The locally resonant sonic material (LRSM) is an artificial metamaterial that can block underwater sound. The low-frequency insulation performance of LRSM can be enhanced by coupling local resonance and Bragg scattering effects. However, such method is hard to be experimentally proven as the best optimizing method. Hence, this paper proposes a statistical optimization method, which first finds a group of optimal solutions of an object function by utilizing genetic algorithm multiple times, and then analyzes the distribution of the fitness and the Euclidean distance of the obtained solutions, in order to verify whether the result is the global optimum. By using this method, we obtain the global optimal solution of the low-frequency insulation of LRSM. By varying parameters of the optimum, it can be found that the optimized insulation performance of the LRSM is contributed by the coupling of local resonance with Bragg scattering effect, as well as a distinct impedance mismatch between the matrix of LRSM and the surrounding water. This indicates coupling different effects with impedance mismatches is the best method to enhance the low-frequency insulation performance of LRSM.

Considering concrete nonlinearity, the wave height limit between small and large amplitude sloshing is defined based on the Bernoulli equation. Based on Navier-Stokes equations, the mathematical model of large amplitude sloshing is established for a Concrete Rectangle Liquid-Storage Structure (CRLSS). The results show that the seismic response of a CRLSS increases with the increase of seismic intensity. Under different seismic fortification intensities, the change in trend of wave height, wallboard displacement, and stress are the same, but the amplitudes are not. The areas of stress concentration appear mainly at the connections between the wallboards, and the connections between the wallboard and the bottom.

This communication reports detection of somaclonal variation among tissue culture-raised plants of Amorphophallus rivieri Durieu, an economically important crop in China, with high content of glucomannan in its corms. A population of regenerated plants was obtained from a single donor plant of A. rivieri via corm organogenesis, and 28 plants were randomly selected as a representative sample and subjected to analysis of somaclonal variation using inter-simple sequence repeat (ISSR) markers. Of the 26 ISSR primers screened, 13 gave distinct and reproducible band patterns, yielding 131 bands with an average of 10.1 bands per primer. Ten primers were polymorphic and generated 16 polymorphic bands with 12.2% mean polymorphism. Based on the ISSR data from the regenerated plants and the donor plant, Jaccard's similarity coefficients were calculated; they ranged from 0.961 to 1.000 with a mean of 0.982. A dendrogram was constructed using the unweighted pair group method with arithmetic mean (Upgma); it showed that a majority of regenerated plants (including the donor plant) clustered closely, with a mean similarity coefficient of 0.987. Low somaclonal variation observed in the regenerated plants indicates that rapid propagation of A. rivieri via corm organogenesis is a practicable method with a low risk of genetic instability.

Forecasting yield curves with regime switches is important in academia and financial industry. As the number of interest rate maturities increases, it poses difficulties in estimating parameters due to the curse of dimensionality. To deal with such a feature, factor models have been developed. However, the existing approaches are restrictive and largely based on the stationarity assumption of the factors. This inaccuracy creates non-ignorable financial risks, especially when the market is volatile. In this paper, a new methodology is proposed to adaptively forecast yield curves. Specifically, functional principal component analysis (FPCA) is used to extract factors capable of representing the features of yield curves. The local AR(1) model with time-dependent parameters is used to forecast each factor. Simulation and empirical studies reveal the superiority of this method over its natural competitor, the dynamic Nelson-Siegel (DNS) model. For the yield curves of the U.S. and China, the adaptive method provides more accurate 6- and 12-month ahead forecasts.

Accurate force and torque calculations are fundamental to being able to predict the operation of an electromechanical device or system. The Maxwell stress tensor and the virtual work principle are the two major theories for force and torque calculation. However, if local distributions of torque are needed to couple to structural and vibration analyses, the conventional Maxwell stress approach cannot provide this easily. A recently developed approach based on sensitivity analysis has the capability to deliver local stress and torque as well as accurate global results. In addition, this approach divides the total torque into different components which are essential to the design of electrical devices. This paper includes several numerical examples of torque calculations of different electrical machines. The results are verified by a commercial software package using the Maxwell stress based force calculation.

This paper presents a novel strategy of particle filtering for state estimation based on Generalized Gaussian distributions (GGDs). The proposed strategy is implemented with the Gaussian particle pilter (GPF), which has been proved to be a powerful approach for state estimation of nonlinear systems with high accuracy and low computational cost. In our investigations, the distribution which gives the complete statistical characterization of the given data is obtained by exponent parameter estimation for GGDs, which has been solved by many methods. Based on GGDs, an extension of GPF is proposed and the simulation results show that the extension of GPF has higher estimation accuracy and nearly equal computational cost compared with the GPF which is based on Gaussian distribution assumption.

In order to overcome the shortcomings of the dolphin algorithm, which is prone to falling into local optimum and premature convergence, an improved dolphin swarm algorithm, based on the standard dolphin algorithm, was proposed. As a measure of uncertainty, information entropy was used to measure the search stage in the dolphin swarm algorithm. Adaptive step size parameters and dynamic balance factors were introduced to correlate the search step size with the number of iterations and fitness, and to perform adaptive adjustment of the algorithm. Simulation experiments show that, comparing with the basic algorithm and other algorithms, the improved dolphin swarm algorithm is feasible and effective.

The recently proposed q-rung orthopair fuzzy set (q-ROFS) characterized by a membership degree and a non-membership degree is powerful tool for handling uncertainty and vagueness. This paper proposes the concept of q-rung orthopair linguistic set (q-ROLS) by combining the linguistic term sets with q-ROFSs. Thereafter, we investigate multi-attribute group decision making (MAGDM) with q-rung orthopair linguistic information. To aggregate q-rung orthopair linguistic numbers ( q-ROLNs), we extend the Heronian mean (HM) to q-ROLSs and propose a family of q-rung orthopair linguistic Heronian mean operators, such as the q-rung orthopair linguistic Heronian mean (q-ROLHM) operator, the q-rung orthopair linguistic weighted Heronian mean (q-ROLWHM) operator, the q-rung orthopair linguistic geometric Heronian mean (q-ROLGHM) operator and the q-rung orthopair linguistic weighted geometric Heronian mean (q-ROLWGHM) operator. Some desirable properties and special cases of the proposed operators are discussed. Further, we develop a novel approach to MAGDM within q-rung orthopair linguistic context based on the proposed operators. A numerical instance is provided to demonstrate the effectiveness and superiorities of the proposed method.

Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

In order to research the losses and heat of damper bars thoroughly, a multislice moving electromagnetic field-circuit coupling FE model of tubular hydro-generator and a 3D temperature field FE model of the rotor are built respectively. The factors such as rotor motion and non-linearity of the time-varying electromagnetic field, the stator slots skew, the anisotropic heat conduction of the rotor core lamination and different heat dissipation conditions on the windward and lee side of the poles are considered. Furthermore, according to the different operating conditions, different rotor structures and materials, compositive calculations about the losses and temperatures of the damper bars of a 36 MW generator are carried out, and the data are compared with the test. The results show that the computation precision is satisfied and the generator design is reasonable.

This paper presents a Kalman filter based method for diagnosing both parametric and catastrophic faults in analog circuits. Two major innovations are presented, i.e., the Kalman filter based technique, which can significantly improve the efficiency of diagnosing a fault through an iterative structure, and the Shannon entropy to mitigate the influence of component tolerance. Both these concepts help to achieve higher performance and lower testing cost while maintaining the circuit.s functionality. Our simulations demonstrate that using the Kalman filter based technique leads to good results of fault detection and fault location of analog circuits. Meanwhile, the parasitics, as a result of enhancing accessibility by adding test points, are reduced to minimum, that is, the data used for diagnosis is directly obtained from the system primary output pins in our method. The simulations also show that decision boundaries among faulty circuits have small variations over a wide range of noise-immunity requirements. In addition, experimental results show that the proposed method is superior to the test method based on the subband decomposition combined with coherence function, arisen recently.

The BeiDou navigation satellite system (BDS) is one of the four global navigation satellite systems. More attention has been paid to the positioning algorithm of the BDS. Based on the study on the Kalman filter (KF) algorithm, this paper proposed a novel algorithm for the BDS, named as the minimum dispersion coefficient criteria Kalman filter (MDCCKF) positioning algorithm. The MDCCKF algorithm adopts minimum dispersion coefficient criteria (MDCC) to remove the influence of noise with an alpha-stable distribution (ASD) model which can describe non-Gaussian noise effectively, especially for the pulse noise in positioning. By minimizing the dispersion coefficient of the positioning error, the MDCCKF assures positioning accuracy under both Gaussian and non-Gaussian environment. Compared with the original KF algorithm, it is shown that the MDCCKF algorithm has higher positioning accuracy and robustness. The MDCCKF algorithm provides insightful results for potential future research.

In this paper, a discrete wavelet transform (DWT) based approach is proposed for power system frequency estimation. Unlike the existing frequency estimators mainly used for power system monitoring and control, the proposed approach is developed for fundamental frequency estimation in the field of energy metering of nonlinear loads. The characteristics of a nonlinear load is that the power signal is heavily distorted, composed of harmonics, inter-harmonics and corrupted by noise. The main idea is to predetermine a series of frequency points, and the mean value of two frequency points nearest to the power system frequency is accepted as the approximate solution. Firstly the input signal is modulated with a series of modulating signals, whose frequencies are those frequency points. Then the modulated signals are decomposed into individual frequency bands using DWT, and differences between the maximum and minimum wavelet coefficients in the lowest frequency band are calculated. Similarities among power system frequency and those frequency points are judged by the differences. Simulation results have proven high immunity to noise, harmonic and inter-harmonic interferences. The proposed method is applicable for real-time power system frequency estimation for electric energy measurement of nonlinear loads.

In order to identify the modal parameters of civil structures it is vital to distinguish the defective data from that of appropriate and accurate data. The defects in data may be due to various reasons like defects in the data collection, malfunctioning of sensors, etc. For this purpose Exploratory Data Analysis (EDA) was engaged toenvisage the distribution of sensor’s data and to detect the malfunctioning with in the sensors. Then outlier analysis was performed to remove those data points which may disrupt the accurate data analysis. Then Data Driven Stochastic Sub-space Identification (DATA-SSI) was engaged to perform the modal parameter identification. In the end to validate the accuracy of the proposed method stabilization diagrams were plotted. Sutong Bridge, one of the largest span cable stayed bridge was used as a case study and the suggested technique was employed. The results obtained after employing the above mentioned techniques are very valuable, accurate and effective.

Abstract This paper present a new fuzzy iterative learning control design to solve the trajectory tracking problem and performing repetitive tasks for rigid robot manipulators. Several times’ iterations are needed to make the system tracking error converge, especially in the first iteration without experience. In order to solve that problem, fuzzy control and iterative learning control are combined, where fuzzy control is used to tracking trajectory at the first learning period, and the output of fuzzy control is recorded as the initial control inputs of ILC. The new algorithm also adopts gain self-tuning by fuzzy control, in order to improve the convergence rate. Simulations illustrate the effectiveness and convergence of the new algorithm and advantages compared to traditional method.

There are an increasing number of binaural systems embedded with head-related transfer functions (HRTFs), so listeners can experience virtual environments via conventional stereo loudspeakers or head- phones. As HRTFs vary from person to person, it is difficult to select appropriated HRTFs from already existing databases for users. Once the HRTFs in a binaural audio device hardly match the real ones of the users, poor localization happens especially on the cone of confusion. The most accurate way to obtain personalized HRTFs might be doing practical measurements. It is, however, expensive and time consuming. Modifying non-individualized HRTFs may be an effort-saving way, though the modifications are always accompanied by undesired audio distortion. This paper proposes a flexible HRTF adjustment system for users to define their own HRTFs. Also, the system can keep sounds from suffering intolerable distortion based on an objective measurement tool for evaluating the quality of processed audio.

The Gaussian mixture model (GMM) method is popular and efficient for voice conversion (VC), but it is often subject to overfitting. In this paper, the principal component regression (PCR) method is adopted for the spectral mapping between source speech and target speech, and the numbers of principal components are adjusted properly to prevent the overfitting. Then, in order to better model the nonlinear relationships between the source speech and target speech, the kernel principal component regression (KPCR) method is also proposed. Moreover, a KPCR combined with GMM method is further proposed to improve the accuracy of conversion. In addition, the discontinuity and oversmoothing problems of the traditional GMM method are also addressed. On the one hand, in order to solve the discontinuity problem, the adaptive median filter is adopted to smooth the posterior probabilities. On the other hand, the two mixture components with higher posterior probabilities for each frame are chosen for VC to reduce the oversmoothing problem. Finally, the objective and subjective experiments are carried out, and the results demonstrate that the proposed approach shows greatly better performance than the GMM method. In the objective tests, the proposed method shows lower cepstral distances and higher identification rates than the GMM method. While in the subjective tests, the proposed method obtains higher scores of preference and perceptual quality.

The variation law of dissolved silica (DSi), dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP) and nutrition structure after the Three Gorges Project (TGP) impounding as well as their ecological effect were analyzed according to monitoring survey of the Yangtze River Estuary in spring (May) and summer (August) from 2004-2009. The results showed that after impounding, DSi and DIN concentration decreased and increased, respectively. During the study period, DSi decreased by about 63%, while DIN almost tripled. DIP concentration fluctuated slightly. With respect to nutrition structure, N:P increased, whereas Si:P and Si:N declined. According to chemometry standard of nutrient limits, nutrition structure tended to be imbalanced and the limiting factor of phytoplankton growth (P) was studied. Changes of nutrition structure have largely decreased diatom and caused different composition of dominant phytoplankton species. This may change ecosystem structure of the Yangtze River Estuary.

Accurate prediction of power load plays a crucial role in the power industry and provides economic operation decisions for the power operation department. Due to the unpredictability and periodicity of power load, an improved method to deal with complex nonlinear relation was adopted, and a short-term load forecasting model combining FEW (fuzzy exponential weighting) and IHS (improved harmonic search) algorithms was proposed. Firstly, the domain space was defined, the harmony memory base was initialized, and the fuzzy logic relation was identified. Then the optimal interval length was calculated using the training sample data, and local and global optimum were updated by optimization criteria and judging criteria. Finally, the optimized parameters obtained by an IHS algorithm were applied to the FEW model and the load data of the Huludao region (2013) in Northeast China in May. The accuracy of the proposed model was verified using an evaluation criterion as the fitness function. The results of error analysis show that the model can effectively predict short-term power load data and has high stability and accuracy, which provides a reference for application of short-term prediction in other industrial fields.