Search Results (452)

The sealing performance of ultra-high-pressure solenoid valves faces significant challenges, particularly under low-temperature conditions. Due to the difference in thermal expansion coefficients between the valve seat and the sealing tube, combined with material contraction at low temperatures, the bolt preload decreases, and consequently the contact force on the sealing surface and the average sealing specific pressure are reduced. This may result in an average sealing specific pressure falling below the required sealing specific pressure, causing leakage and failure of the ultra-high-pressure solenoid valve. To address this problem, this study utilizes theoretical and simulation analysis to examine the preload status in low-temperature environments and the causes of sealing failure in ultra-high-pressure solenoid valves. A corresponding optimization scheme is proposed, which involves increasing the torque from 120 N·m to 130 N·m and applying sealant to the threaded connection to enhance the sealing performance of the ultra-high-pressure solenoid valve. Following the increase in tightening torque and the application of thread sealant, the helium leakage rate at −40 °C is significantly reduced. Specifically, at a test pressure of 87.5 MPa, the helium leakage rate decreases from $1.6\times {10}^{-5}$ mbar·L/s to approximately $1.4\times {10}^{-6}$ mbar·L/s. At test pressures of 1.4 MPa and 10 MPa, the leakage rate is approximately $3.0\times {10}^{-7}$ mbar·L/s. Experimental verification shows that the proposed solution can significantly enhance the sealing reliability of ultra-high-pressure solenoid valves under extreme operating conditions. Full article

Commitment schemes represent foundational cryptographic primitives enabling secure verification protocols across diverse applications, from blockchain systems to zero-knowledge proofs. This paper presents a systematic survey of vector, polynomial, and functional commitment schemes, analyzing their evolution from classical constructions to post-quantum secure alternatives. We examine the strengths and limitations of RSA-based, Diffie–Hellman, and lattice-based approaches, highlighting the critical shift toward quantum-resistant designs necessitated by emerging computational threats. The survey reveals that while lattice-based schemes (particularly those using the Short Integer Solution problem) offer promising security guarantees, they face practical challenges in proof size and verification efficiency. Functional commitments emerge as a powerful generalization, though their adoption is constrained by computational overhead and setup requirements. Key findings identify persistent gaps in adaptive security, composability, and real-world deployment, while proposed solutions emphasize optimization techniques and hybrid approaches. By synthesizing over 90 research works, this paper provides both a comprehensive reference for cryptographic researchers and a roadmap for future developments in commitment schemes, particularly in addressing the urgent demands of post-quantum cryptography and decentralized systems. Full article

In high-precision fields such as advanced manufacturing, semiconductor processing, aerospace assembly, and precision machining, motion control systems often face challenges such as large tracking errors and low control efficiency due to complex dynamic environments. To address this, this paper innovatively proposes a data-driven feedforward compensation control strategy based on a Parallel Gated Recurrent Unit (GRU)–Transformer. This method does not require an accurate model of the controlled object but instead uses motion error data and controller output data collected from actual operating conditions to complete network training and real-time prediction, thereby reducing data requirements. The proposed feedforward control strategy consists of three main parts: first, a Parallel GRU–Transformer prediction model is constructed using real-world data collected from high-precision sensors, enabling precise prediction of system motion errors after a single training session; second, a nonlinear PD controller is introduced, using the prediction errors output by the Parallel GRU–Transformer network as input to generate the primary correction force, thereby significantly reducing reliance on the main controller; and finally, the output of the nonlinear PD controller is combined with the output of the main controller to jointly drive the precision motion platform. Verification on a permanent magnet synchronous linear motor motion platform demonstrates that the control strategy integrating Parallel GRU–Transformer feedforward compensation significantly reduces the tracking error and fluctuations under different trajectories while minimizing moving average (MA) and moving standard deviation (MSD), enhancing the system’s robustness against environmental disturbances and effectively alleviating the load on the main controller. The proposed method provides innovative insights and reliable guarantees for the widespread application of precision motion control in industrial and research fields. Full article

Proof of Storage-Time (PoST) is the core verification mechanism for blockchain data storage, ensuring the integrity and continuous availability of data throughout the storage period. Although the current mainstream Compact Proofs of Storage-Time (cPoST) and Practical and Client-Friendly Proof of Storage-Time (ePoST) solutions have seen significant progress in engineering implementation, their security fundamentally relies on the algebraic structure assumptions underlying their verifiable delay function (VDF) components. In addition, if there are small-order elements that can be efficiently calculated in the underlying group structure, it will directly lead to the failure of the soundness properties of the VDF; thus, the entire PoST system will face systemic security risks. To address the above issues, we propose an innovative PoST protocol based on the modular Lucas sequence. By constructing a delay function through the modular Lucas sequence, the security condition is transferred from the strong security assumption to the weak security assumption, which enhances the security of the protocol: when the protocol encounters an algorithmic breakthrough that causes the modular square security assumption to fail, the soundness of the protocol can still be guaranteed. Secondly, we map all elements to the target $\lambda$-strong groups through homomorphic mapping technology, a domain input restriction mechanism, and a non-unique representation strategy of elements, effectively avoiding the security risks caused by small-order elements in the group structure. Compared with traditional protocols, our protocol achieves significant improvements in security and reliability, providing a more robust framework for decentralized storage and data verification. Full article

Pine wilt disease endangers the ecological stability of China’s coniferous woodlands. In a specific region, the number of dead pine trees has exhibited a consistent year-on-year increase, highlighting the urgent need for efficient and sustainable monitoring strategies. However, UAV-based remote sensing methods currently face challenges in complex environments, including insufficient feature-capture capabilities, interference from visually similar objects, and limited localization accuracy. This study developed a remote sensing workflow leveraging high-resolution UAV imagery to oversee pine trees affected with pine wilt disease. An enhanced YOLOv5 detection model was employed to identify symptomatic trees. To strengthen feature extraction capabilities—particularly for color and texture traits indicative of infection—different types of attention mechanisms, for instance SE, CBAM, ECA, and CA, were integrated as part of the model. Furthermore, a BiFPN structure was incorporated to enhance the fusion of features across multiple scales, and the EIoU loss function was adopted to boost the accuracy of bounding box prediction, ultimately enhancing detection precision. Experimental results show that the enhanced SEBiE-YOLOv5 framework achieved a precision of 89.4%, with an AP of 86.1% and an F1-score of 83.1%. UAV-based monitoring conducted during the spring and autumn of 2023 identified 616 dead trees, with field verification accuracy ranging from 88.91% to 92.42% and localization errors within 1–10 m. These findings validate the method’s high accuracy and spatial precision in complex mountainous forest environments. By integrating attention mechanisms, BiFPN, and the EIoU loss function, the proposed SEBiE-YOLOv5 model substantially enhances the recognition accuracy of key features in infected trees as well as their localization performance, and offers a practical and computationally efficient approach for the long-term surveillance of pine wilt disease in challenging terrain. Full article

In order to determine thermally safe driving parameters of ignition coils for hydrogen internal combustion engines (ICE), a reliable estimation of internal power losses is essential. These losses include resistive winding losses, magnetic core losses due to hysteresis and eddy currents, dielectric losses in the insulation, and electronic switching losses. Direct experimental assessment is difficult because the components are inaccessible, while conventional computer-aided engineering (CAE) approaches face challenges such as the need for accurate input data, the need for detailed 3D models, long computation times, and uncertainties in loss prediction for complex structures. To address these limitations, we propose an artificial intelligence (AI)-based framework for estimating internal losses from external temperature measurements. The method relies on an artificial neural network (ANN), trained to capture the relationship between external coil temperatures and internal power losses. The trained model is then employed within an optimization process to identify losses corresponding to experimental temperature values. Validation is performed by introducing the identified power losses into a CAE thermal model to compare predicted and experimental temperatures. The results show excellent agreement, with errors below 3% across the −30 °C to 125 °C range. This demonstrates that the proposed hybrid ANN–CAE approach achieves high accuracy while reducing experimental effort and computational demand. Furthermore, the methodology allows for a straightforward determination of the coil safe operating area (SOA). Starting from estimates derived from fitted linear trends, the SOA limits can be efficiently refined through iterative verification with the CAE model. Overall, the ANN–CAE framework provides a robust and practical tool to accelerate thermal analysis and support coil development for hydrogen ICE applications. Full article

This study explores the effectiveness of 3D Digital Image Correlation (DIC) for measuring displacement and strain of a propeller undergoing angular motion. Traditional methods, such as strain gauges, face limitations including physical interference, technical difficulties in sensor connections, and restricted measurement points, leading to inaccuracies in capturing true conditions. To overcome these challenges, this research utilizes non-contact 3D DIC technology, enabling measurement of surface displacements and deformations without interfering with the tested component. Experiments were conducted using the model aircraft propellers mounted on a custom-built test stand for partial angular motion. The 1 Mpx high-speed cameras captured strain and displacement data across the propeller blades during motion. The DIC strain measurements were then compared to strain gauge data to evaluate their accuracy and reliability. The results demonstrate that 3D DIC enables precise displacement measurements, while strain measurements are subject to certain limitations. Displacement measurements were achieved with a noise level of ±10 μm, while strain measurement noise ranged from 26 to 174 µm/m depending on direction. Strain gauge measurements were also performed for verification of the DIC measurements and calibration of the filtering procedure. Two types of non-metallic materials were used in the study: Nylon LGF60 PA6 for the propeller and 3D-printed PC ABS for the cantilever beam used in strain measurement validation. This study underscores the potential of DIC for monitoring rotating components, with a particular focus on measuring strains that are often overlooked in publications addressing similar topics. Additionally, it focuses on comparing DIC strain measurements with strain gauge data on rotating components, addressing a critical gap in existing literature, as strain measurement in rotating structures remains underexplored in current research. Full article

This article comprehensively analyzes the new developments and challenges faced by several typical prediction models in the field of radiation effects in recent years. The models discussed include the RPP model, the extended RPP (rectangular parallelepiped) model, and the IRPP (integral rectangular parallelepiped) model. The article conducts a comprehensive analysis of the limitations of the assumption that uses the linear energy transfer (LET) of incident particles and the SEU (single-particle upset) cross-section (without considering the energy and type of ions) to predict the rate of single-particle effects (SEUs). Additionally, the article points out that with the continuous progress of integrated circuit technology, the geometric shape of the target circuit, the energy of the incident particles, the type of particles, and more precise physical models corresponding to the interaction between radiation and matter have become increasingly important in evaluating the sensitivity to single-particle effects (SEEs). Subsequently, based on the probability characteristics of SEE, a series of general estimation equations for the SEE rate are derived, considering particle energy, particle type, and the probability of influence at a specific moment. Then, by introducing the concept of interaction volume, the concept of sensitive volume is further expanded, and using these general equations, the relationship between the SEE rate cross-section and the SEE projected area is derived, simplifying the SEU rate prediction equation to a form that can be directly used in engineering applications. Finally, the article emphasizes a complete method of applying the general prediction equation to engineering to estimate the radiation disturbance performance of two typical verification circuits, and provides the corresponding prediction results. Full article

The current digital delivery of surface engineering in oil and gas fields faces challenges such as difficulty in integrating multiple heterogeneous data sources, low efficiency in quality reviews, and a lack of unified evaluation standards, which seriously restrict the implementation of intelligent operation and maintenance. Based on this, this study constructs multi-dimensional control rules for data quality covering the entire lifecycle. Based on the characteristics of structured, semi-structured, and unstructured data, five-dimensional review criteria and quantification methods for normative, integrity, consistency, accuracy, and timeliness were developed. At the same time, by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM), a combined subjective and objective weight evaluation model was established to achieve scientific quantitative calculation of quality indicators. Verification with a project by Southwest Oil and Gas Field shows that the system effectively achieves quantifiable diagnosis and traceability of engineering data quality, revealing the differentiation characteristics of different data types in the quality dimension. The research results provide core methodological support for the establishment of an integrated data governance paradigm of “collection—review—operation and maintenance” in oil and gas fields, facilitating the implementation of intelligent operation and maintenance. Full article

With coal mines’ mining depth increasing, the stress environment in deep mining (including key factors such as high ground stress, strong disturbance, and complex geological structures, as well as stress redistribution after deformation of surrounding roadway rock) is complex, which leads to increasingly prominent deformation and failure problems for goaf-side roadways in thick coal seams. Surrounding rock deformation is difficult to control, and mine pressure behavior is violent, making traditional support technologies no longer able to meet the mining safety requirements of roadways in deep thick coal seams. Taking the 6311 working face of Tangkou Coal Mine as the engineering research background, this paper systematically summarizes the deformation and failure characteristics of goaf-side roadways in deep thick coal seams through field monitoring, borehole peeping, and other means, and conducts in-depth analysis of their failure mechanisms and influencing factors. Aiming at these problems, a synergistic support–unloading control method for goaf-side roadways is proposed, which integrates roof blasting pressure relief, coal pillar grouting reinforcement, and constant-resistance energy-absorbing anchor cable support. The effects of the unsupported scheme, original support scheme, and synergistic support–unloading control scheme are compared and analyzed through FLAC^3D numerical simulation. Further verification through field application shows that it has remarkable effects in controlling roadway convergence deformation, roof separation, and bolt (cable) stress. Specifically, compared with the original support schemes, the horizontal displacement on the coal pillar side is reduced by 89.5% compared with the original support scheme, and the horizontal displacement on the solid coal side is reduced by 79.3%; the vertical displacement on the coal pillar side is reduced by 45.8% and the vertical displacement on the solid coal side is reduced by 42.4%. Compared with the original support scheme, the maximum deformation of the roadway’s solid coal rib, roof, and coal pillar rib is reduced by 76%, 83%, and 88%, respectively, while the separation between the shallow and deep roof remains at a low level. The coal stress continues fluctuating stably during the monitoring period; the force on the bolts (cables) does not exceed the designed anchoring force, with sufficient bearing reserve space (47% remaining), and no breakage occurs, which fully proves the feasibility and effectiveness of the synergistic support–unloading control technology scheme. This technology realizes the effective control of on-site roadways and provides technical reference for the support engineering of coal mine goaf-side roadways under similar conditions. Full article

Radio frequency identification (RFID) is popular and attaining momentum in manifold sectors, including, but not limited to, pharmaceuticals, retail, defense, transport, healthcare and currently security. Utilizing RFID solely as a solution does not result in effective security. Conventional systems have integrated only one solution, such as GSM, cryptography, wireless sensors, biometrics or a One-Time Password (OTP); however, the security provided is limited since each incorporated technology has its disadvantages. Our paper proposes improving the conventional methods in the field by proposing an intelligent door-lock system prototype implementing two-step authentication, providing double-layered security provisions in, for instance, highly sensitive zones. The suggested technique, firstly based on RFID technology and then a password (PIN) during the authentication process, results in a hybrid system that is more accurate and efficient compared to a traditional, single-method system. The Arduino micro-controller is interfaced with RFID, with a keypad that receives the input to the micro-controller, a Liquid Crystal Display to output the authentication status and finally a motor connected to the door for automation within a limited time-frame. Adding biometric verification, such as fingerprints and face recognition, can enhance the proposed design further by providing an additional layer of security from external intruders. Full article

The primary challenge that robots face in disaster rescue is to precisely and efficiently construct disaster maps and achieve autonomous navigation. This paper proposes a method for air–ground collaborative map construction. It utilizes the flight capability of an unmanned aerial vehicle (UAV) to achieve rapid three-dimensional space coverage and complex terrain crossing for rapid and efficient map construction. Meanwhile, it utilizes the stable operation capability of an unmanned ground vehicle (UGV) and the ground detail survey capability to achieve precise map construction. The maps constructed by the two are accurately integrated to obtain precise disaster environment maps. Among them, the map construction and positioning technology is based on the FAST LiDAR–inertial odometry 2 (FAST-LIO2) framework, enabling the robot to achieve precise positioning even in complex environments, thereby obtaining more accurate point cloud maps. Before conducting map fusion, the point cloud is preprocessed first to reduce the density of the point cloud and also minimize the interference of noise and outliers. Subsequently, the coarse and fine registrations of the point clouds are carried out in sequence. The coarse registration is used to reduce the initial pose difference of the two point clouds, which is conducive to the subsequent rapid and efficient fine registration. The coarse registration uses the improved sample consensus initial alignment (SAC-IA) algorithm, which significantly reduces the registration time compared with the traditional SAC-IA algorithm. The precise registration uses the voxelized generalized iterative closest point (VGICP) algorithm. It has a faster registration speed compared with the generalized iterative closest point (GICP) algorithm while ensuring accuracy. In reinforcement learning navigation, we adopted the deep deterministic policy gradient (DDPG) path planning algorithm. Compared with the deep Q-network (DQN) algorithm and the A* algorithm, the DDPG algorithm is more conducive to the robot choosing a better route in a complex and unknown environment, and at the same time, the motion trajectory is smoother. This paper adopts Gazebo simulation. Compared with physical robot operation, it provides a safe, controllable, and cost-effective environment, supports efficient large-scale experiments and algorithm debugging, and also supports flexible sensor simulation and automated verification, thereby optimizing the overall testing process. Full article

Large-diameter rock-socketed monopiles supporting offshore wind turbines in soft clay strata face significant geotechnical risks from negative skin friction (NFS) induced by construction surcharges. While the effects of NFS on axial drag loads are documented, the critical interaction between horizontal pile loading and NFS development remains poorly understood. This research bridges this gap using a rigorously validated 3D finite element model that simulates the complex coupling of vertical substructure loads (5 MN), horizontal loading, and surcharge-induced consolidation. The model’s accuracy was confirmed through comprehensive verification against field data for both NFS evolution under surcharge and horizontal load–displacement behavior. The initial analysis under representative conditions (10 MN horizontal load, 100 kPa surcharge, 3600 days consolidation) revealed that horizontal loading fundamentally distorts NFS distribution in the upper pile segment (0 to −24 m), transforming smooth profiles into distinct dual-peak morphologies while increasing the maximum NFS magnitude by 57% (from −45.4 kPa to −71.5 kPa) and relocating its position 21 m upward. This redistribution was mechanistically linked to horizontal soil displacement patterns. Crucially, the NFS neutral plane remained invariant at the clay–rock interface (−39 m), demonstrating complete independence from horizontal loading effects. A systematic parametric study evaluated key operational factors: (1) consolidation time progressively increased NFS magnitude throughout the clay layer, evolving from near-linear to dual-peaked distributions in the upper clay (0 to −18 m); NFS stabilized in the upper clay after 720 days while continuing to increase in the lower clay (−18 to −39 m) due to downward surcharge transfer, accompanied by neutral plane deepening (from −36.5 m to −39.5 m) and 84% maximum axial force escalation (12.5 MN to 23 MN); (2) horizontal load magnitude amplified upper clay NFS peaks at −3.2 m and −9.3 m, with the shallow peak magnitude increasing linearly with load intensity, though it neither altered lower clay NFS nor neutral plane position; (3) surcharge magnitude increased overall NFS, but upper clay NFS (0 to −18 m) stabilized beyond 100 kPa, while lower clay NFS continued rising with higher surcharges, and the neutral plane descended progressively (from −38 m to −39.5 m). These findings demonstrate that horizontal loading critically exacerbates peak NFS values and redistributes friction in upper pile segments without influencing the neutral plane, whereas surcharge magnitude and consolidation time govern neutral plane depth, total NFS magnitude, and maximum drag load. This research delivers essential theoretical insights and practical guidelines for predicting NFS-induced drag loads and ensuring the long-term safety of offshore wind foundations in soft clays under complex multi-directional loading scenarios. Full article

The traditional manufacturing industry is facing the challenge of digital transformation, which involves the enhancement of intelligence and production efficiency. Many robotic applications have been discussed to enable collaborative robots to perform operations smartly rather than just automatically. This article tackles the issues of intelligent robots with cognitive and coordination capability by introducing cyber-physical integration technology. The authors propose a system architecture with open-source software and low-cost hardware based on the 5C hierarchy and then conduct experiments to verify the proposed framework. These experiments involve the collection of real-time data using a depth camera, object detection to recognize obstacles, simulation of collision avoidance for a robotic arm, and cyber-physical integration to perform a robotic task. The proposed framework realizes the scheme of the 5C architecture of Industry 4.0 and establishes a digital twin in cyberspace. By utilizing connection, conversion, calculation, simulation, verification, and operation, the robotic arm is capable of making independent judgments and appropriate decisions to successfully complete the assigned task, thereby verifying the proposed framework. Such a cyber-physical integration system is characterized by low cost but good effectiveness. Full article

The proliferation of biometric technology across various domains including user identification, financial services, healthcare, security, law enforcement, and border control introduces convenience in user identity verification while necessitating robust protection mechanisms for sensitive biometric data. While chaos-based encryption systems offer promising solutions, many existing chaos-based encryption schemes exhibit inherent shortcomings including deterministic randomness and constrained key spaces, often failing to balance security robustness with computational efficiency. To address this, we propose a novel dual-layer cryptographic framework leveraging a four-dimensional (4D) Qi hyperchaotic system for protecting biometric templates and facilitating secure feature matching operations. The framework implements a two-tier encryption mechanism where each layer independently utilizes a Qi hyperchaotic system to generate unique encryption parameters, ensuring template-specific encryption patterns that enhance resistance against chosen-plaintext attacks. The framework performs dimensional normalization of input biometric templates, followed by image pixel shuffling to permutate pixel positions before applying dual-key encryption using the Qi hyperchaotic system and XOR diffusion operations. Templates remain encrypted in storage, with decryption occurring only during authentication processes, ensuring continuous security while enabling biometric verification. The proposed system’s framework demonstrates exceptional randomness properties, validated through comprehensive NIST Statistical Test Suite analysis, achieving statistical significance across all 15 tests with p-values consistently above 0.01 threshold. Comprehensive security analysis reveals outstanding metrics: entropy values exceeding 7.99 bits, a key space of ${10}^{320}$, negligible correlation coefficients (<${10}^{-2}$), and robust differential attack resistance with an NPCR of 99.60% and a UACI of 33.45%. Empirical evaluation, on standard CASIA Face and Iris databases, demonstrates practical computational efficiency, achieving average encryption times of 0.50913s per user template for 256 × 256 images. Comparative analysis against other state-of-the-art encryption schemes verifies the effectiveness and reliability of the proposed scheme and demonstrates our framework’s superior performance in both security metrics and computational efficiency. Our findings contribute to the advancement of biometric template protection methodologies, offering a balanced performance between security robustness and operational efficiency required in real-world deployment scenarios. Full article