In the calculation of the acoustic performance of mufflers, the walls of mufflers are usually treated rigidly without considering the acoustic-structural coupling, but the results so calculated differ significantly from the actual situation. Based on the basic equations, the article derives the finite element equations of the muffler system while considering the acoustic-structural coupling effect and theoretically analyses the connection between the acoustic-structural coupling system and the structural and acoustic modes. The structural and acoustic modes of the muffler are calculated and the reasons for the mutation of the transmission loss curve of the muffler when the acoustic-structural coupling is considered are analysed. The results show that the acoustic-structural coupling is the result of the interaction between the structure and the air inside the expansion chamber under acoustic excitation, which manifests mutations in the sound pressure inside the muffler in some frequency bands. Then, using a single-chamber muffler as an example, the transmission loss is used to characterise the performance of the muffler. The effects of different factors such as shell thickness, structure, porous media material lining, and restraint method on the acoustic-structural coupling effect of the muffler are analysed, and the structure of a double-chamber muffler is successfully optimised according to the conclusions.

Cephus fumipennis Eversmann is a key insect pest of wheat crops in Qinghai, China. Its field population densities were surveyed by using both the back-loaded insect vacuum and a sweep net. Mean densities in township-level were calculated and a quantitative relation, ŷ = 0.664 + 0214x, was established between the two sampling methods. The empirical relationship may be applicable in density monitoring and Integrated Pest Management program of the insect.

A speaker recognition system based on joint factor analysis (JFA) is proposed to improve whispering speakers’ recognition rate under channel mismatch. The system estimated separately the eigenvoice and the eigenchannel before calculating the corresponding speaker and the channel factors. Finally, a channel-free speaker model was built to describe accurately a speaker using model compensation. The test results from the whispered speech databases obtained under eight different channels showed that the correct recognition rate of a recognition system based on JFA was higher than that of the Gaussian Mixture Model-Universal Background Model. In particular, the recognition rate in cellphone channel tests increased significantly.

The active distribution network (ADN) represents the future development of distribution networks, whether the islanding phenomenon occurs or not determines the control strategy adopted by the ADN. The best wavelet packet has a better time-frequency characteristic than traditional wavelet analysis in the different signal processing, because it can extract better and more information from the signal effectively. Based on wavelet packet energy and the neural network, the islanding phenomenon of the ADN can be detected. Firstly, the wavelet packet is used to decompose current and voltage signals of the public coupling point between the distributed photovoltaic (PV) system and power grid, and calculate the energy value of each decomposed frequency band. Secondly, the network is trained using the constructed energy characteristic matrix as a neural network learning sample. At last, in order to achieve the function of identification for islanding detection, lots of samples are trained in the neural network. Based on the actual circumstance of PV operation in the ADN, the MATLAB/SIMULINK simulation model of the ADN is established. After the simulation, there are good output results, which show that the method has the characteristics of high identification accuracy and strong generalization ability.

Abstract This paper investigates the problem of adaptive robust simultaneous stabilization (ARSS) of two dissipative Hamiltonian systems (DHSs), and proposes a number of results on the controller parameterization design. Firstly, an adaptive H∞ control design approach is presented by using the dissipative Hamiltonian structural for the case that there are both external disturbances and parametric uncertainties in two DHSs. Secondly, an algorithm for solving tuning parameters of the controller is proposed using symbolic computation. The proposed controller parameterization method avoids solving Hamilton-Jacobi-Issacs (HJI) equations and the obtained controller is easier as compared to some existing ones. Finally, an illustrative example is presented to show that the ARSS controller obtained in this paper works very well.

A questionnaire survey was conducted in the residential quarters of Guangzhou, for which 582 elderly people over 60 years old were randomly recruited. The hearing impairment of the participants was evaluated using the Hearing Handicap Inventory for the Elderly (HHIE), The participants’ subjective responses to the acoustical environment of their living place and the impact of the living acoustical environment (LAE) on the participants were investigated. The results show that the participants with a low HHIE score and no hearing impairment evaluated their LAE more favourably, and they considered that the effect of the LAE on their daily life was weak. However, those with a high HHIE score and severe hearing impairment evaluated their LAE poorly, and considered its effect on their daily lives to be significant. For the elderly, the worse the hearing is, the higher their demand for a better LAE. Traffic, construction, residential quarters, and noise from next door or upstairs neighbours were the main noise sources in the elderly’s living places, and traffic noise, construction noise, and noise from next door and upstairs were the most influential sources. 28.9% of the respondents had trouble hearing what their family said in their living place. The elderly without hearing impairment considered that continuous noise was the main reason that they could not hear what their family said in their living place, while those with hearing impairment believed that their own hearing problem was a contributing factor.

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 accuracy and reliability of Kalman filter are easily affected by the gross errors in observations. Although robust Kalman filter based on equivalent weight function models can reduce the impact of gross errors on filtering results, the conventional equivalent weight function models are more suitable for the observations with the same noise level. For Precise Point Positioning (PPP) with multiple types of observations that have different measuring accuracy and noise levels, the filtering results obtained with conventional robust equivalent weight function models are not the best ones. For this problem, a classification robust equivalent weight function model based on the t-inspection statistics is proposed, which has better performance than the conventional equivalent weight function models in the case of no more than one gross error in a certain type of observations. However, in the case of multiple gross errors in a certain type of observations, the performance of the conventional robust Kalman filter based on the two kinds of equivalent weight function models are barely satisfactory due to the interaction between gross errors. To address this problem, an improved classification robust Kalman filtering method is further proposed in this paper. To verify and evaluate the performance of the proposed method, simulation tests were carried out based on the GPS/BDS data and their results were compared with those obtained with the conventional robust Kalman filtering method. The results show that the improved classification robust Kalman filtering method can effectively reduce the impact of multiple gross errors on the positioning results and significantly improve the positioning accuracy and reliability of PPP.

Since late 2011, porcine infections with highly virulent and antigenic variant of pseudorabies virus (PRV) cause great economic loss in the swine industry in China, and its emergence leads to variable protection efficacy of the commercially available PRV vaccine.

In the present study, the potential cross-protective efficacy of two live virus vaccines, includ- ing a commercial vaccine, and an attenuated low pathogenic PRV variant (rPRVTJ-delTK/gE/gI) against a PRV variant Tianjing (TJ) was evaluated in piglets. Vaccination of piglets with the live vaccine Bartha-K61 could not reduce the clinical signs, and was partially efficacious in the reduc- tion of viral loads upon PRV variant TJ challenge, indicating that this live vaccine provided limited cross-protection efficacy against the PRV variant infection. Additionally, rPRVTJ-delTK/gE/gI appeared to exert some beneficial efficiency in shortening the period of clinical fever and improv- ing the growth performance of the challenged pigs.

Our findings give a valuable guidance for the choice and use of PRV vaccines to control PRV variant infection in the field.

The microstructures and mechanical properties of T92 martensitic steel/Super304 austenitic steel weld joints with three welding consumables were investigated. Three types of welding materials ERNiCr-3, ERNiCrCoMo-1and T-304H were utilized to obtain dissimilar welds by using gas tungsten arc weld (GTAW). The results show that heat affect zone (HAZ) of T92 steel consists of coarse-grained and fine-grained tempered martensites. The microstructures of joints produced from ERNiCrCoMo-1 consist of equiaxed dendrite and columnar dendrite grains, which are more complicated than that of ERNiCr-3. In the tensile tests, joints constructed from ERNiCrCoMo-1 and T-304H met the ASME standard. The highest fracture energy was observed in specimens with the welding material ERNiCrCoMo-1. Ni content in weld seam of ERNiCrCoMo-1 was highest, which was above 40%. In conclusion, the nickel alloy ERNiCrCoMo-1 was the most suitable welding material for joints produced from T92 martensitic steel/Super304 austenitic steel.

Abstract In this paper, the method of periodically pinning intermittent control is introduced to solve the problem of outer synchronization between two complex networks. Based on the Lyapunov stability theory, differential inequality method and adaptive technique, some simple synchronous criteria have been derived analytically. At last, both the theoretical and numerical analysis illustrate the effectiveness of the proposed control methodology. This method not only reduces the conservatism of control gain but also saves the cost of production.These advantages make this method having a large application scope in the real production process.

This paper proposes a speech enhancement method using the multi-scales and multi-thresholds of the auditory perception wavelet transform, which is suitable for a low SNR (signal to noise ratio) environment. This method achieves the goal of noise reduction according to the threshold processing of the human ear's auditory masking effect on the auditory perception wavelet transform parameters of a speech signal. At the same time, in order to prevent high frequency loss during the process of noise suppression, we first make a voicing decision based on the speech signals. Afterwards, we process the unvoiced sound segment and the voiced sound segment according to the different thresholds and different judgments. Lastly, we perform objective and subjective tests on the enhanced speech. The results show that, compared to other spectral subtractions, our method keeps the components of unvoiced sound intact, while it suppresses the residual noise and the background noise. Thus, the enhanced speech has better clarity and intelligibility.