Search Results (796)

Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. An ADASYN-Tomek Links hybrid strategy first addresses class imbalances. The core innovation of FFT-RDNet lies in its novel two-dimensional spatial feature modeling approach, realized through a dedicated dual-path feature embedding module. One branch extracts discriminative statistical features in the time domain, while the other branch transforms the data into the frequency domain via Fast Fourier Transform (FFT) to capture the essential energy distribution characteristics. These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. This network effectively captures complex periodic attack patterns with minimal computational overhead. Comprehensive evaluation on the NSL-KDD and CIC-IDS2018 datasets shows that FFT-RDNet outperforms state-of-the-art neural network IDSs across accuracy, precision, recall, and F1 score (improvements: 0.22–1%). Crucially, it achieves superior accuracy with a significantly reduced computational complexity, demonstrating high efficiency for resource-constrained IoT security deployments. Full article

With the continuous development of the Internet, people have put forward higher requirements for the stability and availability of the network. Although we constantly strive to take measures to avoid network failures, it is undeniable that network failures are unavoidable. Therefore, in this situation, enhancing the stability and reliability of the network to cope with possible network failures has become particularly crucial. Therefore, researching and developing high fault protection rate intra-domain routing protection schemes has become an important topic and is the subject of this study. This study aims to enhance the resilience and service continuity of networks in the event of failures by proposing innovative routing protection strategies. The existing methods, such as Loop Free Alternative (LFA) and Equal Cost Multiple Paths (ECMP), have some shortcomings in terms of fast fault detection, fault response, and fault recovery processes, such as long fault recovery time, limitations of routing protection strategies, and requirements for network topology. In response to these issues, this article proposes a new routing protection scheme, which is an intra-domain routing protection scheme based on the minimum cross-degree backup path. The core idea of this plan is to find the backup path with the minimum degree of intersection with the optimal path, in order to avoid potential fault areas and minimize the impact of faults on other parts of the network. Through comparative analysis and performance evaluation, this scheme can provide a higher fault protection rate and more reliable routing protection in the network. Especially in complex networks, this scheme has more performance and protection advantages than traditional routing protection methods. The proposed scheme in this paper exhibits a high rate of fault protection across multiple topologies, demonstrating a fault protection rate of 1 in the context of real topology. It performs commendably in terms of path stretch, evidenced by a figure of 1.06 in the case of real topology Ans, suggesting robust path length control capabilities. The mean intersection value is 0 in the majority of the topologies, implying virtually no common edge between the backup and optimal paths. This effectively mitigates the risk of single-point failure. Full article

The article presents the quantum signal-induced heap transform (QsiHT) method of the QR-decomposition of multi-qubit operations. This transform can be generated by a given signal, by using different paths, or orders, of processing the data. We propose using the concept of the fast path of calculation of the QsiHT and applying such transforms on each stage of the matrix decomposition. This allows us to build quantum circuits for multi-qubit unitary operation without permutations. Unitary operations with real and complex matrices are considered. The cases of 3- and 4-qubit operations are described in detail with quantum circuits. These circuits use a maximum of 28 and 120 Givens rotation gates for 3- and 4-qubit real operations, respectively. All rotations are performing only on adjacent bit planes. For complex unitary operation, each of the Givens gates is used in pairs with two Z-rotation gates. These two types of rotations and the global phase gate are the universal gate set for multi-qubit operations. The presented approach can be used for implementing quantum circuits for $n$-qubits when $n\ge 2$, with a maximum of ($4^n/2-2^{n-1})$ Givens rotations and no permutations. 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

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 autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

In this study, the neutron and gamma radiation-shielding properties of serpentinites from the Guleman ophiolite complex were investigated, and results were evaluated in comparison with rare earth element (REE) content. The linear and mass attenuation coefficients (LAC and MAC), half-value layer (HVL), mean free path (MFP), and effective atomic numbers (Z_eff) of serpentinite samples were experimentally measured in the energy range of 80.99–383.85 keV. Theoretical MAC values were calculated. Additionally, fast neutron removal cross-sections, as well as thermal and fast neutron macroscopic cross-sections, were theoretically determined. The absorbed equivalent dose rates of serpentinite samples were also measured. The radiation protection efficiency (RPE) for gamma rays and neutrons were determined. It was observed that the presence of rare earth elements within serpentinite structure has a significant impact on thermal neutron cross-sections, while crystalline water content (LOI) plays an influential role in fast neutron cross-sections. Moreover, it has been observed that the concentration of gadolinium exerts a more substantial influence on the macroscopic cross-sections of thermal neutrons than on those of fast neutrons. The research results reveal the mineralogical, geochemical, morphological and radiation-shielding properties of serpentinite rocks contribute significantly to new visions for the use of this naturally occurring rock as a geological repository for nuclear waste or as a wall-covering material in radiotherapy centers and nuclear facilities instead of concrete. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

With the rapid development of smart connected vehicles, vehicle network communications demand high-speed data transmission to support advanced automotive services. Millimeter Wave (mmWave) communication offers fast data rates, strong anti-interference capabilities, high precision localization and low-latency, making it suitable for high-speed in-vehicle communications. However, mmWave communication performance in vehicular networks is hindered by high path loss and frequent beam alignment updates, significantly degrading the coverage and connectivity of vehicle nodes (VNs). In addition, atmospheric propagation attenuation further deteriorates signal quality and limits system performance due to raindrop absorption and scattering. Therefore, the pure mmWave networks cannot meet the high requirements of highway vehicular communications. To address these challenges, this paper proposes a hybrid mmWave and microwave network architecture to improve VNs’ coverage and connectivity performances through the strategic deployment of Roadside Units (RSUs). Using Radio Access Technology (RAT), mmWave and microwave RSUs are symmetrically deployed on both sides of the road to communicate with VNs located at the road center. This symmetric RSUs deployment significantly improves the network reliability. Analytical expressions for coverage and connectivity in the proposed hybrid networks are derived and compared with the pure mmWave networks, accounting for rainfall attenuation. The study results show that the proposed hybrid network shows better performance than the pure mmWave network in both coverage and connectivity. Full article

This study investigates the evolution of axial loads in the secondary air system following shaft failure in aeroengines. It addresses a significant gap in the existing literature regarding the effects of inertial forces within the cavity, as well as the unclear mechanisms by which the geometric parameters of the flow path influence these forces. A combined approach of three-dimensional simulation and experimental validation is utilized to propose a method for analyzing the evolution of axial loads during the fast transient response process, based on changes in the Cavity Inertial Force Dominant Zone (CIDZ). The research examines both single cavities and cavity–tube combination flow paths to explore the impact of inertial forces on the axial load response process and, subsequently, the influence of flow path geometric parameters on this response. The results demonstrate that inertial forces within the cavity and the geometric parameters of the flow path significantly affect the axial load response process by influencing the intensity, phase, and minor oscillation amplitude of the axial load response at various end faces within the cavity. The variation in a single geometric parameter in this study resulted in a maximum impact exceeding 500% on the differences in axial loads at different end faces within the cavity. The study offers theoretical support for the load response analysis of the secondary air system in the context of shaft failure, serving as a foundation for safety design related to this failure mode. Full article

This study aimed to explore the independent and interactive effects of varying squat depths and movement speeds on dynamic postural stability during the Part the Wild Horse’s Mane (PWHM) movement. Thirteen male participants (age: 25.86 ± 1.35 years; height: 174.26 ± 6.09 cm; body mass: 68.64 ± 8.15 kg) performed the PWHM movement at three different squat heights, high squat (HS), middle squat (MS), low squat (LS), and two different speeds, fast and slow. Dynamic postural stability (DPSI) was assessed through the center-of-mass (CoM) trajectory and the center-of-pressure (CoP) trajectory. The analyses used two-factor repeated-measures ANOVA and statistical nonparametric mapping, with key metrics including anteroposterior stability (APSI), mediolateral stability (MLSI), vertical stability (VSI), DPSI indices, and the path lengths of the CoP and CoM. LS exhibited significantly greater CoP and CoM path lengths compared with MS and HS (p < 0.01). Furthermore, fast movements demonstrated higher VSI and DPSI than slow movements (p < 0.05). Tai Chi with different squat depths and speeds can affect postural stability. To reduce the fall risk, older adults and individuals with balance impairments should prioritize slower Tai Chi movements, particularly when using high squat postures. Full article

With the modernization of agricultural production, single-arm machine systems in agriculture are unable to meet the needs of future agricultural development. In order to further improve agricultural operation efficiency, the collaborative operation of multi-robotic arms has become a hot topic in current research. This paper focuses on the task allocation problem in the collaborative operation of agricultural multi-robotic arms and summarizes the main algorithms currently used, including the genetic algorithm, particle swarm algorithm, etc., in terms of the aspects of work area division and task planning order. On this basis, further analysis is conducted on the path planning problem of agricultural multi-robotic arms. This paper summarizes the key technologies used in current research, including heuristic algorithms, fast search rapidly exploring random trees, reinforcement learning algorithms, etc., and focuses on reviewing the present applications of cutting-edge reinforcement learning algorithms in agricultural robotic arms. In summary, the agricultural multi-robot arms system can help with agricultural mechanization and intelligent production. Full article

Autonomous exploration is a fundamental challenge for various applications of unmanned aerial vehicles (UAVs). To enhance exploration efficiency in large-scale unknown environments, we propose a Fast Autonomous Exploration Framework (FAEM) designed to enable efficient autonomous exploration and real-time mapping by UAV quadrotors in unknown environments. By employing a hierarchical exploration strategy that integrates geometry-constrained, occlusion-free ellipsoidal viewpoint generation with a global-guided kinodynamic topological path searching method, the framework identifies a global path that accesses high-gain viewpoints and generates a corresponding highly maneuverable, energy-efficient flight trajectory. This integrated approach within the hierarchical framework achieves an effective balance between exploration efficiency and computational cost. Furthermore, to ensure trajectory continuity and stability during real-world execution, we propose an adaptive dynamic replanning strategy incorporating dynamic starting point selection and real-time replanning. Experimental results demonstrate FAEM’s superior performance compared to typical and state-of-the-art methods in existence. The proposed method was successfully validated on an autonomous quadrotor platform equipped with LiDAR navigation. The UAV achieves coverage of 8957–13,042 m³ and increases exploration speed by 23.4% compared to the state-of-the-art FUEL method, demonstrating its effectiveness in large-scale, complex real-world environments. Full article

In the fast-growing new energy vehicle (NEV) industry, selecting an appropriate technological innovation strategy is vital for enterprises to achieve a competitive market position while effectively coordinating their resources to align with their technical capabilities. This paper integrates ambidextrous innovation theory and the resource-based view to propose a configurational model that examines how the synergy between technological innovation and resources influences NEV firm performance. Using regression analysis and qualitative comparative analysis (QCA) for 52 listed Chinese NEV companies, this study uncovered multiple growth paths and mechanisms. The findings include the following: (1) No single factor was a necessary condition for performance, but effective combinations of innovation strategies and resource elements led to multiple success paths. (2) Government subsidies and R&D investment emerged as key drivers of performance. (3) Four distinct configuration paths were identified, with variations across firms with different resource bases. (4) In response to reduced government subsidies, NEV firms must shift from policy-driven strategies to resource- and market-driven innovation approaches. These insights provide strategic guidance for NEV enterprises in selecting innovation strategies suited to their unique resource bases in the evolving post-subsidy market environment. Full article

With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article