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This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

We consider a class of nonconvex regularized optimization problems, which appear frequently in machine learning and data processing. Due to the structure of the problems, the iteratively reweighted algorithm was developed and applied to the consensus optimization. In this paper, we propose the acceleration of this scheme by adding an inertial term in each iteration. The proposed algorithms inherit the advantages of classical decentralized algorithms: they can be implemented over a connected network, in which the agents communicate with their neighbors and perform local computations. We also employ the diminishing stepsizes technique for the iteratively reweighted algorithm and consider its acceleration. In specific cases, our algorithms reduce to existing decentralized schemes and also indicate novel ones. Mathematically, we prove the convergence for both algorithms with several assumptions on the objective functions. With Kurdyka-Łojasiewicz property, convergence rates can be derived for constant stepsize case. Numerical results demonstrate the efficiency of the algorithms. [ABSTRACT FROM AUTHOR]

Abstract: In many practical data mining applications such as web page classification, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore, semi-supervised learning algorithms such as Tri-training have attracted much attention. However, mislabeling the unlabeled data during the learning process is an inevitable problem and harms the performance improvement of the hypothesis. To solve this problem, a novel human cognitive paradigm is constructed for semi-supervised learning in this paper. In detail, based on local distribution of feature space, the majority voting scheme is substituted by an estimation of the probability of sample to belong to a certain class as an efficient strategy for data editing. It considers the form of the underlying probability distribution in the neighborhood of a point to identify and remove the mislabeled data. Validation of the proposed method is performed with extensive experiments. Results demonstrate that compared with Tri-training method, our method can more effectively and stably exploit unlabeled data to enhance the learning performance. [Copyright &y& Elsevier]

Estimating clean seismic signals from noisy single‐channel records is a hot research topic in the field of microseismic data processing. Due to the existence of strong random noise, the signal‐to‐noise ratio of such data is low, presenting a challenge for signal restoration. In this paper, we propose a denoising method of microseismic data based on a single‐channel phase space reconstruction and independent component analysis algorithm. Specifically, we first apply the phase space reconstruction to transform the one‐dimensional seismic signal into a high‐dimensional phase space in which signal and noise have different trajectories. We then employ independent component analysis to the reconstructed data for noise separation. Experimental results on real microseismic data verify that our proposed denoising method is useful for noise suppression and can enhance the signal‐to‐noise ratio of data, which can be further used for signal identification and event positioning in microseismic monitoring. [ABSTRACT FROM AUTHOR]

The ground-airborne time-domain electromagnetic (GATEM) method, as a new type of electromagnetic method, offers detection efficiency and large investigation depth. It is an efficient electromagnetic detection technique for geological exploration and groundwater exploration. The field data are disturbed by motion noise and mixed noises, such as white noise and electromagnetic noises, which will reduce the data quality and accuracy of data interpretation. The characteristics of GATEM responses are analysed, and a denoising algorithm based on variational Bayesian-based adaptive Kalman filter (VBAKF) is proposed. Considering the attenuation characteristics of the GATEM response, the exponentially fitted data input into the VBAKF. Then the VBAKF with inverse Wishart priors is used to denoise GATEM field data, and the unknown process noise covariance matrix (PNCM) and measurement noise covariance matrix (MNCM) are estimated by variational Bayesian. The VBAKF algorithm is tested on the responses with different noises. It is verified on a layered geological model and compared with the adaptive Kalman filter (AKF) algorithm and the combined wavelet filter (CWF) algorithm. It is also tested on synthetic data of an anomalous model. Through synthetic data, the VBAKF algorithm can effectively suppress noise of GATEM data. Finally, the algorithm is applied to field data in Leshan, Jilin province, China. It effectively improves the quality of field data. • Focus on obtain high-precision GATEM data. • Improve robustness of VBAKF with inverse Wishart priors. • Verify the VBAKF method through synthetic and field data. [ABSTRACT FROM AUTHOR]

Recent studies on three-dimensional (3D) point cloud data (PCD) simplification have played significant roles in computer-aided models for alleviating computational and storage burden. However, existing simplification methods are not suitable for the high-volume PCD with billions of point number, especially in construction industry. In this paper, a decomposed simplification method is developed to handle the high-volume buildings' PCD. Based on the divide-and-conquer philosophy, the new decomposed approach effectively reduces the memory usage. In the proposed approach, PCD is divided into several subsets according to the relationship of natural neighbor, and decomposed graph filtering with adaptive resampling rate is designed. It can be proved that the decomposed simplification results have no variance in the comparison with the optimal simplification of entire PCD. Verification experiments are conducted on different sizes of PCD, which indicate the effectiveness and feasibility of the proposed approach. • A decomposed method based on graph filtering is successfully designed for the high-volume PCD simplification problem. • A decomposed feature extraction algorithm is developed in high-volume PCD simplification algorithm. • Theoretical results guarantee the invariance of simplification in the proposed decomposed framework. [ABSTRACT FROM AUTHOR]

Herein, a processing method is proposed for accurate microdisplacement measurements from a 2D Fabry–Perot (F-P) fringe pattern. The core of the processing algorithm uses the F-P interference imaging concentric ring pattern to accurately calculate the centre coordinates of the concentric ring. The influencing factors of measurement were analysed, and the basic idea of data processing was provided. In particular, the coordinate rotation by the 45-degree method (CR) was improved; consequently, the virtual pixel interval was reduced by half, and the calculation accuracy of the circle centre coordinate was improved. Experiments were conducted to analyse the influence of the subdivision and circle fitting methods. The results show that the proposed secondary coordinate rotation (SCR) by 45 degrees method can obtain higher accuracy of the centre coordinate than the CR method, and that the multichord averaging method (MCAM) is more suitable for calculation of the centre coordinate than the circular regression method (CRM). Displacement measurement experiments were performed. The results show that the standard experimental deviation of the centre of the circle is approximately 0.009 µm, and the extended uncertainty of the displacement measurement in the range of 5 mm is approximately 0.03 μm. The data processing method studied in this study can be widely used in the field of F-P interferometry. [ABSTRACT FROM AUTHOR]

The photoplethysmography (PPG) is inevitably corrupted by many kinds of noise no matter whether its acquisition mode is transmittance or reflectance. To enhance the quality of PPG signals, many studies have made great progress in PPG denoising by adding extra sensors and developing complex algorithms. Considering the reasonable cost, compact size, and real-time and easy implementation, this study proposed a simple real-time denoising method based on double median filters which can be integrated in microcontroller of commercial or portable pulse oximeters without adding extra hardware. First, we used the boundary extension to preserve the signal boundary distortion and designed a first median filter with the time window at approximately 78 ms to eliminate the high-frequency components of the signal. Then, through the second median filter with a time window which was about 780 ms, we estimated the low-frequency components. Finally, we removed the estimated low-frequency components from the signal to obtain the denoised signal. Through comparing the multiple sets of signals under calmly sitting and slightly moving postures, the PPG signals contained noises no matter whether collected by the transmittance-mode or the reflectance-mode. To evaluate the proposed method, we conducted measured, simulated experiments and a strong noisy environment experiment. Through comparing the morphology distortions, frequency spectra, and the signal-to-noise ratios (SNRs), the results showed that the proposed method can suppress noise effectively and preserve the essential morphological features from PPG signals. As a result, the proposed method can enhance the quality of PPG signals and, thus, can contribute to the improvement of the calculation accuracy of the subsequent physiological parameters. In addition, the proposed method could be a good choice to address the real-time noise reduction of portable PPG measuring instruments. [ABSTRACT FROM AUTHOR]

Masonry materials has been widely used in building construction. During the design of a masonry structure, engineers need to manually arrange the position of each masonry unit to reduce the waste of masonry units on construction site. This manual approach is inefficient and error-prone. With the development of computer-aided design, attention has been paid to the use of a computer-aided method to improve the efficiency of masonry design. However, the existing work has focused on the visual representation of masonry structures, while lacking the study on automatic masonry unit layout. Therefore, in this paper, inspired by the 0–1 knapsack problem, a 2-step memetic algorithm is proposed first for the automatic masonry unit layout considering the Chinese code for construction of masonry structures engineering (GB 50924–2014) and the feasibility of construction. The models for layout results are then automatically generated through the tool which developed by the Revit API. Validation experiments are conducted on five sets of masonry structures with the different lengths and a building case, which confirmed the effectiveness and efficiency of the proposed approach. • An approach with memetic algorithm and BIM for masonry unit layout is firstly proposed. • The masonry unit layout is modeled as a 0–1 knapsack problem. • A 2-step memetic algorithm is proposed to accomplish the task of masonry unit layout. • A re-development based on BIM is developed to generate the masonry structure BIM model. • The proposed approach automatically accomplish the masonry unit layout and BIM model generation. [ABSTRACT FROM AUTHOR]