A mathematical model to determine the optimal production lot size for a deteriorating production system under an extended product inspection policy is developed. The last-K product inspection policy is considered so that the nonconforming items can be reduced, under which the last K products in a production lot are inspected and the nonconforming items from those inspected are reworked. Consider that the products produced towards the end of a production lot are more likely to be nonconforming, is proposed an extended product inspection policy for a deteriorating production system. That is, in a production lot, product inspections are performed among the middle K1 items and after inspections, all of the last K2 products are directly reworked without inspections. Our objective here is the joint optimization of the production lot size and the corresponding extended inspection policy such that the expected total cost per unit time is minimized. Since there is no closed form expression for our optimal policy, the existence for the optimal production inspection policy and an upper bound for the optimal lot size are obtained. Furthermore, an efficient solution procedure is provided to search for the optimal policy. Finally, numerical examples are given to illustrate the proposed model and indicate that the expected total cost per unit time of our product inspection model is less than that of the last-K inspection policy.

The problem of scheduling radar dwells in multifunction phased array radar systems is addressed. A novel dwell scheduling algorithm is proposed. The whole scheduling process is based on an online pulse interleaving technique. It takes the system timing and energy constraints into account. In order to adapt the dynamic task load, the algorithm considers both the priorities and deadlines of tasks.  The simulation results demonstrate that compared with the conventional adaptive dwell scheduling algorithm, the proposed one can improve the task drop rate and system resource utility effectively.

In the design problem of low earth orbit (LEO) reconnaissance satellite constellation, optimization of coverage performance is the design goal in most current methods. However, in the using process, the user only concerns with the detection capabilities rather than coverage performance. To establish the relationship between these two aspects, the reconnaissance processes of normal stochastic targets are considered and the mathematic models of detection processes are built. The indicators of coverage performance are used to evaluate the detection probability and expectation of detection time delay, which are important factors in reconnaissance constellation estimation viewed from military intelligence discipline. The conclusions confirmed by the final simulation will be useful in LEO reconnaissance constellation design, optimization and evaluation.

In a cloud-native era, the Kubernetes-based workflow engine enables workflow containerized execution through the inherent abilities of Kubernetes. However, when encountering continuous workflow requests and unexpected resource request spikes, the engine is limited to the current workflow load information for resource allocation, which lacks the agility and predictability of resource allocation, resulting in over and under-provisioning resources. This mechanism seriously hinders workflow execution efficiency and leads to high resource waste. To overcome these drawbacks, we propose an adaptive resource allocation scheme named adaptive resource allocation scheme (ARAS) for the Kubernetes-based workflow engines. Considering potential future workflow task requests within the current task pod’s lifecycle, the ARAS uses a resource scaling strategy to allocate resources in response to high-concurrency workflow scenarios. The ARAS offers resource discovery, resource evaluation, and allocation functionalities and serves as a key component for our tailored workflow engine (KubeAdaptor). By integrating the ARAS into KubeAdaptor for workflow containerized execution, we demonstrate the practical abilities of KubeAdaptor and the advantages of our ARAS. Compared with the baseline algorithm, experimental evaluation under three distinct workflow arrival patterns shows that ARAS gains time-saving of 9.8% to 40.92% in the average total duration of all workflows, time-saving of 26.4% to 79.86% in the average duration of individual workflow, and an increase of 1% to 16% in centrol processing unit (CPU) and memory resource usage rate.

Intuitionistic fuzzy sets (IFSs) are useful means to describe and deal with vague and uncertain data. An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed. In each stage of the intuitionistic fuzzy C-means method the seeds are modified, and for each IFS a membership degree to each of the clusters is estimated. In the end of the algorithm, all the given IFSs are clustered according to the estimated membership degrees. Furthermore, the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets (IVIFSs). Finally, the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.

By deploying the ubiquitous and reliable coverage of low Earth orbit (LEO) satellite networks using optical inter satellite link (OISL), computation offloading services can be provided for any users without proximal servers, while the resource limitation of both computation and storage on satellites is the important factor affecting the maximum task completion time. In this paper, we study a delay-optimal multi-satellite collaborative computation offloading scheme that allows satellites to actively migrate tasks among themselves by employing the high-speed OISLs, such that tasks with long queuing delay will be served as quickly as possible by utilizing idle computation resources in the neighborhood. To satisfy the delay requirement of delay-sensitive task, we first propose a deadline-aware task scheduling scheme in which a priority model is constructed to sort the order of tasks being served based on its deadline, and then a delay-optimal collaborative offloading scheme is derived such that the tasks which cannot be completed locally can be migrated to other idle satellites. Simulation results demonstrate the effectiveness of our multi-satellite collaborative computation offloading strategy in reducing task complement time and improving resource utilization of the LEO satellite network.

Intelligent wars can take place not only in the physical domain and information domain but also in the cognitive domain. The cognitive domain will become the key domain to win in the future intelligent war. A Lanchester equation considering cognitive domain is proposed to fit the development tendency intelligent wars in this paper. One party is considered to obtain the exponential enhancement advantage on combat forces in combat if it can gain an advantage in the cognitive domain over the other party according to the systemic advantage function. The operational effectiveness of the cognitive domain in war is considered to consist of a series of indicators. Hesitant fuzzy sets and linguistic term sets are powerful tools when evaluating indicators, hence the indicators are scored by experts using hesitant fuzzy linguistic terms sets here. A unique hesitant fuzzy hybrid arithmetical averaging operator is used to aggregate the evaluation.

Traditional multi-band frequency selective surface (FSS) approaches are hard to achieve a perfect resonance response in a wide band due to the limit of the onset grating lobe frequency determined by the array. To solve this problem, an approach of combining elements in different period to build a hybrid array is presented. The results of series of numerical simulation show that multi-periodicity combined element FSS, which are designed using this approach, usually have much weaker grating lobes than the traditional FSS. Furthermore, their frequency response can be well predicted through the properties of their member element FSS. A prediction method for estimating the degree of expected grating lobe energy loss in designing multi-band FSS using this approach is provided.

An improved circular synthetic aperture radar (CSAR) imaging algorithm of omega-k (ω-k) type mainly for reconstructing an image on a cylindrical surface is proposed. In the typical CSAR ω-k algorithm, the rage trajectory is approximated by Taylor series expansion to the quadratic terms, which limits the valid synthetic aperture length and the angular reconstruction range severely. Based on the model of the CSAR echo signal, the proposed algorithm directly transforms the signal to the two-dimensional (2D) wavenumber domain, not using approximation processing to the range trajectory. Based on form of the signal spectrum in the wavenumber domain, the formula for the wavenumber domain interpolation of the ω-k algorithm is deduced, and the wavenumber spectrum of the reference point used for bulk compression is obtained from numerical method. The improved CSAR ω-k imaging algorithm increases the valid synthetic aperture length and the angular area greatly and hence improves the angular resolution of the cylindrical imaging. Additionally, the proposed algorithm can be repeated on different cylindrical surfaces to achieve three dimensional (3D) image reconstruction. The 3D spatial resolution of the CSAR system is discussed, and the simulation results validate the correctness of the analysis and the feasibility of the algorithm.

Code acquisition is the kernel operation for signal synchronization in the spread-spectrum receiver. To reduce the computational complexity and latency of code acquisition, this paper proposes an efficient scheme employing sparse Fourier transform (SFT) and the relevant hardware architecture for field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) implementation. Efforts are made at both the algorithmic level and the implementation level to enable merged searching of code phase and Doppler frequency without incurring massive hardware expenditure. Compared with the existing code acquisition approaches, it is shown from theoretical analysis and experimental results that the proposed design can shorten processing latency and reduce hardware complexity without degrading the acquisition probability.

With the development of global position system (GPS), wireless technology and location aware services, it is possible to collect a large quantity of trajectory data. In the field of data mining for moving objects, the problem of anomaly detection is a hot topic. Based on the development of anomalous trajectory detection of moving objects, this paper introduces the classical trajectory outlier detection (TRAOD) algorithm, and then proposes a density-based trajectory outlier detection (DBTOD) algorithm, which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense. The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented, which show the effectiveness of the algorithm.

This paper presents a path planning approach for rotary unmanned aerial vehicles (R-UAVs) in a known static rough terrain environment. This approach aims to find collision-free and feasible paths with minimum altitude, length and angle variable rate. First, a three-dimensional (3D) modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs. Considering the length, height and tuning angle of a path, the path planning of R-UAVs is described as a tri-objective optimization problem. Then, an improved multi-objective particle swarm optimization algorithm is developed. To render the algorithm more effective in dealing with this problem, a vibration function is introduced into the collided solutions to improve the algorithm efficiency. Meanwhile, the selection of the global best position is taken into account by the reference point method. Finally, the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine. Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.

Detecting the forgery parts from a double compressed image is very important and urgent work for blind authentication. A very simple and efficient method for accomplishing the task is proposed. Firstly, the probabilistic model with periodic effects in double quantization is analyzed, and the probability of quantized DCT coefficients in each block is calculated over the entire image. Secondly, the posteriori probability of each block is computed according to Bayesian theory and the results mentioned in first part. Then the mean and variance of the posteriori probability are to be used for judging whether the target block is tampered. Finally, the mathematical morphology operations are performed to reduce the false alarm probability. Experimental results show that the method can exactly locate the doctored part, and through the experiment it is also found that for detecting the tampered regions, the higher the second compression quality is, the more exact the detection efficiency is.

Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators are also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.

Rotating machinery is widely used in the industry. They are vulnerable to many kinds of damages especially for those working under tough and time-varying operation conditions. Early detection of these damages is important, otherwise, they may lead to large economic loss even a catastrophe. Many signal processing methods have been developed for fault diagnosis of the rotating machinery. Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs). In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines. First, the LMD is described. The advantages, disadvantages and some improved LMD methods are presented. Then, a comprehensive review on applications of LMD in fault diagnosis of the rotating machinery is given. The review is divided into four parts:fault diagnosis of gears, fault diagnosis of rotors, fault diagnosis of bearings, and other LMD applications. In each of these four parts, a review is given to applications applying the LMD, improved LMD, and LMD-based combination methods, respectively. We give a summary of this review and some future potential topics at the end.

Deep learning has achieved excellent results in various tasks in the field of computer vision, especially in fine-grained visual categorization. It aims to distinguish the subordinate categories of the label-level categories. Due to high intra-class variances and high inter-class similarity, the fine-grained visual categorization is extremely challenging. This paper first briefly introduces and analyzes the related public datasets. After that, some of the latest methods are reviewed. Based on the feature types, the feature processing methods, and the overall structure used in the model, we divide them into three types of methods: methods based on general convolutional neural network (CNN) and strong supervision of parts, methods based on single feature processing, and methods based on multiple feature processing. Most methods of the first type have a relatively simple structure, which is the result of the initial research. The methods of the other two types include models that have special structures and training processes, which are helpful to obtain discriminative features. We conduct a specific analysis on several methods with high accuracy on public datasets. In addition, we support that the focus of the future research is to solve the demand of existing methods for the large amount of the data and the computing power. In terms of technology, the extraction of the subtle feature information with the burgeoning vision transformer (ViT) network is also an important research direction.

The wheel brake system safety is a complex problem which refers to its technical state, operating environment, human factors, etc., in aircraft landing taxiing process. Usually, professors consider system safety with traditional probability techniques based on the linear chain of events. However, it could not comprehensively analyze system safety problems, especially in operating environment, interaction of subsystems, and human factors. Thus, we consider system safety as a control problem based on the system-theoretic accident model, the processes (STAMP) model and the system theoretic process analysis (STPA) technique to compensate the deficiency of traditional techniques. Meanwhile, system safety simulation is considered as system control simulation, and Monte Carlo methods are used which consider the range of uncertain parameters and operation deviation to quantitatively study system safety influence factors in control simulation. Firstly, we construct the STAMP model and STPA feedback control loop of the wheel brake system based on the system functional requirement. Then four unsafe control actions are identified, and causes of them are analyzed. Finally, we construct the Monte Carlo simulation model to analyze different scenarios under disturbance. The results provide a basis for choosing corresponding process model variables in constructing the context table and show that appropriate brake strategies could prevent hazards in aircraft landing taxiing.

The acquisition, analysis, and prediction of the radar cross section (RCS) of a target have extremely important strategic significance in the military. However, the RCS values at all azimuths are hardly accessible for non-cooperative targets, due to the limitations of radar observation azimuth and detection resources. Despite their efforts to predict the azimuth-dimensional RCS value, traditional methods based on statistical theory fails to achieve the desired results because of the azimuth sensitivity of the target RCS. To address this problem, an improved neural basis expansion analysis for interpretable time series forecasting (N-BEATS) network considering the physical model prior is proposed to predict the azimuth-dimensional RCS value accurately. Concretely, physical model-based constraints are imposed on the network by constructing a scattering-center module based on the target scattering-center model. Besides, a superimposed seasonality module is involved to better capture high-frequency information, and augmenting the training set provides complementary information for learning predictions. Extensive simulations and experimental results are provided to validate the effectiveness of the proposed method.

The rapid development of data communication in modern era demands secure exchange of information. Steganography is an established method for hiding secret data from an unauthorized access into a cover object in such a way that it is invisible to human eyes. The cover object can be image, text, audio, or video. This paper proposes a secure steganography algorithm that hides a bitstream of the secret text into the least significant bits (LSBs) of the approximation coefficients of the integer wavelet transform (IWT) of grayscale images as well as each component of color images to form stego-images. The embedding and extracting phases of the proposed steganography algorithms are performed using the MATLAB software. Invisibility, payload capacity, and security in terms of peak signal to noise ratio (PSNR) and robustness are the key challenges to steganography. The statistical distortion between the cover images and the stego-images is measured by using the mean square error (MSE) and the PSNR, while the degree of closeness between them is evaluated using the normalized cross correlation (NCC). The experimental results show that, the proposed algorithms can hide the secret text with a large payload capacity with a high level of security and a higher invisibility. Furthermore, the proposed technique is computationally efficient and better results for both PSNR and NCC are achieved compared with the previous algorithms.

Beam-hopping technology has become one of the major research hotspots for satellite communication in order to enhance their communication capacity and flexibility. However, beam hopping causes the traditional continuous time-division multiplexing signal in the forward downlink to become a burst signal, satellite terminal receivers need to solve multiple key issues such as burst signal rapid synchronization and high-performance reception. Firstly, this paper analyzes the key issues of burst communication for traffic signals in beam hopping systems, and then compares and studies typical carrier synchronization algorithms for burst signals. Secondly, combining the requirements of beam-hopping communication systems for efficient burst and low signal-to-noise ratio reception of downlink signals in forward links, a decoding assisted bidirectional variable parameter iterative carrier synchronization technique is proposed, which introduces the idea of iterative processing into carrier synchronization. Aiming at the technical characteristics of communication signal carrier synchronization, a new technical approach of bidirectional variable parameter iteration is adopted, breaking through the traditional understanding that loop structures cannot adapt to low signal-to-noise ratio burst demodulation. Finally, combining the DVB-S2X standard physical layer frame format used in high throughput satellite communication systems, the research and performance simulation are conducted. The results show that the new technology proposed in this paper can significantly shorten the carrier synchronization time of burst signals, achieve fast synchronization of low signal-to-noise ratio burst signals, and have the unique advantage of flexible and adjustable parameters.

A proper weapon system is very important for a national defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multipleattribute decision making (MADM) problem. This paper proposes a new mathematical model based on the response surface method (RSM) and the grey relational analysis (GRA). RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to analyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.

According to the Doppler sensitive of the phase coded pulse compression signal, a Doppler estimating and compensating method based on phase is put forward to restrain the Doppler sidelobes, raise the signal-to-noise ratio and improve measuring resolution. The compensation method is used to decompose the echo to amplitude and phase, and then compose the new compensated echo by the amplitude and the nonlinear component of the phase. Furthermore the linear component of the phase can be used to estimate the Doppler frequency shift. The computer simulation and the real data processing show that the method has accurately estimated the Doppler frequency shift, successfully restrained the energy leakage on spectrum, greatly increased the echo signal-to-noise ratio and improved the detection performance of the radio system in both time domain and frequency domain.

A novel approach for engineering application to human error probability quantification is presented based on an overview of the existing human reliability analysis methods. The set of performance shaping factors is classified as two subsets of dominant factors and adjusting factors respectively. Firstly, the dominant factors are used to determine the probabilities of three behavior modes. The basic probability and its interval of human error for each behavior mode are given. Secondly, the basic probability and its interval are modified by the adjusting factors, and the total probability of human error is calculated by a total probability formula. Finally, a simple example is introduced, and the consistency and validity of the presented approach are illustrated.

With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM(1,1) usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model (EOGM(1,1)), based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry’s GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.

A generalized multiple-mode prolate spherical wave functions (PSWFs) multi-carrier with index modulation approach is proposed with the purpose of improving the spectral efficiency of PSWFs multi-carrier systems. The proposed method, based on the optimized multi-index modulation, does not limit the number of signals in the first and second constellations and abandons the concept of limiting the number of signals in different constellations. It successfully increases the spectrum efficiency of the system while expanding the number of modulation symbol combinations and the index dimension of PSWFs signals. The proposed method outperforms the PSWFs multi-carrier index modulation method based on optimized multiple indexes in terms of spectrum efficiency, but at the expense of system computational complexity and bit error performance. For example, with $n $=10 subcarriers and a bit error rate of 1×10^?5, spectral efficiency can be raised by roughly 12.4%.

The concept of unmanned weapon system-of-systems (UWSoS) involves a collection of various unmanned systems to achieve or accomplish a specific goal or mission. The mission reliability of UWSoS is represented by its ability to finish a required mission above the baselines of a given mission. However, issues with heterogeneity, cooperation between systems, and the emergence of UWSoS cannot be effectively solved by traditional system reliability methods. This study proposes an effective operation-loop-based mission reliability evaluation method for UWSoS by analyzing dynamic reconfiguration. First, we present a new connotation of an effective operation loop by considering the allocation of operational entities and physical resource constraints. Then, we propose an effective operation-loop-based mission reliability model for a heterogeneous UWSoS according to the mission baseline. Moreover, a mission reliability evaluation algorithm is proposed under random external shocks and topology reconfiguration, revealing the evolution law of the effective operation loop and mission reliability. Finally, a typical 60-unmanned-aerial-vehicle-swarm is taken as an example to demonstrate the proposed models and methods. The mission reliability is achieved by considering external shocks, which can serve as a reference for evaluating and improving the effectiveness of UWSoS.

The non-minimum phase feature of tail-controlled missile airframes is analyzed. Three selection strategies for desired performance indexes are presented. An acceleration autopilot design methodology based on output feedback and optimization is proposed. Performance and robustness comparisons between the two-loop and classical three-loop topologies are made. Attempts to improve the classical three-loop topology are discussed. Despite the same open-loop structure, the classical three-loop autopilot shows distinct characteristics from a two-loop autopilot with PI compensator. Both the two-loop and three-loop topologies can stabilize a static unstable missile. However, the finite actuator resource is the crucial factor dominating autopilot function.

To solve discrete optimization difficulty of the spectrum allocation problem, a membrane-inspired quantum shuffled frog leaping (MQSFL) algorithm is proposed. The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm, which is an effective discrete optimization algorithm. Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems. By hybridizing the quantum frog colony optimization and membrane computing, the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure. The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time. Simulation results for three utility functions of  a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.

Measuring the business-IT alignment (BITA) of an organization determines its alignment level, provides directions for further improvements, and consequently promotes the organizational performances. Due to the capabilities of enterprise architecture (EA) in interrelating different business/IT viewpoints and elements, the development of EA is superior to support BITA measurement. Extant BITA measurement literature is sparse when it concerns EA. The literature tends to explain how EA viewpoints or models correlate with BITA, without discussing where to collect and integrate EA data. To address this gap, this paper attempts to propose a specific BITA measurement process through associating a BITA maturity model with a famous EA framework: DoD Architectural Framework 2.0 (DoDAF2.0). The BITA metrics in the maturity model are connected to the meta-models and models of DoDAF2.0. An illustrative ArchiSurance case is conducted to explain the measurement process. Systematically, this paper explores the process of BITA measurement from the viewpoint of EA, which helps to collect the measurement data in an organized way and analyzes the BITA level in the phase of architecture development.

Prior research on the resilience of critical infrastructure usually utilizes the network model to characterize the structure of the components so that a quantitative representation of resilience can be obtained. Particularly, network component importance is addressed to express its significance in shaping the resilience performance of the whole system. Due to the intrinsic complexity of the problem, some idealized assumptions are exerted on the resilience-optimization problem to find partial solutions. This paper seeks to exploit the dynamic aspect of system resilience, i.e., the scheduling problem of link recovery in the post-disruption phase. The aim is to analyze the recovery strategy of the system with more practical assumptions, especially inhomogeneous time cost among links. In view of this, the presented work translates the resilience-maximization recovery plan into the dynamic decisionmaking of runtime recovery option. A heuristic scheme is devised to treat the core problem of link selection in an ongoing style. Through Monte Carlo simulation, the link recovery order rendered by the proposed scheme demonstrates excellent resilience performance as well as accommodation with uncertainty caused by epistemic knowledge.

In real-time strategy (RTS) games, the ability of recognizing other players’ goals is important for creating artifical intelligence (AI) players. However, most current goal recognition methods do not take the player ’s deceptive behavior into account which often occurs in RTS game scenarios, resulting in poor recognition results. In order to solve this problem, this paper proposes goal recognition for deceptive agent, which is an extended goal recognition method applying the deductive reason method (from general to special) to model the deceptive agent’s behavioral strategy. First of all, the general deceptive behavior model is proposed to abstract features of deception, and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning (IRL) method. Final, to interfere with the deceptive behavior implementation, we construct a game model to describe the confrontation scenario and the most effective interference measures.

Moth-flame optimization (MFO) is a novel metaheuristic algorithm inspired by the characteristics of a moth's navigation method in nature called transverse orientation. Like other metaheuristic algorithms, it is easy to fall into local optimum and leads to slow convergence speed. The chaotic map is one of the best methods to improve exploration and exploitation of the metaheuristic algorithms. In the present study, we propose a chaos-enhanced MFO (CMFO) by incorporating chaos maps into the MFO algorithm to enhance its performance. The chaotic map is utilized to initialize the moths' population, handle the boundary overstepping, and tune the distance parameter. The CMFO is benchmarked on three groups of benchmark functions to find out the most efficient one. The performance of the CMFO is also verified by using two real engineering problems. The statistical results clearly demonstrate that the appropriate chaotic map (singer map) embedded in the appropriate component of MFO can significantly improve the performance of MFO.

The measurement of the rolling angle of the projectile is one of the key technologies for the terminal correction projectile. To improve the resolution accuracy of the rolling angle in the laser seeker weapon system, the imaging model of the detector, calculation model of the position and the signal-to-noise ratio (SNR) model of the circuit are built to derive both the correlation between the resolution error of the rolling angle and the spot position, and the relation between the position resolution error and the SNR. Then the influence of each parameter on the SNR is analyzed at large, and the parameters of the circuit are determined. Meanwhile, the SNR and noise voltage of the circuit are calculated according to the SNR model and the decay model of the laser energy. Finally, the actual photoelectric detection circuit is built, whose SNR is measured to be up to 53 dB. It can fully meet the requirement of 0.5° for the resolution error of the rolling angle, thereby realizing the analysis of critical technology for photoelectric detection of laser seeker signals.

A system of systems (SoS) composes a set of independent constituent systems (CSs), where the degree of authority to control the independence of CSs varies, depending on different SoS types. Key researchers describe four SoS types with descending levels of central authority: directed, acknowledged, collaborative and virtual. Although the definitions have been recognized in SoS engineering, what is challenging is the difficulty of translating these definitions into models and simulation environments. Thus, we provide a goal-based method including a mathematical baseline to translate these definitions into more effective agent-based modeling and simulations. First, we construct the theoretical models of CS and SoS. Based on the theoretical models, we analyze the degree of authority influenced by SoS characteristics. Next, we propose a definition of SoS types by quantitatively explaining the degree of authority. Finally, we recognize the differences between acknowledged SoS and collaborative SoS using a migrating waterfowl flock by an agent-based model (ABM) simulation. This paper contributes to the SoS body of knowledge by increasing our understanding of the degree of authority in an SoS, so we may identify suitable SoS types to achieve SoS goals by modeling and simulation.

In the process of performing a task, autonomous unmanned systems face the problem of scene changing, which requires the ability of real-time decision-making under dynamically changing scenes. Therefore, taking the unmanned system coordinative region control operation as an example, this paper combines knowledge representation with probabilistic decision-making and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences. Firstly, according to utility value decision theory, the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned. Then, multi-entity Bayesian network is introduced for situation assessment, by which scenes and their uncertainty related to the operation are semantically described, so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty. Finally, the effectiveness of the proposed method is verified in a virtual task scenario. This research has important reference value for realizing scene cognition, improving cooperative decision-making ability under dynamic scenes, and achieving swarm level autonomy of unmanned systems.