Search

In recent years, the rapid development of mobile communication technology has led to the development of positioning services, and the demand for indoor positioning technology has been increasing strictly. In the Internet information era, location-based services (LBS) have been applied in various fields after the emergence of location-based information technology, which brings new experience and convenience to people. In order to improve the indoor positioning accuracy of wireless sensor networks, an indoor positioning algorithm with a rotating arc triangle layout is proposed. This layout uses a circular arc triangle to deploy the beacon nodes and changes the position of the beacon nodes by rotating. Compared with the square, triangle, and equi-arc triangle layout, the rotating arc triangle layout reduces beacon nodes and is easy to rotate, but the layout after rotation has not changed. Experiments show that the layout of the rotating arc triangle layout is simple. By rotating the beacon node, multiple positioning prediction positions are superposed, and the positioning accuracy is improved. The rotating arc triangle layout is better than the square, the traditional triangle, the improved triangle, and the equal arc triangle. The average positioning accuracy of the arc triangle has been increased by 69.9%, 51.8%, 16.6%, 35.6%, and 32.9% respectively, which has certain use value and popularization.

Laser nonlinear wave modulation spectroscopy (LNWMS) has been used to detect nonlinear ultrasonic signatures caused by fatigue cracks in materials. The nonlinear feature extraction from the spectral plot plays a significant role in LNWMS. This paper uses the bispectrum to distinguish harmonically related signals which result from nonlinear interactions and to remove those which do not. A feature called bispectrum damage factor (BDF), which is defined as the ratio of the bispectrum peak difference value of the damage and intact signals to the bispectrum peak value of intact signals at frequencies F1, F2, is extracted from the bispectral plot to characterize the crack features. The basic premise of the proposed laser nonlinear wave modulation bispectrum (LNWMBS) is that this BDF value will vary regularly as the level of crack-induced nonlinearity increases. A finite element model and a noncontact laser ultrasonic system have been built for LNWMBS measurement. The simulation results show that the BDF value is sensitive to crack depth and angle, and is insensitive to crack length and detecting position. BDF values of reflected Rayleigh waves are positive and approximately linearly increase with an increase of the crack depths in the range of 0.1 λ ∼ 0.6 λ (λ is the corresponding wavelength of adopted frequency); meanwhile, the values of the transmitted waves are negative and decreasing. The BDF values are larger than the crack angle at 90° when they are less than 90°, which is beneficial for crack detection. The proposed LNWMBS technique has been successfully tested for detecting artificial sub-millimeter surface-breaking cracks in aluminum alloy plates and closed fatigue cracks of torque load simulator connection blocks. [ABSTRACT FROM AUTHOR]

In this present contribution, thermal conductivity ofethene (TCE) above the critical temperature has been studied. The present data cover the temperature range from 283.46 to 425.00 K and the pressure range from 0.1 to 100 MPa. In the present investigation, various network-based strategies, named as artificial neural network (ANN) optimized with two evolutionary algorithms, including genetic algorithm (GA) and differential evolution (DE), were developed for assessing tTCE in supercritical region. The most comprehensive source of data, including around 256 experimental points, was utilized for ANN modeling. Data index plot, scatter plot, relative deviation diagram and root mean square error (RMSE), and coefficient of determination (R2) as the statistical parameters were used in this examination to evaluate the comprehensiveness of the developed ANN model. Results indicate that the GA-ANN is more accurate than DE-ANN to predict TCE in supercritical region. Also, among optimization algorithms, GA has the largest ability for optimizing the ANN network modeling with the RMSE of 4.2966 and determination coefficient (R2) of 0.9640. [ABSTRACT FROM AUTHOR]

Online recognition of glass defects plays an important role in improving the quality of glass products. This paper presents a conjugate gradient neural network-based method, which combines phase map and the defective image for recognition of glass defects. The boundary coordinates of the connected defect region are calculated and used to extract the defect region in the defective image correspondingly. The piecewise linear gray-level transformation is designed to reduce the noise and to enhance the signal-to-noise ratio of the defective image. The second iteration segmentation based on gray range is applied to calculate the low and high thresholds, and the ternary-valued defective image is acquired. The seven features calculated by Hu invariant moment and four features extracted from the ternary-valued defective image are used as inputs of the conjugate gradient neural network to recognize the defect type. Experimental results show that the accuracy of the recognition reaches up to 93 %. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]