A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage
space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.

To enhance the capacity of the radar-reconnaissance interception receiver recognizing linear frequency modulated (LFM) at a low signal-noise ratio, this paper presents Wigner-Hough transform (WHT) of the LFM signal and its corresponding characteristics, derives the probability density functions of the LFM signal and Gaussian white noise within WHT based on entropy (WHTE), dimension under different assumptions and puts forward a WHT algorithm based on entropy of slice to improve the capacity of detecting the LFM signal. Entropy of the WHT domain slice is adopted to assess the information size of polar radius or angle slice, which is converted into the weight factor to weight every slice. Double-deck weight is used to weaken the influences of noise and disturbance terms and WHTE treatment and signal detection procedure are also summarized. The rationality of the algorithm is demonstrated through theoretical analysis and formula derivation, the efficiency of the algorithm is verified by simulation comparison between WHT, fractional Fourier transform and periodic WHT, and it is highlighted that the WHTE algorithm has better detection accuracy and range of application against strong noise background.

The analog-to-information convertor (AIC) is a successful practice of compressive sensing (CS) theory in the analog signal acquisition. This paper presents a multi-narrowband signals sampling and reconstruction model based on AIC and block sparsity. To overcome the practical problems, the block sparsity is divided into uniform block and non-uniform block situations, and the block restricted isometry property and sub-sampling limit in different situations are analyzed respectively in detail. Theoretical analysis proves that using the block sparsity in AIC can reduce the restricted isometric constant, increase the reconstruction probability and reduce the sub-sampling rate. Simulation results show that the proposed model can complete sub-sampling and reconstruction for multi-narrowband signals. This paper extends the application range of AIC from the finite information rate signal to the multi-narrowband signals by using the potential relevance of support sets. The proposed receiving model has low complexity and is easy to implement, which can promote the application of CS theory in the radar receiver to reduce the burden of analog-todigital convertor (ADC) and solve bandwidth limitations of ADC.

The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm
with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.

Based on the theory of the passive hydrogen maser, along with the technology of frequency modulation and modulation transfer spectroscopy, the theoretical expression of the single frequency modulation for the passive hydrogen maser and the function of the cavity and H line error signals separation are derived,
which are basically coincident with the experiment. The absorption and dispersion spectrum curves with different resonance widths show that the cavity and hydrogen transition serve as discriminators, and the two error signals can be separated. Through the calculations of the two error signals in the passive hydrogen maser, it analyzes the traditional method of the two error signals separation, and then describes a new improved method for the passive hydrogen servo loops consisting in the use of a single modulation frequency and frequency discrimination. A null interaction of the two error signals for the new selection of the phase setting is deduced theoretically and validated by the simulation. The preliminary experimental result confirms the feasibility of this new approach, which can reduce the influence from the cavity frequency variety on the crystal oscillator and contribute significantly to the long term performance of the passive hydrogen maser.

Orthogonal frequency division multiplexing (OFDM) waveform enables radar and communication functions simultaneously, which encounters low angle resolution and poor data rate for traditional single input single output (SISO) systems. To solve these problems, an integrated radar and communication system (IRCS) with multiple input multiple output (MIMO) OFDM waveform is proposed. The different limitations of radar and communication in designing such a system are investigated. Then, an optimization problem is devised to obtain suitable system parameters, including the number of subcarriers, subcarrier spacing, number of symbols, pulse repetition frequency (PRF) and length of cyclic prefix (CP). Finally, to satisfy the requirements of both radar and communication, the IRCS parameters are derived in three typical cases. Several numerical results are presented to illustrate the demands of radar and communication, inconsistent or consistent, for theRCS parameters and the superiority of the proposed system.

This paper studies the development on the first order sea clutter cross section for bistatic high frequency surface wave radar (HFSWR). Based on the received first order electric field expression, a closed-form of cross sections is derived to account for the case of receiving antenna array being mounted on
the shipborne platform. The uniform linear motion and sway motion components are assumed to be responsible for the observed differences in comparison with the bistatic fixed antenna case. Correspondingly, simulations are conducted to study the sea clutter spectral characteristics for these two cases versus different system parameters and sea state conditions. It is shown numerically that the forward motion component will spread the Bragg lines severely and the influence triggered by the sway motion can be explained as the Bessel function modulation of the ordinary sea clutter spectra. The obtained results have important implications in the application of shipborne HFSWR technology to ocean remote sensing and target detection.

The main function of electronic support measure system is to detect threating signals in order to take countermeasures against them. To accomplish this objective, a process of associating each interleaved pulse with its emitter must be done. This process is termed sorting or de-interleaving. A novel point symmetry based radar sorting (PSBRS) algorithm is addressed. In order to deal with all kinds of radar signals, the symmetry measure distance is used to cluster pulses instead of the conventional Euclidean distance. The reference points of the symmetrical clusters are initialized by the alternative fuzzy c-means (AFCM) algorithm to ameliorate the effects of noise and the false sorting. Besides, the density filtering (DF) algorithm is proposed to discard the noise pulses or clutter. The performance of the algorithm is evaluated under the effects of noise and missing pulses. It has been observed that the PSBRS algorithm can cope with a large number of noise pulses and it is completely independent of missing pulses. Finally, PSBRS is compared with some benchmark algorithms, and the simulation results reveal the feasibility and efficiency of the algorithm.

A 2D-direction of arrival estimation (DOAE) for multiinput and multi-output (MIMO) radar using improved multiple temporal-spatial subspaces in estimating signal parameters via rotational invariance techniques method (TS-ESPRIT) is introduced. In order to realize the improved TS-ESPRIT, the proposed algorithm divides the planar array into multiple uniform sub-planar arrays with common reference point to get a unified phase shifts measurement point for all sub-arrays. The TS-ESPRIT is applied to each sub-array separately, and in the same time with the others to realize the parallelly temporal and spatial processing, so that it reduces the non-linearity effect of model and decreases the computational time. Then, the time difference of arrival (TDOA) technique is applied to combine the multiple sub-arrays in order to form the improved TS-ESPRIT. It is found that the proposed method achieves high accuracy at a low signal to noise ratio (SNR) with low computational complexity, leading to enhancement of the estimators performance.

A new efficient coupling relationship description method has been developed to provide an automated and visualized way to multidisciplinary design optimization (MDO) modeling and solving. The disciplinary relation matrix (DRM) is proposed to describe the coupling relationship according to disciplinary input/output variables, and the MDO definition has been reformulated to adopt the new interfaces. Based on these, a universal MDO solving procedure is proposed to establish an automated and efficient way for MDO modeling and solving. Through a simple and convenient initial configuration, MDO problems can be solved using any of available MDO architectures with no further effort. Several examples are used to verify the proposed MDO modeling and solving process. Result shows that the DRM method has the ability to simplify and automate the MDO procedure, and the related MDO framework can evaluate the MDO problem automatically and efficiently.

This paper aims at providing an uncertain bilevel knapsack problem (UBKP) model, which is a type of BKPs involving uncertain variables. And then an uncertain solution for the UBKP is proposed by defining PE Nash equilibrium and PE Stackelberg Nash equilibrium. In order to improve the computational efficiency of the uncertain solution, several operators (binary coding distance, inversion operator, explosion operator and binary back learning operator) are applied to the basic fireworks algorithm to design the binary backward fireworks algorithm (BBFWA), which has a good performance in solving the BKP. As an illustration, a case study of the UBKP model and the PE uncertain solution is applied to an armaments transportation problem.

The reliability of the product sold under a warranty is usually maintained by the manufacturer during the warranty period. After the expiry of the warranty, however, the consumer confronts a problem about how to maintain the reliability of the product. This paper proposes, from the consumer’s perspective, a replacement policy after the extended warranty, under the assumption that the product is sold under the renewable free replacement warranty (RFRW) policy in which the replacement is dependent on the repair-cost threshold. The proposed replacement policy is the replacement after the extended warranty is performed by the consumer based on the repair-cost threshold or preventive replacement (PR) age, which are decision variables. The expected cost rate model is derived from the consumer’s perspective. The existence and uniqueness of the optimal solution that minimizes the expected cost rate per unit time are offered. Finally, a numerical example is presented to exemplify the proposed model.

In the constrained reentry trajectory design of hypersonic vehicles, multiple objectives with priorities bring about more difficulties to find the optimal solution. Therefore, a multi-objective reentry trajectory optimization (MORTO) approach via generalized varying domain (GVD) is proposed. Using the direct collocation
approach, the trajectory optimization problem involving multiple objectives is discretized into a nonlinear multi-objective programming with priorities. In terms of fuzzy sets, the objectives are fuzzified into three types of fuzzy goals, and their constant tolerances are substituted by the varying domains. According to the principle that the objective with higher priority has higher satisfactory degree, the priority requirement is modeled as the order constraints of the varying domains. The corresponding two-side, single-side, and hybrid-side varying domain models are formulated for three fuzzy relations respectively. By regulating the parameter, the optimal reentry trajectory satisfying priorities can be achieved. Moreover, the performance about the parameter is analyzed, and the algorithm to find its specific value for maximum priority difference is proposed. The simulations demonstrate the effectiveness of the proposed method for hypersonic vehicles, and the comparisons with the traditional methods and sensitivity analysis are presented.

The global robust output regulation problem of the singular nonlinear system is investigated. Motivated by the inputoutput linearization of the normal affine nonlinear system, a global diffeomorphism map is designed under the assumption that the singular nonlinear system has a strong relative degree. The global diffeomorphism map transfers the singular nonlinear system into a new singular nonlinear system with a special structure. Attaching an internal model to the new singular nonlinear system yields an augmented singular nonlinear system and the global robust stabilization solution of the augmented system implies the global robust output regulation solution of the original singular nonlinear system. Then the global stabilization problem is solved by some appropriate assumptions and the solvability conditions of the global robust output regulation problem are established. Finally, a simulation example is given to illustrate the design approach.

This paper investigates the consensus problem of second-order nonlinear multi-agent systems (MASs) via the sliding mode control (SMC) approach. The velocity of each agent is assumed to be unmeasurable. A second-order sliding mode observer is designed to estimate the velocity. Then a distributed discontinuous control law based on first-order SMC is presented to solve the consensus problem. Moreover, to overcome the chatting problem, two controllers based on the boundary layer method and the super-twisting algorithm respectively are presented. It is shown that the MASs will achieve consensus under some given conditions. Some examples are provided to demonstrate the effectiveness of the proposed control laws.

A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured,the region scale algorithm is modified and non-isometric multiscale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.

Goals reasoning and management of pilot is a key issue to monitor pilot’s behavior and intention. Traditional modeling methods are based on scenarios or situations, such methods will cause the covering problem due to redundancy and are incapable of depicting interactions among various goals and plans of pilot. Petri net integrated with belief, desire and intention (BDI) theory (BDI Petri net) is designed to solve this problem. Focusing on the BDI theory, goal states of agent are discussed firstly. Belief, desire and intention are modeled by places and transitions based on the  Petri net theory. In order to simplify the network, colored token is introduced to depict various states of belief, and the hierarchy transition is applied to model the intention, together with tokens’ flow demonstrating the interaction among various goals and relationship among belief, desire and intention. A search and rescue mission is used to validate the proposed method and the result indicates that the model can be used to monitor goals and behaviors of pilots.

Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).

This paper considers an optimal sequential inspection schedule for a second-hand product after that the free nonrenewable warranty is expired. The length of warranty is prespecified and during the warranty period, the product is minimally repaired by the dealer when it fails. Following the expiration of the non-renewing warranty, the product is inspected and upgraded sequentially a fixed number of times at the expenses of the customer. At each inspection, the failure rate of the product is reduced proportionally so that the product is upgraded. The product is assumed to deteriorate as it ages and the replacement of the product occurs when a fixed number of inspections are rendered. In addition, the intervals between two successive inspections are assumed to decrease monotonically. The main objective of this paper is to determine the optimal improvement level to upgrade the product at each inspection so that the expected maintenance cost during the life cycle of the product is minimized from the perspective of the customer. Under the given cost structures, we derive an explicit formula to obtain the expected maintenance cost incurred during the life cycle of the product and discuss the method to find the optimal level of the improvement analytically in case the failure times follow the Weibull distribution. Numerical results are analyzed to observe the impact of relevant parameters on the optimal solution.

Determining the number of components is a crucial issue in a mixture model. A moment-based criterion is considered to estimate the number of components arising from a normal mixture model. This criterion is derived from an omnibus statistic involving the skewness and kurtosis of each component. The proposed criterion additionally provides a measurement for the model fit in an absolute sense. The performances of our criterion are satisfactory compared with other classical criteria through Monte-Carlo experiments.

In the constant-stress accelerated life test, estimation issues are discussed for a generalized half-normal distribution under a log-linear life-stress model. The maximum likelihood estimates with the corresponding fixed point type iterative algorithm for unknown parameters are presented, and the least square estimates of the parameters are also proposed. Meanwhile, confidence intervals of model parameters are constructed by using the asymptotic theory and bootstrap technique. Numerical illustration is given to investigate the performance of our methods.