This paper presents the research studies carried out on the application of lattice Boltzmann method (LBM) to computational aeroacoustics (CAA). The Navier-Stokes equation-based solver faces the difficulty of computational efficiency when it has to satisfy the high-order of accuracy and spectral resolution. LBM shows its capabilities in direct and indirect noise computations with superior space-time resolution. The combination of LBM with turbulence models also work very well for practical engineering machinery noise. The hybrid LBM decouples the discretization of physical space from the discretization of moment space, resulting in flexible mesh and adjustable time-marching. Moreover, new solving strategies and acoustic models are developed to further promote the application of LBM to CAA.
A spinal code is the type of rateless code, which has been proved to be capacity- achieving over both a binary symmetric channel (BSC) and an additive white Gaussian noise (AWGN) channel. Rateless spinal codes employ a hash function as a coding kernel to generate infinite pseudo-random symbols. A good hash function can improve the perfor- mance of spinal codes. In this paper, a lightweight hash function based on sponge structure is designed. A permutation function of registers is a nonlinear function. Feedback shift registers are used to improve randomness and reduce bit error rate (BER). At the same time, a pseudo-random number generator adopts a layered and piecewise combination mode, which further encrypts signals via the layered structure, reduces the correlation between input and output values, and generates the piecewise random numbers to compensate the shortcoming of the mixed linear congruence output with fixed length. Simulation results show that the designed spinal code with the lightweight hash function outperforms the original spinal code in aspects of the BER, encoding time and randomness.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
Osteocalcin is a major non-collagenous component of the bone extracellular matrix and is considered to be an indicative factor of osteoblast differentiation. In the present study, we detected osteocalcin expression in different antler areas and growth phases by immunohisto- chemistry. Osteocalcin was highly expressed in all areas during the mineralization period and in mesenchymal cell and chondrocyte areas during the rapid growth period. The nucleotide sequence of the osteocalcin gene in sika deer antler was determined. The open reading frame was 303 bp encoding a protein of 100 amino acids. The estimated molecular mass of osteocalcin was 10.38 kDa and the theoretical isoelectric point was 5.37. The osteocalcin gene with a 6× His-tag at the C-terminus was cloned into the pGEX-4T1 vector and expressed in Escherichia coli under optimal conditions. The recombinant soluble protein fused with GST was purified with Ni-NTA resin. The purified osteocalcin protein exhibited a significant increase in HA adhesion and promoted antler chondrocyte proliferation. Osteocalcin is an important factor in regulating the rapid growth and differentiation of deer antlers.
Overseas mining investment generally faces considerable risk due to a variety of complex risk factors. Therefore, indexes are often based on conditions of uncertainty and cannot be fully quantified. Guided by set pair analysis (SPA) theory, this study constructs a risk evaluation index system based on an analysis of the risk factors of overseas mining investment and determines the weights of factors using entropy weighting methods. In addition, this study constructs an identity-discrepancycontrary risk assessment model based on the 5-element connection number. Both the certainty and uncertainty of the various risks are treated uniformly in this model and it is possible to mathematically describe and quantitatively express complex system decisions to evaluate projects. Overseas mining investment risk and its changing trends are synthetically evaluated by calculating the adjacent connection number and analyzing the set pair potential. Using an actual overseas mining investment project as an example, the risk of overseas mining investment can be separated into five categories according to the risk field, and then the evaluation model is quantified and specific risk assessment results are obtained. Compared to the field investigation, the practicability and effectiveness of the evaluation method are illustrated. This new model combines static and dynamic factors and qualitative and quantitative information, which improves the reliability and accuracy of risk evaluation. Furthermore, this evaluation method can also be applied to other similar evaluations and has a certain scalability.
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.
High voltage direct current (HVDC) emergency control can significantly improve the transient stability of an AC/DC interconnected power grid, and is an important measure to reduce the amount of generator and load shedding when the system fails. For the AC/DC interconnected power grid, according to the location of failure, disturbances can be classified into two categories: 1) interconnected system tie-line faults, which will cause the power unbalance at both ends of the AC system, as a result of the generator rotor acceleration at the sending-end grid and the generator rotor deceleration at the receiving-end grid; 2) AC system internal faults, due to the isolation effect of the DC system, only the rotor of the generator in the disturbed area changes, which has little impact on the other end of the grid. In view of the above two different locations of disturbance, auxiliary power and frequency combination control as well as a switch strategy, are proposed in this paper. A four-machine two-area transmission system and a multi-machine AC/DC parallel transmission system were built on the PSCAD platform. The simulation results verify the effectiveness of the proposed control strategy.
Ladle plays an important role in the metallurgical industry whose maintenance directly affects the production efficiency of enterprises. In view of the problems such as low maintenance efficiency and untimely maintenance in the current ladle passive maintenance scheme, the life prediction mechanism for ladle composite structures is established which bases on the stress analysis of steel shell and ladle lining in the production process, combining conventional fatigue analysis and extended fracture theory. The mechanism is accurate and effective according to the simulation results. Through which, the useful life of steel shell can be accurately predicted by detecting the crack length of it. Due to the large number of factors affecting the life of the lining of the ladle, it is difficult to accurately predict the life of the ladle lining, so a forecasting mean based on the thermal shock method is proposed to predict the service life of the ladle lining in this paper. The life prediction mechanism can provide data support and theoretical guidance for the active maintenance of the ladle, which is the prerequisite for scientifically formulating ladle initiative maintenance program.
7N01-T4 aluminum alloy was welded by metal inert gas welding and the influence of V-groove angle on joint fatigue properties was investigated. The results indicate that the volume of fusion zone (FZ) and the grains in FZ become small when the groove angle decreases to 50° from 70°. Most pores distribute at the FZ edge and fewer pores are formed in the small angle joint. The fatigue crack mainly initiates at the transition region between the weld passes due to the pore concentration. The small angle contributes to increasing joint fatigue properties, especially at the low stress level. The fatigue strength of 50° joint is 103.06 MPa which is 15.3% higher than that of 70°joint.
The pharmacokinetics of a diclofenac sodium was investigated in swine. A single intravenous (i.v.) or intramuscular (i.m.) injection of 5% diclofenac sodium (concentration = 2.5 mg · kg-1) was administered to 8 healthy pigs according to a two-period crossover design. The pharmacokinetic parameters were calculated by non-compartmental analysis with DAS2.1.1 software. After a single i.v. administration, the main pharmacokinetic parameters of diclofenac sodium injection in swine were as follows: the elimination half-time (T1/2β) was 1.32±0.34 h; the area under the curve (AUC) was (55.50±5.50 μg · mL-1 h; the mean residence time (MRT) was 1.60±0.28 h; the apparent volume of distribution (Vd) was 0.50±0.05 L · kg-1; and the body clearance (CLB) was 0.26±0.04 L · (h · kg)-1. After the single i.m. administration, the pharmacokinetic parameters were as follows: peak time (Tmax) was 1.19±0.26 h; and peak concentration (Cmax) was 11.61±5.99 μg mL-1. The diclofenac sodium has the following pharmacokinetic characteristics in swine: rapid absorption and elimination; high peak concentration; and bioavailability.
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 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.
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.
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.
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.
The welfare and healthy growth of poultry under intensive feeding conditions are closely related to their living environment. In spring, the air quality considerably decreases due to reduced ventilation and aeration in cage systems, which influences the meat quality and health of broilers during normal growth stages. In this study, we analyzed the airborne bacterial communities in PM2.5 and PM10 in cage broiler houses at different broiler growth stages under intensive rearing conditions based on the high-throughput 16S rDNA sequencing technique. Our results revealed that PM2.5, PM10 and airborne microbes gradually increased during the broiler growth cycle in poultry houses. Some potential or opportunistic pathogens, including Acinetobacter, Pseudomonas, Enterococcus, Microbacterium, etc., were found in the broiler houses at different growth stages. Our study evaluated variations in the microbial communities in PM2.5 and PM10 and potential opportunistic pathogens during the growth cycle of broilers in poultry houses in the spring. Our findings may provide a basis for developing technologies for air quality control in caged poultry houses.
This paper researches the application of grey system theory in cost forecasting of the coal mine. The grey model (GM(1.1)) is widely used in forecasting in business and industrial systems with advantages of minimal data, a short time and little fluctuation. Also, the model fits exponentially with increasing data more precisely than other prediction techniques. However, the traditional GM(1.1) model suffers from the poor anti-interference ability. Aimed at the flaws of the conventional GM(1.1) model, this paper proposes a novel dynamic forecasting model with the theory of background value optimization and Fourier-series residual error correction based on the traditional GM(1.1) model. The new model applies the golden segmentation optimization method to optimize the background value and Fourier-series theory to extract periodic information in the grey forecasting model for correcting the residual error. In the proposed dynamic model, the newest data is gradually added while the oldest is removed from the original data sequence. To test the new model’s forecasting performance, it was applied to the prediction of unit costs in coal mining, and the results show that the prediction accuracy is improved compared with other grey forecasting models. The new model gives a MAPE & C value of 0.14% and 0.02, respectively, compared to 1.75% and 0.37 respectively for the traditional GM(1.1) model. Thus, the new GM(1.1) model proposed in this paper, with advantages of practical application and high accuracy, provides a new method for cost forecasting in coal mining, and then help decision makers to make more scientific decisions for the mining operation.
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.
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.
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.
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.
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.