Search

A geographically weighted regression (GWR) model with fewer explanatory variables and higher prediction accuracy is required in spatial analysis and other practical applications. This article proposes an l 0 -norm variable adaptive selection method to enhance performances of a GWR by simultaneously performing model selection and coefficient optimization. Specifically, we formulate a regularized GWR model with an additional l 0 -norm constraint to shrink those unimportant regression coefficients toward zero and propose an adaptive variable selection algorithm by iteratively distinguishing the important variables from the variable set. At each location, the best variable subset and optimizing coefficient estimations are simultaneously achieved under the l 0 -GWR framework. Moreover, two novel criteria, the modified Bayesian information criterion and the interpretability of coefficient symbol, which specify the variable selection and model interpretation, respectively, are also introduced to improve the performance of the l 0 -GWR. Experiments on both simulated and actual data sets demonstrate that the proposed algorithm can significantly improve the estimation accuracy of coefficients and can also enhance the interpretative ability of the established model. [ABSTRACT FROM AUTHOR]

Aiming at the problems of short standby time and limited beam coverage in collaborative communication of UAVs, a collaborative beamforming method of UAV based on genetic algorithm is proposed. Based on the random antenna array theory, this method establishes the UAV collaborative beamforming system model, takes the minimum sidelobe level as the optimization goal, and obtains the optimal beam pattern by using genetic algorithm (GA) combined with the selection of optimal node position and optimal beamforming coefficient respectively. The performance comparison of beam pattern with phase error among nodes is analyzed. The simulation results show that the proposed method can effectively improve the beam gain in the target direction, can reduce the peak sidelobe level of the beam pattern, and improve the communication capacity. Among them, the genetic algorithm based on coefficient optimization can reduce the transmission power of each UAV node, save the energy consumption of UAV, and greatly increase the working time of UAV.

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the stages of poultry slaughter and processing, the individuals infected with Campylobacter would pollute the environment which, in turn, would pollute the uninfected individuals, leading to cross-contamination and increasing risks. We employed the Kolmogorov–Smirnov test, indirectly estimating the coefficients by searching the large-scale coefficient space with genetic algorithm, trying to make the model distribution fit the actual distribution as much as possible by this algorithm and further by gradient descent. Finally, we successfully obtained a set of optimal coefficients, quantitatively making clearer the nature of cross-contamination, and thereupon proposed corresponding suggestions for improving the slaughter and process operations.

The coefficient adaptation problem is often encountered in CFD simulations. The accuracy of simulation results depends much on the empirical coefficients of mathematical models. Cavitation simulation is a typical application of CFD. Researchers have proposed methods to optimize the empirical coefficients of the cavitation model. However, these methods can only acquire constant values which aren’t adaptive to all the operating conditions. This paper focused on the condensation and the evaporation coefficients of the Zwart model and considered quasi-steady cavitating flows around a 2-D NACA66(MOD) hydrofoil. For the first time, we gave a formal description of the coefficient adaptation problem, and put forward a method to model the relationship between the best coefficient values and the operating conditions. We designed and implemented the coefficient adaptation platform combining OpenFOAM, and validated the best coefficient values predicted by our method. The overall results show the predicted coefficient values result in an increase of accuracy by 12% in average, compared with the default values and the tuned values by Morgut, thus indicating our method can effectively solve the coefficient adaptation problem for the Zwart model. We believe the proposed method can be extended to other mathematical models in practical uses.

The coefficient adaptation problem is often encountered in CFD simulations. The accuracy of simulation results depends much on the empirical coefficients of mathematical models. Cavitation simulation is a typical application of CFD. Researchers have proposed methods to optimize the empirical coefficients of the cavitation model. However, these methods can only acquire constant values which aren't adaptive to all the operating conditions. This paper focused on the condensation and the evaporation coefficients of the Zwart model and considered quasi-steady cavitating flows around a 2-D NACA66(MOD) hydrofoil. For the first time, we gave a formal description of the coefficient adaptation problem, and put forward a method to model the relationship between the best coefficient values and the operating conditions. We designed and implemented the coefficient adaptation platform combining OpenFOAM, and validated the best coefficient values predicted by our method. The overall results show the predicted coefficient values result in an increase of accuracy by 12% in average, compared with the default values and the tuned values by Morgut, thus indicating our method can effectively solve the coefficient adaptation problem for the Zwart model. We believe the proposed method can be extended to other mathematical models in practical uses. [ABSTRACT FROM AUTHOR]

In this paper, we present a hardware efficient finite impulse response (FIR) filter design using differential evolution (DE) and common sub expression (CSE) elimination algorithm. With the DE algorithm, we first found a set of filter coefficients with reduced number of signed-power-of-two (SPT) terms without compromising on quality of the filter response. After obtaining coefficients, we applied CSE elimination algorithm, and determined the hardware cost in terms of adders. The filters were designed using DE for various word lengths, and the same were implemented in transposed direct form (TDF) structure. The implemented filters were synthesized in Cadence RTL compiler using UMC 90 nm technology. We compared the performances of our filters with recently best published works in terms of area, delay, power and power-delay-product ( PDP ). One of the proposed filters found to improve a PDP gain of 29% compared to Remez algorithm. The proposed approach showed improvements in filter design for the given specifications. [ABSTRACT FROM AUTHOR]

Continuous monitoring of stroke volume (SV) or cardiac output (CO) has long been the subject of numerous studies. The majority of existing methods are calibration-dependent, requiring invasive measurements of CO to initialize the estimation algorithms, thus limiting their application to the clinical setting. In the present study, a new calibration-free method aimed at home-based use has been developed, which allows noninvasive estimation of SV from oscillometric signals measured at the wrist. The estimation equation was constructed based on the PRAM method, with significant modifications to incorporate more patient-specific information. Furthermore, the estimation equation was optimized based on the clinical data acquired from 96 patients (the 'Training' group) to obtain the best comparison of estimated SV with echocardiographic SV. The resulting estimation equation was then applied directly to another patient group (the 'Testing' group) to examine its validity. Obtained results demonstrate that our estimations correlated closely with the measurements in both patient groups. In addition to being noninvasive and calibration-free, the proposed method can be fully automated, which may be valuable for the future development of home-based cardiac monitoring systems. [ABSTRACT FROM AUTHOR]

This paper presents a multiplier power reduction technique for low-power DSP applications through utilization of coefficient optimization. The optimization is implementation dependent in that the multipliers are assumed to be designed in either ASIC or full-custom architectures for general purpose multiplication. The paper first describes a model characterizing the power consumption of the multiplier. Then the coefficient optimized made based on this model. This methodology is applicable to multiplications requiring a large set of coefficients and random data sets. We can accurately estimate the actual power dissipation of the multipliers using the characterization technique. The coefficient optimization based on the power model can save as much as 34.02%. [ABSTRACT FROM AUTHOR]

This paper sets out the analytic solution for the calculation of exact derivatives in linear rational expectations models with reference to the optimal simple rule problem. We argue that there are substantial computational advantages of using analytic derivatives and compare the likely computational costs of using approximate and exact derivatives when calculating optimal coefficients for simple feedback rules. A specific algorithm for finite time optimization is also outlined, which will reduce the computational time required and is simple to implement. We discuss modifications to allow for stochastic models. [ABSTRACT FROM AUTHOR]

Digital Signal Processing (DSP) often involves multiplications with a fixed set of coefficients. This paper presents a novel multiplier design methodology for performing these coefficient multiplications with very low power dissipation. Given bounds on the throughput and the quantization error of the computation, our approach scales the original coefficients to enable the partitioning of each multiplication into a collection of smaller multiplications with shorter critical paths. Significant energy savings are achieved by performing these multiplications in parallel with a scaled supply voltage. Dissipation is further reduced when conventional array multiplier is modified disabling the multiplier rows that do not affect the multiplication's outcome. We have used our methodology to design low-power parallel array multipliers for the Fast Fourier Transform (FFT). Simulation results show that our approach can result in significant up to 76% power savings over conventional array multipliers on 64-coefficient FFT computation. [ABSTRACT FROM AUTHOR]

Today the main optimization parameter of digital filters is the filter order. By the aid of two implemented filters we will show that both power and speed can be enhanced if the optimization effort is made on reducing the filter coefficient lengths rather than minimizing the order. Both filters have been designed from the same specification, one as a standard minimum order filter, the other as a filter with short coefficients found by a computer search. The minimum order filter is of order three with seven bits long coefficients. The coefficient optimized filter is of order six with two bits long coefficients. Both filters were implemented with bit-serial fixed coefficient arithmetic in two's complement representation in a 0.8µ, two metal layers CMOS process. Measurements show an eightfold speedup at half the power consumption and only 30% area cost for the coefficient optimized filter. [ABSTRACT FROM AUTHOR]

Continuous monitoring of stroke volume (SV) or cardiac output (CO) has long been the subject of numerous studies. The majority of existing methods are calibration-dependent, requiring invasive measurements of CO to initialize the estimation algorithms, thus limiting their application to the clinical setting. In the present study, a new calibration-free method aimed at home-based use has been developed, which allows noninvasive estimation of SV from oscillometric signals measured at the wrist. The estimation equation was constructed based on the PRAM method, with significant modifications to incorporate more patient-specific information. Furthermore, the estimation equation was optimized based on the clinical data acquired from 96 patients (the 'Training' group) to obtain the best comparison of estimated SV with echocardiographic SV. The resulting estimation equation was then applied directly to another patient group (the 'Testing' group) to examine its validity. Obtained results demonstrate that our estimations correlated closely with the measurements in both patient groups. In addition to being noninvasive and calibration-free, the proposed method can be fully automated, which may be valuable for the future development of home-based cardiac monitoring systems.