The accurate prediction of Arctic sea ice concentration is essential for polar ecological protection and shipping safety. However, existing prediction methods suffer from insufficient feature representation, which limits their ability to capture the complex spatiotemporal distribution of sea ice. Furthermore, they cannot effectively integrate multi-source, heterogeneous sea ice-related data, resulting in limited prediction accuracy. To address these issues, this paper proposes a Multimodal Feature and Trend analysis (MFT) method for sea ice concentration prediction. In the feature extraction stage, MFT combines a Convolutional Neural Network with a Convolutional Block Attention Module to deeply extract global deep semantic features while also employing the Scale-Invariant Feature Transform algorithm to accurately capture local stable features. To improve processing efficiency for high-dimensional remote sensing data, a coarse-resolution dimensionality reduction strategy is developed to select core spatial features, thereby preserving key spatial distribution information while optimizing computational efficiency. For temporal analysis, the Mann–Kendall (MK) non-parametric test and Sen’s slope method are integrated to quantitatively analyze long-term evolution trends in Arctic sea ice concentration. Experimental results show that the proposed MFT model outperforms random forest (RF), LSTM, and traditional MK methods in both prediction accuracy and computational efficiency.

We found that a heavy target such as Pb is most suitable for determining the proton distribution radii of unstable nuclei through charge-changing cross-section (σ_cc) measurements. As a heavy ion probe, low-Z targets are routinely used to determine nucleon distribution radii of unstable isotopes. This approach has recently been extended to study proton distribution radii from σ_cc measurements. However, empirical scaling factors have to be introduced to apply the Glauber models. In the present work, we systematically investigated the scaling factor using 39 new σ_cc data of 18 p-shell nuclei on hydrogen, carbon, silver, and lead targets at around 240 MeV/nucleon. Together with the existing data, we reveal a universal dependence of the scaling factor on both the masses of target nuclei and the separation energies of projectile nuclei. The scaling factors decrease with increasing target-nucleus mass and converge to 1 for the highest-Z target, making the scaling unnecessary. We conclude that instead of a low-Z target, employing a heavy target such as Pb in σ_cc measurements is the best option to determine the proton distribution radii of unstable nuclei.

Ocean sensor networks (OSNs) can sense long-time series data, which plays a crucial role in tasks that rely on massive sensory data such as ocean ecosystem monitoring and maritime regulation. Unfortunately, ocean nodes are prone to failures, and harsh ocean channels, and natural environments can interfere with the data collection and transmission process, which can lead to large-scale loss of ocean sensory data. Based upon the comprehensive and in-depth analysis of the ocean's time-varying dynamic environment, we proposed a multihead ProbSparse self-attention mechanism (MPSM)-based Model to predict and reconstruct missing ocean data. Specifically, MPSM can learn key features of ocean observation data on both sides of missing gaps with a dual encoder architecture. Subsequently, the MPSM can efficiently capture intersequence dependencies in parallel with lower computational complexity. Moreover, its residual connection will enhance the model's expressive ability and alleviate gradient vanishing. Simulation experiment results demonstrate that the metrics of MPSM are lower than the selected benchmark algorithms on different ocean datasets, which means MPSM has a stronger robust and generalizability. MPSM can provide high-precision, low-latency missing data restoration capabilities for marine monitoring scenarios. It enhances the continuity and reliability of ocean observation systems.

The increasing frequency of maritime activities has fueled a growing demand for advanced wireless communication systems, making accurate channel estimation a crucial technology. Traditional channel estimation algorithms often face limitations when dealing with noise factors. To address this issue, we propose an enhanced channel estimation algorithm based on deep learning, which integrates multiple strategies and is named the IMBP algorithm. This method simulates the insertion of pilot signals at the receiving end and combines the efficiency of mean filter. Additionally, it utilizes random forests to optimize end-to-end information transmission and adjusts strategies through dynamic thresholds. Simultaneously, by incorporating the powerful feature learning capability of deep learning in channel estimation, it upgrades traditional linear mapping to nonlinear mapping. The simulation results demonstrate that the IMBP algorithm proposed in this paper significantly reduces BER in communication, demonstrating superior performance.

Effective path planning in complex underwater environments serves as a critical determinant of autonomous underwater vehicle (AUVs) energy efficiency, while simultaneously influencing sensor operational demands and battery state-of-charge (SOC) dynamics. Systematic trajectory tracking emerges as a pivotal methodology for SOC optimization, enabling enhanced energy management through precision navigation control. This paper proposes a path planning and trajectory tracking control framework for autonomous underwater vehicles (AUVs) combined with battery state of charge (SOC) optimization. The framework incorporates the Grasshopper Optimization Algorithm (GOA) with the Artificial Potential Field Algorithm (APF) to achieve global path planning and local path optimization while minimizing energy consumption as an objective. Specifically, GOA is used for global path planning. APF further optimizes the path by introducing a SOC optimization strategy, in which high SOC consumption points are regarded as repulsive points and low SOC consumption points are regarded as attractive points. In addition, the trajectory tracking control adopts the model predictive control (MPC) method to ensure the accurate tracking of the planned path and dynamically manage the SOC states. Simulation results show that the proposed framework outperforms traditional methods in obstacle avoidance capability and SOC consumption, effectively improving energy efficiency and trajectory tracking accuracy.

The parameters of the path planning algorithm for unmanned surface vehicles (USVs) usually rely on manual settings, which makes it difficult for the algorithm to achieve the optimal solution when considering multiple factors in path planning. This paper proposes a data-driven method to improve the USV path planning algorithm in response to the problem of unreasonable control caused by manual experience parameter settings in traditional algorithms. Firstly, a dataset is constructed by extracting corresponding parameters from traditional fuzzy logic control algorithms. Then, using two existing fuzzy controllers as samples, a fuzzy neural network is designed. Finally, using this dataset, a new fuzzy logic controller is generated through a fuzzy neural network. Compared with traditional fuzzy logic controllers, data-driven controllers exhibit a more reasonable distribution of variable parameters, thus verifying the superiority of neural network-based controllers. Numerical simulations show that the proposed method improves both the path length and the navigation time, while ensuring safety in complex environments.

We present a measurement of elemental fragmentation cross sections (EFCSs) for 32-38S on a carbon target at approximately 200 MeV/nucleon. Our measurement, together with existing data, reveals a universal trend toward unity in the odd-even staggering (OES) magnitude of the EFCSs with increasing projectile isospin and consistency in its strength variation across different isotopic chains. This is well reproduced by the isospin-dependent quantum molecular dynamics model coupled with the GEMINI evaporation model (IQMD+GEMINI). The magnitude of OES exhibits a dependence on the excitation energy distribution (EED) of prefragments in the collision phase, providing a potential method for evaluating the EED using high-precision EFCS data.

We report on the first measurement of the elemental fragmentation cross sections (EFCSs) of Si29−33 on a carbon target at ∼230 MeV/nucleon. The experimental data covering charge changes of ΔZ = 1-4 are reproduced well by the isospin-dependent quantum molecular dynamics (IQMD) coupled with the evaporation GEMINI (IQMD+GEMINI) model. We further explore the mechanisms underlying the single-proton removal reaction in this model framework. We conclude that the cross sections from direct proton knockout exhibit an overall weak dependence on the mass number of Si projectiles. The proton evaporation induced after the projectile excitation significantly affects the cross sections for neutron-deficient Si isotopes, while neutron evaporation plays a crucial role in the reactions of neutron-rich Si isotopes. It is presented that the relative magnitude of one-proton and one-neutron separation energies is an essential factor that influences evaporation processes. Therefore, proton evaporation should be incorporated into the reaction model in addition to the direct proton knockout in analyses of Gade-Tostevin systematic for single-proton removal reactions.

We report the charge-changing cross sections (σcc) of 24 p-shell nuclides on both hydrogen and carbon at about 900A MeV, of which 8,9Li, 10-12Be, 10,14,15B, 14,15,17-22N and 16O on hydrogen and 8,9Li on carbon are for the first time. Benefiting from the data set, we found a new and robust relationship between the scaling factor of the Glauber model calculations and the separation energies of the nuclei of interest on both targets. This allows us to deduce proton radii (Rp) for the first time from the cross sections on hydrogen. Nearly identical Rp values are deduced from both target data for the neutron-rich carbon isotopes; however, the Rp from the hydrogen target is systematically smaller in the neutron-rich nitrogen isotopes. This calls for further experimental and theoretical investigations.

In extreme environments, such as polar oceans, where potential hazards like sea ice are prevalent, deploying autonomous surface vessel (ASV) can enhance operational efficiency and safeguard personnel. As these extreme environments necessitate higher performance standards, particularly in terms of path-following accuracy and control stability, we introduce in this research an ASV path-following control method predicated on an enhanced proportional-integral-derivative (PID) parameters tuning algorithm aimed at reducing path-following errors and bolstering control stability. First, the adaptive line-of-sight (ALOS) guidance algorithm is devised to determine the desired ASV heading by designing the forward-looking range adjustment strategy. Second, the improved sparrow search algorithm (ISSA) is proposed for PID parameters tuning. Since the lack of stability of the standard Sparrow Search Algorithm (SSA), the producer update strategy is modified, and the Brown-Levy mutation strategy is designed to improve the global search ability of the algorithm. Finally, the virtual ASV simulation platform is built, and the real-time PID controller is constructed by designing the PID real-time tuning strategy. The parameters of the Nomoto ship motion model are fitted in the simulation platform according to different marine environments, and the PID controller parameters are updated in real-time by ISSA to improve the path following accuracy. Experimental results of the marine environment simulation test and the real-world experiment show that the ALOS guidance algorithm can effectively generate the current desired rudder angle. The PID controller based on ISSA has the best performance in computer simulation. The average overshoot is 2.79%, and the average convergence time is 20.1 s. In the real-world experiment, the average path following error of ISSA Real-Time is reduced by 51.0% compared with that of SSA and 27.2% compared with that of ISSA. The improved control method can better satisfy the control requirements of the ASV, enhance control stability, and achieve more precise path following.

Elemental fragmentation cross sections (EFCSs) of stable and unstable nuclides have been investigated with various projectile-target combinations at a wide range of incident energies. These data are critical to constrain and develop the theoretical reaction models and to study the propagation of galactic cosmic rays (GCR). In this work, we present a new EFCS measurement for 28Si on carbon at 218 MeV/nucleon performed at the Heavy Ion Research Facility (HIRFL-CSR) complex in Lanzhou. The impact of the target thickness has been well corrected to derive an accurate EFCS. Our present results with charge changes AZ = 1 - 6 are compared to the previous measurements and to the predictions from the models modified EPAX2, EPAX3, FRACS, ABRABLA07, NUCFRG2, and IQMD coupled with GEMINI (IQMD+GEMINI). All the models fail to describe the odd-even staggering strength in the elemental distribution, with the exception of the IQMD+GEMINI model, which can reproduce the EFCSs with an accuracy of better than 3.5% for AZ 5. The IQMD+GEMINI analysis shows that the odd-even staggering in EFCSs occurs in the sequential statistical decay stage rather than in the initial dynamical collision stage. This offers a reasonable approach to understand the underlying mechanism of fragmentation reactions.

The melting of Arctic ice has increased the value of Arctic shipping, making research on the Arctic route a popular topic. However, ships navigating this route will likely encounter randomly distributed sea ice, which poses significant safety hazards to local path planning. The Dynamic Window Approach (DWA) is a suitable method for local path planning, but the DWA results in large ship rotation angles, increasing navigation risk. This study proposes a novel fuzzy control path planning algorithm based on scale factors to address this issue. The proposed algorithm combines a stability fuzzy controller with a collision risk controller to carry out adaptive control of DWA. Two scale factors are defined to improve fuzzy control based on the overall and obstacle avoidance phases. Results show that the proposed algorithm significantly reduces the rotation angle of DWA, improves the effect by 19.52%, shortens distance and time, and increases safety when encountering sea ice.

The path planning and tracking control scheme for unmanned vehicle using multi-dimensional Taylor network (MTN) is proposed. First, the reference path is designed by the A-star algorithm, which is a common method used as path planning. Second, design the MTN controller based on PID controller to control the unmanned vehicle tracking the reference path; and the improved BP algorithm is used as the learning of MTN controller. Finally, the simulation experiment of unmanned vehicle is given to verify the effectiveness of the proposed scheme. The simulation results show that the proposed scheme has optimal path planning and good tracking control performance.

This paper solves the time-optimal path planning for autonomous underwater vehicles (AUVs) in ocean environment with cluttered currents. In this problem, the planner may not find a path in the search space with discrete motion directions, because it leads to a decrease in the available direction. But the search space with the continuous motion directions will greatly increase the computation. To avoid this, the paper presents an approach to improve the lack of discrete motion model by placing Steiner points on each edge. Combining with the ant colony algorithm, the path planner finds the time-optimal path in search space. The effectiveness is verified through simulations using a set of randomly generated current fields.

This paper presents the energy-optimal velocity to solve the problem of energy-optimal path planning for autonomous underwater vehicles (AUVs) in current fields. In the proposed scheme, a sequence of thrust velocities and steering directions is predicted to enable vehicles to utilize/avoid currents and minimize their energy consumption. Each thrust velocity and steering direction are deduced through a vector analysis method under the expected displacement and corresponding current vector, which is the energy-optimal velocity. The proposed energy-optimal velocity combines the global optimization technique and guarantees the minimization of a vehicle’s energy consumption. Several experiments are performed on strong and turbulent current fields to validate the effectiveness of the proposed scheme.

A novel grey multi-dimensional Taylor network (MTN) scheme for nonlinear time series prediction in industrial systems is proposed in this paper. First, we construct the grey MTN model: 1) the GM(1,1) model is used to gain the prediction value and as a group of inputs for the MTN prediction model, which improves the prediction accuracy; 2) we take the MTN model as the prediction model and the conjugate gradient (CG) method as its learning algorithm. Second, the variational mode decomposition (VMD) method is used as data preprocessing for inputs of the prediction model, and the processed data are normalised. Finally, the actual prediction values are obtained by reverse normalization processing. Industrial examples are presented to verify the effectiveness of the proposed scheme. The experimental results show that the proposed prediction scheme is effective. Meanwhile, compared with other schemes, the proposed scheme improves the prediction accuracy and performance considerably.

Localization and tracking of single millimeter wave radar usually make mistakes when outliers and virtual tracks are generated by multipath effect. Cooperation of multiple millimeter can effectively filter outliers and virtual tracks. However, each radar has its own coordinate system, they are different. The basis for cooperation of multiple radars is coordinate calibration. Thus, this paper proposed an algorithm to acquire the translation matrix and rotation angle which are used for coordinate calibration of multiple millimeter wave radars. Finally, numerical experiments validate that tracks generated by real target will coincide together after the optimal translation and rotation angle. On the contrary, if tracks are not generated by real target, they cannot coincide. This method can be used to filter tracks generated by multipath effect effectively.

The predictive uncertainty of ocean currents needs to be considered in the path planning of autonomous underwater vehicles (AUV) in oceanic environments. An interval optimization (IO) scheme is presented in this paper to plan time-optimal paths for AUVs operating in oceanic environments with dynamic and uncertain flow fields; interval currents are established to process the uncertainty of forecasted ocean flows, and the IO scheme is used to search for time-optimal paths in the interval current fields. The IO scheme is a two-layer framework: In the outer layer, quantum-behaved particle swarm optimization is used to generate and optimize candidate paths. In the inner layer, the interval response of the candidate path from the outer layer is calculated under interval current fields, and they are converted into a time-optimal fitness value in accordance with the possibility degree of interval numbers. The influence of predictive uncertainty is analyzed via simulation, and the robustness of the IO scheme is verified.

在时变洋流场环境下,洋流矢量增加了时间维度,在时间角度上可进一步利用洋流以节约自主水下机器人(AUV)能量消耗.此外,在该环境中无后效性不再成立,基于经典贪婪策略的路径规划算法不再适用.鉴于此,结合路径参数选择和双层规划算法,提出一种适用于时变洋流场环境的能耗最优路径规划算法.出发时间和AUV推进速度均可以在时间维度上等待有利洋流,且推进速度与其能量消耗直接相关,因此,引入出发时间和推进速度作为路径参数,在此基础上,针对无后效性不成立问题,使用双层规划作为路径规划算法,分析该算法在时变洋流场环境下的适用性.算法将路径规划任务分为路径规划与路径优化两部分,路径规划部分采用蚁群系统算法构建通道,路径优化部分由量子粒子群算法对路径参数进一步优化,在保证全局最优的同时能够解决传统基于栅格的路径规划算法中机器人运动方向受限的问题.最后以Kongsberg/Hydroid REMUS 600s型水下机器人为模型,对所提出的路径规划算法进行仿真验证.