With the continuous improvement of 5G network infrastructure, the informatization process in the industrial sector is accelerating at an unprecedented pace. The market demand for gateways integrating 5G networks with multi-access edge computing (MEC) technology is growing increasingly strong. There is also a rising requirement for these gateways to possess capabilities such as high reliability, large bandwidth, fast processing speed, and localized data processing. This paper, in response to the practical management needs of photovoltaic power stations and industrial fields, proposes the design of a 5G artificial intelligence (AI) edge computing intelligent gateway with dual 5G modules. The design is based on the i.MX8M Plus processor and integrates dual 5G module design. It supports multiple communication modes, including 5G, Wi-Fi, Ethernet, and serial ports. The software incorporates edge computing scripts, various communication protocols, and a lightweight AI model, enabling real-time data acquisition and processing at the edge. It supports for several industrial standard protocols ultra-low latency acquisition, and intelligent operation features like remote over-the-air upgrade, secure shell secure operation, and configuration hot updates. The system was tested and validated in a typical photovoltaic application scenario. The results demonstrate that, compared with the traditional single-module Internet of Things gateways, the dual 5G AI edge gateway proposed in this paper reduces end-to-end latency by more than 70%, improves data reliability tenfold, enhances bandwidth by 100%–200%, extends coverage capability by 50%, effectively alleviates the bandwidth pressure on the core network, and enhances the overall reliability and security of the system.

To address the significant overhead of the beam training in millimeter wave (mmWave) wireless communications, we propose a balanced multimodal fusion network with bi-directional enhancement in the previous work. It can predict the optimal mmWave beam according to the mmWave wide beam and sub-6 GHz channel state information. Utilizing the interaction and complementarity between different modes can improve the accuracy and robustness of predictions compared to the single mode (mmWave only or sub-6 GHz only). However, the feature extraction of one modality can potentially impact other modalities via parameter backpropagation. Although multimodal models perform better than unimodal models, they may be underutilized. To solve the problem, the adaptive multimodal gradient (AMMG) optimization method is designed to update the gradient of per feature extraction branch adaptively in this paper. Furthermore, a multimodal bi-directional enhancement (MBdE) block is proposed to combine features of other modalities, enhancing the complementarity of mmWave modality and sub-6 GHz modality. As revealed by the numerical results, it is evident that the proposed scheme delivers more precise outcomes with lower overhead compared to conventional and modern deep learning methods.

Previous studies have paid limited attention to the critical role of unmanned aerial vehicle (UAV) altitude variation in determining three-dimensional (3D) search efficiency. To address this gap, this paper introduces a deep reinforcement learning framework integrated with adaptive matrix optimization in multi-UAV target search. The proposed framework explicitply models altitude variations’ impacts on observation range and detection accuracy. To balance flight safety, coverage, and detection precision, deep Q-network (DQN)-altitude matrix optimization (AMO) discretizes 3D trajectories into horizontal paths and vertical altitude decisions, effectively reducing the problem dimensionality. Furthermore, a curriculum learning approach is employed to decompose the search process into phased sub-tasks, each with customized decision rules and reward mechanisms. This hierarchical strategy accelerates agent learning and enhances performance in complex scenarios. Comprehensive experimental evaluations in simulated environments demonstrate that the proposed DQN-AMO outperforms benchmark methods in both robustness and generalization.

The radar cross section (RCS) scale transformation of non-metallic targets is mainly limited by the frequency dispersion relationship of material, making it difficult to be implemented in actual measurements. If the scale dependences of scattering centers can be found to avoid the influence of frequency, it is expected to provide a new solution for the scale measurement of RCS of non-metallic targets. Therefore, based on the high-frequency asymptotic method, this paper first derives the scale dependences of the specular reflection mechanism and the edge diffraction mechanism for three common specular reflection structures and two edge diffraction structures. Then, referring to the expression of the frequency dependent factor in the geometric theory of diffraction (GTD) model, the scale dependent factors of the above scattering mechanisms are analogously proposed. The scale dependent factors are extended to thin-layer coated structures and arbitrary multiple scattering cases. Simulation experiments verified the correctness of the scale dependent factors. Finally, this paper presents an extended expression form of the GTD model, laying a theoretical foundation for the subsequent RCS scale transformation of non-metallic targets based on scattering centers.

Non-terrestrial network (NTN) possesses ubiquitous coverage and access capability, which needs high-reliability and high-throughput transmission supported by the advanced channel coding. In this paper, a cooperative transmission and optimization scheme is proposed based on polar codes in NTN. Firstly, the transmission system model is designed. Then the polar coded cooperation method among the cooperative satellite nodes (CSN) and destination is provided in Rayleigh slow fading channel. Moreover, to improve the overall error probability performance and throughput efficiency, the key coding rate and power allocation of CSN are jointly optimized using multiobjective reinforcement learning algorithm. Finally, simulation and analysis demonstrate the validity of our approach and show the error-correcting performance and throughput advantages of the proposed cooperative transmission scheme in NTN.

Flight mission segments are essential components of aero-engine flight mission profiles. Current recognition methods rely on manual processes, leading to inefficiencies and inaccuracies in aero-engine load analysis. To solve this problem, this paper presents an automatic method for recognizing flight mission segments using bidirectional recurrent neural network (Bi-RNN). Initially, the decomposition of flight mission profiles, manual division of mission segments, labeling of mission segment, and the establishment of maneuver sequence table are introduced, respectively. Secondly, the automatic recognition models are constructed using bidirectional long short-term memory (Bi-LSTM) and bidirectional gated recurrent unit (Bi-GRU). By collecting measured load data from a third-generation aero-engine, including 50 low maneuvering and 50 complex maneuvering flight mission profiles, the dataset is set utilizing K-fold cross-validation and the sliding window method. The optimal parameters for the models are determined through training, and their recognition performance is assessed. The results indicate that the Bi-RNN model achieves over 80% accuracy in recognizing flight mission segments in both low and complex maneuvering flight mission profiles. As a result, the flight mission segment automatic recognition method presented in this study allows for more efficient and precise aero-engine load analysis, which is of great significance for aero-engine life evaluation research.

This paper highlights a Tibetan syllable spelling check method based on memristive computing. Firstly, a rule-based syllable component recognization method is implemented to classify components of syllables into seven types, that is prefix letter, root letter, superscript letter, subscript letter, vowel letter, suffix letter, and farther suffix letter. In order to detect syllable formation errors in Tibetan text, a Tibetan spelling check method is presented based on component recognization by adding mapping principles. Then, a memristor crossbar array with an external control circuit is introduced to make memristive computing for Tibetan spelling check, by analyzing the total current value in a memristor crossbar array to implement rule-based matching and mapping processes. Finally, a stable memristive computing algorithm-based Tibetan spelling check is established. A simulation experiment on the memristor crossbar array demonstrates the stability of our memristive computing-based Tibetan spelling check method. Comparative experiments on 1274 of the most common Tibetan syllables show that the accuracy of our component recognization method is 97.19%, the F1-score of spelling check is 98.58%, beating other rule-based and statistics-based methods in precision and F1-score, demonstrating the accuracy of our method.

In this paper, a cooperative task allocation problem of ground manned and unmanned systems is investigated. Considering roads and threats in the urban security environment, the cooperative scout and strike problem is modeled as a heterogeneous fleet vehicle routing problem with time window. Firstly, a task allocation model for scout and strike of the mixed manned and unmanned systems is constructed, with the objective functions of minimizing the total task completion time and reducing our damage. Secondly, dangerous areas and risky road segments are further taken into account. Then, by integrating the search capability of removal and insertion operation in large-scale neighborhood search (LNS), a hybrid algorithm combining genetic algorithm (GA) and LNS (GA-LNS) is designed, which enhances the local search ability of GA. Finally, the effectiveness of the proposed model and algorithm is evaluated by conducting numerous computational experiments on the Solomon test set and providing a simulated example of urban security exercise. The experimental results show that GA-LNS is more competitive than advanced heuristic algorithms. In addition, compared with the use of manned vehicles alone, the cooperative task allocation of manned and unmanned vehicles in the ground urban security environment has broad application prospects.

A communication and navigation constellation which can cover the whole Cislunar space is useful for deep space exploration. In our previous work, we proposed a configuration of navigation constellations arranged in special long-period orbits of Cislunar space. In order to keep the navigation satellites strictly on the accurate nominal orbit, a terminal sliding mode control (TSMC) strategy based on the disturbance observer is proposed. Since the states of navigation satellites cannot be observed directly, a linear extended state observer (LESO) is designed to recover the state as well as estimate solar gravity to assist in spacecraft orbit determination. Then through the TSMC strategy, high-precision control of the spacecraft on special long-period orbits has been achieved. The stability conditions of the observer are deduced, and the stability of the system is proven using Lyapunov stability theory. The simulation results show that during the entire mission cycle of 76 days, the maximum position error is 259.95 m and the minimum is 1.41 m, and the maximum value of the velocity error is 0.08 m/s and the minimum velocity error is 6.15×10⁻⁶ m/s. The total velocity increment throughout the entire mission cycle is 30.96 m/s.

On-orbit refueling for low-Earth orbit (LEO) mega-constellations requires solving complex access sequence problems. The increasing number of satellites complicates finding optimal solutions. To tackle this, we introduce a data-driven enhanced ant colony optimization (DDEACO) method in this paper. DDEACO uses a two-step approach: initially, it trains a deep Q-network (DQN) using the $\Delta V$ transfer cost as a reward and employs gated recurrent units (GRUs) to learn Q values for selecting the optimal next satellite member for visitation, capturing the sequences’ temporal dependencies. Then, it integrates the pre-trained GRU with traditional ant colony optimization (ACO) to offer new heuristic paths, improving ACO’s exploration and efficiency. The effectiveness of DDEACO is demonstrated through numerical experiments on systems tool kit (STK)-generated datasets with 30 satellites and 200 satellites, showing its significant performance.

Aerial gravity measurements in polar regions require higher accuracy and stability from satellite navigation systems. The performance of conventional Global Positioning System (GPS) significantly deteriorates as one moves from lower latitudes to polar areas. First, a regional comparative analysis of BeiDou Navigation Satellite System (BDS) and GPS positioning performance in polar regions is conducted. Results indicate that in polar environments, BDS demonstrates certain advantages regarding satellite availability and geometric distribution. Based on this analysis, a method for extracting airborne gravity anomalies using BeiDou signals in polar regions is proposed and improved. The process begins by optimizing BeiDou observation data using an altitude angle weighting method combined with precise ephemeris data. An error correction model is constructed to enhance system adaptability in the polar environment. Following this, high-precision position and acceleration estimation is achieved using the precise point positioning (PPP) technique, integrated with inertial navigation information, to extract aerial gravity anomalies in polar regions accurately. Results from Antarctic aerial gravity measurement experiments indicate that the BDS-based model improves accuracy by 30% compared to GPS under the same conditions.

With the transformation of user demands in knowledge economy era, traditional knowledge service operation paradigm and implementation countermeasures cannot meet the demand of users. The previous research on knowledge service system is rich in theoretical content, but most of the research only stays on the shallow analysis of the concept and features. There is lack of information available to the construction of knowledge service system framework in details, especially in the academic area. This paper first contributes to the existing body of knowledge service solution, and then combines general knowledge service solution with integrated definition function (IDEF0) models to produce a knowledge service system framework. These IDEF0 models depict the hierarchy within knowledge service purposes and reveal strategies of meeting user demands in academic research. Finally, a prototype system in knowledge service system shows that the proposed framework can provide effective knowledge service for users, which will help to promote quality and satisfaction in knowledge service process.

This paper investigates a specific operational amplifier to overcome the limitations of traditional accelerated degradation testing, particularly issues related to limited data availability and inaccurate modeling. A particle swarm optimization algorithm is employed to design an optimal testing strategy that achieves a balance between model precision, reliability evaluation confidence, and cost-effectiveness. Utilizing the degradation data derived from experimental procedures, a time-scale function is incorporated into the Wiener process to model the nonlinear degradation behavior of the operational amplifier, accounting for the substantial randomness in environmental influences and the uncertainty associated with actual usage conditions. Furthermore, this study proposes a multivariate performance degradation modeling approach utilizing Copula functions to address product failures arising from competing performance parameters. Compared to conventional multivariate normal models, the proposed approach offers distinct advantages in accurately capturing complex nonlinear dependencies among performance parameters, streamlining the modeling process, and improving both flexibility and applicability. In the presented case study, an optimized reliability testing procedure is applied to a specific operational amplifier, followed by degradation modeling and reliability evaluation utilizing the proposed methodology. The results demonstrate that this integrated approach achieves 14.2% cost reduction, providing engineers with a practical framework for designing cost-effective testing programs without compromising assessment accuracy. The proposed methodology not only advances degradation modeling techniques but also offers direct economic benefits for industrial reliability testing applications.

This paper proposes a two-dimensional (2D) angular super-resolution framework for sparse arrays under the single snapshot condition. The azimuth-elevation 2D angular super-resolution model is established, which shows the relationship between the 2D angular super-resolution image and the signal. Using this model, the 2D angular super-resolution problem is transformed into the beyond linear optimization problem. In order to efficiently and accurately address this optimization problem, we propose the sparse multi-layer iterative (SMLI) algorithm based on beyond linear signal processing (BLiSP) theory. During the layered iterative process, the solution region is continuously narrowed. The constructed nonlinear weighting matrix and sparse constraint coefficient enhance the ability to differentiate between the effect of signal and noise in solving the beyond linear optimization problem. Additionally, the nonlinear weighting matrix can be adaptively updated during the solving process, ensuring high-performance angular super-resolution results under different signal-to-noise ratios (SNRs). Experiments confirm the proposed method’s effectiveness and robustness.

The missiles typically require multiple stages tests to improve hit accuracy. The existing estimation methods seldom consider the tests characteristics of multiple stages growth of missile hit accuracy, which bring difficulties to accurately estimate the missile hit accuracy. Considering the different degrees of damage caused by missile hitting the target in different areas, the Dirichlet distribution is selected as the prior distribution of hit accuracy parameters based on the Bayesian method. The sequence constraint relationship between the hit accuracy parameters of each stage test is established, and the Bayesian estimation model of hit accuracy based on multiple stages growth tests is constructed. The Markov chain-Monte Carlo method combined with Gibbs sampling is used to overcome the problem of solving the posterior high-dimensional integral of the model to complete the model solution. The example shows that this method can fuse multiple stages growth tests information compared with the existing single stage test method, which can provide reference for the estimation of missile hit accuracy during the period of research and development.

A low-profile wideband multi-mode circularly polarized (CP) multiple-input-multiple-output (MIMO) antenna with the parasitic metasurface using characteristic mode analysis (CMA) is presented. The single monopole element of MIMO is composed of a transmission line and a branch after slotting on the ground. Structurally, the transmission line forms a 90° shape with the branch, providing favorable conditions for achieving circular polarization. Through CMA, the difference in characteristic angle (CA) at the intersection of the mode significance (MS) is about 90° with their almost consistent radiation directions. The analysis results show that four modes work together to achieve a circular polarization effect. Secondly, after rotating the monopole antenna at three angles (90°, 180°, 270°), a 4-port MIMO antenna is formed, and the results of their ports on the same level are consistent. Finally, the narrow axial ratio bandwidth (ARBW) of MIMO antennas is compensated by adding two rows of parasitic cross-circular metasurfaces. The measured results of the proposed antenna prototyped are in agreement with the simulated ones, demonstrating an impedance bandwidth (IBW) of 84.1% (3.65-8.95 GHz), a 3-dB ARBW of 52.0% (3.7-6.3 GHz), and a peak gain of 5.8 dBic at 4.9 GHz. The antenna achieves a total efficiency ranging from 72.8% to 98.8% across the bandwidth, and its envelope correlation coefficient (ECC) remains below 0.15 throughout the working frequency range.

In target tracking stage, the integrated sensing and communication (ISAC) system needs to estimate various target parameters, such as the angle and range, while ensuring highly reliable communication. This paper proposes a two-stage constant-modulus waveform design. In the transmission waveform covariance matrix (TWCM) optimization stage, we minimize the Cramer-Rao bound (CRB) of joint estimation for two-dimensional direction of arrival (DOA) and complex reflection coefficients under total power constraint, obtaining an optimal TWCM. In the alternating optimization based on Riemannian steepest descent (RSD) stage, under constant modulus property and multi-user interference constraints, minimize the squared Euclidean distance between actual TWCM and optimal TWCM obtained from the TWCM optimization stage. To address the optimization problem, an alternate optimization algorithm based on the RSD method is proposed. Simulation results demonstrate that the proposed waveform design simultaneously achieve reliable communication and multi-target tracking.

The semiconductor chip industry has become a bottleneck in China’s industrial development. How to improve efficiency has attracted increasing attention. Effective and efficient scheduling decision is the key to improve production efficiency. Oriented towards the wafer manufacturing of micro-electro-mechanical system (MEMS), a reentrant flexible job-shop scheduling problem with assembly constraints (RFJSSP-AC) is modeled to minimize the makespan and the total energy consumption simultaneously. An improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) is developed to solve the problem. The proposed IMOEA/D incorporates three key innovations: a hybrid initialization strategy combining heuristic rules with random generation to produce high-quality initial populations; an objective- oriented search strategy for targeted solution optimization; and an energy consumption inverse optimization mechanism for further energy efficiency improvement. Comprehensive comparative experiments demonstrate the effectiveness of the proposed algorithmic components and the exceptional performance for the RFJSSP-AC problem.

Antenna arrays have become a popular choice for protecting global navigation satellite system (GNSS) receivers in strong radio frequency interference environments. Environmental nonstationarities, which are caused by fast variations in propagation channels and rapid motion of interfering sources or antenna arrays, cause performance degradation of antenna array receivers. The covariance matrix taper (CMT) has been used to widen the adaptive pattern nulls toward the interference sources in GNSS antenna array receivers to address highly dynamic environments. In this paper, it is verified that the loss of the signal to interference and noise ratio (SINR) caused by a CMT cannot be ignored, and a CMT with a fixed null width cannot work well when the dynamic range of the GNSS array receiver is large. To design the modest null width, a CMT with an adaptive null width for space-time adaptive processing (STAP) is introduced. In the adaptive CMT, the proposed subspace leakage in STAP is used to estimate the required null width. In the simulations, the SINRs of adaptive CMT are always close to the SINRs of the optimal fixed null width in different scenarios, and the average SINRs of the adaptive CMT are even more than 10 dB higher than that of CMT with fixed null width in some scenarios. The adaptive CMT can be widely applied to GNSS antenna array receivers with a large dynamic range.

To address long-range impulsive orbital pursuit-defense game scenario, characterized by long decision cycles, sparse actions, and misaligned maneuvering times, a multi-agent asynchronous hierarchical reinforcement learning (MA-AHRL) method is proposed. The method considers practical constraints including space perturbations, thrust limitations, fuel consumption limitations, and perceptual capabilities. Firstly, a relative orbital dynamics model is established, and the pursuit-defense game environment is defined, which clarifies the success criteria for the tasks of both the pursuer and the defender. Secondly, an actor-critic network structure integrating long short-term memory (LSTM) networks with an adaptive attention mechanism is designed. The LSTM extracts features from historical interaction data to compensate for the critic network’s inability to access opponent action information during training. The attention mechanism adaptively adjusts the weights of different time steps, thereby enhancing the ability to extract key state features in the game environment. Then, the action network employs a dual-layer decision structure that integrates dynamics, where the high-level policy outputs the maneuver time interval and the low-level policy outputs impulsive actions, effectively reducing training difficulty and improving training efficiency. Finally, the pursuit-defense game is modeled as a partially observable Markov decision process (POMDP), where the action space, state space, observation space, and reward function are explicitly designed. Simulation comparison demonstrates that MA-AHRL can effectively coordinate the competitive relationship between the pursuer and defender while exhibiting excellent training stability. Testing the decision-making networks, both the pursuer and the defender display strong strategic adaptability and real-time decision-making capabilities, fully validating the effectiveness and reliability of the proposed method in this paper.

This paper presents a time-varying formation tracking strategy for multiagent quadrotor operating in hostile or challenging circumstances, where the position constraints become crucial. A decentralized cooperative control scheme is proposed based on barrier Lyapunov functions in conjunction with linear sliding mode control with directed communication among the quadrotors. The consequence of using the proposed control scheme is to provide practical finite time convergence and disturbance rejection while fulfilling position constraints. The practical finite time convergence of the proposed method is shown for multi-input and multi-output (MIMO) multiagent systems via Lyapunov analysis. The capability of the proposed algorithm is investigated for six quadrotors using MATLAB/Simulink. The simulation results demonstrate the intended performance of the proposed formation tracking controller under modeling uncertainties and external disturbances.

Few studies have sought to integrate the strengths of advanced control technologies with classical control approaches to address the stringent requirements of hyper-agile satellites for both ultra-high precision and ultra-fast maneuverability. The present work addresses this issue by combining active disturbance rejection control (ADRC) and proportional-integral-derivative (PID) control, with a focus on the use of reaction flywheels as control actuators in hyper-agile satellites. The ADRC algorithm enables the rapid correction of significant control errors and avoids excessive control torque by adopting a transition process in a tracking differentiator. In contrast, the PID algorithm applies direct error feedback to achieve ultra-high steady-state precision tracking. The results of numerical simulations demonstrate that the proposed integrated control method not only achieves ultra-fast maneuverability, with an angular displacement of 25° within 10 s, but also delivers ultra-high precision steady-state tracking with an angular error of 1×10⁻⁶° and an angular velocity variability of 2×10⁻⁸ °/s without the reaction flywheel actuator control errors. However, the reaction flywheel output torque errors reduce the velocity control stability by four orders of magnitude and the attitude control accuracy by a factor of two.

In the study of complex networks, the identification of key nodes is crucial but challenging. Traditional centrality indicators (such as degree, betweenness, and closeness centrality) are difficult to achieve a balance between accuracy, effectiveness and time complexity, time-consuming and resource-consuming, or unable to accurately locate key nodes. In order to solve these problems, based on the commonly used Laplacian centrality (LC), a two-hop LC (TLC) index is proposed, which aims to improve the index recognition performance by introducing second-order neighbor node information. The two-hop degree centrality (TDC) is proposed and combined with LC to derive the TLC, whose superiority is verified through multi-dimensional comparative analysis. Meanwhile, the application of higher-order hop degree centrality in the derivation of LC is deduced, and it is proved that it is not feasible to apply three-hop and above degree centrality to the derivation of LC.

To address the issue of local oscillations in the slime mould algorithm (SMA) during path planning within complex environments, a multi-strategy improved hierarchical guidance SMA (MHGSMA) is introduced to enhance the search performance of SMA. Based on a priori analysis of the early-stage search behavior of slime moulds, a hierarchical method is developed to achieve a more rational division between elite and general populations. While adhering to the inherent search mechanism of SMA, the guidance functions for each population are refined to expand the search scope. Additionally, a greedy selection strategy is incorporated further to enhance the search efficiency and accuracy of the SMA. Simulation experiments for path planning are conducted in four distinct obstacle environments by comparing the MHGSMA, SMA, and hierarchical guided SMA (HG-SMA) for smooth path planning. The analysis of convergence speed, global path length, and accuracy reveals that MHGSMA excels in path planning. In complex environments, it shows improvements of 58.6% and 42% in convergence speed, 9.3% and 6% in path length, and 38.5% and 63.6% in accuracy, significantly enhancing SMA’s search performance. At the same time, under the same conditions, MHGSMA also demonstrates strong competitiveness compared to particle swarm optimization and grey wolf optimizer.

A reliable evaluation index system (EIS) is essential for assessing the operational capabilities of equipment system-of-systems (SoS), yet systematic methods for validating its effectiveness in complex scenarios remain lacking. To address this problem, this paper proposes a validation algorithm, which includes a validation framework, a data collection format tailored to operational contexts, and a grey clustering algorithm. First, the validation framework is designed at three levels, syntactic, semantic, and pragmatic, to systematically assess the normativity, logical coherence, and practical utility of the EIS. Second, a data collection format suited to operational backgrounds is introduced, enabling multi-domain coverage across the physical, informational, cognitive, and social dimensions, and accounting for both expected and abnormal scenarios. Third, a grey clustering algorithm is developed, in which a clustering weight calculation model based on the interval fuzzy preference relation matrix ensures consistency among indicators, while rough and fine clustering analyses are combined to achieve quantitative validation of EIS effectiveness. Case studies demonstrate that the proposed method effectively validates EIS performance under diverse operational conditions. Overall, this work provides a systematic basis for EIS validation and practical guidance for enhancing SoS evaluation.

To address the problem of inefficient visual information processing and high cognitive load in manned/unmanned aerial vehicle (M/UAV) cooperative command and control (C2) interfaces under complex battlefield environments, this paper proposes a phased dynamic information presentation strategy tailored to different mission phases. Based on visual attentional capture theory and stimulus characteristics, we designed four dynamic presentation methods—blinking, flowing border, masking, and size scaling—and evaluated them across four typical mission phases: early warning detection, target identification, interception strike, and damage assessment. We conduct a mixed-design experiment with 4 mission phases × 5 presentation methods (including a static baseline) and collected data through eye-tracking, behavioral metrics, and subjective evaluations. Results show that dynamic presentation methods significantly outperform static ones in terms of task completion time, time to first fixation, total fixation duration, and subjective ratings. The optimal dynamic method for each phase is identified as blinking for early warning detection, masking for target identification, size scaling for interception strike, and flowing border for damage assessment. The findings demonstrate that phase-adapted dynamic presentation strategies can effectively enhance visual search efficiency and reduce cognitive load in M/UAV cooperative C2 interfaces, providing practical guidance for human-computer interaction design in high-pressure operational scenarios.

In order to study the influence of the support device on the energy conversion of the electromagnetic coil launcher, five different material and structure Livermore Software Technology Corporation’s Dynamic Analyzer (LS-DYNA) finite element analysis models are established, including the insulation ring column structure model, non-magnetic ring column structure model, magnetic ring column structure model, non-magnetic half ring column structure model, and magnetic half ring column structure model, to investigate the effect of the support device on the energy conversion of the internal drive coil launcher. In order to obtain the circuit characteristics of the internal drive coil launcher, a mathematical model and Maxwell finite element analysis model of the internal drive coil launcher are established, and the circuit characteristics of the launcher are solved based on the established model. By inputting system circuit data into a supported LS-DYNA finite element analysis model, the internal driving three-dimensional non axisymmetric simulation is completed, which solves the problem of Maxwell simulation software being unable to achieve three-dimensional hollow motion domain. The simulation results show that the peak velocities of the five supported launchers are reduced by 2.5%, 61.9%, 42.1%, 29.9%, and 14.7% respectively compared to the unsupported coil launchers. The results show that the influence of insulation materials on the energy conversion of the launcher can be basically ignored, while the influence of metal materials on the energy conversion of the launcher is significant. The use of magnetic high resistance materials and anti eddy current structures can significantly weaken the influence of metal support devices on the energy conversion of the coil launcher, and improve the launch performance of the coil launcher.

Unsupervised methods for defect detection hold significant potential for automated optical inspection (AOI) due to their independence from the challenges of collecting defect data. However, their performance is often limited in scenarios where normal samples exhibit high variability and defect features are subtle. To address this limitation, an unsupervised defect detection method named feature isolation autoencoder (FIAE) is proposed. This method compresses images into two components: one that encodes the information necessary to reconstruct normal samples, and the other that encodes anomalous information. As a result, any non-zero value in the anomalous component indicates the presence of an anomaly in the input images. Additionally, we propose the dynamic defect variability guidance (DDVG) technique and the stabilization-compensation training strategy, which ensure that FIAE achieves the expected training outcomes in the training data and process levels, respectively. Extensive experiments on the transistor dataset compiled in this study and the benchmark dataset MVTec Anomaly Detection dataset demonstrate that FIAE outperforms the baseline methods in most cases, achieving an area under the curve (AUC) of 0.979 and a best F1 score of 0.958 on the transistor dataset. The necessity of the proposed DDVG and the stabilization-compensation training strategy through ablation studies are validated.

Compressive sensing (CS) has been successfully combined with deep-learning neural networks to generate models for image-depth CS. Many existing models face significant limitations, such as their inability to handle tasks with multiple CS ratios and suboptimal performance in image reconstruction. To overcome these challenges, an optimized deep network model, named global multi-attention condition module (GMCM) iterative shrinkage-thresholding algorithm (ISTA)-Net⁺⁺ is proposed. In this model, the CS ratio of an image is incorporated as a network parameter, enabling the network to effectively handle multiple CS ratio tasks. Additionally, a hybrid attention mechanism is employed to preprocess the reconstructed image for feature extraction and to calculate the image complexity score. These elements are then combined with the CS ratio to jointly predict the parameters and iterative reconstruction steps, which are subsequently broadcast to all iterative stages of the model. Extensive experiments on high- and low-resolution image datasets demonstrate that the proposed GMCM ISTA-Net⁺⁺ model achieves a superior performance compared to traditional models. Given its flexibility, enhanced reconstruction quality, and adaptability, this model holds significant potential for deployment in resource-constrained mobile image-processing terminals.

It is a complex system problem to dispatch the robot swarm to complete exploration missions with high quality in uncertain environments. To solve the challenge, an algorithm of the robot swarm exploration mission planning supported by humans is proposed in this paper. Firstly, the target environment is decomposed into several areas, where it takes a price to explore these areas and each robot gets a reward after exploring. Secondly, the exploration problem is modeled as a multi-agent dynamic programming problem with the help of humans. Then, an extensible greedy based planning algorithm is proposed, where the searching policy of each robot is calculated based on the judgment index. Finally, the proposed algorithm is optimal through theoretical analysis, and simulation experiments show that the algorithm can provide high-quality solutions for the swarm exploration.

Lightweight convolutional neural networks (CNNs) have simple structures but struggle to comprehensively and accurately extract important semantic information from images. While attention mechanisms can enhance CNNs by learning distinctive representations, most existing spatial and hybrid attention methods focus on local regions with extensive parameters, making them unsuitable for lightweight CNNs. In this paper, we propose a self-attention mechanism tailored for lightweight networks, namely the brief self-attention module (BSAM). BSAM consists of the brief spatial attention (BSA) and advanced channel attention blocks. Unlike conventional self-attention methods with many parameters, our BSA block improves the performance of lightweight networks by effectively learning global semantic representations. Moreover, BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training, maintaining the network’s lightweight and mobile characteristics. We validate the effectiveness of the proposed method on image classification tasks using the Food-101, Caltech-256, and Mini-ImageNet datasets.

Supportability simulation analysis has emerged as a cost-effective alternative to physical experimentation for enhancing the logistics capacity of equipment support, especially in complex combat scenarios. However, key issues such as support scheme planning, resource optimization, and comprehensive analysis of the reliability, maintainability, supportability, and testability (RMST) indices are seldom explored in depth. This paper proposes mission-driven methods for equipment supportability analysis and optimization. Firstly, a supportability modeling and optimization framework, along with a mission-driven support activity simulation, is presented to describe the complex interaction between large-scale equipment and resources. Then, an optimization model for resource allocation based on simulation is developed, and a heuristic method is introduced to solve the problem. Fault uncertainty is incorporated into the support activities to integrate the RMST indices of the equipment. A Monte Carlo simulation is then used to comprehensively evaluate these RMST indices. Finally, a numerical example is used to validate the proposed methodology. The experimental results demonstrate that the presented approach successfully performs simulation, analysis, and optimization of the support activity, and the equipment deployment results in 93.29% availability, which can provide reliable decision support for equipment operation and logistics.

Traditional optimization algorithms from operations research are not well-suited for addressing the vehicle routing problem (VRP) with a large number of instances and high real-time requirements. Deep reinforcement learning (DRL) introduces a new approach to solving VRP, characterized by its rapid problem-solving capabilities and robust generalization abilities. In the domain of VRP variants, numerous benchmarks are utilized to evaluate the performance of the solving algorithms. However, the limited number of instances in these public benchmarks is insufficient for training DRL algorithms. To address this issue, an effective benchmark augmentation method using distribution derivation is proposed. By modeling the distributions of key VRP variables and estimating their parameters, the method derives distributions closely resembling the original benchmark and subsequently generates a large number of synthetic instances accordingly. The proposed method holds promise for widespread applications across various benchmarks. Experiments on VRP with time windows (VRPTW) and heterogeneous fleet VRP (HFVRP) verify the generalization abilities. The results show that DRL trained using the augmented benchmarks performs much better on public benchmarks than trained using the data customized by the algorithm authors. In addition, the DRL algorithms trained using the augmented benchmarks achieve performance comparable to state-of-the-art algorithms in public benchmarks for various VRP variants while significantly reducing the computation time.

In electronic warfare, radar systems are confronted with significant threats within the complex electromagnetic environment, particularly from the prevalent method of blanket jamming that impairs radar’s detection capabilities. Frequency agility is recognized as an effective anti-jamming method against blanket jamming in practice. However, the traditional frequency agility selects the carrier frequency randomly, which highly limits the potential for robust anti-jamming capabilities, despite offering sufficient optimization space to improve the radar performance. This paper proposes an antijamming decision strategy based on the comb gradient-bandits algorithm (CGBA) to optimize decisions for frequency agility radar under blanket jamming conditions. In this strategy, the reward value is defined as the signal to interference plus noise ratio (SINR) of the radar in each training round, and the radar selects the frequency channel with the highest reward value as the new operational channel. The proposed method has dynamic adaptive ability according to the jamming status, which enables the radar to update its frequency channel in real-time. Theoretical analysis and experimental results show that the proposed model improves the radar’s ability to select anti-jamming strategies to a certain extent.

When detecting weak targets, radar systems often encounter the interference from complex clutter in the field scene, leading to a high false alarm rate and a low detection rate when the traditional radar methods are employed. To address this issue, this paper proposes a clutter suppression method based on generative adversarial networks (GANs) to improve the detection performance of weak targets. This method designs a clutter identification network based on the characteristics of clutter in the range-Doppler domain. By adopting a divide-and-conquer strategy, a clutter suppression network is selected based on the clutter identification results. This method transforms the clutter suppression process into a mapping problem from the clutter-affected data domain to a clutter-free data domain. Experiments results show that the proposed method achieves a 10% improvement in detection rate in rain clutter scenarios, and reduce the false alarm rate by 90% in ground clutter scenarios.

Large-scale group decision-making (LSGDM), as a unique form of group decision-making, has been extensively studied. Its characteristic lies in the large number of decision-makers (DMs), resulting in a certain degree of complexity in the decision-making process. In the social network environment, DMs typically have some form of social relationship, influencing each other’s decision information. Considering the interactive connections among DMs, this paper proposes a model for LSGDM that takes into account community opinion leaders. Firstly, experts use linguistic distributed preference relations (LDPRs) to express assessment opinions. Different from the traditional approach that assigns the same semantic information to each expert, we construct a consistency-driven optimization model to obtain personalized individual semantics (PIS) of experts and language preferences are transformed into fuzzy numerical preferences. Secondly, combining social relationships and preference information among experts, the classic community detection algorithm is improved to partition large-scale groups into communities. Additionally, a feedback mechanism is designed to identify community opinion leaders and provide preference adjustment suggestions for experts. Finally, the feasibility and effectiveness of the proposed consensus decision-making model are verified through a specific case analysis.

To address issues such as model discretization, incomplete coverage of verification elements, and broken requirement traceability in the verification of combat system-of-systems (SoS) architectures, the paper proposes a verification method for combat systems based on model-based system engineering-multi-granularity system simulation verification, architecture data model verification and requirements model verification. To achieve consensus on architectural design elements and avoid deviations caused by terminology logic, a meta-model data set is constructed based on the Department of Defense Architecture Framework (DoDAF) meta-model method. The model-based system engineering methodology is introduced to clarify the design and verification framework of combat SoS from requirements analysis, design, analysis to verification. To address the issue of insufficient verification, a closed-loop system verification approach is proposed from the bottom up, encompassing multi-granularity system simulation verification, architectural data model verification, and requirements verification. An unmanned aircraft system cooperative combat SoS is constructed and verified. The verification method can systematically and comprehensively verify the domain, system, SoS architecture, and requirement models, reflecting the rationality, standardization, and effectiveness of the system verification method design. The verification method provides a systematic verification framework for combat SoS. It has engineering application value for improving the design reliability of combat systems.