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.
A proper weapon system is very important for a national defense system. Generally, it means selecting the optimal weapon system among many alternatives, which is a multipleattribute decision making (MADM) problem. This paper proposes a new mathematical model based on the response surface method (RSM) and the grey relational analysis (GRA). RSM is used to obtain the experimental points and analyze the factors that have a significant impact on the selection results. GRA is used to analyze the trend relationship between alternatives and reference series. And then an RSM model is obtained, which can be used to calculate all alternatives and obtain ranking results. A real world application is introduced to illustrate the utilization of the model for the weapon selection problem. The results show that this model can be used to help decision-makers to make a quick comparison of alternatives and select a proper weapon system from multiple alternatives, which is an effective and adaptable method for solving the weapon system selection problem.
The mean shift tracker has difficulty in tracking fast moving targets and suffers from tracking error accumulation problem. To overcome the limitations of the mean shift method, a new approach is proposed by integrating the mean shift algorithm and frame-difference methods. The rough position of the moving target is first located by the direct frame-difference algorithm and three-frame-difference algorithm for the immobile camera scenes and mobile camera scenes, respectively. Then, the mean shift algorithm is used to achieve precise tracking of the target. Several tracking experiments show that the proposed method can effectively track first moving targets and overcome the tracking error accumulation problem.
The non-minimum phase feature of tail-controlled missile airframes is analyzed. Three selection strategies for desired performance indexes are presented. An acceleration autopilot design methodology based on output feedback and optimization is proposed. Performance and robustness comparisons between the two-loop and classical three-loop topologies are made. Attempts to improve the classical three-loop topology are discussed. Despite the same open-loop structure, the classical three-loop autopilot shows distinct characteristics from a two-loop autopilot with PI compensator. Both the two-loop and three-loop topologies can stabilize a static unstable missile. However, the finite actuator resource is the crucial factor dominating autopilot function.
The realization of the parameter estimation of chirp signals using the fractional Fourier transform (FRFT) is based on the assumption that the sampling duration of practical observed signals would be equal to the time duration of chirp signals contained in the former. However, in many actual circumstances, this assumption seems unreasonable. On the basis of analyzing the practical signal form, this paper derives the estimation error of the existing parameter estimation method and then proposes a novel and universal parameter estimation algorithm. Furthermore, the proposed algorithm is developed which allows the estimation of the practical observed Gaussian windowed chirp signal. Simulation results show that the new algorithm works well.
With the development of global position system (GPS), wireless technology and location aware services, it is possible to collect a large quantity of trajectory data. In the field of data mining for moving objects, the problem of anomaly detection is a hot topic. Based on the development of anomalous trajectory detection of moving objects, this paper introduces the classical trajectory outlier detection (TRAOD) algorithm, and then proposes a density-based trajectory outlier detection (DBTOD) algorithm, which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense. The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented, which show the effectiveness of the algorithm.
To solve discrete optimization difficulty of the spectrum allocation problem, a membrane-inspired quantum shuffled frog leaping (MQSFL) algorithm is proposed. The proposed MQSFL algorithm applies the theory of membrane computing and quantum computing to the shuffled frog leaping algorithm, which is an effective discrete optimization algorithm. Then the proposed MQSFL algorithm is used to solve the spectrum allocation problem of cognitive radio systems. By hybridizing the quantum frog colony optimization and membrane computing, the quantum state and observation state of the quantum frogs can be well evolved within the membrane structure. The novel spectrum allocation algorithm can search the global optimal solution within a reasonable computation time. Simulation results for three utility functions of a cognitive radio system are provided to show that the MQSFL spectrum allocation method is superior to some previous spectrum allocation algorithms based on intelligence computing.
Combining beamlet transform with steerable filters, a new edge detection method based on line gradient is proposed. Compared with operators based on point local properties, the edge-detection results with this method achieve higher SNR and position accuracy, and are quite helpful for image registration, object identification, etc. Some edge-detection experiments on optical and SAR images that demonstrate the significant improvement over classical edge operators are also presented. Moreover, the template matching result based on edge information of optical reference image and SAR image also proves the validity of this method.
In the process of initial alignment for a strapdown inertial navigation system (SINS) on a stationary base, the east gyro drift rate is an important factor affecting the alignment accuracy of the azimuth misalignment angle. When the Kalman filtering algorithm is adopted in initial alignment, it yields a constant error in the estimation of the azimuth misalignment angle because the east gyro drift rate cannot be estimated. To improve the alignment accuracy, a novel alignment method on revolving mounting base is proposed. The Kalman filtering algorithm of extending the measured values is studied. The theory of spectral condition number is utilized to analyze the degrees of observability of states. Simulation results show that the estimation accuracy of the azimuth misalignment angle is greatly improved through a revolving mounting base, and the proposed method is efficient in initial alignment for a medium accurate SINS.
Strapdown inertial navigation system (SINS) requires an initialization process that establishes the relationship between the body frame and the local geographic reference. This process, called alignment, is generally used to estimate the initial attitude angles. This is possible because an accurate determination of the inertial measurement unit (IMU) motion is available based on the measurement obtained from global position system (GPS). But the update frequency of GPS is much lower than SINS. Due to the non-synchronous data streams from GPS and SINS, the initial attitude angles may not be computed accurately enough. In addition, the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters. This paper presents an effective approach of matching the velocities which are provided by GPS and SINS. In this approach, a digital high-pass filter, which implements a pre-filtering scheme of the measured signal, is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS. Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.
The GPS multipath signal model is presented, which indicates that the coherent DLL outputs in multipath environment are the convolution between the ideal DLL outputs and the channel responses. So the channel responses can be estimated by a least square method using the observed curve of the DLL discriminator. In terms of the estimated multipath channels, two multipath mitigation methods are discussed, which are equalization filtering and multipath subtracting, respectively. It is shown, by computer simulation, that the least square method has a good performance in channels estimation and the multipath errors can be mitigated almost completely by either of the methods. However, the multipath subtracting method has relative small remnant errors than equalization filtering.
A novel approach for engineering application to human error probability quantification is presented based on an overview of the existing human reliability analysis methods. The set of performance shaping factors is classified as two subsets of dominant factors and adjusting factors respectively. Firstly, the dominant factors are used to determine the probabilities of three behavior modes. The basic probability and its interval of human error for each behavior mode are given. Secondly, the basic probability and its interval are modified by the adjusting factors, and the total probability of human error is calculated by a total probability formula. Finally, a simple example is introduced, and the consistency and validity of the presented approach are illustrated.
The estimation of generalized exponential distribution based on progressive censoring with binomial removals is presented, where the number of units removed at each failure time follows a binomial distribution. Maximum likelihood estimators of the parameters and their confidence intervals are derived. The expected time required to complete the life test under this censoring scheme is investigated. Finally, the numerical examples are given to illustrate some theoretical results by means of Monte-Carlo simulation.
According to the Doppler sensitive of the phase coded pulse compression signal, a Doppler estimating and compensating method based on phase is put forward to restrain the Doppler sidelobes, raise the signal-to-noise ratio and improve measuring resolution. The compensation method is used to decompose the echo to amplitude and phase, and then compose the new compensated echo by the amplitude and the nonlinear component of the phase. Furthermore the linear component of the phase can be used to estimate the Doppler frequency shift. The computer simulation and the real data processing show that the method has accurately estimated the Doppler frequency shift, successfully restrained the energy leakage on spectrum, greatly increased the echo signal-to-noise ratio and improved the detection performance of the radio system in both time domain and frequency domain.
Traditional multi-band frequency selective surface (FSS) approaches are hard to achieve a perfect resonance response in a wide band due to the limit of the onset grating lobe frequency determined by the array. To solve this problem, an approach of combining elements in different period to build a hybrid array is presented. The results of series of numerical simulation show that multi-periodicity combined element FSS, which are designed using this approach, usually have much weaker grating lobes than the traditional FSS. Furthermore, their frequency response can be well predicted through the properties of their member element FSS. A prediction method for estimating the degree of expected grating lobe energy loss in designing multi-band FSS using this approach is provided.
Detecting the forgery parts from a double compressed image is very important and urgent work for blind authentication. A very simple and efficient method for accomplishing the task is proposed. Firstly, the probabilistic model with periodic effects in double quantization is analyzed, and the probability of quantized DCT coefficients in each block is calculated over the entire image. Secondly, the posteriori probability of each block is computed according to Bayesian theory and the results mentioned in first part. Then the mean and variance of the posteriori probability are to be used for judging whether the target block is tampered. Finally, the mathematical morphology operations are performed to reduce the false alarm probability. Experimental results show that the method can exactly locate the doctored part, and through the experiment it is also found that for detecting the tampered regions, the higher the second compression quality is, the more exact the detection efficiency is.
The problem of combined radar imagery from multiple sparse frequency subbands initially incoherent to each other is of practical importance for radar target discrimination. A new coherent processing technique based on probability density analysis of the subband data is proposed, which is applicable for radar imaging from measurements of two or more initially incoherent radar subbands. The coherence parameters for both amplitude and phase are obtained by combining modern spectral analysis with probability density estimation. The major advantage of the proposed technique lies in that it enables much more robust cohering for the sparse subband data of real-world complex targets.
A non-cooperative game is proposed to perform the sub-carrier assignment and power allocation for the multi-cell orthogonal frequency division multiple access (OFDMA) system. The objective is to raise the spectral efficiency of the system and prolong the life time of user nodes. This paper defines a game player as a cell formed by the unique base station and the served users. The utility function considered here measures the user’s achieved utility per power. Each individual cell's goal is to maximize the total utility of its users. To search the Nash equilibrium (NE) of the game, an iterative and distributed algorithm is presented. Since the NE is inefficient, the pricing of user's transmission power is introduced to improve the NE in the Pareto sense. Simulation results show the proposed game outperforms the water-filling algorithm in terms of fairness and energy efficiency. Moreover, through employing a liner pricing function, the energy efficiency could be further improved.
Using super resolution direction of arrival (DOA) estimation algorithm to reduce the resolution angle is an effective method for passive radar seeker (PRS) to antagonize non-coherent radar decoy. Considering the power and correlation property between radar and non-coherent decoy, an improved subspace DOA estimation method based on traditional subspace algorithm is proposed. Because this new method uses the invariance property of noise subspace, compared with traditional MUSIC algorithm, it shows not only better resolution in condition of closely spaced sources, but also superior performance in case of different power or partially correlated sources. Using this new method, PRS can distinguish radar and non-coherent decoy with good performance. Both the simulation result and the experimental data confirm the performance of the method.
To improve the positioning accuracy in GPS point positioning, the geometric dilution of precision (GDOP) including HDOP, VDOP, TDOP, PDOP is commonly considered. The properties of the DOP for the GPS satellite navigation system are studied and the coordinate system is improved in order to decrease the amount of variables. In the end, by simulation and discussing the results, the corresponding conclusions are presented.
To design approximately linear-phase complex coefficient finite impulse response (FIR) digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter’s coefficients. The approach can deal with both the real
and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.
A new approach for improving the throughputs of multi-
channel packet radio systems is proposed.Based on the charac-
teristics of multi-code CDMA technology,the scheme factitiously
improves the transmission bit rate of a terminal by compressing
the packet transmission time and thereby increases the number
of the orthogonal spreading codes used by the terminal.By this
means,the average interference level of the system is reduced
and the system capacity is improved.Simulation results show that
the proposed scheme exhibits larger throughput compared with
the traditional multi-code CDMA slotted Aloha systems.
Intuitionistic trapezoidal fuzzy numbers and their operational laws are defined. Based on these operational laws, some aggregation operators, including intuitionistic trapezoidal fuzzy weighted arithmetic averaging operator and weighted geometric averaging operator are proposed. Expected values, score function, and accuracy function of intuitionitsic trapezoidal fuzzy numbers are defined. Based on these, a kind of intuitionistic trapezoidal fuzzy multi-criteria decision making method is proposed. By using these aggregation operators, criteria values are aggregated and integrated intuitionistic trapezoidal fuzzy numbers of alternatives are attained. By comparing score function and accuracy function values of integrated fuzzy numbers, a ranking of the whole alternative set can be attained. An example is given to show the feasibility and availability of the method.
The frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is a light-weight, cost-effective, high-resolution imaging radar, which is suitable for a small flight platform. The signal model is derived for FMCW SAR used in unmanned aerial vehicles (UAV) reconnaissance and remote sensing. An appropriate algorithm is proposed. The algorithm performs the range cell migration correction (RCMC) for continuous non-chirped raw data using the energy invariance of the scaling of a signal in the scale domain. The azimuth processing is based on step transform without geometric resampling operation. The complete derivation of the algorithm is presented. The algorithm performance is shown by simulation results.
The class of multiple attribute decision making (MADM) problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.
The transfer alignment problem of the shipborne weapon inertial navigation system (INS) is addressed. Specifically, two transfer alignment algorithms subjected to the ship motions induced by the waves are discussed. To consider the limited maneuver level performed by the ship, a new filter algorithm for transfer alignment methods using velocity and angular rate matching is first derived. And then an improved method using integrated velocity and integrated angular rate matching is introduced to reduce the effect of the ship body flexure. The simulation results show the feasibility and validity of the proposed transfer alignment algorithms.
In the high speed target environment, there exists serious Doppler effect in the low pulse repetition frequency (LPRF) modulated frequency stepped frequency (MFSF) radar signal. The velocity range of the target is large and the velocity is high ambiguous, so the single method is difficult to satisfy the velocity measurement requirement. For this problem, a novel method is presented, it is a combination of cross-correlation inner frame velocity measurement and range-Doppler coupling velocity measurement. The cross-correlation inner frame method, overcoming the low Doppler tolerance of the cross-correlation between frames, can obtain the coarse velocity of the high speed target, and then the precision velocity can be obtained with the range-Doppler coupling method. The simulation results confirm the method is effective, and also it is well real-time and easy to the project application.
To improve the performance of a multiuser MIMO-OFDM system with imperfect channel status information, a downlink adaptive resource allocation algorithm which combines space-time block coding and beam forming (STBC-BF) is proposed. The algorithm allocates the subcarriers with a shared manner. A zero forcing processing with joint Rx-Tx is used to suppress the co-channel interference (CCI) and to construct uncorrelatedchannels for STBC. An adaptive power allocation for the STBC equivalent channels can increase signal to interference and noise ratio at the receiver. Simulation results show that under the condition of an imperfect CSI, the proposed algorithm improves the system performance and reduces the number of BS transmit antennas required.
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.
For the joint time difference of arrival (TDOA) and angle of arrival (AOA) location scene, two methods are proposed based on the rectangular coordinates and the polar coordinates, respectively. The problem is solved perfectly by calculating the target position with the joint TDOA and AOA location. On the condition of rectangular coordinates, first of all, it figures out the radial range between target and reference stations, then calculates the location of the target. In the case of polar coordinates, first of all, it figures out the azimuth between target and reference stations, then figures out the radial range between target and reference stations, finally obtains the location of the target. Simultaneously, simulation analyses show that the theoretical analysis is correct, and the proposed methods also provide the application of the joint TDOA and AOA location algorithm with the theoretical basis.
In order to take full advantage of federated filter in faulttolerant
design of integrated navigation system, the limitation of
fault detection algorithm for gradual changing fault detection and
the poor fault tolerance of global optimal fusion algorithm are the
key problems to deal with. Based on theoretical analysis of the
influencing factors of federated filtering fault tolerance, global faulttolerant
fusion algorithm and information sharing algorithm are
proposed based on fuzzy assessment. It achieves intelligent faulttolerant
structure with two-stage and feedback, including real-time
fault detection in sub-filters, and fault-tolerant fusion and information
sharing in main filter. The simulation results demonstrate that
the algorithm can effectively improve fault-tolerant ability and ensure
relatively high positioning precision of integrated navigation
system when a subsystem having gradual changing fault.
Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing (SUT), test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, and automated system testing for embedded real-time software.
Mobile ad hoc networking (MANET) has become an exciting and important technology in recent years, because of the rapid proliferation of wireless devices. Mobile ad hoc networks is highly vulnerable to attacks due to the open medium, dynamically changing network topology, cooperative algorithms, and lack of centralized monitoring and management point. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features. A distributed intrusion detection approach based on timed automata is given. A cluster-based detection scheme is presented, where periodically a node is elected as the monitor node for a cluster. These monitor nodes can not only make local intrusion detection decisions, but also cooperatively take part in global intrusion detection. And then the timed automata is constructed by the way of manually abstracting the correct behaviours of the node according to the routing protocol of dynamic source routing (DSR). The monitor nodes can verify the behaviour of every nodes by timed automata, and validly detect real-time attacks without signatures of intrusion or trained data. Compared with the architecture where each node is its own IDS agent, the approach is much more efficient while maintaining the same level of effectiveness. Finally, the intrusion detection method is evaluated through simulation experiments.
Gyro’s drift is not only the main drift error which influences gyro’s precision but also the primary factor that affects gyro’s reliability. Reducing zero drift and random drift is a key problem to the output of a gyro signal. A three-layer de-nosing threshold algorithm is proposed based on the wavelet decomposition to dispose the signal which is collected from a running fiber optic gyro (FOG). The coefficients are obtained from the three-layer wavelet packet decomposition. By setting the high frequency part which is greater than wavelet packet threshold as zero, then reconstructing the nodes which have been filtered out noise and interruption, the soft threshold function is constructed by the coefficients of the third nodes. Compared wavelet packet de-noise with forced de-noising method, the proposed method is more effective. Simulation results show that the random drift compensation is enhanced by 13.1%, and reduces zero drift by 0.052 6?/h.
This paper presents a fault diagnosis method for process faults and sensor faults in a class of nonlinear uncertain systems. The fault detection and isolation architecture consists of a fault detection estimator and a bank of adaptive isolation estimators, each corresponding to a particular fault type. Adaptive thresholds for fault detection and isolation are presented. Fault detectability conditions characterizing the class of process faults and sensor faults that are detectable by the presented method are derived. A simulation example of robotic arm is used to illustrate the effectiveness of the fault diagnosis method.
To preserve the sharp features and details of the synthetic aperture radar (SAR) image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform (NSBT) domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges are detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least
square-Gaussian scale mixture (BLS-GSM).