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"A priori Data processing."
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Binocular vision and priori data based intelligent pose measurement method of large aerospace cylindrical components
In the robot finishing process of the assembly interface of large aerospace cylindrical components (short for assembly interface), to realize the high-precision and high-efficiency pose perception of the large component, an intelligent pose measurement method for the large component is proposed based on binocular vision and priori data. In this method, the global coordinate system of the robot finishing system is initially established using laser tracking measurement method and customized reference plates, giving a unified coordinate transformation datum for the interoperation of the finishing system's subsystems. Then, utilizing deep learning and digital image processing technologies, an algorithm for recognizing and locating key features of the large component is developed, which can realize the identification of key feature types and accurate localization of feature centroids. Following that, the global coordinate of the key feature centroid is determined using the proposed binocular vision three-dimensional (3D) coordinate reconstruction method. Meanwhile, by introducing the priori processing data of the large component to match the 3D reconstruction coordinates of the key feature centroids, the spatial pose of the large component can be calculated with high precision. Finally, the proposed method is experimentally validated using a case study of a large aerospace cylindrical component. Experimental results prove that the proposed method can achieve high-precision pose measurement of the large component, which can provide pose data support for the adjustment or modification of the cutting path of the robot that is generated by the as-designed model of the large component, to ensure the correctness of the robotic machining of the assembly interface, and thus the proposed method can meet the robot finishing needs of the large component.
Journal Article
Efficient Nearly Orthogonal and Space-Filling Latin Hypercubes
This article presents an algorithm for constructing orthogonal Latin hypercubes, given a fixed sample size, in more dimensions than previous approaches. In addition, we detail a method that dramatically improves the space-filling properties of the resultant Latin hypercubes at the expense of inducing small correlations between the columns in the design matrix. Although the designs are applicable to many situations, they were developed to provide Department of Defense analysts flexibility in fitting models when exploring high-dimensional computer simulations where there is considerable a priori uncertainty about the forms of the response surfaces.
Journal Article
State Evaluation of Electrical Equipment in Substations Based on Data Mining
This paper explores the combination of a data mining-based state evaluation method for electrical equipment in substations, analyzing the effectiveness and accuracy. First, a Gaussian mixture model is applied to fit all raw data of electrical equipment. The Expectation Maximization algorithm summarizes the data distribution characteristics and identifies outliers. The a priori algorithm is then employed for data mining to derive frequent itemsets and association rules between equipment quality and measurement data. For new equipment samples, conditional probabilities of each feature are independently calculated and combined to classify and evaluate equipment quality. The results suggest that equipment reliability in smart substations can be inferred from historical and real-time operational data using improved association rule algorithms and Naive Bayes classifiers. Finally, the proposed method was applied to analyze statistical data from a 110 kV substation of a power supply company. The states prediction accuracy exceeded 95% when compared with actual equipment quality. The effectiveness evaluation metrics demonstrated that this method outperforms single-category algorithms in terms of accuracy and discrimination ability.
Journal Article
Estimation of the Antenna Phase Center Correction Model for the BeiDou-3 MEO Satellites
Satellite antenna phase center offsets (PCOs) and phase variations (PVs) for BeiDou-3 satellites are estimated based on the tracking data of the Multi-GNSS Experiment (MGEX) and the international GNSS Monitoring and Assessment System (iGMAS) network. However, when estimating the (PCOs) of BeiDou-3 medium Earth orbit (MEO) satellites by pure Extending the CODE Orbit Model (ECOM1), the x-offset estimations of the PCOs have a systematic variation of about 0.4 m with the elevation of the Sun above the orbital plane (β-angle). Thus, a priori box-wing solar radiation pressure (SRP) model of BeiDou-3 MEO was assisted with ECOM1. Then, the satellite type-specific PCOs and common PVs were obtained. The estimations of PCOs and PVs were compared with the MGEX PCOs from the precise orbit and clock offset. When the MGEX PCOs were used, the root mean square (RMS) of 24 h overlap was 6.76, 4.36, 1.46 cm, in along-track, cross-track, and radial directions, respectively; the RMS and standard deviations (STD) of the 24 h clock offset overlap were 0.28 and 0.15 ns; the fitting RMS of the 72 h clock offset of the quadratic polynomial was 0.243 ns. After comparing this with the estimated PCOs and PVs, the RMS of the 24 h orbit overlap was decreased by 6.5 mm (10.54%), 1.8 mm (4.4%), and 1.1 mm (8.03%) in the along-track, cross-track, and radial directions, respectively; the RMS and STD of the 24 h clock offset overlap were decreased by 0.024 ns (8.6%) and 0.020 ns (13.1%), respectively; the fitting RMS of the 72 h clock offset of the quadratic polynomial was reduced by about 0.016 ns (6.5%).
Journal Article
Implicit Is Not Enough: Explicitly Enforcing Anatomical Priors inside Landmark Localization Models
The task of localizing distinct anatomical structures in medical image data is an essential prerequisite for several medical applications, such as treatment planning in orthodontics, bone-age estimation, or initialization of segmentation methods in automated image analysis tools. Currently, Anatomical Landmark Localization (ALL) is mainly solved by deep-learning methods, which cannot guarantee robust ALL predictions; there may always be outlier predictions that are far from their ground truth locations due to out-of-distribution inputs. However, these localization outliers are detrimental to the performance of subsequent medical applications that rely on ALL results. The current ALL literature relies heavily on implicit anatomical constraints built into the loss function and network architecture to reduce the risk of anatomically infeasible predictions. However, we argue that in medical imaging, where images are generally acquired in a controlled environment, we should use stronger explicit anatomical constraints to reduce the number of outliers as much as possible. Therefore, we propose the end-to-end trainable Global Anatomical Feasibility Filter and Analysis (GAFFA) method, which uses prior anatomical knowledge estimated from data to explicitly enforce anatomical constraints. GAFFA refines the initial localization results of a U-Net by approximately solving a Markov Random Field (MRF) with a single iteration of the sum-product algorithm in a differentiable manner. Our experiments demonstrate that GAFFA outperforms all other landmark refinement methods investigated in our framework. Moreover, we show that GAFFA is more robust to large outliers than state-of-the-art methods on the studied X-ray hand dataset. We further motivate this claim by visualizing the anatomical constraints used in GAFFA as spatial energy heatmaps, which allowed us to find an annotation error in the hand dataset not previously discussed in the literature.
Journal Article
Variance based time-frequency mask estimation for unsupervised speech enhancement
Variance based two dimensional time-frequency mask estimation for unsupervised speech enhancement is proposed to improve the speech quality and intelligibility by reducing the low-frequency residual noise distortion in the noisy speech signals. Unlike conventional speech enhancement methods, the proposed method is able to reduce the residual noise distortion by utilizing benefits of the less aggressive Wiener gain and variance based two dimensional time-frequency mask to establish a two-stage speech enhancement method. In the first stage, the less aggressive Wiener gain with modified a priori signal-to-noise (SNR) estimate is applied to the input noisy speech to obtain a reduced noise pre-processed speech signal. In the second stage, variance based features are extracted from the pre-processed speech and compared to a nonparametric adaptive threshold to construct a two dimensional time-frequency mask. The estimated mask is then applied to the pre-processed speech from the first stage to suppress the annoying residual noise distortion. A comparative performance study is included to demonstrate the effectiveness of the proposed method in various noisy conditions. The experimental results showed large improvements in terms of the perceptual evaluation of speech quality (PESQ), segmental SNR (SegSNR), residual noise distortion (BAK) and speech distortion (SIG) over that achieved with competing methods at different input SNRs. To measure the understanding of enhanced speech in different noisy conditions, short-time intelligibility prediction (STOI) is used which reinforced a better performance of the proposed method in terms of the speech intelligibility. The time-varying spectral analysis validated significant reduction of the residual noise components in the enhanced speech.
Journal Article
Distributed Hydrologic Modeling Using Satellite-Derived Potential Evapotranspiration
Satellite-derived potential evapotranspiration (PET) estimates computed from Moderate Resolution Imaging Spectroradiometer (MODIS) observations and the Priestley–Taylor formula (M-PET) are evaluated as input to the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). The HL-RDHM is run at a 4-km spatial and 6-h temporal resolution for 13 watersheds in the upper Mississippi and Red River basins for 2003–10. Simulated discharge using inputs of daily M-PET is evaluated for all watersheds, and simulated evapotranspiration (ET) is evaluated at two watersheds using nearby latent heat flux observations. M-PET–derived model simulations are compared to output using the long-term average PET values (default-PET) provided as part of the HL-RDHM application. In addition, uncalibrated and calibrated simulations are evaluated for both PET data sources. Calibrating select model parameters is found to substantially improve simulated discharge for both datasets. Overall average percent bias (PBias) and Nash–Sutcliffe efficiency (NSE) values for simulated discharge are better from the default-PET than the M-PET for the calibrated models during the verification period, indicating that the time-varying M-PET input did not improve the discharge simulation in the HL-RDHM. M-PET tends to produce higher NSE values than the default-PET for the Wisconsin and Minnesota basins, but lower NSE values for the Iowa basins. M-PET–simulated ET matches the range and variability of observed ET better than the default-PET at two sites studied and may provide potential model improvements in that regard.
Journal Article
An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis
Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.
Journal Article
A priori-driven multivariate statistical approach to reduce dimensionality of MEG signals
A magnetoencephalography (MEG) multivariate data exploratory analysis is described and implemented that combines the variance criterion used in principal component analysis with some prior knowledge about the sensory experimental task. By using the idea of rearranging the data matrix in classification pairs that correspond to the time-varying representation of either stable or stimulus phases of the specific task, the feature extraction method is constrained reducing significantly the number of principal components necessary to represent most of the total variance explained by the MEG signals.
Journal Article