Although a various of existing techniques are able to improve the performance of detection of the weak interesting signal, how to adaptively and efficiently attenuate the intricate noises especially in the case of no available reference noise signal is still the bottleneck to be overcome. According to the characteristics of sonar arrays, a multi-channel differencing method is presented to provide the prerequisite reference noise. However, the ingredient of obtained reference noise is too complicated to be used to effectively reduce the interference noise only using the classical linear cancellation methods. Hence, a novel adaptive noise cancellation method based on the multi-kernel normalized leastmean-square algorithm consisting of weighted linear and Gaussian kernel functions is proposed, which allows to simultaneously consider the cancellation of linear and nonlinear components in the reference noise. The simulation results demonstrate that the output signal-to-noise ratio (SNR) of the novel multi-kernel adaptive filtering method outperforms the conventional linear normalized least-mean-square method and the mono-kernel normalized leastmean- square method using the realistic noise data measured inthe lake experiment.

A communication and navigation receiver is required to remove hostile jamming signals and synchronize receiving signals effectively especially for satellite  cmmunication and navigation whose resources are becoming more and more limited. This paper proposes a novel signal receiving method by combining the processes of anti-jamming and synchronization to reduce the overall computational complexity at the expense of slightly affecting the detection probability, which is analyzed in detail by derivations. Furthermore, this paper introduces sparse Fourier transformation (SFT) into the proposed algorithm to replace fast Fourier transformation (FFT) so as to further reduce the calculation time especially in large frequency offset environments.

In order to deal with the particle degeneracy and impoverishment problems existed in particle filters, a modified sequential importance resampling (MSIR) filter is proposed. In this filter, the resampling is translated into an evolutional process just like the biological evolution. A particle generator is constructed, which introduces the current measurement information (CMI) into the resampled particles. In the evolution, new particles are first produced through the particle generator, each of which is essentially an unbiased estimation of the current true state. Then, new and old particles are recombined for the sake of raising the diversity among the particles. Finally, those particles who have low quality are eliminated. Through the evolution, all the particles retained are regarded as the optimal ones, and these particles are utilized to update the current state. By using the proposed resampling approach, not only the CMI is incorporated into each resampledparticle, but also the particle degeneracy and the loss of diversity among the particles are mitigated, resulting in the improved estimation accuracy. Simulation results show the superiorities of the proposed filter over the standard sequential importance resampling (SIR) filter, auxiliary particle filter and unscented Kalman
particle filter.

With the exploration of the ocean, underwater acoustic communication has attracted more and more attention in recent years. The underwater acoustic channel is considered to be one of the most complicated channls because it suffers from more serious multipath effect, fewer available bandwidths and quite complex noise. Since the signals experience a serious distortion after being transmitted through the underwater acoustic channel, the underwater acoustic communication experiences a high bit error rate (BER). To solve this problem, carrier waveform interdisplacement (CWID) modulation is proposed. It has been proved that CWID modulation is an effective method to decrease BER. The linear frequency modulation (LFM) carrier-waves are used in CWID modulation. The performance of the communication using CWID modulation is sensitive to the change of the frequency band of LFM carrier-waves. The immune particle swarm optimization (IPSO) is introduced to search for the optimal frequency band of the LFM carrier-waves, due to its excellent performance in solving complicated optimization problems. The multi-objective and multipeak optimization nature of the IPSO gives a suitable description of the relationship between the upper band and the lower band of the LFM carrier-waves. Simulations verify the improved performance and effectiveness of the optimization method.

In traditional inverse synthetic aperture radar (ISAR) imaging of moving targets with rotational parts, the micro-Doppler (m-D) effects caused by the rotational parts influence the quality of the radar images. Recently, L. Stankovic proposed an m-D removal method based on L-statistics, which has been proved effective and simple. The algorithm can extract the m-D effects according to different behaviors of signals induced by rotational parts and rigid bodies in time-frequency (T-F) domain. However, by removing m-D effects, some useful short time Fourier transform (STFT) samples of rigid bodies are also extracted, which induces the side lobe problem of rigid bodies. A parameter estimation method for rigid bodies after m-D removal is proposed, which can accurately recover rigid bodies and avoid the side lobe problem by only using m-D removal. Simulations are given to validate the effectiveness of the proposed method.

The antenna geometry strategy for direction finding (DF) with multiple-input multiple-output (MIMO) radars is studied. One case, usually encountered is practical applications, is considered. For a directional antenna geometry with a prior direction, the trace-optimal (TO) criterion (minimizing the trace) on the average Cram´er-Rao bound (CRB) matrix is employed. A qualitative explanation for antenna geometry is provided, which is a combinatorial optimization problem. In the numerical example section, it is shown that the antenna geometries, designed by the proposed strategy, outperform the representative DF antenna geometries.

With appropriate geometry configuration, helicopterborne rotating synthetic aperture radar (ROSAR) can break through the limitations of monostatic synthetic aperture radar (SAR) on forward-looking imaging. With this capability, ROSAR has extensive potential applications, such as self-navigation and self-landing. Moreover, it has many advantages if combined with the frequency modulated continuous wave (FMCW) technology. A novel geometric configuration and an imaging algorithm for helicopter-borne FMCW-ROSAR are proposed. Firstly, by performing the equivalent phase center principle, the separated transmitting and receiving antenna system is equalized to the case of system configuration with antenna for both transmitting and receiving signals. Based on this, the accurate two-dimensional spectrum is obtained and the Doppler frequency shift effect induced by the continuous motion of the platform during the long pulse duration is compensated. Next, the impacts of the velocity approximation error on the imaging algorithm are analyzed in detail, and the system parameters selection and resolution analysis are presented. The well-focused SAR image is then obtained by using the improved Omega-K algorithm incorporating the accurate compensation method for the velocity approximation error. Finally, correctness of the analysis and effectiveness of the proposed algorithm are demonstrated through simulation results.

This paper addresses the problem of four-dimensional angle and Doppler frequency estimation for bistatic multiple-input multiple-output (MIMO) radar with arbitrary arrays in spatial colored noise. A novel method for joint estimation of Doppler frequency, two-dimensional (2D) direction of departure and 2D direction of arrival based on the propagator method (PM) for arbitrary arrays is discussed. A special matrix is constructed to eliminate the influence of spatial colored noise. The four-dimensional (4D) angle and Doppler frequency are extracted from the matrix and the threedimensional (3D) coordinates of the targets are then calculated on the basis of these angles. The proposed algorithm provides a lower computational complexity and has a parameter estimation very close to that of the ESPRIT algorithm and the DOA-matrix algorithm in the high signal to noise ratio and the Cram´er-Rao bound (CRB) is given. Furthermore, multi-dimensional parameters can be automatically paired by this algorithm to avoid performance degradation resulting from wrong pairing. Simulation results demonstrate the effectiveness of the proposed method.

Although opportunistic maintenance strategies are widely used for multi-component systems, all opportunistic maintenance strategies only consider economic dependence and do not take structural dependence into account. An opportunistic maintenance strategy is presented for a multi-component system that considers both structural dependence and economic dependence. The cost relation and time relation among components based on structural dependence are developed. The maintenance strategy for each component of a multi-component system involves one of five maintenance actions, namely, no-maintenance, a minimal maintenance action, an imperfect maintenance action, a perfect maintenance action, and a replacement action. The maintenance action is determined by the virtual age of the component, the life expectancy of the component, and the age threshold values. Monte Carlo simulation is designed to obtain the optimal opportunistic maintenance strategy of the system over its lifetime. The simulation result reveals that the minimum maintenance cost with a strategy that considers structural dependence is less than thatwith a strategy that does not consider structural dependence. The availability with a strategy that considers structural dependence is
greater than that with a strategy that does not consider structural dependence under the same conditions.

This study presents a Bayesian methodology for designing step stress accelerated degradation testing (SSADT) and its application to batteries. First, the simulation-based Bayesian design framework for SSADT is presented. Then, by considering historical data, specific optimal objectives oriented Kullback–Leibler (KL) divergence is established. A numerical example is discussed to illustrate the design approach. It is assumed that the degradation model (or process) follows a drift Brownian motion; the acceleration model follows Arrhenius equation; and the corresponding parameters follow normal and Gamma prior distributions. Using
the Markov Chain Monte Carlo (MCMC) method and WinBUGS software, the comparison shows that KL divergence is better than quadratic loss for optimal criteria. Further, the effect of simulation outliers on the optimization plan is analyzed and the preferred surface fitting algorithm is chosen. At the end of the paper, a NASA lithium-ion battery dataset is used as historical information and the KL divergence oriented Bayesian design is compared with maximum likelihood theory oriented locally optimal design. The results show that the proposed method can provide a much better testing plan for this engineering application.

In the traditional data envelopment analysis (DEA) structure, the efficiency score for one decision making unit (DMU) is calculated by measuring the distance of the evaluated DMU to the best practice frontier. Recent researches have provided the reasonability of considering the worst practice frontier as a supplement to the traditional DEA techniques. The existing researches take only one type of frontier into account, and they cannot compare the evaluated DMU with both the best and the worst performing DMUs. A DEA-based procedure is developed to consider the best and the worst frontiers in the same scenario where the ratio of two distances (RDS) measure is proposed. The principal application of this approach is for ranking, and, as a complement tool, for performance evaluation. The proposed approach can be used in a wide range of applications such as the performance evaluation of employees and others. Finally, a bookstore data set is used to illustrate the proposed approach.

In order to meet the demand of testability analysis and evaluation for complex equipment under a small sample test in the equipment life cycle, the hierarchical hybrid testability modeling and evaluation method (HHTME), which combines the testability structure model (TSM) with the testability Bayesian networks model (TBNM), is presented. Firstly, the testability network topology of complex equipment is built by using the hierarchical hybrid testability modeling method. Secondly, the prior conditional probability distribution between network nodes is determined through expert experience. Then the Bayesian method is used to update the conditional probability distribution, according to history test information, virtual simulation information and similar product information. Finally, the learned hierarchical hybrid testability model (HHTM) is used to estimate the testability of equipment. Compared with the results of other modeling methods, the relative deviation  of the HHTM is only 0.52%, and the evaluation result is the most accurate.

Quality function deployment (QFD) is a well-known customer-oriented product design methodology. Rating the final importance of customer requirements (CRs) is really a very essential starting point in the implementation of QFD, since it largely affects the target setting value of design requirements. This paper aims to propose a novel method to deal with the relative importance ratings (RIRs) of CRs problem considering customers’ diversified requirements and unknown information on customers’ weights, which is an indispensable process for determining the final importance ratings of CRs. First, a new concept of customer’s assessment structure is proposed according to the basic idea of grey relational analysis (GRA), and then a constrained nonlinear optimization model is constructed to describe the assessment information aggregation factors of CRs considering customers’ personalized and diversified requirements. Furthermore, an immune particle swarm optimization (IPSO) algorithm is designed to solve the model, and the weight vector of customers is obtained. Finally, a car door design example is introduced to illustrate the novel hybrid GRA-IPSO method’s potential application in determining the RIRs of CRs.

The observer-based robust fault detection filter design and optimization for networked control systems (NCSs) with uncertain time-varying delays are addressed. The NCSs with uncertain time-varying delays are modeled as parameter-uncertain systems by the matrix theory. Based on the model, an observer-based residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of the linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. To prevent the false alarms, a new threshold function is established, and the solution of the optimization problem is given by using the singular value decomposition (SVD) of the matrix. A numerical example is provided to illustrate the effectiveness of the proposed approach.

An adaptive backstepping-based non-singular terminal sliding mode (NTSM) control method is proposed for a class of uncertain nonlinear systems in the parameteric-strict feedback form. The adaptive control law is combined with the first n − 1 steps of the backstepping method to estimate the unknown parameters of the system. In the nth step, an NTSM control strategy is utilized to drive the last state of the system to converge in a finite time. Furthermore, the derivate estimator is used to obtain the derivates of the states of the error system; the higher-order non-singular terminal sliding mode control (HONTSMC) law is designed to eliminate the chattering and make the system robust to both matched and unmatched uncertainties. Compared to the adaptive backstepping-based linear sliding mode control method (LSMC), the proposed method improves the convergence rate and the steady-state tracking accuracy of the system, and makes the control signal smoother. Finally, the compared simulation results are presented to validate the method.

The control synthesis for switched systems is extended to distributed parameter switched systems in Hilbert space. Based on semigroup and operator theory, by means of multiple Lyapunov method incorporated average dwell time approach, sufficient conditions are derived in terms of linear operator inequalities framework for distributed parameter switched systems. Being applied to one dimensional heat propagation switched systems, these linear operator inequalities are reduced to linear matrix inequalities subsequently. In particular, the state feedback gain matrices and the switching law are designed, and the state decay estimate is explicitly given whose decay coefficient completely depends on the system’s parameter and the boundary condition. Finally, two numerical examples are given to illustrate the proposed method.

This paper investigates a signal difference-based deadband H_∞  control approach for networked control systems (NCSs) with limited resources. The effects of variable network-induced delays, sampling intervals and data transmitting deadbands are considered simultaneously and the model of the NCS is presented. A Lyapunov functional is adopted, which makes full use of the network characteristic information including the bounds of network delay (BND), the bounds of sampling interval (BSI) and the bounds of transmission deadband (BTD). In the meanwhile, the new H_∞  performance analysis and controller design conditions for the NCSs are proposed, which describe the relationship of BND, BSI, BTD and the system’s performance. Three examples are used to illustrate the advantages of the proposed methods. The results have shown that the proposed method not only effectively reduces the data traffic, but also guarantees the system asymptotically stable and achieves the prescribed H∞ disturbance attenuation level.

To accelerate the selection process of feature subsets in the rough set theory (RST), an ensemble elitist roles based quantum game (EERQG) algorithm is proposed for feature selection. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles’ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selection can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Finally, the ensemble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which will greatly improve the feasibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

In modern data centers, because of the deadlineagnostic congestion control in transmission control protocol (TCP), many deadline-sensitive flows can not finish before their deadlines. Therefore, providing a higher deadline meeting ratio becomes a critical challenge in the typical online data intensive (OLDI) applications of data center networks (DCNs). However, a problem named as priority synchronization is found in this paper, which decreases the deadline meeting ratio badly. To solve this problem, we propose a priority probability deceleration (P2D) deadline-aware TCP. By using the novel probabilistic deceleration, P2D prevents the priority synchronization problem. Simulation results show that P2D increases the deadline meeting ratio by 20% compared with D2TCP.

The artificial bee colony (ABC) algorithm is a simple and effective global optimization algorithm which has been successfully applied in practical optimization problems of various fields. However, the algorithm is still insufficient in balancing exploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechaism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which fully utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the selfadaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to enhance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic oppositionbased learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark functions show that the proposed algorithm, especially for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

Support vector machines (SVMs) have been extensively studied and have shown remarkable success in many applications. A new family of twice continuously differentiable piecewise smooth functions are used to smooth the objective function of unconstrained SVMs. The three-order piecewise smooth support vector machine (TPWSSVMd) is proposed. The piecewise functions can get higher and higher approximation accuracy as required with the increase of parameter d. The global convergence proof of TPWSSVMd is given with the rough set theory. TPWSSVMd can efficiently handle large scale and high dimensional problems. Numerical results demonstrate TPWSSVMd has better classification performance and learning efficiency than other competitive baselines.

A semi-supervised vector machine is a relatively new learning method using both labeled and unlabeled data in classification. Since the objective function of the model for an unstrained semi-supervised vector machine is not smooth, many fast optimization algorithms cannot be applied to solve the model. In order to overcome the difficulty of dealing with non-smooth objective functions, new methods that can solve the semi-supervised vector machine with desired classification accuracy are in great demand. A quintic spline function with three-times differentiability at the origin is constructed by a general three-moment method, which can be used to approximate the symmetric hinge loss function. The approximate accuracy of the quintic spline function is estimated. Moreover, a quintic spline smooth semi-support vector machine is obtained and the convergence accuracy of the smooth model to the non-smooth one is analyzed. Three experiments are performed to test the efficiency of the model. The experimental results show that the new model outperforms other smooth models, in terms of classification performance. Furthermore, the new model is not sensitive to the increasing number of the labeled samples, which means that the new model is more efficient.

Reliability, maintainability and testability (RMT) are important properties of equipment, since they have important influence on operational availability and life cycle costs (LCC). Therefore, weighting and optimizing the three properties are of great significance. A new approach for optimization of RMT parameters is proposed. First of all, the model for the equipment operation process is established based on the generalized stochastic Petri nets (GSPN) theory. Then, by solving the GSPN model, the quantitative relationship between operational availability and RMT parameters is obtained. Afterwards, taking history data of similar equipment and operation process into consideration, a cost model of design, manufacture and maintenance is developed. Based on operational availability, the cost model and parameters ranges, an optimization model of RMT parameters is built. Finally, the effectiveness and practicability of this approach are validated through an example.

This article deals with the case of the failure-censored constant-stress partially accelerated life test (CSPALT) for highly reliable materials or products assuming the Pareto distribution of the second kind. The maximum likelihood (ML) method is used to estimate the parameters of the CSPALT model. The performance of ML estimators is investigated via their mean square error. Also, the average confidence interval length (IL) and the associated coverage probability (CP) are obtained. Moreover, optimum CSPALT plans that determine the optimal proportion of the test units allocated to each stress are developed. Such optimum test plans minimize the generalized asymptotic variance (GAV) of the ML estimators of the model parameters. For illustration, Monte Carlo simulation studies are given and a real life example is provided.