited for deployment in real-time smart farming monitoring systems. These results indicate that CF-DETR delivers an improved trade-off between detection performance and computational cost for real-time visual monitoring in intensive poultry production. Full article

Accurate and real-time monitoring of low-frequency electromagnetic field (EMF) is essential in power and industrial environments, yet most conventional approaches still suffer from limited spatial coverage, manual operation, and insufficient digitization. To address these challenges, this paper proposes an intelligent EMF monitoring system that integrates six-axis magnetic field sensing, temperature compensation, vector synthesis, Sub-1 GHz wireless communication, and real-time data visualization. The system supports simultaneous measurement of both AC and DC magnetic fields across the 30 Hz–100 kHz range, with specific optimization for power-frequency conditions (50/60 Hz). Designed with modular integration and low power consumption, it is suitable for portable deployment in field scenarios. Comprehensive laboratory and substation tests demonstrate high accuracy, with maximum measurement errors of 1.17% under zero-field and 1.42% under applied-field conditions—well below the ±5% tolerance defined by international standards. Wireless performance tests further confirm stable long-distance communication, achieving ranges of up to 5 km without significant transmission errors, while overall system measurement error reached as low as 0.015%. These results verify the system’s robustness, fidelity, and compliance with international safety standards. Overall, the proposed platform provides a practical and scalable solution for intelligent EMF monitoring, offering strong potential for deployment in industrial environments and infrastructure-critical applications. Full article

Given the inadequacies in the management of reactive power compensation equipment in distribution networks and insufficient power data mining, existing studies pay little attention to asymmetric reactive power compensation equipment and face pain points such as difficult quantification of nonlinear relationships and challenging evaluation of mechanical switches. First, this paper proposes a data mining-based diagnostic method for the operating status of asymmetric reactive power compensation equipment: it preprocesses data via singular value decomposition and matrix approximation. Second, it classifies load types with K-means clustering, defines “health degree” by introducing mutual information and a reliability coefficient, constructs dual switching criteria, and defines the switching qualification rate. Third, the TOPSIS method is employed for dual-index comprehensive evaluation, and equipment status levels are classified with statistical analysis. Finally, the case analysis demonstrates that the proposed method is accurate, applicable, and easy to implement, which can serve as a basis for equipment troubleshooting and maintenance, thereby filling the relevant research gap. Full article

The Automated Building Information Modeling (BIM) reconstruction of existing building curtain walls is crucial for promoting digital Operation and Maintenance (O&M). However, existing 3D reconstruction technologies are mainly designed for general architectural scenes, and there is currently a lack of research specifically focused on the BIM reconstruction of curtain walls. This study proposes a BIM reconstruction method from unmanned aerial vehicle (UAV) images based on neural radiance field (NeRF) and deep learning-based semantic segmentation. The proposed method compensates for the lack of semantic information in traditional NeRF methods and could fill the gap in the automatic reconstruction of semantic models for curtain walls. A comprehensive high-rise building is selected as a case study to validate the proposed method. The results show that the overall accuracy (OA) for semantic segmentation of curtain wall point clouds is 71.8%, and the overall dimensional error of the reconstructed BIM model is less than 0.1m, indicating high modeling accuracy. Additionally, this study compares the proposed method with photogrammetry-based reconstruction and traditional semantic segmentation methods to further validate its effectiveness. Full article

Natural and unconstrained locomotion remains a fundamental challenge in creating truly immersive virtual reality (VR) experiences. This paper presents the design and control of a novel robotic omnidirectional treadmill (ODT) based on the bilateral symmetry of two cooperative five-bar planar mechanisms designed to replicate realistic walking mechanics. The central contribution is a human in the loop control strategy designed to achieve stable walking in place. This framework employs a specific control strategy that actively repositions the footplates along a dynamically defined ‘Line of Movement’ (LoM), compensating for the user’s motion to ensure the midpoint between the feet remains stabilized and symmetrical at the platform’s geometric center. A comprehensive dynamic model of both the ODT and a coupled humanoid robot was developed to validate the system. Numerical simulations demonstrate robust performance across various gaits, including turning and catwalks, maintaining the user’s locomotion center with a maximum resultant drift error of 11.65 cm, a peak value that occurred momentarily during a turning motion and remained well within the ODT’s safe operational boundaries, with peak errors along any single axis remaining below 9 cm. The system operated with notable efficiency, requiring RMS torques below 22 Nm for the primary actuators. This work establishes a viable dynamic and control architecture for foot-tracking ODTs, paving the way for future enhancements such as haptic terrain feedback and elevation simulation. Full article

A radiation-hardened RCC (Ring Choke Converter) isolated power supply design is proposed, which provides an innovative solution to the challenge of providing stable power to the PWM controller in DC-DC converters under nuclear radiation environments. By optimizing circuit architecture and component selection, and incorporating transformer isolation and dynamic parameter compensation technology, the RCC maintains an 8.9 V output voltage after exposure to neutron irradiation of 3 × 10¹³ n/cm², significantly outperforming conventional designs with a failure threshold of 1 × 10¹³ n/cm². For the first time, the degradation mechanisms of VDMOS devices under neutron irradiation during switching operations are systematically revealed: a 32–36% reduction in threshold voltage (with the main power transistor dropping from 5 V to 3.4 V) and an increase in on-resistance. Based on these findings, a selection criterion for power transistors is established, enabling the power supply to achieve a 2 W output in extreme environments such as nuclear power plant monitoring and satellite systems. The results provide a comprehensive solution for radiation-hardened power electronics systems, covering device characteristic analysis to circuit optimization, with significant engineering application value. Full article

Absolute linear encoders have emerged as a core technical enabler in the fields of high-end manufacturing and precision displacement measurement, owing to their inherent advantages such as the elimination of the need for homing operations and the retention of position data even upon power failure. However, there remains a notable scarcity of comprehensive review materials that can provide systematic guidance for practitioners engaged in the field of absolute linear encoder measurement technology. The present study aims to address this gap by offering a practical reference to professionals in this domain. In this research, we first systematically delineate the three fundamental categories of measurement principles underlying absolute linear encoders. Subsequently, we analyze the evolutionary trajectory of coding technologies, encompassing the design logics and application characteristics of quasi-absolute coding (including non-embedded and embedded variants) as well as absolute coding (covering multi-track and single-track configurations). Furthermore, we summarize the primary error sources that influence measurement accuracy and explore the operational mechanisms of various types of errors. This study clarifies the key technical pathways and existing challenges associated with absolute linear encoders, thereby providing practitioners in relevant fields with a decision-making guide for technology selection and insights into future development directions. Moving forward, efforts should focus on achieving breakthroughs in critical technologies such as high fault-tolerant coding design, integrated manufacturing, and error compensation, so as to advance the development of absolute linear encoders toward higher precision, miniaturization, cost reduction, and enhanced reliability. Full article

This study tackles the challenges associated with permanent magnet synchronous motor (PMSM) position control under complex operating conditions—characterized by strong coupling, nonlinearity, and time-varying parameters—which often lead to slow response, low control accuracy, and weak disturbance rejection capability. A high-performance control system is developed based on an active disturbance rejection controller (ADRC), with three key improvements proposed. Firstly, a modified nonlinear function is designed to suppress chattering. Secondly, a delay compensation module is integrated to synchronize the input signals of the extended state observer (ESO). Finally, an automated parameter tuning method is introduced using the Newton-Raphson optimization algorithm. Comparative simulations are conducted to validate the effectiveness of the proposed system, demonstrating its advantages of rapid response, minimal overshoot, and enhanced disturbance rejection capability. For the proposed strategy, the maximum position tracking error is 0.1 rad, the adjustment time is 0.15 s, the dynamic speed drop is 0.025 rad, and the recovery time is 0.15 s—all comprehensive performance indicators outperform those of other control strategies. Additionally, automated parameter tuning eliminates the need for manual adjustments, reduces operational complexity, and improves tuning accuracy, thereby significantly advancing the position control performance of PMSMs. Full article

As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article

With the advancement of precision agriculture, the autonomous navigation of unmanned agricultural ground vehicles (UAGVs) has emerged as a critical research topic. As a fundamental component of autonomous navigation, path-tracking control is essential for ensuring the accurate and stable operation of UAGVs. However, achieving high-precision and robust tracking in agricultural environments remains challenging due to unstructured terrain, variable wheel slip, and complex dynamic disturbances. This review provides a structured and comprehensive survey of modeling and control methodologies for UAGVs, with particular emphasis on control-theoretic formulations and their applicability across diverse agricultural scenarios. In contrast to prior reviews, the modeling approaches are systematically classified into geometric, kinematic, and dynamic models, including extended formulations that incorporate wheel slip and external disturbances. Furthermore, this paper systematically reviews commonly adopted path-tracking strategies for UAGVs, including proportional–integral–derivative (PID) control, pure pursuit (PP), Stanley control, sliding mode control (SMC), model predictive control (MPC), and learning-based approaches. Emphasis is placed on their theoretical underpinnings, tracking accuracy, adaptability to unstructured field environments, and computational efficiency. In addition, several key technical challenges are identified, such as terrain-adaptive vehicle modeling, slip compensation mechanisms, real-time implementation under hardware constraints, and the cooperative control of multiple UAGVs operating in dynamic agricultural scenarios. By presenting a detailed review from a control-centric perspective, this study aims to serve as a valuable reference for researchers and practitioners developing intelligent agricultural vehicle systems. Full article

With the rapid advancement of intelligent technologies, permanent magnet synchronous motor (PMSM) servo systems have seen increasing applications in industrial fields, accompanied by continuously rising control performance demands. Moreover, the adjustment of controller parameters is pivotal for the performance optimization of servo systems. This paper presents an optimization method for PMSM servo systems based on the coupling technique of the neural network surrogate model and intelligent optimization algorithm. A hybrid model is constructed by the proposed method, integrating a mathematical model based on transfer functions with an artificial neural network surrogate model, which is employed to compensate for the discrepancies between the mathematical model and the actual measured values. The accuracy and superiority of the hybrid model are comprehensively validated through training and validation loss analysis, fitting plot construction, and ablation experiments. Subsequently, based on the hybrid model, the qualitative and quantitative comparative analysis of the Pareto fronts of five commonly used multi-objective intelligent optimization algorithms is conducted. The optimal algorithm is determined through experimental validation of the optimization results to obtain the optimal result. The optimal result demonstrates that, compared to the initial result before optimization, the overshoot is reduced by 89.7%, and the settling time is reduced by 80.1%. Additionally, several other non-dominated solutions are available for selection, and all optimized results are superior to the initial result. This study provides a novel idea and method for the performance optimization of PMSM servo systems, contributing to the field with a robust and effective approach to enhance system control performance. Full article

Wellbore washing technology is a basic operation in wellbore maintenance. Problems such as low automation levels, long processing times, the fact that it is easy to cause downhole falling, and cleaning blind areas greatly affect the use and maintenance of traditional cleaning equipment. These problems usually come from design defects such as a complicated installation process, a lack of an anti-impact structure, and a fixed jet direction. To address the aforementioned issues, this paper proposes an efficient and integrated rapid-disassembly and -assembly automatic filtration rotary jet cleaning device. The device is divided into two main units and further subdivided into four modules. The quick-assembly unit comprises an elastic connection module and a downstroke quick-assembly module, which can automatically compensate for deviations in equipment position during the installation process, ensuring the reliability of the installation process and the sealing of the equipment and facilitating the rapid connection and separation of the tool string. The wellbore cleaning unit includes a hydraulic rotary washing module and a rotary filtration storage module. The wellbore is jet-flushed by hydraulic drive, and the solid particles are separated and filtered during the cleaning fluid circulation process to realize the purification and reuse of the cleaning fluid. The device reduces the installation operation time and labor cost, improves the reliability of equipment in the well, improves the flushing coverage area and the cleaning efficiency, realizes the reuse of the cleaning liquid in the wellbore, reduces the energy consumption of the flowback treatment, and comprehensively improves the cleaning efficiency and the energy utilization efficiency. Full article

To advance the application of sustainable recycled aggregate concrete (RAC) in bridge engineering, this study introduces a novel reinforced RAC-filled circular steel tubular (RRACFCST) column, leveraging the dual confinement of an external steel tube and an internal reinforcement cage. Its primary novelty is a comprehensive analytical framework integrating a new theoretical model by using limit analysis, ferrule theory, and the twin shear unified strength theory. Then, a rigorously validated nonlinear finite element model that incorporated material nonlinearity and interface effects was used to validate the proposed theoretical model. The results demonstrate the significant performance of the steel reinforcement cage, which enhanced the axial bearing capacity by 17.86%, and an optimal recycled aggregate replacement rate of 70% yielded the bearing capacity, with 100% replacement still achieving a 13.3% higher capacity than unconfined conventional concrete, demonstrating how effective confinement can compensate for and overcome the inherent deficiencies of RCA. Conversely, larger diameter–thickness ratios would reduce the strength by 33.7%. These quantifiable findings provide critical design insights and a validated predictive tool, establishing the RRACFCST column as a promising and high-performance sustainable solution for bridge structures. Full article

To address the challenges of multiple cost-influencing factors, high risks, and difficult control in highway construction projects, this study conducts a cost risk assessment based on the full-process perspective of project owners. Considering the long duration and distinct phases of highway construction projects, the study employs a literature-based statistical method to identify the factors influencing cost risks and establishes an evaluation index system for cost risk factors throughout the entire construction process. Based on questionnaire surveys, the study applies the Analytic Hierarchy Process (AHP) to calculate the initial weights of the cost risk factors. Then, the improved entropy weight method is used to compute the correction coefficients for the initial weights and determine the final weight of each influencing factor. By integrating the results from AHP, the comprehensive weights of all factors are obtained, thereby identifying the key factors affecting cost risks throughout the entire highway construction process. Additionally, cost risk prevention and control measures are proposed. The research findings indicate that among the 42 evaluation factors, the ten factors with the greatest impact on project cost risks are project positioning changes, price inflation, unclear or erroneous contract terms, lack of supervision by design units, delayed compensation payments, collusion in bidding (including bid-rigging and cover bidding), lack of coordination among different departments leading to schedule risks, construction claims risks, risks associated with bidding methods, and financing risks. These ten key factors are analyzed in detail, and corresponding risk prevention and control measures are proposed. Full article

Wear and overheating in cycloidal systems significantly affect motion transmission efficiency, component lifespan, and maintenance costs. Numerous solutions have been proposed in the patent literature to address these issues. This paper identifies, analyzes, and classifies these solutions based on their working principles, strategies (Preventive, Compensative, Mitigative), and implementation methods. The results indicate that preventive strategies are predominant, mainly through friction reduction achieved via rolling elements. Compensative strategies rely on lubrication, while mitigative strategies, though rare, involve restoring contact surfaces through reshaping. This review serves as a comprehensive reference for the development of new solutions in the field. Full article