Advanced Materials Research Vols. 989-994

Abstract: This paper presents a modified harmony search (MHS) algorithm for solving numerical optimization problems. MHS employs a novel self-learning strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. In the proposed MHS algorithm, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory. The other two key parameters PAR and bw adjust dynamically with generation number. Based on a large number of experiments, MHS has demonstrated stronger convergence and stability than original harmony search (HS) algorithm and its two improved algorithms (IHS and GHS).

Abstract: In this paper, a modified differential evolution algorithm (MDE) developed to solve unconstrained numerical optimization problems. The MDE algorithm employed random position updating and disturbance operation to replaces the traditional mutation operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the MDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and its two improved algorithms (JADE and SaDE) that reported in recent literature.

Abstract: In order to improve the accuracy of people counting in video surveillance, the method for people counting based on the moving feature of the mass is proposed. We obtain the orientation and energy density of mass through the optical flow algorithm, and get the information about the size of mass to design the feature of mass. The people counting model is obtained by training a support vector machine (SVM) classifier with the moving feature and shape feature of mass. The experimental results confirm that our approach improves the accuracy of people counting.

Abstract: With the continuous expansion of the scale data storage, cloud storage technology for its high performance and low cost to get a lot of attention and support. However, the security issues of cloud storage data hinder its further promotion. For the current cloud storage applications of data stored encrypted, a cloud storage encryption scheme based on the separated key and encryption policy is proposed. By strengthening the data encryption key management and data encryption algorithm, the system achieves a more secure storage of data assurance in the technical level.

Abstract: The multi-attribute decision making (MADM) problem is studied, in which the information about attribute weights is unknown and the decision maker (DM) has avail preference information on alternatives. Firstly, a quadratic programming model based on the minimum deviation between the objective decision-making information and the subjective preference information on alternatives is established. Secondly, the existence of solution to the model is proved and the calculated formula of the attribute weights are given, thus the overall values of the alternatives are gained by using the additive weighting method. Based on these values, the selecting the best on alternatives is processed. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.

Abstract: In recent years, a variety of manifold-based learning dimensionality reduction techniques have been proposed, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure. Among them, marginal fisher analysis (MFA) achieved high performance for face recognition. However, the optimal basis vectors obtained by MFA are non-orthogonal and MFA usually deteriorates when labeled information is insufficient. In order to resolve these problems, we present a new method called orthogonal semi-supervised marginal fisher analysis (OSMFA), which not only extracts all the orthogonal discriminant vectors but also preserves the global structure of labeled and unlabeled samples to learn a better subspace for classification. Experimental results on ORL database demonstrate the effectiveness of the proposed algorithm.

Abstract: Geomagnetic matching (GM) is a new developmental technology in recent years. For resolving the cumulate errors of position, velocity, attitude in an inertial navigation system (INS), utilizing artificial fish swarm algorithm (AFSA) to carry on GM is proposed, and then the attained matching position regarded as measurement of filter to achieve the emendation of INS. Firstly, affine transformation model between inertial and real trajectories is displayed and the principle of INS/GM is explained. Secondly, the state of artificial fish (AF), distance and food consistence are defined afresh, and then the flow chart based on AFSA is given. Lastly, simulation analysis is performed in actual geomagnetic reference map (GRM). The results show that the algorithm actualizes the GM when the inertial system exist position, velocity and attitude error, that is the algorithm got the optimized global solution, with that, the data fusing is finished with the results that not only reduce error evidently but also verify the validity and feasible of the algorithm.

Abstract: A currency crisis is typically a kind of rare event, but it hurts sustainable economic development when it occurs. A novel method of wavelet-based support vector machine (SVM) is proposed to predict financial crisis events for early-warning purposes in this paper. In the proposed method, currency exchange rate, a typical currency indicator that usually reflects economic fluctuations, is first chosen. Then a wavelet decomposition algorithm is applied to the currency exchange rate series. Using the wavelet decomposition procedure, some details and features of the currency exchange rate series, with different scales, can be obtained. Using these details and features, a wavelet-based SVM learning paradigm is used to predict future currency crisis events, based upon some historical data. For illustration purpose, the proposed wavelet-based SVM learning paradigm is applied to exchange rate data of two Asian countries to evaluate the state of currency crisis. Experimental results reveal that the proposed wavelet-based SVM learning paradigm can significantly improve the generalization performance relative to some popular forecasting methods.

Abstract: This paper proposes an improved speech enhancement algorithm based on Wiener-Filtering, which addresses the problems of speech distortion and musical noise. The proposed algorithm adopts the masking properties of human auditory system on calculating the gain of spectrum point, in order that the signal in the enhanced speech whose energy is lower than the threshold will not be decreased further and the less distortion will be brought to enhanced speech by the trade-off between the noise elimination and speech signal distortion. What’s more, in order to eliminate the “musical noise”, a spectrum-shaping technology using averaging method between adjacent frames is adopted. And to guarantee the real-time application, two-stage moving-average strategy is adopted. The computer simulation results show that the proposed algorithm is superior to the traditional Wiener method in the low CPU cost, real-time statistics, the reduction of the speech distortion and residual musical noise.

Abstract: In order to improve the license plate recognition accuracy under complex environment, a new license location algorithm combining vertical edge detection, color information of the license plate and mathematical morphology is presented in this paper. For balance of computing load and recognition accuracy, a “200-d” character feature rule is designed, and the “200-d” feature is used as the input of BP neural network to recognize the characters. Based on the above-mentioned methods, a license plate recognition system is set up, which can locate and recognize the license plate effectively, even when the resolution of pictures and the position of vehicles in the pictures are not fixed. Experimental results indicate that the recognition rate of the algorithm reaches 90.5%.