Improvement in Ridge Coefficient Optimization Criterion for Ridge Estimation-Based Dynamic System Response Curve Method in Flood Forecasting

The ridge estimation-based dynamic system response curve (DSRC-R) method, which is an improvement of the dynamic system response curve (DSRC) method via the ridge estimation method, has illustrated its good robustness. However, the optimization criterion for the ridge coefficient in the DSRC-R method still needs further study. In view of this, a new optimization criterion called the balance and random degree criterion considering the sum of squares of flow errors (BSR) is proposed in this paper according to the properties of model-simulated residuals. In this criterion, two indexes, namely, the random degree of simulated residuals and the balance degree of simulated residuals, are introduced to describe the independence and the zero mean property of simulated residuals, respectively. Therefore, the BSR criterion is constructed by combining the sum of squares of flow errors with the two indexes. The BSR criterion, L-curve criterion and the minimum sum of squares of flow errors (MSSFE) criterion are tested on both synthetic cases and real-data cases. The results show that the BSR criterion is better than the L-curve criterion in minimizing the sum of squares of flow residuals and increasing the ridge coefficient optimization speed. Moreover, the BSR criterion has an advantage over the MSSFE criterion in making the estimated rainfall error more stable.

A Miniaturized Transmitting LPDA Design for 2 MHz–30 MHz Uses

A miniaturized horizontal polarized high frequency transmitting LPDA is presented. In use of the dipole transformation and antenna coefficient optimization methods, a 65% reduction in the size was achieved with the electrical performance kept in a competitive level. Full-wave simulation results showed a stable directional pattern and lower VSWR over the impedance bandwidth of 2 to 30 MHz. The gain bandwidth can reach the range of 4–30 MHz, meanwhile, there is only minor degradation on gain in frequencies under 4 MHz.

Dual channel speech enhancement using particle swarm optimization

Adaptive processing for canceling noise is a powerful technology for signal processing that can completely remove background noise. In general, various adaptive filter algorithms are used, many of which can lack the stability to handle the convergence rate, the number of filter coefficient variations, and error accuracy within tolerances. Unlike traditional methods, to accomplish these desirable characteristics as well as to efficiently cancel noise, in this paper, the cancelation of noise is formulated as a problem of coefficient optimization, where the particle swarm optimization (PSO) is employed. The PSO is structured to minimize the error by using a very short segment of the corrupted speech. In contrast to the recent and conventional adaptive noise cancellation methods, the simulation results indicate that the proposed algorithm has better capability of noise cancelation. The results show great improvement in signal to noise ratio (SNR) of 96.07 dB and 124.54 dB for finite impulse response (FIR) and infinite impulse response (IIR) adaptive filters respectively.

Reducing error accumulation of optimized finite-difference scheme using the minimum norm

The finite-difference (FD) scheme is popular in the field of seismic exploration for numerical simulation of wave propagation; however, its accuracy and computational efficiency are restricted by the numerical dispersion caused by numerical discretization of spatial partial derivatives using coarse grids. The constant-coefficient optimization method is used widely for suppressing the numerical dispersion by tuning the FD weights. Although gaining a wider effective bandwidth under a given error tolerance, this method undoubtedly encounters larger errors at low wavenumbers and accumulates significant errors. We have developed an approach to reduce the error accumulation. First, we construct an objective function based on the [Formula: see text] norm, which can constrain the total error better than the [Formula: see text] and [Formula: see text] norms. Second, we translated our objective function into a constrained [Formula: see text]-norm minimization model, which can be solved by the alternating direction method of multipliers. Finally, we perform theoretical analyses and numerical experiments to illustrate the accuracy improvement. The proposed method is shown to be superior to the existing constant-coefficient optimization methods at the low-wavenumber region; thus, we can obtain higher accuracy with less error accumulation, particularly at longer simulation times. The widely used objective functions, defined by the [Formula: see text] and [Formula: see text] norms, could handle a relatively wider range of accurate wavenumbers, compared with our objective function defined by the [Formula: see text] norm, but their actual errors would be much larger than the given error tolerance at some azimuths rather than axis directions (e.g., about twice at 45°), which greatly degrade the overall numerical accuracy. In contrast, our scheme can obtain a relatively even 2D error distribution at various azimuths, with an apparently smaller error. The peak error of the proposed method is only 40%–65% that of the [Formula: see text] norm under the same error tolerance, or only 60%–80% that of the [Formula: see text] norm under the same effective bandwidth.