In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most effective and reliable maintenance ...policy. Prognostics, as one of the key enablers of CBM, involves the core task of estimating the remaining useful life (RUL) of the system. Neural networks-based approaches have produced promising results on RUL estimation, although their performances are influenced by handcrafted features and manually specified parameters. In this paper, we propose a multiobjective deep belief networks ensemble (MODBNE) method. MODBNE employs a multiobjective evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives. The eventually evolved DBNs are combined to establish an ensemble model used for RUL estimation, where combination weights are optimized via a single-objective differential evolution algorithm using a task-oriented objective function. We evaluate the proposed method on several prognostic benchmarking data sets and also compare it with some existing approaches. Experimental results demonstrate the superiority of our proposed method.
Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge appearance differences. To combat this ...problem, we propose an unsupervised change detection method that contains only a convolutional autoencoder (CAE) for feature extraction and the commonality autoencoder for commonalities exploration. The CAE can eliminate a large part of redundancies in two heterogeneous images and obtain more consistent feature representations. The proposed commonality autoencoder has the ability to discover common features of ground objects between two heterogeneous images by transforming one heterogeneous image representation into another. The unchanged regions with the same ground objects share much more common features than the changed regions. Therefore, the number of common features can indicate changed regions and unchanged regions, and then a difference map can be calculated. At last, the change detection result is generated by applying a segmentation algorithm to the difference map. In our method, the network parameters of the commonality autoencoder are learned by the relevance of unchanged regions instead of the labels. Our experimental results on five real data sets demonstrate the promising performance of the proposed framework compared with several existing approaches.
Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many ...scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 bound-constrained numerical optimization problems and compares favorably with the conventional DE and several state-of-the-art parameter adaptive DE variants.
Hyperspectral image classification is a challenging problem. Among existing approaches to addressing this problem, the active learning (AL) and semisupervised learning (SSL) techniques have attracted ...much attention in recent years. AL usually involves a labor-intensive human-labeling process while SSL, although avoiding human labeling by assigning pseudolabels to unlabeled data, may introduce incorrect pseudolabels and thus deteriorate classification performance. To overcome these drawbacks, a novel approach named collaborative active and semisupervised learning (CASSL) is proposed in this paper. CASSL combines AL and SSL to invoke a collaborative labeling process by both human experts and classifiers. Specifically, an AL-based pseudolabel verification procedure is performed for gradually improving the pseudolabeling accuracy to facilitate SSL. Meanwhile, only those unlabeled data with low pseudolabeling confidence in SSL will become the query candidates in AL. We evaluate the performance of CASSL on three hyperspectral data sets and compare it with that of two state-of-the-art hyperspectral image classification methods. Experimental results reveal the superiority of CASSL.
This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other ...particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes available from http://www.ntu.edu.sg/home/epnsugan) on multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Ackley, and Schwefel and composition functions both with and without coordinate rotation. The results demonstrate good performance of the CLPSO in solving multimodal problems when compared with eight other recent variants of the PSO.
A region-based unsupervised segmentation and classification algorithm for polarimetric synthetic aperture radar (SAR) imagery that incorporates region growing and a Markov random field edge strength ...model is designed and implemented. This algorithm is an extension of the successful Iterative Region Growing with Semantics (IRGS) segmentation and classification algorithm, which was designed for amplitude only SAR imagery, to polarimetric data. Polarimetric IRGS (PolarIRGS) extends IRGS by incorporating a polarimetric feature model based on the Wishart distribution and modifying key steps such as initialization, edge strength computation, and the region growing criterion. Like IRGS, PolarIRGS oversegments an image into regions and employs iterative region growing to reduce the size of the solution search space. The incorporation of an edge penalty in the spatial context model improves segmentation performance by preserving segment boundaries that traditional spatial models will smooth over. Evaluation of PolarIRGS with Flevoland fully polarimetric data shows that it improves upon two other recently published techniques in terms of classification accuracy.
Methane (CH
4
) is an important greenhouse gas; however, there is a lack of large-scale studies on ground-level CH
4
concentrations. We estimated global ground-level CH
4
concentrations based on the ...CH
4
columns from the Copernicus Sentinel-5 precursor satellite (S5P) and vertical profiles of CH
4
concentrations simulated from the Atmospheric Chemical Transport Model (GEOS-Chem). The proposed approach had achieved a high predictive accuracy for monthly ground-level CH
4
concentrations, with a correlation coefficient of 0.93 (p < 0.01) and RMSE of 29.93 ppb between the estimated CH
4
concentrations and those of ground measurements from the World Data Centre for Greenhouse Gases (WDCGG). Compared with the S5P CH
4
columns, the estimated ground-level CH
4
concentration has a close spatial relationship with emissions. The high CH
4
concentrations occurred in eastern China, northern India, western Russia, eastern U.S., and central Europe. Furthermore, the estimated ground-level CH
4
concentrations could reflect the seasonal variations of the observations, with correlation coefficients from 0.14 to 0.92. Our findings highlight the importance of satellite observations on atmospheric CH
4
in understanding the spatial and temporal emissions.
Multivariate image segmentation is a challenging task, influenced by large intraclass variation that reduces class distinguishability as well as increased feature space sparseness and solution space ...complexity that impose computational cost and degrade algorithmic robustness. To deal with these problems, a Markov random field (MRF) based multivariate segmentation algorithm called "multivariate iterative region growing using semantics" (MIRGS) is presented. In MIRGS, the impact of intraclass variation and computational cost are reduced using the MRF spatial context model incorporated with adaptive edge penalty and applied to regions. Semantic region growing starting from watershed over-segmentation and performed alternatively with segmentation gradually reduces the solution space size, which improves segmentation effectiveness. As a multivariate iterative algorithm, MIRGS is highly sensitive to initial conditions. To suppress initialization sensitivity, it employs a region-level k -means (RKM) based initialization method, which consistently provides accurate initial conditions at low computational cost. Experiments show the superiority of RKM relative to two commonly used initialization methods. Segmentation tests on a variety of synthetic and natural multivariate images demonstrate that MIRGS consistently outperforms three other published algorithms.
Photoresponsive materials that change in response to light have been studied for a range of applications. These materials are often metastable during irradiation, returning to their pre‐irradiated ...state after removal of the light source. Herein, we report a polymer gel comprising poly(ethylene glycol) star polymers linked by Cu24L24 metal–organic cages/polyhedra (MOCs) with coumarin ligands. In the presence of UV light, a photosensitizer, and a hydrogen donor, this “polyMOC” material can be reversibly switched between CuII, CuI, and Cu0. The instability of the MOC junctions in the CuI and Cu0 states leads to network disassembly, forming CuI/Cu0 solutions, respectively, that are stable until re‐oxidation to CuII and supramolecular gelation. This reversible disassembly of the polyMOC network can occur in the presence of a fixed covalent second network generated in situ by copper‐catalyzed azide‐alkyne cycloaddition (CuAAC), providing interpenetrating supramolecular and covalent networks.
Supramolecular polymer metal–organic cage (polyMOC) gels with Cu24L24 cuboctahedral junctions featuring a high density of coumarin ligands were prepared. These robust, dynamic materials could be reversibly switched between CuII, CuI, and Cu0 states, each with distinct mechanical, optical, and catalytic properties. In particular, the CuI state was used to catalyze covalent network formation, providing novel polyMOC covalent double networks.
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