Search Results (70)

In order to describe the heavy ion collision dynamics which implies the formation of hot and very dense nuclear matter in the overlapping region of the two colliding nuclei, we used simulated numerical calculations for FAIR available energies. We used the anti-$k_T$ jet-detection algorithm for highlighting the main directions of flow in Au-Au collisions at CBM energies, thus obtaining structures of the events depending on the number of flow streams. The jet-finder algorithm identified domains in the y-$\psi$ (rapidity-azimuthal angle) plane, where the number of charged particles, momenta and energy take higher values compared to other areas of this plane. The anisotropic flow coefficients $v_n$ may offer information about the pressure gradients in the early stages of the collision and about the high-density nuclear matter properties. The observation of $K^{+}$ mesons in heavy ion collisions is of interest since $K^{+}$ mesons, due to their strangeness, have a mean free path that exceeds the dimensions of the “fireball”. In the numerical calculations the interval of rapidity $0<y<0.8$ is highlighted, for which the fluctuations of the antiparticle to particle ratio excitation functions show non-monotonic behavior in the 10–13 A GeV energy interval. Full article

The utilization of drone swarms for cooperative missions is becoming increasingly prevalent. However, establishing high-concurrency and highly reliable communication links in complex environments remains a significant challenge. Existing methods based on traditional Medium Access Control (MAC) protocols struggle to cope with high-density collisions, while conventional deep reinforcement learning (DRL) approaches often encounter convergence difficulties in non-stationary interference environments, leading to notable limitations in anti-jamming robustness and algorithmic efficiency. To tackle this problem, this paper proposes a dynamic access algorithm based on Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO). Specifically, we adopt a Centralized Training with Decentralized Execution (CTDE) architecture to enable implicit spectrum cooperation within the swarm. Notably, we design a three-stage progressive curriculum learning mechanism—basic collision avoidance, load balancing, and dynamic anti-jamming—coupled with a phased reward reshaping strategy, guiding the agents to progressively master intelligent frequency-hopping decisions in complex environments. Experimental results demonstrate that in simulated scenarios involving dynamic sweep jamming and high-load multi-drone communication, the proposed method significantly outperforms baseline models such as Carrier Sense Multiple Access (CSMA), random frequency hopping, and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in terms of normalized throughput, channel collision rate, and convergence speed. This research provides theoretical support and an algorithmic foundation for achieving highly reliable access in large-scale swarm data links under harsh environmental conditions. Full article

When artificial satellites disintegrate in orbit due to collisions, explosions, or anti-satellite tests, they generate a large amount of space debris. These debris move in orbit through formation-like flight patterns, which we refer to as debris clusters. Compared to constellation satellites in formation flight, debris clusters have closer mutual distances and higher spatial density. To address this phenomenon, this paper proposes a detection method for satellite breakup events through a two-layer clustering strategy using the DBSCAN algorithm in three-dimensional spatiotemporal domains. The method provides a systematic approach for identifying and characterizing breakup debris through orbital backpropagation and density analysis, serving as a screening tool for potential breakup events. Using the 2009 Cosmos 2251-Iridium 33 collision breakup event as a validation case, experimental results demonstrate that our two-layer clustering approach achieves good clustering accuracy (98.92%) and a positive recall rate (93.20%), with a temporal resolution of 5 s and spatial precision of 21.68 km. The methodology was further applied to analyze the 2024 Intelsat 33E breakup event, successfully identifying sparse debris clusters from Intelsat 33E. The detected breakup time showed an 8 min deviation from the Space-track.org official report. Full article

Ice adhesion on exposed structures remains a major operational challenge, motivating the search for passive, material-based anti-icing strategies. Molecular dynamics offers a controlled way to investigate ice–surface interactions beyond the limits of experimental setups. In this work, we develop a simulation framework to model the impact of solid hexagonal ice droplets on metallic substrates. Ice impacts are simulated across a range of velocities (10–120 m/s), temperatures (120–250 K), and face-centred cubic surface materials (gold, copper, silver, aluminum, and nickel). Using LAMMPS, mW water force-field, EAM/Alloy metal potentials, and Lennard-Jones water–surface interactions, we quantify phase evolution through angular order parameter and quasi-liquid layer measurements, complemented by the CHILL+ algorithm in OVITO. By isolating all external factors, we show that melting increases with velocity and temperature and correlates with substrate properties: metals with high thermal diffusivity and low Young’s modulus tend to decrease post-collision ice melting. The ratio of the former to the latter, a derived index of merit Υ, significantly correlates with melting percentage and identifies silver as the most effective anti-ice material examined. Statistical analyses strongly suggest that these surface properties influence interfacial melting, supporting the use of this modelling framework for screening and designing anti-icing materials. Full article

Autonomous path planning in obstacle-dense environments remains challenging for swarm intelligence methods due to infeasible initialization, insufficient exploration–exploitation balance, and poor trajectory smoothness for real-robot execution. To address these issues, this paper proposes a Kinematic-Aware Improved Hippo Optimization algorithm (KA-IHO) for mobile robot path planning. The proposed method integrates four components: an elite safety pool initialization strategy to improve feasible solution generation in dense maps, a hierarchical elite-scout update mechanism to better balance global exploration and local exploitation, anti-stagnation mechanisms including a Population Stagnation Restart strategy and a 10-Direction Radial Micro-Search to guarantee high feasibility rates across all map complexities, and a late-stage Laplacian Line-of-Sight Ironing Operator to reduce path redundancy and improve trajectory smoothness. Comparative experiments are conducted on five reproducible grid maps with different complexity levels ($40\times 40$ and $80\times 80$), where KA-IHO is evaluated against six representative algorithms, including HO, SBOA, PSO, GWO, ARO, and INFO, over 20 independent runs. The results show that KA-IHO consistently achieves collision-free planning and obtains lower mean fitness values with smaller standard deviations than the compared methods, indicating improved robustness and solution quality. In addition, hardware closed-loop experiments on a differential-drive mobile robot demonstrate that the planned paths can be executed reliably in real environments, with trajectory tracking errors controlled within $\pm 4$ cm. Full article

Passive radio frequency identification (RFID) sensing systems integrate wireless energy transfer with information identification. However, conventional passive RFID systems still face three key challenges in practical applications: low RF energy harvesting efficiency, high power consumption of sensor loads, and high complexity of tag anti-collision algorithms. To address these issues, this paper proposes a hardware–software co-optimized RFID sensor system. For hardware, low threshold RF Schottky diodes are selected, and an input inductor is introduced into the voltage multiplier rectifier to boost the signal amplitude, thereby enhancing the radio frequency to direct current (RF-DC) energy conversion efficiency. In terms of loading, a low-power management strategy is implemented for the power supply and control logic of the sensor node to minimize the overall system energy consumption. For algorithmic implementation, a Dual-Threshold Stepped Dynamic Frame Slotted ALOHA (DTS-DFSA) anti-collision algorithm is proposed, which adaptively adjusts the frame length based on the observed collision ratio, eliminating the need for complex tag population estimation. The algorithm features low computational complexity and is well suited for resource constrained embedded platforms. Through simulation validation, we compare the conversion efficiency of the RF energy harvesting circuit before and after improvement, the current of the sensor load in active and idle states, and the performance of the proposed algorithm against the low-complexity DFSA (LC-DFSA). The results show that the maximum conversion efficiency of the improved RF energy harvesting circuit has increased from 60.56% to 68.69%; specifically, the sensor load current drastically drops from approximately 2.0 $\mathrm{m}\mathrm{A}$ in the active state to around 74 $\mathsf{\mu }\mathrm{A}$ in the idle state, validating the efficacy of the proposed power gating strategy, and the proposed DTS-DFSA algorithm outperforms existing low-complexity schemes in both identification efficiency and computational complexity. Full article

In the dynamic marine environment, the high mobility of intrusion targets, complex interference, and insufficient multi-vessel coordination accuracy pose significant challenges to the cooperative interception mission of multiple unmanned surface vehicles (USVs). This paper proposes an adaptive dynamic prediction-based cooperative interception control algorithm and establishes a “mission planning—anti-interference control—phased coordination” system. Specifically, it ensures interception accuracy through threat-level-oriented target assignment and extended Kalman filter multi-step prediction, offsets environmental interference by separating the cooperative encirclement and anti-interference modules using an improved Two-stage architecture, and optimizes the movement of nodes to form a stable blockade through the “target navigation—cooperative encirclement” strategy. Simulation results show that in a 1000 m × 1000 m mission area, the node trajectory deviation is reduced by 40% and the heading angle fluctuation is decreased by 50, compared with the limit cycle encirclement algorithm, the average interception time is shortened by 15% and the average final distance between the intrusion target and the guarded target is increased by 20%, when the target attempts to escape, the relevant collision rates are all below 0.3%. The TFMUSV framework ensures the stable optimization of the algorithm and significantly improves the efficiency and reliability of multi-USV cooperative interception in complex scenarios. This paper provides a highly adaptable technical solution for practical tasks such as maritime security and anti-smuggling. Full article

High-reliability localization is essential for underground mining autonomous vehicle, as inaccurate positioning triggers collision risks and limits deployment in safety-critical environments. Underground mining localization faces unique challenges: kilometer-scale signal-free tunnels restrict traditional technologies, while wheel slippage-induced non-Gaussian noise and geometric-degraded tunnel localization failures further reduce accuracy—issues existing methods cannot address simultaneously. To resolve these bottlenecks, this study develops a scenario-adapted, self-correcting positioning system for underground autonomous vehicles, fusing multi-source onboard sensor data to suppress slip noise and ensure feature-deficient environment robustness. We propose a three-stage cascaded filtering system: it first fuses LiDAR, IMU, wheel speed, and steering angle data for a self-contained framework, then adds two dedicated modules for core challenges. For wheel slippage noise, an anti-slip prior estimation algorithm integrates kinematic models with IMU data, plus a low-adhesion mine surface-tailored slip compensation mechanism to ensure reliable state estimation and eliminate slip deviations. For geometrically degraded tunnel failures, an anti-degradation algorithm uses point cloud degradation-derived regularization constraints and regularized Kalman filtering to enable stable positioning updates. Experiments show that the system achieves sub-meter accuracy and full-area coverage underground, with improved performance under severe wheel slip and in feature-deprived zones. This work fills the gap in high-reliability, self-contained localization for kilometer-scale underground mining vehicles and provides a safety-oriented paradigm for autonomous vehicle scaling, aligning with critical scenario driving safety demands. Full article

In engineering design for semi-submersible drilling platforms, it is necessary to improve anti-collision performance by optimizing the platform’s column’ structure. However, collision is usually analyzed through numerical analysis methods such as finite element analysis, which comes with high calculation costs. The genetic algorithm (GA) and other traditional optimization methods require massive numerical simulations, with unacceptable computational complexity. To address the above problems, a parallel efficient global optimization (EGO) multi-objective algorithm, based on hybrid criteria for the Kriging approximate model, is put forward in this paper. The proposed algorithm was validated through six typical multi-objective optimization test functions. The results show that it is superior to classic EGO, in terms of both optimization results and computational efficiency. Lastly, the hybrid criterion-based parallel EGO algorithm proposed in this paper was employed for the anti-collision, lightweight design of the column of the first ice zone semi-submersible drilling platform in China. It was found that the anti-collision capacity of the platform column rose by 11.9% and the structural weight declined by 2.7 t in the optimized design, suggesting obvious optimization effects with respect to the original design. Full article

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset—a 2.4 percentage point improvement over the baseline YOLO11—while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions. Full article

This paper addresses the issue of the poor collision avoidance effect of underactuated surface vehicles (USVs) during local path tracking. A virtual ship group control method is suggested by using Freiner coordinates and a model predictive control (MPC) algorithm. We track the planned path using the MPC algorithm according to the known vessel state and build a hierarchical weighted cost function to handle the state of the virtual vessel, to ensure that the vessel avoids obstacles while tracking the path. In addition, the control system incorporates an Extended Kalman Filter (EKF) algorithm to minimize the state estimation error by continuously updating the ship state and providing more accurate state estimation for the system in a timely manner. In order to validate the anti-interference and robustness of the control system, the simulation experiment is carried out with the “Yukun” as the research object by adding the interference of wind and wave of level 6. The outcome shows that the algorithm suggested in this paper can accurately perform the trajectory-tracking task and make collision avoidance decisions under six levels of external interference. Compared with the original MPC algorithm, the improved MPC algorithm reduces the maximum rudder angle output value by 58%, the integral absolute error by 46%, and the root mean square error value by 46%. The control method provides a new technical choice for trajectory tracking and collision avoidance of USVs in complex marine environments, with a reliable theoretical basis and practical application value. Full article

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations. Full article

Current methods for designing three-dimensional trajectories rarely account for complex formation constraints, focusing primarily on geometric relationships. However, trajectory adjustments are often necessary during drilling operations. These field adjustments typically lack systematic optimization, resulting in suboptimal trajectories. This study introduces a novel trajectory optimization framework that integrates formation fitness for curve construction and proactive anti-collision trajectory adjustment (PACTA). The framework begins by incorporating PACTA and optimizing the initial trajectory to minimize total measured depth (TMD) using a genetic algorithm. Subsequently, a second optimization phase identifies curve sections passing through formations with low build-up fitness, automatically splitting them into combinations of curves and straight lines. Dynamic trajectory equations are then constructed based on these adjustments, and the final trajectory is optimized accordingly. Case studies demonstrate that the proposed method effectively adjusts curve positions in the presence of multiple formations with low build-up fitness while avoiding wellbore collisions. The approach achieves an average 10% reduction in total drilling time when minimizing TMD and an average 19.7% reduction in drillstring torque when torque minimization is prioritized. This new trajectory design method is expected to significantly reduce well construction costs. Full article

This paper proposes a standard applications of Radio Frequency Identification (RFID) system to manage vehicles at electronic toll gate system without compromising the vehicle speed. The existing electronic toll gate system has low latency as each car needs to wait in a queue for passing the gate. Installation of reader antenna at each lane makes the system expensive. We propose a two-ray ground model for using minimal reader antenna to define required antenna height and downtilt angle to cover the total gate area. We estimate that reader antenna at a height of 15 m and 45.4° downtilit angle are required for optimal signal reception from the car at a distance of 35–38 m. Modified Pulse Protocol algorithm is suggested for mitigating reader collision and increase throughput and utilization of the system by 14.71% and 15.2% respectively. The probability distribution function of the contention window is modified to triangular function. The M-ary Trimming Q-tree protocol is suggested to resolve the tag collision by trimming the idle nodes from the query tree, which reduces identification time. The total time slot decreases with higher elements, and the total throughput also increases to 98%. The “no-lane” strategy established a fast, efficient and cost effective vehicle toll collection system. Full article

The installation distance between the speed sensor of the traditional rolling or sliding bearing permanent magnet synchronous motor and the rotor was very close, and the rotor of the magnetic levitation motor supported by Active Magnetic Bearings (AMBs) was in suspension. When the motor was running at high speed, the radial trajectory of the rotor changed all the time. The same frequency vibration caused by the unbalanced mass of the rotor made it easy to cause mechanical collision between the sensor and the rotor, resulting in direct damage of the sensor. Therefore, the sensorless speed estimation method was needed for the rotor control system of the magnetic levitation motor (MLM) to achieve high performance closed-loop control of speed and position. More importantly, in order to control or compensate the unbalanced force of the electromagnetic bearing rotor system, the rotor rotation speed signal should be obtained as accurately as possible. Therefore, the principle of adaptive sliding mode observer (SMO) was analyzed in detail by taking the rotor system of MLM as an example. Then, the sliding mode surface was designed, the speed estimation algorithm based on adaptive SMO was derived, and the stability analysis was completed. Finally, in order to verify the anti-disturbance performance of the system and the static and dynamic tracking performance of the motor, the dynamic performance was verified by increasing and decreasing the speed and load. The results showed that the speed estimation method based on adaptive SMO could achieve accurate speed estimation and had good static and dynamic performance. Full article