Search Results (113)

This work presents the experimental validation of a reset control mode for a Quasi-Spherical Parallel Manipulator (qSPM), designed as a master device for bilaterally teleoperated telesurgical systems. The reset functionality enables autonomous repositioning of the master device to its central configuration via a joint-space path planning algorithm, executed entirely within the local control loop. Given the non-convex nature of the joint space, the algorithm computes feasible trajectories using a simplified optimization scheme that ensures compliance with mechanical and kinematic constraints. The algorithm was implemented within an ROS Noetic framework and tested across multiple scenarios, including both simulated and physical configurations. The experimental results confirm the device’s ability to reset to the central position in approximately 5 s, maintaining an average residual error below $0.25°$. Computational evaluations demonstrate that each path is generated in less than 10 milliseconds, supporting real-time execution. Additional trials show successful motion toward arbitrary points within the joint space, suggesting the potential for future integration of user-driven repositioning features. These findings highlight the responsiveness, reliability, and experimental feasibility of the proposed control mode, marking a key step toward improving usability in telesurgical environments. Full article

The phase detection of abnormal gait and the prediction of lower-limb angles are key challenges in controlling lower-limb exoskeletons. This study simulated three types of abnormal gaits: scissor gait, foot-drop gait, and staggering gait. To enhance the recognition capability for abnormal gait phases, a four-discrete-phase division for a single leg is proposed: pre-swing, swing, swing termination, and stance phases. The four phases of both legs further constitute four stages of walking. Using the Euler angles of the ankle joints as inputs, the capabilities of a Convolutional Neural Network and a Support Vector Machine in recognizing discrete gait phases are verified. Based on these discrete gait phases, a continuous phase estimation is further performed using an adaptive frequency oscillator. For predicting the lower-limb motion angle, this study innovatively proposes an input scheme that integrates three-axis ankle joint angles and continuous gait phases. Comparative experiments confirmed that this information fusion scheme improved the limb angle prediction accuracy, with the Convolutional Neural Network–Long Short-Term Memory network yielding the best prediction results. Full article

As metal mines advance into deep mining, the increase in tectonic stress and horizontal stress leads to a higher degree of joint and fissure development in roadway surrounding rocks, along with a significant rise in both the fragmentation degree of the rock mass and the support cost. This paper adopts field monitoring and numerical simulation methods to analyze the characteristics of mining-induced stress rotation after unloading due to deep roadway excavation in the Jinchuan mining area, and proposes corresponding surrounding rock control countermeasures and optimized schemes for the original support. The research results show that after the unloading caused by the excavation of deep roadway surrounding rock, the magnitudes and directions of the maximum, intermediate, and minimum principal stresses all exhibit a trend of slow change, followed by drastic change, and finally gradual stabilization. When the roadway advances to 4 m in front of the monitor section, the adjustment of the magnitude of principal stress of the surrounding rock is the most drastic. Moreover, as the working face moves away from the monitor section, the principal stress gradually stabilizes and becomes lower than the initial stress value. When the roadway advances to 6 m in front of the monitor section, the adjustment of the direction of the principal stress of the surrounding rock is the most drastic. The rotation angle of the maximum principal stress shows a trend of first increasing and then decreasing with the increase in the excavation step, while the rotation angles of the intermediate and minimum principal stresses show a trend of first decreasing and then increasing as the excavation step increases. Based on the spatial distribution characteristics of joints and fissures in the roadway surrounding rock, the sensitive area for the rotation of mining-induced stress direction is defined. By changing the advancing direction of the roadway, the rotation trajectory of the principal stress can be deviated from the sensitive area, thereby improving the self-stabilization ability of the roadway surrounding rock. It is proposed that asymmetric coupling support be adopted to reinforce the positions where the principal stress rotation of the rock mass around the anchorage is severe, which can effectively reduce the range of the plastic zone in the roadway surrounding rock. The research results provide new ideas for the surrounding rock control of deep roadways, as well as a theoretical basis for the design and optimization of roadway support parameters in similar mines. Full article

Driven by advancements in communication technology, the Internet of Vehicles (IoV) has gained significant importance. Its capability for real-time information exchange and processing substantially enhances data transmission performance within multi-node distributed systems. Among core physical layer transmission technologies, beamforming and power allocation are crucial for optimizing system efficiency. However, the real-time joint optimization of the transmitter, receiver, and power allocation in MIMO-based IoV systems remains insufficiently addressed in existing research. To bridge this gap, this paper proposes a framework for the real-time joint optimization of beamforming and power allocation, aiming to maximize transmission efficiency while satisfying constant modulus constraints and power limitations. The proposed framework decomposes the problem and utilizes the CVX library to obtain a local optimum for the joint scheme. The simulation results show that compared with traditional beamforming methods, this scheme has better performance in multiple indicators, increasing the transmission rate of the system by 43%, having faster convergence speed, and improving spectral efficiency. Thus, this study achieves real-time joint optimization of MIMO beamforming and power allocation for IoV scenarios, providing crucial technical support for related designs. Full article

The Internet of Things (IoT) has promoted emerging applications that require massive device collaboration, heavy computation, and stringent latency. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems can provide flexible services for user devices (UDs) with wide coverage. The optimization of both latency and energy consumption remains a critical yet challenging task due to the inherent trade-off between them. Joint association, offloading, and computing resource allocation are essential to achieving satisfying system performance. However, these processes are difficult due to the highly dynamic environment and the exponentially increasing complexity of large-scale networks. To address these challenges, we introduce a carefully designed cost function to balance the latency and the energy consumption, formulate the joint problem into a partially observable Markov decision process, and propose two multi-agent deep-reinforcement-learning-based schemes to tackle the long-term problem. Specifically, the multi-agent proximal policy optimization (MAPPO)-based scheme uses centralized learning and decentralized execution, while the closed-form enhanced multi-armed bandit (CF-MAB)-based scheme decouples association from offloading and computing resource allocation. In both schemes, UDs act as independent agents that learn from environmental interactions and historic decisions, make decision to maximize its individual reward function, and achieve implicit collaboration through the reward mechanism. The numerical results validate the effectiveness and show the superiority of our proposed schemes. The MAPPO-based scheme enables collaborative agent decisions for high performance in complex dynamic environments, while the CF-MAB-based scheme supports independent rapid response decisions. Full article

To solve the problem of traditional engineering drilling rig propulsion systems being difficult to adapt to complex working conditions due to their bulky structure and poor load adaptability, this study proposes a new type of mechanical hydraulic composite electro-hydraulic proportional propulsion system. The system innovatively adopts a composite design of parallel hydraulic cylinders and movable pulley groups in mechanical structure, aiming to achieve system lightweighting through displacement multiplication effect. In terms of control strategy, a fuzzy adaptive PID controller based on position feedback was designed to improve the dynamic tracking performance and robustness of the system under nonlinear time-varying loads. The study established a multi physics domain mathematical model of the system and conducted joint simulation using AMESim and MATLAB/Simulink to deeply verify the overall performance of the proposed scheme. The simulation results show that the mechanical structure can stably achieve a 2:1 displacement multiplication effect, providing a feasible path for shortening the system size. Compared with traditional PID control, the proposed fuzzy adaptive PID control strategy significantly improves the positioning accuracy of the system. The maximum tracking errors of the master and slave hydraulic cylinders are reduced from 6.3 mm and 10.4 mm to 2.3 mm and 5.6 mm, respectively, and the accuracy is improved by 63.49% and 46.15%, providing theoretical support and technical reference for the design of engineering drilling rig propulsion control systems. Full article

Virtual power plants (VPPs) enable the efficient participation of distributed renewable energy resources in electricity markets by aggregating them. However, the profitability of VPPs is challenged by market volatility and regulatory constraints, such as price caps and imbalance penalties. This study examines the joint impact of varying price cap levels and imbalance penalty structures on the bidding strategies and revenues of VPPs. A stochastic optimization model was developed, where a three-stage scenario tree was utilized to capture the uncertainty in electricity prices and renewable generation output. Simulations were performed under various market conditions using real-world price and generation data from the Korean electricity market. The analysis reveals that higher price cap coefficients lead to greater revenue and more segmented bidding strategies, especially under asymmetric penalty structures. Segment-wise analysis of bid price–quantity pairs shows that over-bidding is preferred under upward-only penalty schemes, while under-bidding is preferred under downward-only ones. Notably, revenue improvement tapers off beyond a price cap coefficient of 0.8, which indicates that there exists an optimal threshold for regulatory design. The findings of this study suggest the need for coordination between price caps and imbalance penalties to maintain market efficiency while supporting renewable energy integration. The proposed framework also offers practical insights for market operators and policymakers seeking to balance profitability, adaptability, and stability in VPP-integrated electricity markets. Full article

Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in performance, capacity, and cost-effectiveness. To tackle frequency regulation challenges in remote desert-based renewable energy hubs—where traditional power infrastructure is unavailable—this study introduces a planning framework for an electro-hydrogen energy storage system with grid-forming capabilities, designed to supply both inertia and frequency response. At the system design stage, a direct current (DC) transmission network is modeled, integrating battery and hydrogen storage technologies. Using this configuration, the capacity settings for both grid-forming batteries and hydrogen units are optimized. This study then explores how hydrogen systems—comprising electrolyzers, storage tanks, and fuel cells—and grid-forming batteries contribute to inertial support. Virtual inertia models are established for each technology, enabling precise estimation of the total synthetic inertia provided. At the operational level, this study addresses stability concerns stemming from renewable generation variability by introducing three security indices. A joint optimization is performed for virtual inertia constants, which define the virtual inertia provided by energy storage systems to assist in frequency regulation, and primary frequency response parameters within the proposed storage scheme are optimized in this model. This enhances the frequency modulation potential of both systems and confirms the robustness of the proposed approach. Lastly, a real-world case study involving a 13 GW renewable energy base in Northwest China, connected via a ±10 GW HVDC export corridor, demonstrates the practical effectiveness of the optimization strategy and system configuration. Full article

Due to the long operation period of Beijing Metro Line 2 and the complex surrounding building environment, this paper comprehensively studied the mechanical properties of new tunnels using close-fitting undercrossing based on pre-support technology. To control structural deformation caused by the expansion project, methods such as laboratory tests, numerical simulation, and field tests were adopted to systematically analyze the tunnel mechanics during the undercrossing of existing metro lines. First, field tests were carried out on the existing Line 2 and Line 3 tunnels during the construction period. It was found that the close-fitting construction based on pre-support technology caused small deformation displacement in the subway tunnels, with little impact on the smoothness of the existing subway rail surface. The fluctuation range was −1 to 1 mm, ensuring the safety of existing subway operations. Then, a refined finite difference model for the close-fitting undercrossing construction process based on pre-support technology was established, and a series of field and laboratory tests were conducted to obtain calculation parameters. The reliability of the numerical model was verified by comparing the monitored deformation of existing structures with the simulated structural forces and deformations. The influence of construction methods on the settlement changes of existing line tracks, structures, and deformation joints was discussed. The research results show that this construction method effectively controls the settlement deformation of existing lines. The settlement deformation of existing lines is controlled within 1~3 cm. The deformation stress of the existing lines is within the concrete strength range of the existing structure, and the tensile stress is less than 3 MPa. The maximum settlement and maximum tensile stress of the station in the pre-support jacking scheme are −5.27 mm and 2.29 MPa. The construction scheme with pre-support can more significantly control structural deformation, reduce stress variations in existing line structures, and minimize damage to concrete structures. Based on the monitoring data and simulation results, some optimization measures were proposed. Full article

Community-based systems present a key option for water services, especially in rural areas. Our goal is to achieve a state-of-the-art understanding of joint rural water supply development in Finland over 150 years. A mixed-methods approach was used: a literature survey and a questionnaire to selected experts. Based on the literature, a table including 23 decisions considered the most influential strategic events from 1872 to 2022 was produced. The table was sent to 10 selected experts known to be deeply familiar with the theme, all of whom replied. Joint rural water services in Finland have evolved based on demand through co-operative principles. The first documented scheme was constructed in 1872, while governmental financial support to rural water services started in 1951. It expanded in various forms until it dramatically declined in recent years. Multi-locality may increase the need for these services in the future. The expert survey revealed the following most influential long-term decisions: the first official water co-operative established in 1907, the land reform for immigrants and war veterans introduced in 1945, the Committee for Rationalisation of Households established in 1950, the start of domestic manufacturing of plastic pipes in 1954, and the Water Act enacted in 1962 to start water pollution control. This paper reminds us that urban and rural services are not contradictory but can supplement each other. Full article

The increasing threat of quantum computing to traditional cryptographic systems has prompted intense research into post-quantum schemes. Despite SNOVA’s potential for lightweight and secure digital signatures, its performance on embedded devices (e.g., ARMv8 platforms) remains underexplored. This research addresses this gap by presenting the optimal SNOVA implementations on embedded devices. This paper presents a performance-optimized implementation of the SNOVA post-quantum digital signature scheme on ARMv8 processors. SNOVA is a multivariate cryptographic algorithm under consideration in the NIST’s additional signature standardization. Our work targets the performance bottlenecks in the SNOVA scheme. Specifically, we employ matrix arithmetic over ${\mathbb{GF}}_{16}$ and AES-CTR-based pseudorandom number generation by exploiting the NEON SIMD extension and tailoring the computations to the matrix rank. At a low level, we develop rank-specific SIMD kernels for addition and multiplication. Rank 4 matrices (i.e., 16 bytes) are handled using fully vectorized instructions that align with 128-bit-wise registers, while rank 2 matrices (i.e., 4 bytes) are processed in batches of four to ensure full SIMD occupancy. At the high level, core routines such as key generation and signature evaluation are structurally refactored to provide aligned memory layouts for batched execution. This joint optimization across algorithmic layers reduces the overhead and enables seamless hardware acceleration. The resulting implementation supports 12 SNOVA parameter sets and demonstrates substantial efficiency improvements compared to the reference baseline. These results highlight that fine-grained SIMD adaptation is essential for the efficient deployment of multivariate cryptography on modern embedded platforms. Full article

Accurate preform design in forging processes is critical for improving part quality, conserving material, reducing manufacturing costs, and eliminating secondary operations. This paper presents a finite element (FE) simulation-based methodology for preform design aimed at achieving flashless and near-flashless forging. The approach leverages material point backtracking within FE models to generate physics-informed preform geometries that capture complex material flow, die geometry interactions, and thermal gradients. An iterative scheme combining backtracking, surface reconstruction, and point-cloud solid modeling was developed and applied to several three-dimensional forging case studies, including a cross-joint and a three-lobe drive hub. The methodology demonstrated significant reductions in flash formation, particularly in parts that traditionally exhibit severe flash under conventional forging. Beyond supporting the development of new flashless forging sequences, the method also offers a framework for modifying preforms during production to minimize waste and for diagnosing preform defects linked to variability in frictional conditions, die temperatures, or material properties. Future integration of the proposed method with design of experiments (DOE) and surrogate modeling techniques could further enhance its applicability by optimizing preform designs within a localized design space. The findings suggest that this approach provides a practical and powerful tool for advancing both new and existing forging production lines toward higher efficiency and sustainability. Full article

In 5G-and-beyond (B5G) Internet of Things (IoT) networks, the integration of intelligent reflecting surfaces (IRSs) with millimeter-wave (mmWave) multiple-input multiple-output (MIMO) techniques can significantly improve signal quality and increase network capacity. However, a single fixed IRS lacks the dynamic adjustment capability to flexibly adapt to complex environmental changes and diverse user demands, while mmWave MIMO is constrained by limited coverage. Motivated by these challenges, we investigate the application of drone-mounted IRS-assisted MIMO communications in B5G IoT networks, where multiple IRS-equipped drones are deployed to provide real-time communication support. To fully exploit the advantages of the proposed MIMO-enabled air-to-ground integrated information transmission framework, we formulate a joint optimization problem involving beamforming, phase shift design, and drone deployment, with the objective of maximizing the sum of achievable weighted data rates (AWDRs). Given the NP-hard nature of the problem, we develop an iterative optimization algorithm to solve it, where the optimization variables are tackled in turn. By employing the quadratic transformation technique and the Lagrangian multiplier method, we derive closed-form solutions for the optimal beamforming and phase shift design strategies. Additionally, we optimize drone deployment by using a distributed discrete-time convex optimization approach. Finally, the simulation results show that the proposed scheme can improve the sum of AWDRs in comparison with the state-of-the-art schemes. Full article

Maize is vital for global and Chinese food security. Yet, in Henan Province, a key maize-growing region in China, water scarcity, uneven rainfall, and inefficient irrigation and fertilization limit its yield and quality. This study combines a two-year field experiment (2023–2024) with the DSSAT model to optimize irrigation and fertilization for typical hydrological years (wet, normal, and dry). After calibration and validation with field data, the DSSAT model showed strong performance. Results indicate that optimal irrigation timing and volume vary with hydrological years: no irrigation is needed in wet years, one 30 mm irrigation at the tasseling (VT) stage in normal years, and three irrigations (total 90 mm) at the emergence (VE), jointing (VT), and grain filling (R2) stages in dry years. The optimal nitrogen fertilizer is 240 kg·ha⁻¹ in water-rich and normal years and 180 kg·ha⁻¹ in dry years. These optimized schemes can achieve 98–100% of maximum potential maize yields across hydrological years, offering practical insights for enhancing agricultural water and nutrient management in central Henan to support sustainable development and reduce environmental impacts. Full article

Image steganalysis has been a key technology in information security in recent years. However, existing methods are mostly limited to the binary classification for detecting steganographic images used in digital watermarking, privacy protection, illicit data concealment, and security images, such as unaltered cover images or surveillance images. They cannot identify the steganography algorithms used in steganographic images, which restricts their practicality. To solve this problem, this paper proposes a general steganography algorithms recognition scheme based on image big data matching with improved ResNet50. The scheme first intercepts the image region with the highest complexity and focuses on the key features to improve the analysis efficiency; subsequently, the original image of the image to be detected is accurately located by the image big data matching technique and the steganographic difference feature image is generated; finally, the ResNet50 is improved by combining the pyramid attention mechanism and the joint loss function, which achieves the efficient recognition of the steganography algorithm. To verify the feasibility and effectiveness of the scheme, three experiments are designed in this paper: verification of the selection of the core analysis region, verification of the image similarity evaluation based on Peak Signal-to-Noise Ratio ($PSNR$), and performance verification of the improved ResNet50 model. The experimental results show that the scheme proposed in this paper outperforms the existing mainstream steganalysis models, such as ZhuNet and YeNet, with a detection accuracy of 96.11%, supports the recognition of six adaptive steganography algorithms, and adapts to the needs of analysis of multiple sizes and image formats, demonstrating excellent versatility and application value. Full article