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.

In the applications of joint control and robot movement, the joint torque estimation has been treated as an effective technique and widely used. Researches are made to analyze the kinematic and compliance model of the robot joint with harmonic drive to acquire high precision torque output. Through analyzing the structures of the harmonic drive and experiment apparatus, a scheme of the proposed joint torque estimation method based on both the dynamic characteristics and unscented Kalman filter (UKF) is designed and built. Based on research and scheme, torque estimation methods in view of only harmonic drive compliance model and compliance model with the Kalman filter are simulated as guidance and reference to promote the research on the torque estimation technique. Finally, a promoted torque estimation method depending on both harmonic drive compliance model and UKF is designed, and simulation results compared with the measurements of a commercial torque sensor, have verified the effectiveness of the proposed method.

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle (UAV) real-time path planning problem, a real-time UAV path planning algorithm based on long short-term memory (RPP-LSTM) network is proposed, which combines the memory characteristics of recurrent neural network (RNN) and the deep reinforcement learning algorithm. LSTM networks are used in this algorithm as Q-value networks for the deep Q network (DQN) algorithm, which makes the decision of the Q-value network has some memory. Thanks to LSTM network, the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment. Besides, the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning, so that the UAV can more reasonably perform path planning. Simulation verification shows that compared with the traditional feed-forward neural network (FNN) based UAV autonomous path planning algorithm, the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

Focusing on obstacle avoidance in three-dimensional space for unmanned aerial vehicle (UAV), the direct obstacle avoidance method in dynamic space based on three-dimensional velocity obstacle spherical cap is proposed, which quantifies the influence of threatening obstacles through velocity obstacle spherical cap parameters. In addition, the obstacle avoidance schemes of any point on the critical curve during the multi-obstacles avoidance are given. Through prediction, the insertion point for the obstacle avoidance can be obtained and the flight path can be replanned. Taking the Pythagorean Hodograph (PH) curve trajectory re-planning as an example, the three-dimensional direct obstacle avoidance method in dynamic space is tested. Simulation results show that the proposed method can realize the online obstacle avoidance trajectory re-planning, which increases the flexibility of obstacle avoidance greatly.

The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.

Enterprise architecture (EA) development is always a superior way to address business-IT alignment (BITA) issue. However, most EA design frameworks are inadequate to allocate IT resources, which is an important metric of BITA maturity. Under this situation, the idea of IT resource allocation is combined with the EA design process, in order to extend prior EA research on BITA and to demonstrate EAos capability of implementing IT governance. As an effective resource allocation method, portfolio decision analysis (PDA) is used to align business functions of business architecture and applications of system architecture. Furthermore, this paper exhibits an illustrative case with the proposed framework.

The fast growth of datacenter networks, in terms of both scale and structural complexity, has led to an increase of network failure and hence brings new challenges to network management systems. As network failure such as node failure is inevitable, how to find fault detection and diagnosis approaches that can effectively restore the network communication function and reduce the loss due to failure has been recognized as an important research problem in both academia and industry. This research focuses on exploring issues of node failure, and presents a proactive fault diagnosis algorithm called heuristic breadth-first detection (HBFD), through dynamically searching the spanning tree, analyzing the dial-test data and choosing a reasonable threshold to locate fault nodes. Both theoretical analysis and simulation results demonstrate that HBFD can diagnose node failures effectively, and take a smaller number of detection and a lower false rate without sacrificing accuracy.

Remaining useful life (RUL) prediction is one of the most crucial components in prognostics and health management (PHM) of aero-engines. This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold. Firstly, a random-coefficient regression (RCR) model is used to model the degradation process of aero-engines. Then, the RUL distribution based on fixed failure threshold is derived. The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation (MLE) method and the random coefficient is updated in real time under the Bayesian framework. The failure threshold in this paper is defined by the actual degradation process of aero-engines. After that, a expectation maximization (EM) algorithm is proposed to estimate the underlying failure threshold of aero-engines. In addition, the conditional probability is used to satisfy the limitation of failure threshold. Then, based on above results, an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold (RFT) is derived in a closed-form. Finally, a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

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.

A novel multi-view 3D face registration method based on principal axis analysis and labeled regions orientation called local orientation registration is proposed. The pre-registration is achieved by transforming the multi-pose models to the standard frontal model’s reference frame using the principal axis analysis algorithm. Some significant feature regions, such as inner and outer canthus, nose tip vertices, are then located by using geometrical distribution characteristics. These regions are subsequently employed to compute the conversion parameters using the improved iterative closest point algorithm, and the optimal parameters are applied to complete the final registration. Experimental results implemented on the proper database demonstrate that the proposed method significantly outperforms others by achieving 1.249 and 1.910 mean root-mean-square measure with slight and large view variation models, respectively.

System of systems architecture (SoSA) has received increasing emphasis by scholars since Zachman ignited its flame in 1987. Given its complexity and abstractness, it is critical to validate and evaluate SoSA to ensure requirements have been met. Multiple qualities are discussed in the literature of SoSA evaluation, while research on functionality is scarce. In order to assess SoSA functionality, an extended influence diagram (EID) is developed in this paper. Meanwhile, a simulation method is proposed to elicit the conditional probabilities in EID through designing and executing SoSA. An illustrative anti-missile architecture case is introduced for EID development, architecture design, and simulation.

The time dependent vehicle routing problem with time windows (TDVRPTW) is considered. A multi-type ant system (MTAS) algorithm hybridized with the ant colony system (ACS) and the max-min ant system (MMAS) algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection (NNS) mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm's good performance with all these strategies on classic vehicle routing problem with time windows (VRPTW) benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

Unmanned combat air vehicles (UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.

Platform planning is one of the important problems in the command and control (C2) field. Hereto, we analyze the platform planning problem and present nonlinear optimal model aiming at maximizing the task completion qualities. Firstly, we take into account the relation among tasks and build the single task nonlinear optimal model with a set of platform constraints. The Lagrange relaxation method and the pruning strategy are used to solve the model. Secondly, this paper presents optimization-based planning algorithms for efficiently allocating platforms to multiple tasks. To achieve the balance of the resource assignments among tasks, the m-best assignment algorithm and the pair-wise exchange (PWE) method are used to maximize multiple tasks completion qualities. Finally, a series of experiments are designed to verify the superiority and effectiveness of the proposed model and algorithms.

To reduce complexity, the combat effectiveness simulation system (CESS) is often decomposed into static structure, physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge (DIK) and application variant knowledge (AVK) levels. This study concentrates on the specification of CESS's physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering (MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.

Ordering based search methods have advantages over graph based search methods for structure learning of Bayesian networks in terms on the efficiency. With the aim of further increasing the accuracy of ordering based search methods, we first propose to increase the search space, which can facilitate escaping from the local optima. We present our search operators with majorizations, which are easy to implement. Experiments show that the proposed algorithm can obtain significantly more accurate results. With regard to the problem of the decrease on efficiency due to the increase of the search space, we then propose to add path priors as constraints into the swap process. We analyze the coefficient which may influence the performance of the proposed algorithm, the experiments show that the constraints can enhance the efficiency greatly, while has little effect on the accuracy. The final experiments show that, compared to other competitive methods, the proposed algorithm can find better solutions while holding high efficiency at the same time on both synthetic and real data sets.

The path-following control of the asymmetry underactuated unmanned surface vehicle (USV) under external disturbances such as unknown constant and irrational ocean currents is discussed, and an adaptive sliding-mode path-following control system is proposed, which comprises a path-variable updated law, a modified integral line-of-sight (ILOS) guidance law based on a time-varying lookahead distance and adaptive feedback linearizing controllers combined with sliding-mode technique. A more accurate USV model without the assumption of having diagonal inertia and damping matrices is first presented, aiming at improving the performance of the path-following control. Next, the coordinate transformation is adopted to decouple the sway dynamic from the rudder angle, and the path-following errors dynamics without non-singular problem are presented in the moving Frenet-Serret frame. Then, based on the cascaded theorem and the adaptive sliding-mode method, the adaptive control law of position errors and course error are designed, among which the lookahead distance and integral gain are all computed as different functions of cross-track error to estimate and compensate the sideslip angle caused by external disturbances adaptively. Finally, according to the Lyapunov and cascaded theorem, the control system proposed is proved to be uniform globally asymptotic stability (UGAS) and uniform semiglobal exponential stability (USGES) when the control objectives are all achieved. Simulation results illustrate the precision and high-quality performance of this new controller.

How to recognize targets with similar appearances from remote sensing images (RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network (CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However, the training and testing of CNN mainly rely on single machine. Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure. When a model is complex or the training data is relatively small, overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore, Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Naïve Bayes classifier, a distributed Naïve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.

This paper studies the problem of tracking a ground target for a fixed-wing unmanned aerial vehicle (UAV) based on the proposed guidance law. The algorithm ensures that a UAV continuously overflies the target whether it is fixed or moving. The requirements of the UAV flight constraints such as bounded airspeed and acceleration are considered. A Lyapunov function is constructed to prove the stability of the proposed guidance law, and parameter design criteria have been developed. Considering the fixed and moving ground targets, numerical simulations are performed to verify the feasibility and benefits of the proposed guidance algorithm.

Reliability enhancement testing (RET) is an accelerated testing which hastens the performance degradation process to surface its inherent defects of design and manufacture. It is an important hypothesis that the degradation mechanism of the RET is the same as the one of the normal stress condition. In order to check the consistency of two mechanisms, we conduct two enhancement tests with a missile servo system as an object of the study, and preprocess two sets of test data to establish the accelerated degradation models regarding the temperature change rate that is assumed to be the main applied stress of the servo system during the natural storage. Based on the accelerated degradation models and natural storage profile of the servo system, we provide and demonstrate a procedure to check the consistency of two mechanisms by checking the correlation and difference of two sets of degradation data. The results indicate that the two degradation mechanisms are significantly consistent with each other.

This paper proposes a parallel cyclic shift structure of address decoder to realize a high-throughput encoding and decoding method for irregular-quasi-cyclic low-density parity-check (IR-QC-LDPC) codes, with a dual-diagonal parity structure. A normalized min-sum algorithm (NMSA) is employed for decoding. The whole verification of the encoding and decoding algorithm is simulated with Matlab, and the code rates of 5/6 and 2/3 are selected respectively for the initial bit error ratio as 6% and 1.04%. Based on the results of simulation, multi-code rates are compatible with different basis matrices. Then the simulated algorithms of encoder and decoder are migrated and implemented on the field programmable gate array (FPGA). The 183.36 Mbps throughput of encoder and the average 27.85 Mbps decoding throughput with the initial bit error ratio 6% are realized based on FPGA.

A joint resource allocation scheme concerned with the sensor subset, power and bandwidth for range-only target tracking in multiple-input multiple-output (MIMO) radar systems is proposed. By selecting an optimal subset of sensors with the predetermined size and implementing the power allocation and bandwidth strategies among them, this algorithm can help achieving a better performance within the same resource constraints. Firstly, the Bayesian Cramer-Rao bound (BCRB) is derived from it. Secondly, a criterion for minimizing the BCRB at the target location among all targets tracking in a certain range is derived. Thirdly, the optimization problem involved with three variable vectors is formulated, which can be simplified by deriving the relationship between the optimal power allocation vector and the bandwidth allocation vector. Then, the simplified optimization problem is solved by the cyclic minimization algorithm incorporated with the sequential parametric convex approximation (SPCA) algorithm. Finally, the validity of the proposed method is demonstrated with simulation results.

With the development of micro-satellite technology, traditional monolithic satellites can be replaced by micro-satellite clusters to achieve high flexibility and dynamic reconfiguration capability. For satellite clusters based on the frequency division-code division multiple access (FD-CDMA) communication system, the inter-satellite ranging precision is usually constrained due to the influence of multi-address interference (MAI). The multi-user detection (MUD) is a solution to MAI, which can be divided into two categories: the linear detector (LD) and the non-linear detector (NLD). The general idea of the LD is aiming to make a better decision during the symbol decision process by using the information of all channels. However, it is not beneficial for the signal phase tracking precision. Instead, the principle of the NLD is to rebuild the interference signal and cancel it from the original one, which can improve the ranging performance at the expense of considerable delays. In order to enable simultaneous ranging and communication and reduce multi-node ranging performance degradation, this paper proposes an NLD scheme based on a delay locked loop (DLL), which simplifies the receiver structure and introduces no delay in the decision process. This scheme utilizes the information obtained from the interference channel to reconstruct the interference signal and then cancels it from the original delayed signal. Therefore, the DLL input signal-to-interference ratio (SIR) of the desired channel can be significantly improved. The experimental results show that with the proposed scheme, the standard deviation of the tracking steady error is decreased from 5.59 cm to 3.97 cm for SIR = 5 dB, and 13.53 cm to 5.77 cm for SIR = -5 dB, respectively.

Time-limited dispatching (TLD) analysis of the full authority digital engine control (FADEC) systems is an important part of the aircraft system safety analysis and a necessary task for the certification of commercial aircraft and aeroengines. In the time limited dispatch guidance document ARP5107B, a single-fault Markov model (MM) approach is proposed for TLD analysis. However, ARP5107B also requires that the loss of thrust control (LOTC) rate error calculated by applying the single-fault MM must be less than 5% when performing airworthiness certification. Firstly, the sources of accuracy errors in three kinds of MM are analyzed and specified through a case study of the general FADEC system, and secondly a two-fault MM considering maintenance policy is established through analyzing and calculating the expected repair time when two related faults happen. Finally, a specific FADEC system is given to study on the influence factors of accuracy error in the single-fault MM, and the results show that the accuracy error of the single-fault MM decreases with the increase of short or long prescribed dispatch time, and the range values of short time (ST) and long time (LT) are determined to satisfy the requirement of accuracy error within 5%.

In this paper, the 40-Gbps orthogonal frequency division multiple access (OFDMA) technology enabled by subcarrier allocation in the form of integrated architecture for the intra-cell is proposed in the downlink transmission passive broadband optical access system. The data-carrying subcarriers in the inverse fast Fourier transform/fast Fourier transform (IFFT/FFT) size of 1 024 points are successfully divided into three sub-channels, in which each sub-channel has 256 useful subcarriers, by using adaptive dynamic bandwidth allocation (DBA). Taking the inherent advantages of M-ary quadrature amplitude modulation (MQAM) modulation mechanism into account, the performance of the absolutely identical MQAM format over the different sub-channels for the downstream OFDMA-passive optical network (PON) is investigated based on the intensity modulation direct detection (IMDD) system by simulations. The results show that three parallel 4QAM or 16QAM or 64QAM OFDMA data, which are transmitted over three sub-channels, is more suitable for different sub-channel allocations, respectively. In addition, comparing with single port 4/16/64QAM OFDM over the same access system, the receiver sensitivity economizes -0.6 dBm, 0.6 dBm, 4.6 dBm at the bit error rate (BER) value of 10$^{ - 3}$ respectively.

Person re-identification (re-id) involves matching a person across nonoverlapping views, with different poses, illuminations and conditions. Visual attributes are understandable semantic information to help improve the issues including illumination changes, viewpoint variations and occlusions. This paper proposes an end-to-end framework of deep learning for attribute-based person re-id. In the feature representation stage of framework, the improved convolutional neural network (CNN) model is designed to leverage the information contained in automatically detected attributes and learned low-dimensional CNN features. Moreover, an attribute classifier is trained on separate data and includes its responses into the training process of our person re-id model. The coupled clusters loss function is used in the training stage of the framework, which enhances the discriminability of both types of features. The combined features are mapped into the Euclidean space. The L2 distance can be used to calculate the distance between any two pedestrians to determine whether they are the same. Extensive experiments validate the superiority and advantages of our proposed framework over state-of-the-art competitors on contemporary challenging person re-id datasets.

Not confined to a certain point, such as waveform, this paper systematically studies the low-intercept radio frequency (RF) stealth design of synthetic aperture radar (SAR) from the system level. The study is carried out from two levels. In the first level, the maximum low-intercept range equation of the conventional SAR system is deduced firstly, and then the maximum low-intercept range equation of the multiple-input multiple-output SAR system is deduced. In the second level, the waveform design and imaging method of the low-intercept RF SAR system are given and verified by simulation. Finally, the main technical characteristics of the low-intercept RF stealth SAR system are given to guide the design of low-intercept RF stealth SAR system.

In this paper, a bit error ratio (BER)-based relay selection strategy is investigated under opportunistic relay selection. The challenging problem is to design the relay selection rule so that the relay is able to measure the performance of the cooperative system at the destination exactly with low computation costs. This paper derives a closed-form expression of the end-to-end bit error rate firstly. Then, an approximate BER expression based on the relationship between the instantaneous signal-to-noise ratio (SNR) of the relay-to-destination link and the probability of error propagation is derived. Finally, a simplified relay selection formula is proposed. Simulation results prove that the proposed relay selection rule can reflect the BER of each relay properly as well.

The advancement of small satellites is promoting the development of distributed satellite systems, and for the latter, it is essential to coordinate the spatial and temporal relations between mutually visible satellites. By now, dual one-way ranging (DOWR) and two-way time transfer (TWTT) are generally integrated in the same software and hardware system to meet the limitations of small satellites in terms of size, weight and power (SWaP) consumption. However, studies show that pseudo-noise regenerative ranging (PNRR) performs better than DOWR if some advanced implementation technologies are employed. Besides, PNRR has no requirement on time synchronization. To apply PNRR to small satellites, and meanwhile, meet the demand for time difference measurement, we propose the round-way time difference measurement, which can be combined with PNRR to form a new integrated system without exceeding the limits of SWaP. The new integrated system can provide distributed small satellite systems with on-orbit high-accuracy and high-precision distance measurement and time difference measurement in real time. Experimental results show that the precision of ranging is about 1.94 cm, and that of time difference measurement is about 78.4 ps, at the signal to noise ratio of 80 dBHz.

This paper examines the direction of arrival (DOA) estimation for polarized signals impinging on a sparse vector sensor array which is based on the maximum interelement spacing constraint (MISC). The vector array effectively utilizes the polarization domain information of incident signals, and the quaternion model is adopted for signals polarization characteristic maintenance and computational burden reduction. The features of MISC arrays are crucial to the mutual coupling effects reduction and higher degrees of freedom (DOFs). The quaternion data model based on vector MISC arrays is established, which extends the scalar MISC array into the vector MISC array. Based on the model, a quaternion multiple signal classification (MUSIC) algorithm based on vector MISC arrays is proposed for DOA estimation. The algorithm combines the advantages of the quaternion model and the vector MISC array to enhance the DOA estimation performance. Analytical simulations are performed to certify the capability of the algorithm.

Neuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the Von Neumann architecture. Inspired by the real characteristics of physical memristive devices, we propose a threshold-type nonlinear voltage-controlled memristor mathematical model which is used to design a novel memristor-based crossbar array. The presented crossbar array can simulate the synaptic weight in real number field rather than only positive number field. Theoretical analysis and simulation results of a 2×2 image inversion operation validate the feasibility of the proposed crossbar array and the necessary training and inference functions. Finally, the presented crossbar array is used to construct the neural network and then applied in the handwritten digit recognition. The Mixed National Institute of Standards and Technology (MNIST) database is adopted to train this neural network and it achieves a satisfactory accuracy.

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.

According to the signal processing characteristic of MIMO radars, an adaptive dwell scheduling algorithm is proposed. It is based on a novel pulse interleaving technique, which makes full use of transmitting, waiting and receiving durations of radar dwells. The utilization of transmitting duration is unique for MIMO radars and is realized through transmitting duration overlapping. Simulation results show that, compared with the conventional scheduling algorithm, the scheduling performance of MIMO radars can be improved effectively by the proposed algorithm, and the scheduling rule can be chosen arbitrarily when using the proposed algorithm.