The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks (TSFNN) with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory. Based on linear matrix inequalities (LMIs), we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs. Compared with the existing literature, this paper removes the assumptions on the neuron activations such as Lipschitz conditions, bounded, monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point. Thus, the results are more general and wider. Finally, two numerical examples are given to show the effectiveness of the proposed stability results.
In the spaceborne/airborne forward-looking bistatic synthetic aperture radar (SA-FBSAR), due to the system platforms’ remarkable velocity difference and the forward-looking mode, the range cell migration (RCM) not only depends on the target’s twodimensional location, but also varies with the range location nonlinearly. And the nonlinearity is not just the slight deviation from the linear part, but exhibits evident nonlinear departure in the RCM trajectory. If the RCM is not properly corrected, nonlinear image distortions would occur. Based on the RCM model, a modified two-step RCM compensation (RCMC) method for SA-FBSAR is proposed. In this method, firstly the azimuth-dependent RCM is compensated by the scaling Fourier transform and the phase multiplication. And then the range-dependent RCM is removed through interpolation. The effectiveness of the proposed RCMC method is verified by the simulation results of both point scatterers and area targets.
To improve the agility, dynamics, composability, reusability, and development efficiency restricted by monolithic federation object model (FOM), a modular FOM is proposed by high level architecture (HLA) evolved product development group. This paper reviews the state-of-the-art of HLA evolved modular FOM. In particular, related concepts, the overall impact on HLA standards, extension principles, and merging processes are discussed. Also permitted and restricted combinations, and merging rules are provided, and the influence on HLA interface specification is given. The comparison between modular FOM and base object model (BOM) is performed to illustrate the importance of their combination. The applications of modular FOM are summarized. Finally, the significance to facilitate compoable simulation both in academia and practice is presented and future directions are pointed out.
Sparse representation has recently been proved to be a powerful tool in image processing and object recognition. This paper proposes a novel small target detection algorithm based on this technique. By modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem, the proposed apporach successfully improves and optimizes the small target representation with innovation. Furthermore, the sparsity concentration index (SCI) is creatively employed to evaluate the coefficients of each block representation and simpfy target identification. In the detection frame, target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model (GIM), and then sparse model solvers are applied to finding sparse representation for each sub-image block. Finally, SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target position. The effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.
The existing direction of arrival (DOA) estimation algorithms based on the electromagnetic vector sensors array barely deal with the coexisting of independent and coherent signals. A two-dimensional direction finding method using an L-shape electromagnetic vector sensors array is proposed. According to this method, the DOAs of the independent signals and the coherent signals are estimated separately, so that the array aperture can be exploited sufficiently. Firstly, the DOAs of the independent signals are estimated by the estimation of signal parameters via rotational invariance techniques, and the influence of the coherent signals can be eliminated by utilizing the property of the coherent signals. Then the data covariance matrix containing the information of the coherent signals only is obtained by exploiting the Toeplitz property of the independent signals, and an improved polarimetric angular smoothing technique is proposed to de-correlate the coherent signals. This new method is more practical in actual signal environment than common DOA estimation algorithms and can expand the array aperture. Simulation results are presented to show the estimating performance of the proposed method.
The observing failure and feedback instability might happen when the partial sensors of a satellite attitude control system (SACS) go wrong. A fault diagnosis and isolation (FDI) method based on a fault observer is introduced to detect and isolate the fault sensor at first. Based on the FDI result, the object system state-space equation is transformed and divided into a corresponsive triangular canonical form to decouple the normal subsystem from the fault subsystem. And then the KX fault-tolerant observers of the system in different modes are designed and embedded into online monitoring. The outputs of all KX fault-tolerant observers are selected by the control switch process. That can make sense that the SACS is part-observed and in stable when the partial sensors break down. Simulation results demonstrate the effectiveness and superiority of the proposed method.
The pruning algorithms for sparse least squares support vector regression machine are common methods,and easily com- prehensible,but the computational burden in the training phase is heavy due to the retraining in performing the pruning process, which is not favorable for their applications.To this end,an im- proved scheme is proposed to accelerate sparse least squares support vector regression machine.A major advantage of this new scheme is based on the iterative methodology,which uses the previous training results instead of retraining,and its feasibility is strictly verified theoretically.Finally,experiments on bench- mark data sets corroborate a significant saving of the training time with the same number of support vectors and predictive accuracy compared with the original pruning algorithms,and this speedup scheme is also extended to classification problem.
Traditional data envelopment analysis (DEA) theory assumes that decision variables are regarded as inputs or outputs, and no variable can play the roles of both an input and an output at the same time. In fact, there exist some variables that work as inputs and outputs simultaneously and are called dual-role variables. Traditional DEA models cannot be used to appraise the performance of decision making units containing dual-role variables. The paper analyzes the structure and properties of the production systems comprising dual-role variables, and proposes a DEA model integrating dual-role variables. Finally the proposed model is illustrated to evaluate the efficiency of university departments.
Orthogonal netted radar systems (ONRS) can fundamentally improve the radar performance by using a group of specially designed orthogonal polyphase code signals which require a very low aperiodic autocorrelation peak sidelobe level, low aperiodic cross-correlation, and a good resilience to small Doppler shifts. However, the existing numerical solutions degrade severely in the presence of small Doppler shifts. A new set of polyphase sequences is presented with good correlation properties as well as resilience to Doppler shifts. These sequences are built by using numerical optimization based on correlation properties as well as the Doppler effects on matched filter outputs, which maintains the Doppler tolerance. The statistical simulated annealing algorithm and the greedy code search method are used to optimize the sequences. Correlation and Doppler results are compared with the best-known sequences and show to be superior.
Due to defects of time-difference of arrival localization, which influences by speed differences of various model waveforms and waveform distortion in transmitting process, a neural network technique is introduced to calculate localization of the acoustic emission source. However, in back propagation (BP) neural network, the BP algorithm is a stochastic gradient algorithm virtually, the network may get into local minimum and the result of network training is dissatisfactory. It is a kind of genetic algorithms with the form of quantum chromosomes, the random observation which simulates the quantum collapse can bring diverse individuals, and the evolutionary operators characterized by a quantum mechanism are introduced to speed up convergence and avoid prematurity. Simulation results show that the modeling of neural network based on quantum genetic algorithm has fast convergent and higher localization accuracy, so it has a good application prospect and is worth researching further more.
To correct the range walk through resolution cell in Doppler beam sharpening (DBS) imaging, a new DBS imaging algorithm based on Keystone transform is proposed. Without the exact values of the movement parameters and the look angle of the radar platform in the multi-targets environment, a linear transform on the received data is employed to correct different range walk values accurately under the condition of Doppler frequency ambiguity in this algorithm. This method can realize the coherent integration in azimuth dimension and improve the azimuth resolution. In order to reduce the computational burden, a fast implementation of Keystone transform is used. Theoretical analysis and simulation results demonstrate the effectiveness of the new algorithm. And through comparing the computational load of the fast implementation with several other algorithms, the real-time processing ability of the proposed algorithm is superior to that of other algorithms.
It is now well known that the time-varying sliding mode control (TVSMC) is characterized by its global robustness against matched model uncertainties and disturbances. The accurate tracking problem of the mechanical system in the presence of the parametric uncertainty and external disturbance is addressed in the TVSMC framework. Firstly, an exponential TVSMC algorithm is designed and the main features are analyzed. Especially, the control parameter is obtained by solving an optimal problem. Subsequently, the global chattering problem in TVSMC is considered. To reduce the static error resulting from the continuous TVSMC algorithm, a disturbance observer based time-varying sliding mode control (DOTVSMC) algorithm is presented. The detailed design principle and the stability of the closed-loop system under the composite controller are provided. Simulation results verify the effectiveness of the proposed algorithm.
The problem of the quantized dynamic output feedback controller design for networked control systems is mainly discussed. By using the quantized information of the system measurement output and the control input, a novel networked control system model is described. This model includes many networkinduced features, such as multi-rate sampled-data, quantized signal, time-varying delay and packet dropout. By constructing suitable Lyapunov-Krasovskii functional, a less conservative stabilization criterion is established in terms of linear matrix inequalities. The quantized control strategy involves the updating values of the quantizer parameters μi(i = 1, 2)(μi take on countable sets of values which dependent on the information of the system measurement outputs and the control inputs). Furthermore, a numerical example is given to illustrate the effectiveness of the proposed method.
Multi-disciplinary virtual prototypes of complex products are increasingly and widely used in modern advanced manufacturing. How to effectively address the problems of unified modeling, composition and reuse based on the multi-disciplinary heterogeneous models has brought great challenges to the modeling and simulation (M&S) science and technology. This paper presents a top-level modeling theory based on the meta modeling framework (M2F) of the COllaborative SIMulation (COSIM) theory of virtual prototyping to solve the problems. Firstly the fundamental principles of the top-level modeling theory are decribed to expound the premise, assumptions, basic conventions and special requirements in the description of complex heterogeneous systems. Next the formalized definitions for each factor in top level modeling are proposed and the hierarchical nature of them is illustrated. After demonstrating that they are self-closing, this paper divides the toplevel modeling into two views, static structural graph and dynamic behavioral graph. Finally, a case study is discussed to demonstrate the feasibility of the theory.
The location of a moving target based on signal fitting and sub-aperture tracking from an airborne multi-channel radar is dealt with. The proposed approach is applied in two steps: first, the ambiguous slant-range velocity is derived with a modified single-snapshot multiple direction of arrival estimation method, and second, the unambiguous slant-range velocity is found using a track-based criterion. The prominent advantage of the proposed approach is that the unambiguous slant-range velocity can be very large. Besides, the first stage is carried out at the determinate range-Doppler test cell by azimuth searching for fitting best to the moving target signal, therefore, the location performance would not be sacrificed in order to suppress clutter and/or interference. The effectiveness and efficiency of the proposed method are validated with a set of airborne experimental data.
This paper proposes a scheme to construct timefrequency codes based on protograph low density parity check (LDPC) codes in orthogonal frequency division multiplexing (OFDM) communication systems. This approach synthesizes two techniques: protograph LDPC codes and OFDM. One symbol of encoded information by protograph LDPC codes corresponds to one sub-carrier, namely the length of encoded information equals to the number of sub-carriers. The design of good protograph LDPC codes with short lengths is given, and the proposed protograph LDPC codes can be of fast encoding, which can reduce the encoding complexity and simplify encoder hardware implementation. The proposed approach provides a higher coding gain in the Rayleigh fading channel. The simulation results in the Rayleigh fading channel show that the bit error rate (BER) performance of the proposed time-frequency codes is as good as random LDPCOFDM codes and is better than Tanner LDPC-OFDM codes under the condition of different fading coefficients.
Reduction of conservatism is one of the key and difficult problems in missile robust gain scheduling autopilot design based on multipliers. This article presents a scheme of adopting linear parameter-varying (LPV) control approach with full block multipliers to design a missile robust gain scheduling autopilot in order to eliminate conservatism. A model matching design structure with a high demand on matching precision is constructed based on the missile linear fractional transformation (LFT) model. By applying full block S-procedure and elimination lemma, a convex feasibility problem with an infinite number of constraints is formulated to satisfy robust quadratic performance specifications. Then a grid method is adopted to transform the infinite-dimensional convex feasibility problem into a solvable finite-dimensional convex feasibility problem, based on which a gain scheduling controller with linear fractional dependence on the flight Mach number and altitude is derived. Static and dynamic simulation results show the effectiveness and feasibility of the proposed scheme.
A new adaptive neural network (NN) output-feedback stabilization controller is investigated for a class of uncertain stochastic nonlinear strict-feedback systems with discrete and distributed time-varying delays and unknown nonlinear functions in both drift and diffusion terms. First, an extensional stability notion and the related criterion are introduced. Then, a nonlinear observer to estimate the unmeasurable states is designed, and a systematic backstepping procedure to design an adaptive NN output-feedback controller is proposed such that the closed-loop system is stable in probability. The effectiveness of the proposed control scheme is demonstrated via a numerical example.
This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion time. For this NP-hard problem, the largest sum of release date, processing time and delivery time first rule is designed to determine a certain machine for each job, and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine, and then a novel algorithm for the scheduling problem is built. To evaluate the performance of the proposed algorithm, a lower bound for the problem is proposed. The accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 jobs. The computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%, therefore the solutions obtained by the proposed algorithm are very accurate.
Technology management is recognized as a key for organizations to achieve competitiveness. How to promote an organization’s technology management capability is of great significance in creating efficiencies and achieving a competitive edge. The knowledge essence of technology management capability is introduced and then the correlation between knowledge diffusion and the development of technology management capability is discussed. Further, the basic and extended dynamic models of the development of technology management capability are constructed, and is applied into an enterprise. The results show that the dynamic models can well explain how the knowledge improves the development of technology management capability, and they can be used as an useful tool by an enterprise to promote technology management capability. Finally, the managerial implications of the models are discussed.
To avoid uneven energy consuming in wireless sensor networks, a clustering routing model is proposed based on a Bayesian game. In the model, Harsanyi transformation is introduced to convert a static game of incomplete information to the static game of complete but imperfect information. In addition, the existence of Bayesian nash equilibrium is proved. A clustering routing algorithm is also designed according to the proposed model, both cluster head distribution and residual energy are considered in the design of the algorithm. Simulation results show that the algorithm can balance network load, save energy and prolong network lifetime effectively.
A new spectral matching algorithm is proposed by using nonsubsampled contourlet transform and scale-invariant feature transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency image. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix (or matching result) reflecting the matching degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
A methodology for automatically generating risk scenarios is presented. Its main idea is to let the system model “express itself” through simulation. This is achieved by having the simulation model driven by an elaborated simulation engine, which: (i) manipulates the generation of branch points, i.e. event occurrence times; (ii) employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point. In addition, a backtracking technique, as an extension, is implemented to recover some missed risk scenarios. A widely discussed dynamic reliability example (a holdup tank) is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.
A collaborative optimization model for maintenance and spare ordering of a single-unit degrading system is proposed in this paper based on the continuous detection. A gamma distribution is used to model the material degradation. The degrading decrement after the imperfect maintenance action is assumed as a random variable normal distribution. This model aims to obtain the optimal maintenance policy and spare ordering point with the expected cost rate within system lifecycle as the optimization objective. The rationality and feasibility of the model are proved through a numerical example.
A new method for discretization of continuous attributes is put forward to overcome the limitation of the traditional rough sets,which cannot deal with continuous attributes.The method is based on an improved algorithm to produce candidate cut points and an algorithm of reduction based on variable precision rough information entropy.With the guarantee of consistency of decision system,the method can reduce the number of cut points and im- prove efficiency of reduction.Adopting variable precision rough information entropy as measure criterion,it has a good tolerance to noise.Experiments show that the algorithm yields satisfying reduction results.
A generalization of the linguistic aggregation functions (or operators) is presented by using generalized and quasiarithmetic means. Firstly, the linguistic weighted generalized mean (LWGM) and the linguistic generalized ordered weighted averaging (LGOWA) operator are introduced. These aggregation functions use linguistic information and generalized means in the weighted average (WA) and in the ordered weighted averaging (OWA) function. They are very useful for uncertain situations where the available information cannot be assessed with numerical values but it is possible to use linguistic assessments. These aggregation operators generalize a wide range of aggregation operators that use linguistic information such as the linguistic generalized mean (LGM), the linguistic OWA (LOWA) operator and the linguistic ordered weighted quadratic averaging (LOWQA) operator. We also introduce a further generalization by using quasi-arithmetic means instead of generalized means obtaining the quasi-LWA and the quasi-LOWA operator. Finally, we develop an application of the new approach where we analyze a decision making problem regarding the selection of strategies.
The novel compensating method directly demodulates the signals without the carrier recovery processes, in which the carrier with original modulation frequency is used as the local coherent carrier. In this way, the phase offsets due to frequency shift are linear. Based on this premise, the compensation processes are: firstly, the phase offsets between the base band neighbor-symbols after clock recovery are unbiasedly estimated among the reference symbols; then, the receiving signals symbols are adjusted by the phase estimation value; finally, the phase offsets after adjusting are compensated by the least mean squares (LMS) algorithm. In order to express the compensation processes and ability clearly, the quadrature phase shift keying (QPSK) modulation signals are regarded as examples for Matlab simulation. BER simulations are carried out using the Monte-Carlo method. The learning curves are obtained to study the algorithm’s convergence ability. The constellation figures are also simulated to observe the compensation results directly.
Power efficiency and link reliability are of great importance in hierarchical wireless sensor networks (HWSNs), especially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are considered as players. By applying a double-pricing scheme in the definition of OHSs’ utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm.
Aiming at a class of nonlinear systems that contains faults, a novel iterative learning scheme is applied to fault detection, and a novel algorithm of fault detection and estimation is proposed. This algorithm first constructs residual signals by the output of the practical system and the output of the designed fault tracking estimator, and then uses the residuals and the differencevalue signal of the adjacent two residuals to gradually revise the introduced virtual faults, which can cause the virtual faults to close to the practical faults in systems, thereby achieving the goal of fault detection for systems. This algorithm not only makes full use of the existing valid information of systems and has a faster tracking convergent speed than the proportional-type (P-type) algorithm, but also calculates more simply than the proportional-derivative-type (PD-type) algorithm and avoids the unstable effects of differential operations in the system. The final simulation results prove the validity of the proposed algorithm.
Local invariant algorithm applied in downward-looking image registration, usually computes the camera’s pose relative to visual landmarks. Generally, there are three requirements in the process of image registration when using these approaches. First, the algorithm is apt to be influenced by illumination. Second, algorithm should have less computational complexity. Third, the depth information of images needs to be estimated without other sensors. This paper investigates a famous local invariant feature named speeded up robust feature (SURF), and proposes a highspeed and robust image registration and localization algorithm based on it. With supports from feature tracking and pose estimation methods, the proposed algorithm can compute camera poses under different conditions of scale, viewpoint and rotation so as to precisely localize object’s position. At last, the study makes registration experiment by scale invariant feature transform (SIFT), SURF and the proposed algorithm, and designs a method to evaluate their performances. Furthermore, this study makes object retrieval test on remote sensing video. For there is big deformation on remote sensing frames, the registration algorithm absorbs the Kanade-Lucas-Tomasi (KLT) 3-D coplanar calibration feature tracker methods, which can localize interesting targets precisely and efficiently. The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping (vSLAM) in a period of time.
The problem of designing fuzzy static output feedback controller for T-S discrete-time fuzzy bilinear system(DFBS)is presented.Based on parallel distribution compensation method, some sufficient conditions are derived to guarantee the stability of the overall fuzzy system.The stabilization conditions are further formulated into linear matrix inequality(LMI)so that the desired controller can be easily obtained by using the Matlab LMI toolbox. In comparison with the existing results,the drawbacks,such as coordinate transformation,same output matrices,have been elim- inated.Finally,a simulation example shows that the approach is effective.
A novel algorithm is proposed to solve the poor performance problem of the Tent chaos-based frequency modulation (FM) signal for range-Doppler imaging, which takes it into complex multi-segment system by increasing its segments. The simulation results show that the effectiveness of the proposed algorithm, as well as the performance of the improved Tent FM signal is obvious in a multipath or noise propagation environment.
Only in the presence of sidelobe jamming (SLJ), can the conventional adaptive monopulse technique null the jamming effectively and maintain the monopulse angle estimation accuracy simultaneously. While mainlobe jamming (MLJ) exists, the mainlobe of adaptive pattern will subject to serious distortion, which results in a failure of detecting and tracking targets by monopulse technique. Therefore, a monopulse angle estimation algorithm based on combining sum-difference beam and auxiliary beam is presented. This algorithm utilizes both high gain difference beams and high gain auxiliary beams for cancelling the mainlobe jammer and multiple sidelobe jammers (SLJs) while keeping an adaptive monopulse ratio. Theoretical analysis and simulation results indicate that the serious invalidation of monopulse technique in MLJ and SLJs scenarios is resolved well, which improves the monopulse angle accuracy greatly. Furthermore, the proposed algorithm is of simple implementation and low computational complexity.
Distribution-based degradation models (or graphical approach in some literature) occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models’ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based degradation models.
Efficiency and linearity of the microwave power amplifier are critical elements for mobile communication systems. A memory polynomial baseband predistorter based on an indirect learning architecture is presented for improving the linearity of an envelope tracing (ET) amplifier with application to a wireless transmitter. To deal with large peak-to-average ratio (PAR) problem, a clipping procedure for the input signal is employed. Then the system performance is verified by simulation results. For a single carrier wideband code division multiple access (WCDMA) signal of 16-quadrature amplitude modulation (16-QAM), about 2% improvement of the error vector magnitude (EVM) is achieved at an average output power of 45.5 dBm and gain of 10.6 dB, with adjacent channel leakage ratio (ACLR) of -64.55 dBc at offset frequency of 5 MHz. Moreover, a three-carrier WCDMA signal and a third-generation (3G) long term evolution (LTE) signal are used as test signals to demonstrate the performance of the proposed linearization scheme under different bandwidth signals.
