The evaluation of air combat decision-making has garnered significant attention due to its potential to effectively mitigate losses resulting from erroneous decisions. However, existing research primarily focuses on static evaluation methods. Therefore, this paper proposes a dynamic multi-round decision evaluation method based on the characteristics of multi-round unmanned aerial vehicle air combat under opponent’s optimal strategy. In order to determine objective weights, an improved multi-attribute decision making method is proposed, which incorporates the proximity as a correction coefficient for evaluation indicators, utilizing the cosine similarity instead of Euclidean distance, and incorporating both actual and theoretical objective weights to prevent data mutations. Subsequently, the game theory is employed to reasonably adjust subjective and objective weights to obtain comprehensive weights. To address the issues related to the ambiguity and randomness during the evaluation process, a reverse cloud generator is utilized to determine the center of gravity of the cloud model using comprehensive weights while employing the weighted deviation degree for evaluating air combat decision-making effectiveness. By activating the cloud generator through the cloud model, the optimal strategies for each round of air combat are determined, thereby completing the dynamic evaluations for multi-round sequential decision-making processes. Finally, the feasibility and effectiveness of the proposed method are verified through simulations.

This research proposes an odd-even dimension RUNge Kutta algorithm (ODRUN) to solve global optimization and a well-known NP-hard problem in inventory management. The rpoposed algorithm integrates odd-even dimensional, fourth-order Runge-Kutta method, and neighbor search strategies. This hybrid approach significantly improves population diversity, avoids local optima, and enhances convergence accuracy. To validate the performance of the proposed algorithm, a widely recognized benchmark function suit from CEC2022 is first employed. Results confirm that ODRUN achieves an overall effectiveness ratio of 66.67% across three statistical indicators (best, mean, and standard deviation) for 12 benchmark functions. The test shows this algorithm is ranked first compared to seven state-of-the-art metaheuristic algorithms. Furthermore, ODRUN is applied to the joint replenishment problem with imperfect items and trade credit. Numerical examples from 600 randomly generated large-scale instances highlight that the algorithm’s performance remains unaffected by an increase in problem scale. The significant cost savings brought by the ODRUN algorithm, with the maximum improvement ratio in average cost and best-found total cost ranging from 14.81% to 19.5%, are achieved in comparison to other algorithms. In conclusion, ODRUN is an effective and robust tool for complex optimization problems.

To enhance the real-time performance and accuracy of guidance command generation, we propose an online reentry guidance algorithm based on analytical solutions of the hypersonic glide trajectory (HGT). Initially, an altitude-velocity profile is designed in the longitudinal plane to satisfy both path and terminal constraints. Based on this profile, we derive analytical solutions for the flight path angle (FPA) and bank angle. Subsequently, by employing the Newton-Raphson method to linearize the reentry motion equations, analytical solutions for the latitude and heading angle are obtained. Furthermore, we introduce an improved particle swarm optimization (IPSO) algorithm to optimize the profile parameters. This approach significantly enhances the algorithm’s global convergence by narrowing the parameter optimization range and adaptively adjusting the inertia weight and cognitive factors. Finally, we present an online guidance algorithm that combines the HGT analytical solutions with the IPSO algorithm. This algorithm effectively achieves longitudinal and lateral guidance by continuously updating the altitude-velocity profile and bank angle symbol in real time. Simulation results demonstrate that the proposed algorithm is fast, efficient, accurate, and holds significant potential for broader application.

The paper presents a full-exchange streaming adaptive processor architecture with nested parallel sampling covariance matrix estimation and adaptive weight computation, to achieve polarization-space-time adaptive processing (P-STAP), adaptive digital beamforming (ADBF) and multiple side-lobe canceller (MSLC) within a configurable computational framework. An effective method for real-time numerical calculation is proposed by reducing truncation errors, ensuring robust convergence, and low time complexity through the lower-diagnoal-lower transpose (LDLT) right multiplication iterative (LDLT-RMI) method. To fully implement P-STAP for high-intensity interference clutter adaptive robust suppression, a series of intellectual property (IP) cores are designed for high-order, high-condition symmetric positive definite matrix inversion. The adaptive processor can operate on a frame-structured data flow, accommodating various space-time-frequency-polarization multi-domain combinations, which supports 8 to 100 channels with a dynamic range of up to 80 dB. Even the interferences reach a high intensity of 65 dB, the adaptive processor can still work stably.

This paper proposes a space target attitude estimation method with inverse synthetic aperture radar (ISAR) images to address the challenges of difficult feature extraction, low estimation accuracy, and inefficiency in the attitude estimation of space targets. This method relies on ISAR contour projection plane alignment. Unlike traditional contour matching methods, this approach introduces an image plane alignment algorithm for different radar lines of sight (LOS). The algorithm aligns image planes from various frames, defined by their corresponding LOS and rotation axis, with those of a reference template library. This alignment resolves attitude mismatches during template matching caused by differences in image planes. The attitude estimation method first uses the Hausdorff distance as the contour matching criterion to enhance matching speed. Second, the contour projection plane alignment algorithm outputs the actual attitude for each frame. Third, the multi-solution attitude problem is addressed by combining the actual attitudes of each frame using the minimum distance method. Finally, an optimization algorithm is employed to improve estimation accuracy. This method demonstrates higher accuracy and efficiency compared to traditional approaches in scatter point imaging and electromagnetic simulation experiments.

To solve the problem of the precise strike for seeker-less missile, a cooperative guidance method of seeker-less missile and the beacon aircraft is proposed. Firstly, the guidance law considering the miss distance and line-of-sight (LOS) angle constraint is designed to achieve the precise strike on the target and satisfy the LOS angle constraint. On this basis, the tangential load of the beacon aircraft is designed to ensure that the remaining flight time (time-to-go) for the missile and the beacon aircraft converge to the same value within a finite time, thus the seeker-less missile can indirectly strike the target precisely. Simulation results validate the effectiveness of the proposed method in addressing the problem of cooperative strike on target, and compared with the cooperative guidance method in reference, the proposed cooperative guidance method is better in strike accuracy and demand overload.

The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters, including air density, aerodynamic parameters, and engine thrust, which often exhibit deviation. Predicting the trajectory range of the launch vehicle under the influence of uncertainty is essential before launch, and uncertainty quantification serves as a crucial method to address this challenge. In traditional uncertainty quantification for launch vehicles, unknown parameters are often assigned specific distributions based on prior knowledge. However, prior knowledge is sometimes subjective, and unknown parameters are often assigned conservative ranges to meet safety margins. In addition, the flight data of the past launch is precious, especially in quantifying the uncertainty of reusable or same-type launch vehicles. This paper utilizes flight data to estimate parameters base on Bayesian methods and integrates the estimation results with prior knowledge, which can more objectively set the distribution of uncertain parameters. Reasonable distribution has a positive impact on uncertainty quantification, which can avoid control strategies that are not robust enough or overly redundant. Therefore, the uncertainty quantification for launch vehicles is discussed under different information sources. In addition, the algorithm is accelerated based on Gaussian process regression and polynomial chaos expansions.

In this paper, the system we consider has multiple inventory warehouses and multiple pieces of equipment with multiple repairable components, where the joint planning of spare components and maintenance workers with lateral and cross-echelon transshipment is studied. Firstly, the characteristics of inventory system is analyzed, and the scheduled relationship of maintenance resources is carded. Based on this, a total system cost model is proposed, incorporating holding, ordering, and maintenance costs under an average waiting time constraint. A hyper-heuristic algorithm is then introduced to efficiently solve larger-scale problems with improved computational speed, and is applied to derive an optimized inventory allocation plan for maintenance resources. Finally, a maintenance system is analyzed, comprising four local warehouses, three central warehouses, and one plant that serves five machine groups. Each group contains four machines, each warehouse supports one or two machines, and every machine includes five independently failing key components. By analyzing the effect on reducing total cost, improving maintenance demand satisfaction rate, the effectiveness of the proposed optimization approach is verified.

Direction-of-arrival (DOA) estimation provides the angle information about interesting targets for array radar systems. However, conventional algorithms are designed for line-of-sight (LOS) scenarios and thus cannot be used to estimate non-LOS (NLOS) targets. Recently, intelligent reflecting surface (IRS) has been applied to DOA estimation for NLOS environments. This paper proposes a geometric model utilizing IRS for DOA estimation in NLOS scenarios. First, we establish an IRS-aided DOA estimation framework, where one-pair-IRSs is used to manipulate the propagation direction of the detection signals. Then, we formulate the estimation task using the elliptic positioning technique. Subsequently, we derive a closed-form solution for the resultant problem. Furthermore, we improve the architecture using multi-pair-IRSs and then design a binary weighting method to enhance the accuracy of DOA estimation. In comparison with the existing methods, the proposed algorithm is able to estimate two-dimensional (2D) DOA for NLOS targets. Numerical simulations are conducted to validate the effectiveness of IRS for DOA estimation in NLOS environments.

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.

Forest fires are characterized by their abrupt onset and highly destructive nature, resulting in significant annual property losses. Hence, regular surveillance is imperative for forest fire prevention and mitigation. The fundamental challenge in patrolling is akin to the problem of helicopter route planning. Conventional unmanned aerial vehicle (UAV) path planning commonly entails single-trip missions. Considering the extensive and complex forest environments, we advocate a multi-stage UAV reconnaissance strategy to address the daily inspection route planning conundrum. This approach facilitates UAVs to conduct round-trip flights between designated surveillance points and the base station at diverse time intervals, effectively satisfying the requirements for multi-tiered, hierarchical reconnaissance. Furthermore, we develop an advanced multi-strategy genetic algorithm (MSGA) to optimize the multi-stage reconnaissance model. Experimental outcomes underscore the superior performance of the enhanced MSGA, achieving a reduction of nearly 20% in total flight path length relative to the traditional genetic algorithm. This methodology significantly enhances the efficacy of daily forest patrols.

Weapon target assignment (WTA) problem is a critical problem in multiplatform confrontation. This paper studies a static WTA problem with heterogeneous weapons in multi-platform air combat scenarios, called heterogeneous WTA (HWTA) problem. Heterogeneous indicates that the engagement platforms carry multiple kinds of weapons for different tactical purposes. The targets assigned and the weapons used by one side’s platforms will affect the survival probability and capability of the other side’s platforms. The goal of each side in HWTA is to find a solution to determine the kind of weapon used and the target assigned for each platform, so as to maximize their combat effectiveness. The problem is formulated as a two-player noncooperative game model with considering the conflicts between the engaged sides. The Nash equilibrium is an effective solution to the game in which no player has an incentive to deviate. However, the number of pure strategies in HWTA increases exponentially with the engagement platforms. To improve computing efficiency, a double oracle algorithm with constructive heuristic (DOCH) is developed, within which the constructive heuristic is embedded to solve the oracle subproblems efficiently. Numerical experiments are conducted to verify the effectiveness of the DOCH. The results show that the DOCH can find effective strategies for platforms to improve combat effectiveness. Moreover, the DOCH can find high-quality solutions in seconds, significantly outperforming the state-of-the-art algorithms in terms of computational efficiency, especially for large-scale problems.

In environments with complex electromagnetic characteristics, signals from active repeater mainlobe deception jamming are closely aligned with radar emissions, significantly impairing radar functionality. This paper explores the efficacy of frequency diverse array (FDA) radar in combating active mainlobe jamming. Initially, the signal model for FDA multi-input multi-output (FDA-MIMO) radar is introduced. Building on this, a frequency offset optimization approach for FDA radar using the differential artificial bee colony (DEABC) algorithm is developed. Following this, the steepest descent linear minimum variance distortionless response beamforming algorithm is implemented in an FDA early warning radar to mitigate mainlobe jamming. Simulations are conducted to validate the effectiveness of the proposed strategies. The results from these simulations demonstrate that the methodologies can create a focused beam aimed at the target while effectively neutralizing jamming signals within the mainlobe area.

Sign language recognition (SLR) can be improved by using millimeter-wave radar, which safeguards the privacy of those who are hearing-impaired. However, existing SLR methods do not fully utilize the unique features of radar echoes, resulting in limited accuracy. Sign language is composed of an individual’s poses and hand movements. To obtain these recognition features, this paper presents a multi-modal-feature-based SLR (MMF-SLR) network framework. This method first constructs a transformer-pose network to extract the human skeleton information, which represents the poses in sign language, from the radar images. Additionally, hand movement information can be represented by the range-Doppler sequence and micro-Doppler signatures. The human skeleton and hand movement information are input into a multimodal fusion network to achieve high-accuracy SLR. The experimental results demonstrate that the proposed method can enhance the recognition accuracy of the sign language with similar poses or movements compared to the traditional SLR methods.

The consensus mechanism in multi-agent networks has attracted considerable attention in both control and computer science. However, current advancements in consensus-based control theory lack a general framework to optimize the communication complexity required to reach consensus. This gap highlights the necessity of robust analytical frameworks to advance the field. Our proposed method, termed hierarchical random networks, decomposes the entire network into multiple random sub-swarms and constructs a hierarchical structure among these sub-swarms. First, we establish a simplified condition to ensure the connectivity of hierarchical random networks. Further, we prove that the expected number of network connections in hierarchical random networks can be reduced to its lower bound as the size of sub-swarms approaches the square root of the total number of agents. At the end of the paper, we validate the effectiveness of the proposed network topology through simulation case studies on maneuvering target tracking. The results demonstrate that combining hierarchical random networks with consensus-based filters can achieve maneuvering target tracking while reducing communication complexity.

As is well known, mutual coupling between array elements has a significant negative impact on direction of arrival (DOA) estimation. To achieve DOA estimation under unknown mutual coupling, this paper proposes a low computational complexity Newton-like method. Firstly, a block sparse model based on the signal subspace is established, and the Lagrangian function is established according to the block sparse model. Secondly, since the Hessian matrix of the Lagrangian function cannot always ensure positive definiteness and the computational complexity of the inverse matrix of the Hessian matrix is enormous, the Newton method is no longer applicable. Therefore, this paper proposes a Newton-like method to achieve DOA estimation under mutual coupling and reduce the computational complexity by matrix inversion lemma. Finally, compared with existing methods of DOA estimation under array mutual coupling, the simulation results validate the effectiveness of the proposed method.

In this paper, we propose evaluation approaches for the spatial target localization precision based on the observation matrix conditional number. Three evaluation approaches for the spatial target localization precision are derived including relative condition numbers, absolute condition numbers, and volume condition numbers by properties of vectors and matrix norms. The theoretical analysis shows that the proposed methods are the upper certainty bound of the magnification of measurement error. Meanwhile, the proposed methods are able to account for variations of localization accuracy by exploiting geometric variations in the composition between the target and measurement stations. Finally, the proposed methods perform better, compared with the traditional evaluation methods. This is of great significance for the accuracy evaluation of high-precision measuring equipment, optimization of workstation layout and geometric configuration. Simulation experiments corroborate the effectiveness of the proposed methods.

To address the attitude control problem under the uncertainty, external disturbance, and actuator failure, a predefined-time fault-tolerant control method based on a predefined time disturbance observer is proposed. First, the dynamics model of hypersonic morphing vehicle (HMV) is established, and the control system is designed as an outer-loop attitude angle control loop and an inner-loop angular rate control loop considering the actuator failure problem. Secondly, a predefined-time disturbance observer is designed to estimate the comprehensive disturbances, and compensate in the control law. By integrating back-stepping control with predefined-time theory, a predefined-time attitude tracking control method is proposed, enabling the convergence time of the attitude tracking error to be designed through a simple parameter. Rigorous Lyapunov function analysis has demonstrated that the attitude tracking error can converge to an arbitrarily small neighborhood around the origin within a predefined time, and all signals in the closed-loop system are bounded. Finally, comparative simulations validate the effectiveness of the proposed method.

A dual-frequency aperiodic planar scanning array with diversified radiation elements is proposed. The proposed element consists of an elliptic patch and two parasitic rectangular patches, which can work both at 5.28?5.33 GHz with difference radiation beam and 5.78?5.83 GHz with sum radiation beam. A four equivalent magnetic currents model has been established to explain the radiation principle of the proposed element. Compared to the beamwidth of the sum beam, the 3 dB beamwidth of the difference beam is broadened about 30% and the radiation gain of the sum beam is obviously improved about 48%. A 64-element aperiodic array with diversified radiation elements is constructed. The array can scan at a wide angle of ±70° with no grating lobes at difference radiation mode and can scan in the range of ±60° with high gain at sum radiation mode.

The multi-body dynamics in the launch process of a space platform deploying a server, as well as the optimal double impulse rendezvous guidance law between the server and the target spacecraft, are studied. Firstly, the space platform enters into orbit around the target, keeping its launch tube axis aiming at it. After receiving the launch command, the server shoots out from the launch tube, flying to the target. Due to body coupling, the platform’s attitude is disturbed, preventing the server from accurately aiming at the target during separation. The server uses its small rocket engine to apply two velocity pulses: the first one to adjust its trajectory for rendezvous, and the second near the target to reduce relative velocity to zero for soft docking. A two-body dynamics model is established using the Newton-Euler method, and a virtual prototype is developed in ADAMS for validation. To solve the multi-objective optimization subject to energy consumption and flight time for rendezvous, an improved non-dominated sorting genetic algorithm II (NSGA-II) algorithm is proposed. Simulation results show that launch-induced perturbations are non-negligible, and the proposed algorithm effectively derives the optimal guidance law that balances energy use and flight time.

To address the problem of sea ice collisions threatening offshore drilling operations in polar regions, this paper proposes a Bayesian network–based collision risk assessment model for drillships. The model integrates large ice floe/iceberg conditions, natural environmental factors, and geometric factors derived from the ship’s shape, size, distance, and azimuth. Using iceberg routes, scenario simulations are conducted to evaluate collision probabilities and provide time-dependent risk values. Results demonstrate that the method yields reasonable and consistent assessments of drillship–ice interactions. The proposed method enables automatic collision risk assessment and can be applied to unattended management systems to enhance the safety of polar drilling operations.

The survivability of the infrastructure network, especially the combat system, is a core index to reflect the merits of the system. The cascade effect caused by the failure of a single or a few nodes is the focus of the research on the resistance. Many scholars have studied this problem and put forward some useful cascading failure models. Considering node resilience, a cascade failure model is proposed in which nodes have three states: normal, overload and failure. In addition, the conditions for the failure of connected edges are given. The model fully considers the redundancy design of the real system, and can objectively reflect the performance of the system to deal with cascade failure. We study the effect of node resilience on network cascade failure in both typical network and real network. Experimental results show that the proposed model can reduce the scale of cascade failures with higher cost utilization compared with the classical model that is widely used, especially when the network capacity is small.

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.

This paper proposes a hybrid differential evolution and ant colony optimization (DE-ACO) algorithm for multi-unmanned aerial vehicle (UAV) task allocation and path planning, addressing dynamic task allocation in heterogeneous UAV clusters. The algorithm integrates task order constraints, uniform target point weights, and path planning to avoid terrain obstacles and radar threats, ensuring efficient resource utilization. Simulations demonstrate balanced task allocation, rapid convergence, and robustness under UAV failure and communication range reduction scenarios. Compared to a baseline ant colony optimization approach, the hybrid DE-ACO method offers enhanced flexibility, making it a promising solution for real-world multi-UAV missions.

The physical architecture of optical convolution restricts its capacity to capture multi-scale features from targets, thus impeding the precision of network recognition. In this work, we propose a multi-scale optical convolutional neural network (MS-OCNN), which uses convolution kernels with different resolutions in the convolution layer to extract different scale features, along with attention mechanism and residual structure to analyze features. By separating the training and inference platforms of the network, we facilitate the electronic training of model parameters on a computer and the optical deployment on a system equipped with a spatial light modulator, enabling efficient target classification. The proposed MS-OCNN exhibits 1% to 3% improvement in classification performance on the modified national institute of standards and technology (MNIST) and Fashion-MNIST datasets compared to single-scale optical inference models. Online experimental systems in real-world scenarios have validated the target recognition capabilities of this method, which yielded classification accuracies of 97% and 87% on the MNIST and Fashion-MNIST datasets, respectively. This work enhances the feature acquisition capabilities of optical convolutional networks, elevates network recognition accuracy, and significantly propels the application of optical computing in domains such as guidance systems, autonomous driving, and robotics.

To predict the ranking of the country’s innovation capability in the world in real-time, this study designs a two-phased prediction model based on the pairwise comparison. Data from the global innovation index (GII) reports are employed in this study. Countries with different income levels have shown different development inertias, the two-phased prediction model is thus proposed. In the first phase, the GII data from the previous year are applied to predict the ranking of innovation capability for high-income countries. In the second phase, more years of historical data are adopted to predict the innovation ranking for other countries. The global innovation rankings for all countries and economies are thus obtained. Experiments have proved that the model requires only a few indicators to get accurate results. The model provides real-time decision support for decision-makers to formulate innovative development policies.

With the improvement of the informatization and intelligence level of logistics equipment, the interactive and collaborative relationships between equipment entities become complex, and the uncertainty problems emerge in the equipment system-of-systems. Herein, a heterogeneous network model is built to describe logistics equipment system-of-systems, which considers the heterogeneity and complex connections of different logistics equipment nodes. Next, the topological structure properties of this model are analyzed. On this basis, the experiments on the logistics equipment system-of-systems under attack strategies including degree attacks, betweenness centrality attacks and random attacks are taken to assess the changes of structural invulnerability. Results show that the logistics equipment system-of-systems heterogeneous network has similar topological structure characteristics of typical complex networks, namely small-world effect and scale-free characteristics, indicating that the flow, sharing, and synchronization between logistics equipment entities in the network are relatively easy. Meantime, the key logistics equipment nodes with large values such as degree, closeness centrality, and betweenness centrality should be protected in the logistics equipment system-of-systems heterogeneous network against deliberate attacks. The current work provides a perspective for demonstration and affords the theoretical support for development and decision-making of logistics equipment system-of-systems.

Aiming at the issues where traditional direction-of-arrival (DOA) estimation algorithms experience substantial performance degradation in low signal-to-noise ratio environments, and deep learning-based DOA estimation methods rely on massive training data with prolonged model training cycles, this paper proposes two efficient and high-precision DOA estimation methods based on ensemble learning. By formulating DOA estimation as a multi-label classification problem and leveraging the classification chain paradigm, data-driven models, classification chain-random forest (CC-RF) and classification chain-eXtreme gradient boosting (CC-XGBoost), are constructed, which are capable of handling multi-label classification tasks. To verify the effectiveness of the proposed methods, a multi-dimensional comparative experiment is designed to benchmark their performance against the traditional multiple signal classification (MUSIC) algorithm and a convolutional neural network (CNN) model. Experimental results indicate that in both single-source and multi-source scenarios, the proposed CC-RF algorithm exhibits excellent performance, achieving DOA estimation accuracy comparable to the MUSIC algorithm; in multi-source estimation scenarios, both proposed models demonstrate strong noise adaptability. Compared with the traditional MUSIC and CNN algorithms, the estimation error of the CC-XGBoost and CC-RF models is reduced by up to nearly 30 times while maintaining low time complexity, with the single estimation time reduced by approximately 90% compared to traditional methods. This study provides a technical pathway for DOA estimation in complex environments and holds significant application value in fields such as radar detection and wireless communication.

The implementation of timely monitoring and preventive maintenance plays a fundamental role to ensure the reliable operation of complex systems. Condition-based maintenance strategy offers an effective means to leverage system remaining life information, enabling the application of targeted measures to reduce maintenance costs and elevate overall operational efficiency. This study delves into a performance degradation system affected by external random shocks, utilizing the Wiener process model to characterize the continuous degradation process. Within this framework, two distinct condition-based monitoring schemes are proposed: one is the real-time condition monitoring and the other is the dynamic periodic monitoring. Through the optimization of maintenance strategies for each scheme based on the long-term average cost, the study aims to optimize the preventive maintenance threshold for system failure. The Monte Carlo simulation algorithm is adopted to solve the optimization problem. Finally, a comprehensive numerical example is provided to validate the efficiency of both the models and the proposed maintenance strategies.

Deep reinforcement learning algorithms are revolutionizing intelligent decision-making in air combat, drawing widespread attention and extensive research. However, air combat agents trained with these algorithms face significant challenges, such as limited decision-making capacities due to adversarial training against relatively fixed and singular expert strategies, and a lack of interpretability and reliability in their decision-making processes. To tackle these issues, this paper proposes a self-play training mechanism based on policy switching and opponent selection, allowing air combat agents to refine their capabilities via engaging with previous versions of themselves. Additionally, an explainable decision tree model is developed to clarify the decision logic of these agents. Simulations and results demonstrate that the proposed self-play training approach significantly enhances the decision-making abilities of air combat agents, with late-stage agents showing a 38% improvement over early-stage agents in confrontations with an expert strategy. Moreover, the explainable decision tree model effectively elucidates the decision logic and achieves an 86% win rate against the expert strategy, comparable to the 88% win rate of the air combat agents.

Accurately sensing the channel state of heterogeneous networks is key to matching users’ diverse service communication demands with the channel state, and is an effective way to improve the utilization efficiency of network resource. However, existing channel state perception methods are not suitable for heterogeneous network, and their perception performance is easily affected by interference uncertainty. In order to achieve channel state perception of heterogeneous networks, this paper adopts a centralized collaborative perception model, where each node obtains local channel state perception results based on statistical pulse parameters at the physical layer. In order to reduce the impact of interference on perception performance, this paper uses the Jousselme distance to quantify the degree of difference among nodes caused by interference. Using the average credibility as a threshold, nodes in the sensing area are classified. On this basis, the local perception results of each node are performed classification-based correction to improve the accuracy and reliability of channel state perception. Simulation results indicate that the proposed method has good adaptability for channel state perception in complex electromagnetic environments. The perception results can accurately reflect the actual channel state, which is conducive to improving the network throughput.

A millimeter-wave (mm-Wave) dual circularly polarized (CP) antenna in gap waveguide (GWG) technology with high port isolation is proposed in this paper. It is consisted of a simplified orthomode transducer (OMT) and an improved multi-section hexagonal waveguide CP horn antenna. The OMT is composed of two metal layers without the traditional septum or iris, which makes the structure simpler. The CP horn antenna can be easily integrated with the OMT without mode conversion. The principle analysis as well as the simulated and measured results of the proposed antenna are given in this paper. The simulated and measured results agree very well with each other. The port isolation of more than 27 dB over bandwidth of 26.5?31 GHz (|S₁₁|< ?15 dB) is achieved with relative bandwidth of 15.7%. The axial ratio (AR) lower than 2.5 dB for both left-hand and right-hand CP (LHCP and RHCP) are achieved over the bandwidth. The proposed antenna is a candidate for mm-Wave satellite communications or beyond fifth-generation (5G) communications applications.

Resolving conflict and achieving consensus among social groups with diverse opinions becomes a critical issue in today’s extensively connected society. Despite the ubiquitous heterogeneity of connection or contact patterns, the study of how topological characteristics of network structure affect opinion convergence is still insufficient. Based on Deffuant and colleagues’ bounded confidence model and the transformable network structure between random network and typical complex network types, including small-world network and scale-free network, we analyze the critical factors affecting continuous opinion convergence. We find that the network density plays a crucial role in the aggregated process of opinions in the social group, followed by the modularized level and the average shortest path length of the social network. However, the structural features have little impact on the consensus phase transition threshold. The further simulation experiments under real networks can be well understood based on the interplay of these three main factors. These findings confirm the paramount importance of creating a high-frequency and widely communicated atmosphere to mitigate conflict and efficiently reach consensus.

Validation for simulation models often confronts challenges with small samples due to the costs of time and money. To address this issue, this paper presents a validation method for small-sample dynamic outputs based on Gaussian process regression (GPR) models. Firstly, a validation framework based on Bayes statistics is proposed, shifting the focus from merely analyzing validation data to a more comprehensive analysis of posterior distributions. Subsequently, the posterior distributions of both the simulation outputs and the reference data are separately captured through segmented GPR. Then, the consistency of these posterior distributions is evaluated in terms of the central tendency and the distribution range. This consistency serves as a quantitative measure of the simulation model’s credibility, expressed as a value ranging from 0 to 1, where a value closer to 1 indicates higher credibility. Finally, the effectiveness of this validation method is demonstrated through a numerical example and an application example, highlighting its capability in uncertainty description and adaptability to small samples.

To address the energy consumption issues caused by task lengths in task scheduling on heterogeneous multi-core systems, this paper proposes an adaptive parameterized improved simulated annealing algorithm based on the directed acyclic graph task model. The algorithm employs feedback from acceptance rates to dynamically adjust the temperature and neighborhood size of the simulated annealing process. Additionally, it introduces a security mechanism to enhance convergence speed and global search capabilities. Compared against classical simulated annealing and standard heuristic algorithms, the proposed algorithm achieves reductions exceeding 54% in convergence generations, 50% in task slots, and 10% in scheduling time, providing a direction for low-power task scheduling.

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.

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.

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.