Small Amount Of Measurement Data Research Articles

An Improved Laplace Satellite Tracking Method Based on the Kalman Filter

When photoelectric measuring equipment is used to track satellites, the extraction of the short-term or long-term target often fails because the target is weak, clouds block the target, and/or the sun’s angle is too small, resulting in the loss of the tracking target. In this study, an improved Laplacian satellite tracking method based on the Kalman filter is proposed. Firstly, the improved Laplacian algorithm was used for the initial fitting of the equation of motion of a small amount of measurement data. Judgment of the validity and Kalman filtering was carried out on the current frame’s measurement data to calculate the optimal estimate of the current frame’s orbit data, and the accurate equation of motion was iteratively fitted to obtain high-precision data for predicting the satellite’s orbit frame by frame. Numerical tracking of the equipment was carried out. This method was experimentally validated on an actual optical measurement device. The test results showed that this method can make up for the frequent loss of short-term targets. Under the condition that the maximum deviation is less than 3”, the length of extrapolated data can be up to 30 s and the length of the measurement data was less than 30 s. This method may improve the stability of tracking equipment as well as the accuracy and integrity of the measurement data.

Endmember extraction and abundance estimation algorithm based on double-compressed sampling

Based on double-compressed sampling, a hyperspectral spectral unmixing algorithm (SU_DCS) is proposed, which could directly complete the endmember extraction and abundance estimation. On the basis of the linear mixed model (LMM), we designed spatial and spectral sampling matrices, obtained spatial and spectral measurement data, and constructed a joint unmixing model containing endmember and abundance information. By using operator separation and Lagrangian multiplier algorithm, the endmember matrix, abundance matrix and remixing image can be quickly obtained by matrix operation. The parameters of the unmixing algorithm, including regularization parameter, convergence threshold and spatial sampling rate, are determined using synthetic simulated hyperspectral data. The proposed algorithm is applied to two kinds of real hyperspectral data, with or without ground truth, in order to verify the effectiveness and reliability of the algorithm. Firstly, we provide the performance of the algorithm on real datasets without ground truth. Compared with algorithm VCA_FCLS and algorithm CPPCA_VCA_FCLS, the endmember spectral curve extracted by the proposed SU_DCS is almost consistent with that obtained by VCA_FCLS, and is more smooth than that of obtained by CPPCA_VCA_FCLS. Additionally, the abundance estimation map estimated by the SU_DCS has consistency with the results obtained by VCA_FCLS. Moreover, the proposed SU_DCS has higher peak signal-to-noise ratio (PSNR) for remixing images with higher computational efficiency. Secondly, we provide the performance of the proposed algorithm on four real datasets with ground truth, including dataset Cuprite, dataset Samson, dataset Jasper and dataset Urban. We provide the results of endmember extraction and abundance estimation from the compressed data under different sampling rate conditions. The extracted endmember maintains good consistency with the true spectral curves, and the estimated abundance map can also maintain good spatial consistency with the ground truth. The comparison results with other four comparative algorithms also indicate that the proposed algorithm can obtain relatively accurate endmembers and abundance information from compressed data, the reliability and validity of the proposed algorithm have been proved. In summary, the main innovation of the proposed algorithm is that it can extract endmembers and estimate abundance with high accuracy from a small amount of measurement data.

WaveNets: physics-informed neural networks for full-field recovery of rotational flow beneath large-amplitude periodic water waves

AbstractWe formulate physics-informed neural networks (PINNs) for full-field reconstruction of rotational flow beneath nonlinear periodic water waves using a small amount of measurement data, coined WaveNets. The WaveNets have two NNs to, respectively, predict the water surface, and velocity/pressure fields. The Euler equation and other prior knowledge of the wave problem are included in WaveNets loss function. We also propose a novel method to dynamically update the sampling points in residual evaluation as the free surface is gradually formed during model training. High-fidelity data sets are obtained using the numerical continuation method which is able to solve nonlinear waves close to the largest height. Model training and validation results in cases of both one-layer and two-layer rotational flows show that WaveNets can reconstruct wave surface and flow field with few data either on the surface or in the flow. Accuracy in vorticity estimate can be improved by adding a redundant physical constraint according to the prior information on the vorticity distribution.

Multi-Objective Surrogate Modeling Through Transfer Learning for Telescopic Boom Forklift

Simulation and optimization methods have been widely used in forklift design due to their cost-effectiveness. However, this type of method involves challenges such as the accuracy of the simulation model and the simulation solution time. These challenges reduce the stability and precision of the surrogate model and hence generate further optimization errors. In this paper, a multi-objective surrogate modeling (MSM) method for telescopic boom forklifts based on closed-loop transfer learning is proposed in order to solve these challenges. The MSM consists of the following two steps: to pre-train an initial deep neural network model (deep model) with a large amount of existing simulation data from the same type of forklift and to transfer the model with a small amount of measurement data collected on the current forklift. A general framework for deep neural network (DNN) training is introduced to improve the approximation ability of the initial model. Moreover, a novel uncertainty-analysis-based sampling method is suggested for measurement data development, and combined with transfer learning to form a closed-loop mode to improve the stability of the final model. The superiority of MSM is demonstrated through comparative studies with the fine-tuning method on a telescopic boom forklift with two objectives. The experimental results show that the Correlation coefficient (R) of the deep model can reach 0.9971 by using only 80 sets of training data. In addition, it can also achieve an improvement of at least a 13.25% reduction in Root Mean Squared Error (RMSE) and a 9.19% reduction on average in Maximum Absolute Error (MAE), as well as stronger robustness compared to the benchmarks. Furthermore, it will provide a valuable reference for the simulation optimization of complex electromechanical products.

A novel fault diagnosis and self-calibration method for air-handling units using Bayesian Inference and virtual sensing

The Air-handling unit (AHU) is a critical and energy-consuming component in heating, ventilation, and air conditioning (HVAC) systems. However, sensor and component faults influence the AHU operation and energy consumption. This study proposed a novel fault detection, diagnosis (FDD), and self-calibration method based on the Bayesian Inference coupling with virtual sensing to estimate various faults, including the sensor and component faults. The method uses a small amount of measurement data for estimation. Specifically, virtual sensing was employed to represent variables that were difficult to measure directly. The Markov Chain Monte Carlo (MCMC) algorithm was used to derive the statistical characteristics of fault levels. Subsequently, a detailed criterion was conducted based on the statistical characteristics. A series of fault scenarios in a typical AHU system were designed to comprehensively verify the performance of the proposed method. Accordingly, the proposed method effectively recognized the system operating state and identified the fault positions. Following self-calibration, the decreased deviation rate was high up to 98.0% for most fault scenarios. It is confirmed that the proposed method exhibits effective performance even with a small number of datasets. The results highlight the potentials of the proposed method in fault detection, diagnosis and self-calibration of HVAC systems.

Universal Framework of Bayesian Creep Model Selection for Steel

The creep deformation process is constructed by complex interactions of multiple factors, and the measurement of creep deformation requires enormous economic costs and a long experimental time, so there is a small amount of measurement data. In such a situation, multiple models are often proposed to explain the same experimental data. The coexistence of multiple models based on different physical assumptions makes it difficult to understand the creep deformation process. The purpose of this study is to construct a framework to compare and evaluate coexistence models based on measurement data using the Bayesian model selection framework. Basically, in the creep deformation model, basis functions, usually the exponential function eat or power function tb, corresponding to the primary creep stage and the tertiary creep stage are set, and the time series of the whole creep deformation process is regressed as the linear sum of these bases. The parameters a and b of the basis functions are parameters that must also be optimized to regress the time series data. Izuno et al. proposed the Bayesian model selection method for the creep deformation process, and it was confirmed that the model with the linear term corresponding to the secondary creep stage, called the modified theta method, is a good model compared to the model without adding the linear term. To obtain the criterion of Bayesian model selection analytically, which can reduce the computational cost greatly, Izuno et al. treated only the regression coefficients as the probabilistic parameter of the creep model. In this method, parameters a and b of the basis functions are optimized by a grid search. Because the grid search requires significant computational costs if multiple basis functions are set for each creep stage, as in the model proposed by Kimura et al., it is difficult to apply the algorithm to models with multiple bases. In addition, if parameters a and b of the basis functions correlate with the regression coefficients, the method of Izuno et al. would offer a biased evaluation of the models. To improve such an existing method, we proposed a Bayesian model selection method that treats the parameters of the basis functions as probabilistic parameters. The large computational cost to treat the parameters of the basis functions as probabilistic parameters was reduced using the exchange Monte Carlo method, which is an efficient sampling method. By applying our proposed method to the evaluation of the creep deformation model, information was obtained to understand the creep deformation process deeply. For example, it was confirmed that there exists a model in which there is a correlation between the parameter of basis function and the regression coefficients.

An improved algebraic reconstruction technique for reconstructing tomographic gamma scanning image

Tomographic gamma scanning (TGS) is one of the most advanced non-destructive techniques for assaying the radioactive waste drum. When traditional algorithms are adopted to accurately reconstruct TGS images, the measurement data must be completely sampled by TGS system, which inevitably leads to a long-term assay. In order to save time, small number of data is measured by dividing the drum into several large voxels, which leads to inaccurate TGS images. In this work, an improved algebraic reconstruction technique (IART) is proposed to reconstruct TGS images. The total variation minimization method and the self-adaptive relaxation factor are applied to improve the iterative process of traditional algebraic reconstruction technique (ART). Experimental results show that this IART algorithm can accurately reconstruct TGS images simply based on small amount of measurement data. Compared with traditional technique, this method can reduce the mean square error and improve the signal-to-noise ratio of transmission images. And it can improve the positioning accuracy of radioisotope and the accuracy of reconstructed activity.

Image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix

To solve the problem of high time and space complexity of traditional image fusion algorithms, this paper elaborates the framework of image fusion algorithm based on compressive sensing theory. A new image fusion algorithm based on improved K-singular value decomposition and Hadamard measurement matrix is proposed. This proposed algorithm only acts on a small amount of measurement data after compressive sensing sampling, which greatly reduces the number of pixels involved in the fusion and improves the time and space complexity of fusion. In the fusion experiments of full-color image with multispectral image, infrared image with visible light image, as well as multispectral image with full-color image, this proposed algorithm achieved good experimental results in the evaluation parameters of information entropy, standard deviation, average gradient, and mutual information.

Kriging-Based Interference Power Constraint: Integrated Design of the Radio Environment Map and Transmission Power

This paper proposes a probabilistic interference constraint method with a radio environment map (REM) for spatial spectrum sharing. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging interpolation for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the primary user is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically. Additionally, we compare the performance of the proposed technique with three methods: the perfect estimation, the path loss-based method, and the Kriging-based method without the error prediction. The comparison results show that the proposed method has a higher spectrum sharing opportunity than the path loss-based method, even if only a small amount of measurement data is available. It is also shown that the proposed method dramatically improves the outage probability of the interference power compared to the conventional Kriging-based method.

Internal Dose from Food and Drink Ingestion in the Early Phase after the Accident

Activity concentrations in food and drink, represented by water and vegetables, have been monitored continuously since the Fukushima Daiichi Nuclear Power Plant accident, with a focus on radioactive cesium. On the other hand, iodine-131 was not measured systematically in the early phase after the accident. The activity concentrations of iodine-131 in food and drink are important to estimate internal exposure due to ingestion pathway. When the internal dose from ingestion in the evacuation areas is estimated, water is considered as the main ingestion pathway. In this study, we estimated the values of activity concentrations in water in the early phase after the accident, using a compartment model as an estimation method. The model uses measurement values of activity concentration and deposition rate of iodine-131 onto the ground, which is calculated from an atmospheric dispersion simulation. The model considers how drinking water would be affected by radionuclides deposited into water. We estimated the activity concentrations of water on Kawamata town and Minamisouma city during March of 2011 and the committed effective doses were 0.08 mSv and 0.06 mSv. We calculated the transfer parameters in the model for estimating the activity concentrations in the areas with a small amount of measurement data. In addition, we estimated the committed effective doses from vegetables using atmospheric dispersion simulation and FARMLAND model in case of eating certain vegetables as option information.

Photon correlation spectroscopy of the small amount of data based on auto-regressive power spectrum

Dynamic light scattering (DLS) technology is a powerful approach for measuring ultrafine particle size. The correlation method and photon correlation spectroscopy (PCS) are two methods of DLS technology. However, measurement accuracy of the correlation method and PCS based on traditional power spectrum is lower under the condition of the small amount of measurement data. For measurement problem of the small amount of data, PCS based on auto-regressive (AR) power spectrum is proposed in this paper. According to existing small amount of measurement data, this method can forecast subsequent data by an AR model, and then estimate the power spectrum of the data, which can achieve the measurement accuracy of the large amount of data. Besides, the optimal fast Fourier transform (FFT) points of 50nm–1000nm particles are obtained by analyzing the mean square error (MSE) of the AR power spectrum and its theoretical power spectrum in different FFT points. The results of simulation and experiment data demonstrate that the AR power spectrum method might serve as an effective approach to PCS measurement problem with the small amount of data.

基于改进的分块压缩感知红外图像重建

Regarding to the reconstruction problem of an infrared image in compressed sensing, we proposed a modified block compressed sensing method. First, the original infrared image is divided into small blocks, each of which is sampled with a Gaussian random matrix to generate a small amount of measurement data. Second, Spectral Graph Wavelet transform with excellent sparse features is applied into reconstruction process of projected Landweber algorithm, and the hybrid median filter is used for enhancing image smoothness and reducing block artifacts. Finally, the high-quality infrared image satisfied termination conditions is obtained. Experimental results on various types of infrared images show that the proposed method attains much better performance in CS recovery than the conventional ones and can obtain higher quality infrared images.

OPAX: A new transfer path analysis method based on parametric load models

Since its first publication in the beginning of the 1980s, transfer path analysis (TPA) has evolved into a widely used tool for noise and vibration troubleshooting and internal load estimation, for single source and multivariate problems. One of the main bottlenecks preventing its even more widespread use in the vehicle development process is the test time needed to build the full data model, requiring not only in-operation tests but also extensive frequency response function (FRF) measurements.As a consequence, several new approaches, such as operational TPA, have appeared over the past years attempting to circumvent this limitation. These methods attract quite some attention as they only require operational data measured at the path references and target locations. However, despite being time-efficient, these methods suffer from several limitations that can lead to incorrect path contribution interpretations and wrong engineering decisions.Hence, a new TPA approach is proposed, providing a good compromise between path accuracy and measurement time. The method is referred to as OPAX as it essentially uses in-operation data complemented with a minimal set of extra tests with forced excitation. The key idea of OPAX is the use of parametric models for identifying the operational loads. This makes the method scalable, enabling the engineer to use a simple model based on a small amount of measurement data for quick troubleshooting or increase accuracy using a more complex model together with additional measurements.