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• A novel blind deconvolution method based on explicit and implicit salient edges selection is proposed. • The method helps the blur kernel estimation by selecting salient edges and removing disadvantageous structures in latent images. • An effective optimization scheme based on mean curvature filter and half-quadratic splitting method is provided. • The superiority of the proposed method is supported by extensive experiments on benchmark datasets and real-world blurry images. Blind image deconvolution is an ill-posed problem since there exists infinite pairs of blur kernels and latent images. To obtain reasonable results of this problem, most previous methods have emphasized the importance of selecting salient edges for blur kernel estimation. In this paper, a blind deconvolution method based on explicit and implicit salient edges selection is proposed. Explicit edge selection is achieved by using mutually guided image filtering, while mean curvature regularization is adopted to remove disadvantageous structures implicitly. Besides, the proposed model can be efficiently optimized by using half-quadratic penalty method and mean curvature filter. Extensive experiments show that the proposed method performs favorably against the state-of-the-art blind deconvolution methods on both synthetic and real-world blurred images. [ABSTRACT FROM AUTHOR]

This paper presents a blind image deblurring algorithm by utilizing Gaussian curvature (GC) of the image surface. GC is an intrinsic geometric feature and related to the developability of the surface. In recent years, numerous variational models based on GC for image denoising and image reconstruction have been proposed. In this paper, we show that GC regularization is also effective for blind image deblurring when combines it with L 0 -norm of image gradients. By minimizing the combined regularization, our algorithm gradually preserves sharp edges and removes detrimental structures and noises in intermediate latent images. The sharp latent images can then accurately guide the estimation of the blur kernel. However, a complicated optimization problem will occur once the proposed regularization has been involved. As we know, traditional diffusion methods for minimizing the GC regularization not only converges slowly but also requires the differentiability of signals. Moreover, the L 0 -norm of image gradients is non-convex. Consequently, Gaussian curvature filter (GCF) and the half-quadratic splitting strategy are adopted to solve the optimization problem. Extensive experimental results show that the proposed deblurring method achieves state-of-the-art results on benchmark datasets and performs favorably on real-world blurry images. • A novel regularization based on Gaussian curvature (GC) of the image surface for blind image deblurring is proposed. • Minimizing the proposed regularization is able to preserve sharp edges and remove detrimental structures for blur kernel estimation. • An effective optimization scheme based on Gaussian curvature filter (GCF) and half-quadratic splitting method is provided. • Effectiveness of the proposed method is evaluated on synthetic datasets and real-world blurry images. [ABSTRACT FROM AUTHOR]

The enhanced local maximum intensity prior is proposed in this paper. We find that the intermediate latent image restoration can be enhanced by joining the local maximum intensity with the local maximum gradient, and the quality of the recovered kernel is hereby improved. Our method is based on the fact that the maximum value of local patch pixels and gradients will decrease with the blurring process, which has been proved mathematically and experimentally. This joint prior has been named enhanced local maximum intensity prior. Based on this prior, a new energy function is established and the image deblurring is successfully carried out by alternating optimization strategy. Extensive experiments have been conducted, which shows that our method is more effective compared with the state-of-the-art methods. • We design an enhanced local maximum intensity prior for blind image deblurring. • We strengthen the robustness of the channel-based prior. • We verify the validity of the proposed prior both mathematically and experimentally. • We analyze the convergence of the proposed algorithm. • Our approach achieves comparable results compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The original publication of the article contains incorrect notes in Fig. 1. The correct version of notes in Fig. 1 is provided in this erratum. [ABSTRACT FROM AUTHOR]

The natural gas resources in the Sichuan Basin are abundant and exhibit various phenomena, for example, mixed sources, mixed maturities and overlapping combinations, resulting in great difficulties in identifying natural gas sources. In this study, several groups of thermal simulation experiments were conducted on various types of kerogens using closed and/or semi‐open systems to investigate the characteristics of the carbon isotope compositions of light hydrocarbon gases derived from different typical kerogens. The results showed that the carbon isotope compositions and variation trends of methane and ethane are controlled by the kerogen type, thermal simulation system and thermal evolution degree. The carbon isotope compositions of light hydrocarbon gases derived from different types of kerogens exhibit some differences with increased thermal evolution degree. When the thermal simulation temperature increases from 320 to 550°C, the methane carbon isotope of typically marine source rocks, mainly type I and type II1 kerogens, is slightly higher than or equivalent to that of ethane. However, the ethane carbon isotope of terrestrial source rocks, primarily types II2 and III kerogens, is significantly heavier than methane. By comparison, the evolution trend of carbon isotope compositions of actual natural gas is well consistent with that of light hydrocarbon gases cracked from thermal simulation experiments with increased thermal evolution degree. These findings are of great importance to identify the source rock of actual natural gas with high thermal maturities, as well as to determine the sources of deep shale gas in the southern Sichuan Basin, the Yuanba gas field and the Puguang gas field. Finally, a fitting method of carbon isotope values of methane and ethane of light hydrocarbon gases can be used to more finely differentiate the sources of natural gas in complex areas with mixed sources, mixed maturities and overlapping combinations. [ABSTRACT FROM AUTHOR]

In recent years, the typical crude oil from the Cambrian Lower Ordovician source rocks has been found in the Tarim Basin, and it is characterized by 13C enrichment. The stable carbon isotope of the oil is concentrated at approximately −28‰, which is 3‰–6‰ heavier than that of the crude oil from the Middle and Upper Ordovician source rocks. The stable carbon isotope between crude oil and its kerogen is characterized by inversion, with an amplitude of approximately 3‰–4‰. However, the origin of this phenomenon remains controversial, which restricts the next exploration of crude oil in the Cambrian–Lower Ordovician in the Tarim Basin. Samples were collected from seven outcrop profiles in the Kuruketage region. The basic geochemical features of the Cambrian strata, including total organic carbon abundance, δ13Corg, δ13Ccarb, and δ18Ocarb, were studied and compared with those of well TD2. The results show that the lithologies, isotope stratigraphic evolutional curves, and correlations between the organic carbon and inorganic carbon of the profiles from the northern Kuruketage region are successively comparable with those of the profiles from the southern Kuruketage region and the well TD2. In this study, the carbon isotope records are supposed to indicate a vertical gradient Cambrian oceanic mode, including the shallow decoupled isotope zone, middle gradient‐coupled isotope zone, and deep isotope‐decoupled zone. The discovery of this isotope record reflects the formation of stable carbon and oxygen isotope stratified oceans during the Cambrian period. Additionally, it provides important insights into the origin of the 13C enrichment observed in some Lower Cambrian crude oils in the Tarim Basin. [ABSTRACT FROM AUTHOR]

In order to perform accurate physical analysis of digital core, the reconstruction of high-quality digital core image has become a problem to be resolved at present. In this paper, a digital core image reconstruction method based on the residual self-attention generative adversarial networks is proposed. In the process of digital core image reconstruction, the traditional generative adversarial networks (GANs) can obtain high resolution detail features only by the spatial local point generation in low resolution details, and the far away dependency can only be processed by multiple convolution operations. In view of this, in this paper the residual self-attention block is introduced in the traditional GANs, which can strengthen the correlation learning between features and extract more features. In order to analyze the quality of generated shale images, in this paper the Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) are used to evaluate the consistency of Gaussian distribution between reconstructed shale images and original ones, and the two-point covariance function is used to evaluate the structural similarity between reconstructed shale images and original ones. Plenty experiments show that the reconstructed shale images by the proposed method in the paper are closer to the original images and have better effect, compared to those of the state-of-art methods. [ABSTRACT FROM AUTHOR]

Deep learning for solving partial differential equations (PDEs) has been a major research hotspot. Various neural network frameworks have been proposed to solve nonlinear PDEs. However, most deep learning-based methods need labeled data, while traditional numerical solutions do not need any labeled data. Aiming at deep learning-based methods behaving as traditional numerical solutions do, this paper proposed an approximation-correction model to solve unsteady compressible seepage equations with sinks without using any labeled data. The model contains two neural networks, one for approximating the asymptotic solution, which is mathematically correct when time tends to 0 and infinity, and the other for correcting the error of the approximation, where the final solution is physically correct by constructing the loss function based on the boundary conditions, PDE, and mass conservation. Numerical experiments show that the proposed method can solve seepage equations with high accuracy without using any labeled data, as conventional numerical solutions do. This is a significant breakthrough for deep learning-based methods to solve PDE. [ABSTRACT FROM AUTHOR]

Generalized B-spline bases are generated by monotone increasing and continuous 'core' functions; thus generalized B-spline curves and surfaces not only hold almost the same perfect properties which classical B-splines hold but also show more flexibility in practical applications. Geometric iterative method (also known as progressive iterative approximation method) has good adaptability and stability and is popular due to its straight geometric meaning. However, in classical geometric iterative method, the number of control points is the same as that of data points. It is not suitable when large numbers of data points need to be fitted. In order to combine the advantages of generalized B-splines with those of geometric iterative method, a fresh least square geometric iterative fitting method for generalized B-splines is given, and two different kinds of weights are also introduced. The fitting method develops a series of fitting curves by adjusting control points iteratively, and the limit curve is weighted least square fitting result to the given large data points. Detailed discussion about choosing of core functions and two kinds of weights are also given. Plentiful numerical examples are also presented to show the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a new combined approximating and interpolating ternary 4-point subdivision scheme with multiple parameters is proposed. A set of nice properties, such as support, continuity and polynomial generation, are briefly discussed. The new combined scheme not only contains a lot of classical ternary schemes as special cases, but also generates brand-new ternary schemes. Compared to other approximating subdivision schemes, limit curves generated by the new scheme are more consistent with the corresponding control polygons and keep detail features better. Examples are given to show the effectiveness of the scheme. Furthermore, fractal property is analyzed and fractal curves are also given. [ABSTRACT FROM AUTHOR]

This paper addresses the secure quasi-synchronization issue of heterogeneous complex networks (HCNs) under aperiodic denial-of-service (DoS) attacks with dynamic event-triggered impulsive scheme (ETIS). The heterogeneity of networks and the aperiodic DoS attacks, which hinder communication channels and synchronization goals, present challenges to the analysis of secure quasi-synchronization. The ETIS leverages impulsive control and dynamic event-triggered scheme (ETS) to handle the network heterogeneity and the DoS attacks. We give specific bounds on the attack duration and frequency that the network can endure, and obtain synchronization criteria that relate to event parameters, attack duration, attack frequency, and impulsive gain by the variation of parameter formula and recursive methods. Moreover, we prove that the dynamic ETS significantly reduces the controller updates, saves energy without sacrificing the system decay rate, and prevents the Zeno phenomenon. Finally, we validate our control scheme with a numerical example., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

This article presents a prescribed-time output feedback control (PTOFC) algorithm for cyber-physical systems (CPSs) under output constraint occurring in any finite time interval (OC-AFT) and malicious attacks. The OC-AFT meaning that the output constraint only occurs during a finite number of time periods while being absent in others, which is more general and complex than traditional infinite-time/deferred output constraints. A stretch model-based nonlinear mapping function is constructed to handle the OC-AFT, and a salient advantage is that the proposed algorithm is also suit for CPSs with infinite-time/deferred output (or funnel) constraints, as well as those that are constraint-free, without necessitating changes to the control structure. The uncertain terms (including system model uncertainties, malicious attacks, and external disturbances) are compensated by fuzzy logic systems. Furthermore, a novel practical prescribed-time stability criterion is proposed, under which a novel PTOFC scheme is given. The results demonstrate that the proposed scheme can ensure that both tracking error and observation error converge to a neighborhood centered on zero within a prescribed time, while accommodating the OC-AFT and malicious attacks. Additionally, the settling time remains unaffected by control parameters and initial states, and the limitations of excessive initial control inputs and singularity problems in existing prescribed-time control algorithms are eliminated. The developed algorithm is exemplified through simulation instances.

This article presents a unified adaptive fuzzy control approach for high-order nonlinear systems (HONSs) with multitype state constraints. Existing methods always require the upper and lower constraint boundaries are strictly positive and negative functions (or constants), respectively, which is often inconsistent with the actual constraints. In this article, "multitype state constraint" means that the upper and lower constraint boundaries include multiple types, such as both being strictly positive (or negative), sometime be positive or negative, and so on (cases ①-⑥). By designing a unified mapping function (UMF), the multitype state constraints are processed under removal the feasibility conditions (FCs). Furthermore, a technical design makes the proposed method also applicable to unconstrained HONSs without changing the control structure. By means of a fuzzy-logic system (FLS) and fixed-time stability theory (FTST), the proposed algorithm can ensure that the tracking error converges to a zero-centered neighborhood within a fixed time, and the singularity which often appears in the existing fixed-time control (FTC) methods of HONSs is effectively avoided. Simulation results demonstrate the scheme developed.

The issue of decentralized adaptive safe tracking control for interconnected large-scale nonlinear systems (ILSNSs) with conflicted output constraints is discussed in this paper. By "conflicted output constraints", we mean that the output constraint functions conflict with reference signal, i.e., the reference signal is not completely constrained within the constraint range. In existing methods, it is always assumed that the reference signal is constrained within the constraint region. In practice, the constraints may be detected during system operation and conflict with the reference signal given in advance. In this particular case, the existing methods based on barrier Lyapunov function (BLF) or nonlinear transformation function (NTF) are invalid. From a new point of view, this article designs a new safety reference signal (SRS) which is completely restricted within the constraint range by using the boundary protection approach. Meanwhile, a prescribed performance function which can arbitrarily define the convergence time and tracking accuracy is introduced so that the system output can better track the SRS. Then, combining backstepping technique and radial basis function neural network (RBFNN), a new controller is constructed, under which a desired tracking trajectory can be obtained under the premise of ensuring safety performance. Furthermore, by adding a dynamic event triggering mechanism (DETM) between the actuator and the plant, the communication burden is effectively reduced. Simulation results verify the scheme developed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

Axial Vertebral Rotation (AVR) is a significant indicator of adolescent idiopathic scoliosis (AIS). A host of methods are provided to measure AVR on coronal plane radiographs or 3D vertebral model. This paper provides a method of automatic AVR measurement in 3D vertebral model that is based on point cloud segmentation neural network and the tip of the spinous process searching algorithm. An improved PointNet using multi-input and attention mechanism named Multi-Input PointNet is proposed, which can segment the upper and lower endplates of the vertebral model accurately to determine the transverse plane of vertebral model. An algorithm is developed to search the tip of the spinous process according to the special structure of vertebrae. AVR angle is measured automatically using the midline of vertebral model and projection of y -axis on the transverse plane of vertebral model based on points obtained above. We compare automatic measurement results with manual measurement results on different vertebral models. The experiment shows that automatic results can achieve accuracy of manual measurement results and the correlation coefficient of them is 0.986, proving our automatic AVR measurement method performs well., (© 2021 IOP Publishing Ltd.)

Objective: Scoliosis is a complex spinal deformity in 3D space that commonly occurs in teenagers, especially teenage girls, and judging the actual deformed spine situation using only CT images is difficult. However, using 3D finite element models to help doctors analyse the deformed spine is also time-consuming and laborious. Therefore, software that can quickly and easily perform scoliosis correction analysis is needed. To achieve rapid preoperative simulation of scoliosis correction in 3D space and help doctors construct surgical programmes faster, a morphology-based system was developed for simulating scoliosis correction performance., Methods: The simulation system first takes advantage of the centre point of each vertebra on the entire spine model to fit a space curve. Then the system obtains information from the models and the space curve, and finally, uses the information to simulate scoliosis correction. The deformed spine model in the system can be corrected to a better state., Results: During the simulation process, doctors can easily and clearly see how the vertebral models move, and the deformed spine parameters are also updated and shown. Using this system, doctors can easily simulate scoliosis correction according to their experience and quickly construct a surgical programme., Conclusions: The experimental results show that this system is capable of simulating scoliosis correction according to a doctor's own experience to speed up the operation and provides a scientific basis for the development of surgical programmes., (Copyright © 2018 Elsevier Ltd. All rights reserved.)