Forward Propagation Method Research Articles

Chromatic correction for super multi-view 3D display based on diffraction theory.

The traditional analysis method for super multi-view 3D display based on geometric optics, which approximates the lenticular lenses as a series of pinhole structures, ignored the chromatic aberration. In this paper, the optimization method based on diffraction theory is proposed for super multi-view 3D display, where the wavefronts are evaluated accurately by the forward propagation method, and the chromatic aberration of the synthetic viewpoint image is reduced dramatically by the backward reconstruction optimization method (BROM). The optical experiment is performed to verify the feasibility of the method, which is consistent with numerical simulation results. It is proved that the proposed method simulates the physical propagation process of super multi-view 3D display and improves the reconstructed image quality. In the future, it can be used to achieve the super multi-view 3D light field technology with low crosstalk.

Kinematic calibration of over-constrained robot with geometric error and internal deformation

In the over-constrained robot with geometric errors, internal forces would occur and lead to deformation due to redundant constraints and common constraints. In current kinematic calibration approaches, since the deformation, geometric errors and pose errors cannot be described in a unified framework, the error propagation fails to be solved intuitively and generically. In this article, a general calibration framework of over-constrained robots is proposed to unify geometric errors and internal deformation through the least action principle. In the framework, considering the interaction between errors and deformation, the difference map between geometric errors and pose errors is explicitly and analytically revealed. The mapping matrix can degenerate to be consistent with the previous research under non-over-constrained robots. In addition, the error forward propagation method and the error identification algorithm are proposed to solve the error nonlinear propagation and the ill-posed identification equations, respectively. Finally, the calibration simulation and experiment results of a 5-DOF over-constrained hybrid robot show that the proposed method can accurately predict the pose of the end-effector under geometric errors robustly and effectively.

Wind Turbine Prediction using Deep Learning and Long Short Term Memory (LSTM)

Accurate forecasting is essential for the long-term success of adding wind energy to the national power system. In this study, we look at forecasting wind turbine using a LSTM deep learning model. To forecast potential outcomes for a time series, it is sufficient to initially obtain pertinent details from past data. While many methods struggle with understanding the long-term dependencies encoded in data sets, LSTM options, an instance of the strategy in deep learning, show potential for efficiently overcoming this challenge. An overview of LSTM's architecture and forward propagation method is provided initially. LSTM network is applied to the wind turbine prediction dataset. This dataset has 9 features and 6575 records. There are four performance matrices used to test the model. The four matrices are mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). MAPE obtained the least error.

A Novel Forward-Propagation Workflow Assessment Method for Malicious Packet Detection.

In recent times, there has been a huge upsurge in malicious attacks despite sophisticated technologies in digital network data transmission. This research proposes an innovative method that utilizes the forward-propagation workflow of the convolutional neural network (CNN) algorithm to detect malicious information effectively. The performance comparison of this approach was accomplished using accuracy, precision, false-positive and false-negative rates with k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. To detect malicious packets in the original dataset, an experiment was carried out using CNN’s forward-propagation workflow method (N = 11) as well as the KNN and the SVM machine learning algorithms with a significant value of 0.005. The accuracy, precision, false-positive and false-negative rates were evaluated to detect malicious packets present in normal data packets. The mean performance measures of the proposed forward-propagation method of the CNN algorithm were evaluated using the Statistical Package for the Social Sciences (SPSS) tool. The results showed that the mean accuracy (98.84%) and mean precision (99.08%) of the proposed forward propagation of the CNN algorithm appeared to be higher than the mean accuracy (95.55%) and mean precision (95.97%) of the KNN algorithm, as well as the mean accuracy (94.43%) and mean precision (94.58%) of the SVM algorithm. Moreover, the false-positive rate (1.93%) and false-negative rate (3.49%) of the proposed method appeared to be significantly higher than the KNN algorithm’s false-positive (4.04%) and false-negative (6.24%) as well as the SVM algorithm’s false-positive (5.03%) and false-negative rate (7.21%). Hence, it can be concluded that the forward-propagation method of the CNN algorithm is better than the KNN and SVM algorithms at detecting malicious information.

Fast Construction of Dual-Field Superfocusing Based on Forward-Propagation Design

A three-port plasmonic array is proposed to achieve a dual-field superfocusing using the forward-propagation method based on spatial Fourier transform. The algorithm of fast Fourier transform is used to facilitate the design process. Both the electric and magnetic fields can be superfocused on the focal plane of z = 0.1λ (λ is the free-space wavelength) based on an effect of radiationless electromagnetic interference. The full widths of the half-maximum of both two fields are 0.060λ and 0.072λ, respectively. Besides, the metrics of the dual-field superfocusing are 1.67 and 1.39. The proposed theory can simplify the design process of superfocusing, and the plasmonic array with brief three ports can sufficiently facilitate the construction of the desired source. This letter can be beneficial for the development of beam steering, sophisticated holography, super-resolution imaging, and other applications of near- or far-field devices.

Electricity consumption prediction based on LSTM with attention mechanism

Power data analysis in power system, such as electricity consumption prediction, has always been the basis for the power department to adjust electricity price, substation regulation, total load prediction and peak avoidance management. In this paper, a short‐term time‐phased electricity consumption prediction model based on Long Short‐Term Memory (LSTM) with an attention mechanism is proposed. First, the attention mechanism is used to assign weight coefficients to the input sequence data. Then, the output value of every cell of LSTM is calculated according to the forward propagation method, and the error between the real value and the predicted value is calculated using the back‐propagation method. The gradient of each weight is calculated according to the corresponding error term, and the weight of the model is updated by the gradient descent direction to make the error smaller. Using modeling and predicting experiments on different types of electricity consumption, the results show that the prediction accuracy of the model proposed increased by 6.5% compared to the state‐of‐the‐art model. The model has a good effect on electricity consumption prediction. Not only can it be close to actual results numerically, but it can also better predict the development trend of data. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

마이크로폰 어레이를 이용한 회전하는 소음원 가시화에 관한 연구

Acoustic analysis of a moving sound source required that the measured sound signals be do-Dopplerized and restored as of the original emission signals. The purpose of this research is development of beamforming technique can be applied to the rotor noise source identification. For the do-Dopplerization and reconstruction of emitted sound wave, Forward Propagation Method is applied to the time domain beamforming technique. And validation test were performed using rotating sound source constructed by bended pipe and horn driver. In the validation test using sinusoidal sound wave, sufficient performance of signal processing can be seen, and the effect of measuring duration for accuracy was compared. In the prop-rotor measurements, the acoustic source locations were successfully verified in varying positions for different frequencies and collective pitch angle, in hover condition.

빔형성방법을 이용한 회전하는 음원의 위치 판별에 관한 연구

The positions of rotating sound sources have been localized by experiments with the Doppler effects removed. In order to de-Dopplerize the sound signals emitted from moving sources, two kinds of signal reconstruction methods were applied. One is the forward propagation method and the other is the backward propagation method. Forward propagation method analyze the source emission time based on the instantaneous distance between sensors and the assumed source position, then the signals are reconstructed with respect to the emission time. On the other hand, the backward method uses time delay to do-Dopplerize the acquired data for the received time of reference. In both techniques. the reconstructed signal data were processed using beamforming algorithm to produce power distributions at the frequencies of interest. Experiments have been carried out for varying frequencies, rotating speeds and the object distances. It is shown that the forward propagation method gives better performance in locating source position than the backward propagation method.

Rapid geoacoustic inversion with a curved horizontal array

Real-time geoacoustic inversions with a towed array are of interest for rapidly characterizing the sediment properties over changing regions, and require an efficient and accurate forward propagation model. A wavenumber integration solution using the computationally efficient method of undetermined coefficients and the Fast-Field Program is implemented with a simulated annealing optimization method and a coordinate rotation technique. The source function of interest may be broadband ship self-noise or a controlled source. The parameters sought in the inversion include, but are not limited to, geoacoustic sediment properties and receiver array geometry; the water column is assumed known. The forward propagation method is efficient and accurate for very short ranges. Results are presented of simulations with a controlled source, as well as preliminary MAPEX2000 ship self-noise. [Work supported by ONR.]

A fuzzy inference model based on an uncertainty forward propagation approach

The management of uncertainty and imprecision is becoming more and more important in knowledge-based systems. Fuzzy logic provides a systematic basis for representing and inferring with this kind of knowledge. This paper describes an approach for fuzzy inference based on an uncertainty forward propagation method and a change in the granularity of the elements involved. The proposed model is able to handle very general kinds of facts and rules, and it also verifies the most usual properties required by a fuzzy inference model.

Diamagnetic Rydberg atoms and negative-energy Coulomb functions.

The complicated quadratic Zeeman spectrum in a laboratory magnetic field is analyzed with the aid of accurate Coulomb functions. Different Rydberg manifolds are masked by the generalized Gailitis average based on the multichannel quantum-defect theory. Fundamental resonances are thereby accentuated. An efficient and reliable numerical procedure is developed for evaluating the negative-energy Coulomb functions and their derivatives. The method is based on the three-term recursions and employs Steed's forward-propagation method [A. R. Barnett et al., Comput. Phys. Commun. 8, 377 (1974)] as well as the backward-propagation method of Stegun and Abramowitz [Phys. Rev. 98, 1851 (1955)].