Optimal oppor tunistic maintenance model of multi-unit systems

An opportunistic maintenance model is presented for a continuously deteriorating series system with economical de-pendence. The system consists of two kinds of units, which are respectively subjected to the deterioration failure described by Gamma process and the random failure described by Poisson process. A two-level opportunistic policy defined by three decision parameters is proposed to coordinate the different maintenance actions and minimize the long-run maintenance cost rate of the system. A computable expression of the average cost rate is established by using the renewal property of the stochastic process of the maintained system state. The optimal values of three deci- sion parameters are derived by an iteration approach based on the characteristic of Gamma process. The behavior of the proposed policy is illustrated through a numerical experiment. Comparative study with the widely used corrective maintenance policy demonstrates the advantage of the proposed opportunistic maintenance method in significantly reducing the maintenance cost. Simultane- ously, the applicable area of this opportunistic model is discussed by the sensitivity analysis of the set-up cost and random failure rate.     

Design of continuous long slot leaky-wave antenna for millimeter wave application

A simple and efficient design scheme of the continuous long slot leaky-wave antenna is developed. The key steps involved in the scheme are summarized. First, the cut-off frequencies of slot waveguides with different slot offsets are obtained by 3D finite-difference time-domain (FDTD) method. Second, the attenuation function αra is estimated by the aperture distribution, and the attenuation function αrs is determined by the slot radiation.Finally, the attenuation function αra is combined with the attenuation function αrs by the coefficient K. And an example in Ka band is presented. Moreover, the return loss of the E-plane Tee-junction (ET) and the radiation pattern of leaky-wave antenna are simulated. The scheme is verified by comparing with the experimental result.     

Finite element approximation for a class of parameter estimation problems

This paper investigates the finite element approximation of a class of parameter estimation problems which is the form of performance as the optimal control problems governed by bilinear parabolic equations,where the state and co-state are discretized by piecewise linear functions and control is approximated by piecewise constant functions.The authors derive some a priori error estimates for both the control and state approximations.Finally,the numerical experiments verify the theoretical results.     

Fast fine acquisition algorithm of GPS receiver aided by INS information

Acquisition time of global position system (GPS) receiver, which is the main factor contributes to time to first fix (TTFF), can be shortened by estimating the Doppler frequency shift through external inertial navigation system (INS) information and almanac data and reducing the searching area. The traditional fast acquisition is analyzed, the fast acquisition of the GPS receiver aided is presented by INS information, and the signal is fine captured by spectrum zooming. Then the algorithm is simulated by sampled GPS intermediate frequency (IF) signal and the result verifies that this acquisition can dramatically improve the capability of GPS receiver and reduce its acquisition time.     

A model for knowledge transfer in a multi-agent organization based on lattice kinetic model

A study on knowledge transfer in a mutli-agent organization is performed by applying the basic principle in physics such as the kinetic theory.Based on the theoretical analysis of the knowledge accumulation process and knowledge transfer attributes,a special type of knowledge field(KF)is introduced and the knowledge diffusion equation(KDE)is developed.The evolution of knowledge potential is modeled by lattice kinetic equation and verified by numerical experiments.The new equation-based modeling developed in this paper is meaningful to simulate and predict the knowledge transfer process in firms.The development of the lattice kinetic model(LKM)for knowledge transfer can contribute to the knowledge management theory,and the managers can also simulate the knowledge accumulation process by using the LKM.     

Stability of GM(1,1) power model on vector transformation

The morbidity problem of the GM(1,1) power model in parameter identification is discussed by using multiple and rotation transformation of vectors. Firstly we consider the morbidity problem of the special matrix and prove that the condition number of the coefficient matrix is determined by the ratio of lengths and the included angle of the column vector, which could be adjusted by multiple and rotation transformation to turn the matrix to a well-conditioned one. Then partition the corresponding matrix of the GM(1,1) power model in accordance with the column vector and regulate the matrix to a well-conditioned one by multiple and rotation transformation of vectors, which completely solve the instability problem of the GM(1,1) power model. Numerical results show that vector transformation is a new method in studying the stability problem of the GM(1,1) power model.     

Region-based classification by combining MS segmentation and MRF for POLSAR images

Speckle effects on classification results can be suppressed to some extent by introducing the contextual information.An unsupervised classification algorithm is proposed for polarimetric synthetic aperture radar(POLSAR) images based on the mean shift(MS) segmentation and Markov random field(MRF).First,polarimetric features are exacted by target decomposition for MS segmentation.An initial classification is executed by using the target decomposition and the agglomerative hierarchical clustering algorithm.Thereafter,a classification step based on MRF is performed by using the mean coherence matrices obtained for each segment.Under the MRF framework,the smoothness term is defined according to the distance between neighboring areas.By using POLSAR images acquired by the German Aerospace Centre and National Aeronautics and Space Administration/Jet Propulsion Laboratory,the experimental results confirm that the proposed method has higher accuracy and better regional connectivity than other classification methods.     

ON LIMITATIONS OF THE SAMPLED—DATA FEEDBACK FOR NONPARAMETRIC DYNAMICAL SYSTEMS

In this paper, we study a basic class of first order sampled-data control systems with unknown nonlinear structure and with sampling rate not necessarily fast enough, aiming at understanding the capability and limitations of the sampled-data feedback. We show that if the unknown nonlinear function has a linear growth rate with its "slope" (denoted by L) being a measure of the "size" of uncertainty, then the sampling rate should not exceed 1/L multiplied by a constant (≈ 7.53) for the system to be globally stabilizable by the sampled-data feedback. If, however, the unknown nonlinear function has a growth rate faster than linear, and if the system is disturbed by noises modeled as the standard Brownian motion, then an example is given, showing that the corresponding sampled-data system is not stabilizable by the sampled-data feedback in general, no matter how fast the sampling rate is.     

L ∞-estimates of mixed finite element methods for general nonlinear optimal control problems

This paper investigates L~∞-estimates for the general optimal control problems governed by two-dimensional nonlinear elliptic equations with pointwise control constraints using mixed finite element methods.The state and the co-state are approximated by the lowest order Raviart-Thomas mixed finite element spaces and the control is approximated by piecewise constant functions.The authors derive L~∞-estimates for the mixed finite element approximation of nonlinear optimal control problems.Finally,the numerical examples are given.     

Dynamic model and simulation of aseismatic system about laser gyro strapdown inertial measure assembly based on MSTMM

The precision of the laser gyro used in tactical missiles is poor because of dithering frequency,actuating by vibration,shock and overload in dynamical environment.This paper introduces the transfer matrix method of the multibody system(MSTMM),establishes the dynamic model of the laser gyro strapdown inertial measure assembly aseismatic system,and analyzes the precision affected by dithering of the laser gyro and shocking of the tactical missile.And the dynamic response of the laser gyro strapdown inertial measure assembly aseismatic system is obtained by simulating the multibody system model.The simulation result indicates a theoretical idea to design the vibration isolation for the laser gyro strapdown inertial measure assembly.     

Data-driven fault diagnosis method for analog circuits based on robust competitive agglomeration

The data-driven fault diagnosis methods can improve the reliability of analog circuits by using the data generated from it. The data have some characteristics, such as randomness and incompleteness, which lead to the diagnostic results being sensitive to the specific values and random noise. This paper presents a data-driven fault diagnosis method for analog circuits based on the robust competitive agglomeration (RCA), which can alleviate the incompleteness of the data by clustering with the competing process. And the robustness of the diagnostic results is enhanced by using the approach of robust statistics in RCA. A series of experiments are provided to demonstrate that RCA can classify the incomplete data with a high accuracy. The experimental results show that RCA is robust for the data needed to be classified as well as the parameters needed to be adjusted. The effectiveness of RCA in practical use is demonstrated by two analog circuits.     

Method for uncertain multi-attribute decisionmaking with preference information in the form of interval numbers complementary judgment matrix

The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker's preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging (C-OWA) operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.     

RETRACTION NOTE： EXPLORING THE VALUE OF INSTALLED BASE： PRICING INFORMATION GOODS UNDER VALUE DEPRECIATION AND CONSUMER SOCIAL LEARNING

The article Exploring the value of installed base： Pricing information goods under value depreciation and consumer social learning published in Volume 22, Issue 3, pages 362-382 by Yifan Dou and Tianliang Liu, has been retracted by agreement between the authors and the journal Editor-in-Chief. The core contributions in this article have been used without significant acknowledgement and approval by the project research team.     

Parameter estimation for dual-rate sampled Hammerstein systems with dead-zone nonlinearity

The identification of nonlinear systems with multiple sampled rates is a difficult task.The motivation of our paper is to study the parameter estimation problem of Hammerstein systems with dead-zone characteristics by using the dual-rate sampled data.Firstly,the auxiliary model identification principle is used to estimate the unmeasurable variables,and the recursive estimation algorithm is proposed to identify the parameters of the static nonlinear model with the dead-zone function and the parameters of the dynamic linear system model.Then,the convergence of the proposed identification algorithm is analyzed by using the martingale convergence theorem.It is proved theoretically that the estimated parameters can converge to the real values under the condition of continuous excitation.Finally,the validity of the proposed algorithm is proved by the identification of the dual-rate sampled nonlinear systems.     

Further research of a new kind of series repairable system

This paper is devoted to studying the uniqueness and existence of the system dynamic solution by using Co-semigroup theory and discussing its exponential stability by analyzing the spectral distribution of system operator and its quasi-compactness.Some primary reliability indices are discussed with the eigenfunction of system operator and the optimal vacation time to get the maximum system profit is analyzed at the end of paper.     

QUADRATIC REPRESENTATION FOR ROADWAY PROFILE THAT MINIMIZES EARTHWORK COST

Roadway design usually involves choices regarding grade selection and earthwork (transportation) that can be solved using linear programming. Previous work considered the road profile as series of interconnected linear segments. In these models, constraints are included in the linear programming formulation to insure continuity of the road, which cause sharp connectivity points at the intersection of the linear segments. This sharp connectivity needs to be smoothed out after l;he linear programming solution is found and the earth in the smoothed portion of the roadway has to be moved to the landfill. In previous research, the smoothing issue is dealt with after an optimal solution is found. This increases the work required by the design engineer and consequently increases the construction cost; furthermore, the optimal solution is violated by this smoothing operation. In this paper, the issue of sharp connectivity points is resolved by representing the road profile by a quadratic function. The continuit     

Solving the State Equation of a System by Walsh Function

In this paper, We deal with the solution of the state equation of a system by the Walsh function, that is, we shall find the solution of a matrix differential equation by the Walsh function, and introduce a solution of the higher-order matrix differential equation. First, after a certain transform, we turn the higher-order matrix differential equation into a state equation. Then we find the solution of the state equation by the Walsh function. Finally after a certain transform, we obtain a solution of the higher-order matrix differential equation.     

Performance Evaluation of Analyst Recommendations in Chinese Stock Market

Using the samples from every Monday‘s column ‘the Most Promising Stocks Recommended by Analysts This Week‘ on Shanghai Securities News during the period from Sep.2000 to Dec.2003, this paper analyzes the performance of the stocks recommended by analysts during the following week. Firstly, the analysts are inclined to recommend the stocks with better performance in the latest week. Secondly, the portfolios formed by the analyst recommendations have remarkably positive abnormal returns on Monday, while they do not outperform the market from Tuesday to Friday, even have remarkably negative abnormal returns.     

THE TRANSITION PROBABILITY MATRIX OF A MARKOV CHAIN MODEL IN AN ATM NETWORK

In this paper we consider a Markov chain model in an ATM network, which has been studied by Dag and Stavrakakis. On the basis of the iterative formulas obtained by Dag and Stavrakakis, we obtain the explicit analytical expression of the transition probability matrix. It is very simple to calculate the transition probabilities of the Markov chain by these expressions. In addition, we obtain some results about the structure of the transition probability matrix, which are helpful in numerical calculation and theoretical analysis.     

On Minimum Norm Control for an Elastic Robot System

In this paper, we discuss a robot system formulated as a vibrating elastic system. By regarding the damping coefficient of structure as the control variable and adjudging its optimality by norm minimum, we have proved the existence, the uniqueness and the approximation of the optimal control element by utilizing space L2(0,L) 's the reflexivity, the smoothness and the strict convexity.