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• A novel three-dmensional chaotic system NewSysm is constructed for keystream. • A fresh model TransM is built for the production of plaintext keys. • A new model GetM is established for computing of the initial values of NewSysm. • Three bit planes of the cipher image are decomposed for hidding respectively. In this paper, a three-dimensional chaotic system is proposed. Based on this chaotic system and two-dimensional compressive sensing, an asymmetric visually meaningful image compression-hiding algorithm is presented. Firstly, in the keystream generation stage, a novel parameter transformation model is constructed to pick up the feature information from the plain image as the plaintext key. Then, Rivest-Shamir-Adleman algorithm is employed to encrypt the plaintext key into the ciphertext key seen as public key. Before generating the initial values for the chaotic system, a new initial value getting model is designed to transform both the plaintext and the ciphertext keys. After solving the chaotic system, the keystream is produced which is then used in the image encryption process. Secondly, in the compression and encryption phase, a novel keystream pre-processing model is built to generate new sequences with a confusion performed on the plain image. Then, a newly constructed measurement matrix is designed to do two-dimensional compressive sensing on confusing the image to get measurements. Before obtaining the cipher image, a double diffusion operation is applied on these measurements. Thirdly, in the image hiding stage, the carrier image is performed by integer wavelet transformation to obtain coefficient matrices. Then, the cipher image is decomposed in decimal, getting the ones, tens and hundreds of pixels to form three bit matrices, of which are embedded into the three medium-high coefficient matrices of integer wavelet transformation, respectively. Finally, after performing inverse integer wavelet transformation, the carrier image containing secrets, i.e., visually meaningful encrypted image, is obtained. Experimental results also show that at a low compression ratio of 0.25, the normalized correlation coefficient between the original plain image and the recovered image is almost equal to one, while the peak signal-to-noise ratio between the carrier image containing secrets and its original carrier can reach as high as 42 dB. In addition, the proposed image compression-hiding algorithm performs good ability consideing the brute force attack and the cropping attack. [ABSTRACT FROM AUTHOR]

The Black Hole Clustering (BHC) algorithm is a density-based partitional clustering method inspired by the Density-based Spatial Clustering of Applications with Noise (DBSCAN). It does not require the number of clusters nor the computation of the pair-wise distance matrix between the data points, making it faster than DBSCAN. Also, it only needs one parameter that is intuitively easier to set than the epsilon parameter of DBSCAN. However, BHC needs the allocation of the so-called black holes that have to be linearly independent, making the algorithm in its current version suitable only for two or three-dimensional data sets. In this paper, we propose a generalized version of the black hole clustering algorithm (GBHC) by introducing a novel black hole allocation procedure for higher-dimensional data spaces. Furthermore, the proposed method is data-independent, so we have to run it once to obtain the black hole positions for all finite-dimensional metric spaces. We performed extensive computational experiments to compare GBHC with DBSCAN. The results show that both algorithms obtain comparable clustering solutions. GBHC, however, outperforms DBSCAN in computational complexity and explainability. • We propose a novel method to place the black holes for high-dimensional data sets. • This method is called Generalized Black Hole Clustering and it is data-independent. • We run the new method once to get black hole positions in all finite metric spaces. • We compare the GBHC and DBSCAN algorithms using several validation measures. [ABSTRACT FROM AUTHOR]

• Space debris detection; • Payload camera breadboard for space surveillance; • Star tracking and space debris detection algorithms; • Hybrid time-index image algorithm. Space debris threatens space activities, demanding continuous observation and tracking by the Space Surveillance Network (SSN) to secure the Earth's orbits. However, SST efforts are limited by the size and brightness of the debris, detecting only a small amount of the total. Seeking to overcome such limitations, this study proposes an alternative payload camera capable of extracting the attitude of a satellite in orbit and detecting under-catalogued debris. This work is a sequential study of previous research, where the camera breadboard was designed and implemented. The contribution of this paper is the evaluation of star tracking and space debris algorithms to be implemented in the payload camera, the development and implementation of a new hybrid algorithm and the elaboration of performance metrics for comparison between the algorithms. The observation data of the previous research was used as input for the algorithms' tests. For star identification and, consequently, attitude extraction, the chosen algorithm was Tetra. The results were compared to the standard star identification software, Astrometry.net , to assess the attitude accuracy. For debris detection, ASTRiDE and the Hybrid time-index image algorithms were used. A comparison of the results was made to establish a performance evaluation metric in terms of detectability, time, and computational cost. [ABSTRACT FROM AUTHOR]

• A computing method for constellation-to-ground coverage is investigated. • The maximum and minimum coverage regions are calculated. • Simulation results demonstrate the validity and effectiveness of the proposed algorithm. The issue of constellation-to-ground coverage is a research focus in Earth observation applications. Traditional calculations often rely on grid point methods or their derivatives, but these can be limited in their application, relatively costly, inefficient, and often do not account for potential errors. This paper proposes a novel, efficient method based on latitude strip division for calculating the ground area coverage of satellite constellations, capable of providing the upper and lower bounds of coverage ratio for any ground area. Initially, the ground target area is divided into several latitude strips, and the target area range is utilized to determine the longitude range of each latitude strip. Subsequently, the upper and lower bounds of coverage of each strip are calculated according to the satellite ground coverage range. On this basis, the coverage boundary function is defined and the coverage ratio is derived through comprehensive statistics analysis. Finally, depending on the accuracy of the latitude strip division, the precise coverage area and coverage ratio with upper and lower bounds are determined for instantaneous, continuous, and cumulative coverage problems. Numerical simulation experiments were carried out and compared with the traditional grid point method to validate the effectiveness and computational efficiency of this algorithm in addressing the coverage issue for arbitrarily shaped ground areas. When compared to the longitude strip method, it was confirmed that for ground areas where the longitude exceeds the latitude range, this approach offers superior computational efficiency than the latitude strip method. [ABSTRACT FROM AUTHOR]

Due to the fast progress of Deep Learning (DL) methods in remote sensing applications, many change detection (CD) algorithms have been recently proposed based on CNN networks and the mechanism of self-attention. These algorithms extract features without focusing on the temporal dependency between features. This shortcoming led to the introduction of the Transformer mechanism. In this paper, we design a convolutional transformer network with a Siamese U-shaped structure and name it CTS-Unet to solve the CD problem. We exploit the ability of the CNN to extract effective semantic features and that of the transformer to extract global information effectively. The Siamese architecture allows using CNN to simultaneously extract effective semantic features from low-resolution bi-temporal images. The transformer part contains an encoder and a decoder, all of which use the Swin transformer module as their basic unit. The encoder processes the features extracted from the CNN using patch merging and the Swin transformer module to produce semantic features. The encoder extracts the detailed information from the features using patch expansion, the Swin transformer module, and convolutional upsampling to create a CD map. The experiments were performed on the widely used LEVIR-CD and DSIFN-CD datasets. Compared with other state-of-the-art CD methods, CTS-Unet provides higher performance with F1-scores of 91.87% and 69.60% for LEVIR-CD and DSIFN-CD datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes a second-order decoupled Backward Euler method plus time filter for the evolutionary Stokes/Darcy model, which allows different variable time steps in the free fluid flow region and the porous media flow region. Furthermore, this algorithm, which is a combination of first order Backward Euler method and time filter scheme, uncouples the Stokes/Darcy model into the Stokes and Darcy problems per time step. In particular, adaptive algorithm is constructed to improve the computational efficiency. Moreover, we mainly deduce the stability and error estimation in theoretical analysis. In addition, numerical experiments are used to verify the effectiveness, second order convergence and efficiency. [ABSTRACT FROM AUTHOR]

Shadow detection is a common computer vision task that identifies the location of shadows. It has been used as a key primitive for many high-level tasks such as object segmentation and video tracking. Due to the complexity and variety of shadows, it is challenging to design a one-size-fits-all shadow detection algorithm in real-world scenarios. Instead, it is often necessary in practice to pick the best shadow detection algorithm for a particular vision application. While shadow detection algorithms can be assessed by comparing the detected shadow with the ground truth, such ground truth is often unavailable in real-world vision systems consistently generating new data. In this paper, we propose a no-reference shadow detection quality assessment network (NSDQA-Net) to rank shadow detection algorithms without needing the ground truth. For a given image, the proposed assessment network first learns a reference map that compensates the false positives and false negatives of multiple input shadow detection algorithms. The reference map, as well as the original image and the input shadow detection results, are then used to regress the quality scores of the shadow detection methods. Our framework supports the assessment of one, two, or multiple algorithms in a single run, making it easier for deployment in practical scenarios. Experiments results show that the proposed network achieves optimal accuracy in no-reference shadow detection assessment. Relying on it for adaptive algorithm selection also improves the performance of shadow detection using a fixed algorithm on benchmark datasets. [Display omitted] • We identify a task of no-reference quality assessment in shadow detection. • We design a novel no-reference shadow detection quality assessment network (NSDQA-Net). • We experimentally demonstrate the state-of-the-art performance of the proposed network. [ABSTRACT FROM AUTHOR]

A self-training algorithm is a classical semi-supervised learning algorithm that uses a small number of labeled samples and a large number of unlabeled samples to train a classifier. However, the existing self-training algorithms consider only the geometric distance between data while ignoring the data distribution when calculating the similarity between samples. In addition, misclassified samples can severely affect the performance of a self-training algorithm. To address the above two problems, this paper proposes a self-training algorithm based on data editing with mass-based dissimilarity (STDEMB). First, the mass matrix with the mass-based dissimilarity is obtained, and then the mass-based local density of each sample is determined based on its k nearest neighbors. Inspired by density peak clustering (DPC), this study designs a prototype tree based on the prototype concept. In addition, an efficient two-stage data editing algorithm is developed to edit misclassified samples and efficiently select high-confidence samples during the self-training process. The proposed STDEMB algorithm is verified by experiments using accuracy and F-score as evaluation metrics. The experimental results on 18 benchmark datasets demonstrate the effectiveness of the proposed STDEMB algorithm. • Mass-based dissimilarity is used to o account for the effects of data distribution. • A prototype tree was designed to efficiently identify high confidence samples. • A two-stage data editing algorithm was developed for editing misclassified samples. • Numerous experiments were conducted to prove the performance of the proposed STDEMB. [ABSTRACT FROM AUTHOR]

Twin support vector machine (TSVM) is a practical machine learning algorithm, whereas traditional TSVM can be limited for data with outliers or noises. To address this problem, we propose a novel TSVM with the symmetric LINEX loss function (SLTSVM) for robust classification. There are several advantages of our method: (1) The performance of the proposed SLTSVM for data with outliers or noise can be improved by using the symmetric LINEX loss function. (2) The introduction of regularization term can effectively improve the generalization ability of our model. (3) An efficient iterative algorithm is developed to solve the optimization problems of our SLTSVM. (4) The convergence and time complexity of the iterative algorithm are analyzed in detail. Furthermore, our model does not involve loss function parameter, which makes our method more competitive. Experimental results on synthetic, benchmark and image datasets with label noises and feature noises demonstrate that our proposed method slightly outperforms other state-of-the-art methods on most datasets. • This paper proposes SLTSVM by introducing symmetric LIENX loss function. • An interpretable method is proposed for selecting the loss parameter. • The convergence of the designed solution algorithm are further analyzed. • Experiments show that our method is efficient for data with outliers or noises. [ABSTRACT FROM AUTHOR]

• A continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. • Combination of modal truncation order and sensor layout optimization by modal strain energy. • The proposed algorithm has better accuracy and noise immunity than SSM and GKFM. • For continuous systems, the proposed algorithm has a solution time of 0.38s and is insensitive to the time step. • The proposed algorithm has good accuracy in experimental validation. Dynamic load identification based on structural responses is an important problem in the field of engineering and plays an important role in the condition assessment of mechanical structures. Current popular load identification methods such as the state-space method (SSM) and the Green's kernel function method (GKFM) are implemented on discrete systems with outstanding performance, but when dealing with complex continuous systems, there are some limitations such as low efficiency and inaccuracy. In this paper, a continuous system load identification algorithm based on SSM and Newmark-β is proposed for the first time. Using modal coordinate transformation and modal truncation methods, the number of infinite vibration differential equations of a continuous system in physical space are converted to a finite number of vibration differential equations in modal space, where the modal truncation order and the optimal layout of the sensors are combined by modal strain energy. The Newmark-β method in modal space is derived and thus combined with SSM to obtain a load identification model Y =HF called SSM-Newmark-β method, which reduces the size of the transfer matrix H. The solution time of the proposed algorithm is 0.38 s for continuous systems, which is rather shorter than that for discrete systems. Furthermore, the simulations show that it has better accuracy and noise immunity than SSM and GKFM in the identification of sinusoidal load, impact load, and random load. The effect of time step is also discussed which reveals that the larger time step has less effect on the proposed algorithm. An experimental study is carried out on a cantilever beam system. The result verifies that the SSM-Newmark-β algorithm has better accuracy in load identification for the continuous system. This research provides a new sight for the real-time identification of dynamic loads for complex structures. [ABSTRACT FROM AUTHOR]

Narrowband SETI algorithms have been in use for many decades. A key processing step in the usual SETI pipeline called "De-Doppler" involves non-coherent integration of linearly-drifting lines in a spectrogram. De- Doppler (DD) is needed because a transmitted constant-frequency tone will be received as a chirp, or a tone with a near-constant frequency rate, due to relative accelerations between the transmitter and receiver. The frequency drift may be an important discriminator against certain types of radio-frequency interference. This paper examines the standard Taylor DD algorithm and finds there are significant signal losses in the integration process. Part of the loss is caused by imperfect indexing in the algorithm. Additional loss is associated with the effect of an FFT filter bank on chirp signals; DD performance is intertwined with the filter bank. Filter bank improvements involving polyphase filter banks are examined. A new DD algorithm called "fastDD" is proposed, as well as extensions to the Taylor algorithm. They both offer significant performance gains and higher resolution in frequency and drift rate in the DD detection plane. The loss mechanisms are now understood. Computational cost trade-offs for various filter bank and DD parameters are presented based on C code functions. Several "best" options are given which offer significant SNR improvements ranging from 2 to 4 dB for modest levels of additional computation. These options have the potential to dramatically improve detection rates. • Goal: (i) Understand and improve the De-Doppler (DD) function for narrowband SETI. (ii) Taylor DD – the gold standard for over a decade – has not been examined in detail. (iii) Need to look at DD as part of an overall end-to-end narrowband SETI detection system. • Results: (i) Significantly improved detection performance is possible: 2–4 dB improved sensitivity, depending on computation and memory budget; Potential SETI detection rate improvements of 2–4 times. (ii) Gains arise from possible use of overlapped polyphase filter banks and higher resolution in the DD plane. (iii) New algorithms have been defined (fastDD and extended Taylor DD) and evaluated. (iv) SNR Gain vs. Computation cost trades have been examined using C language functions: Best options of filter bank and DD parameters are identified. [ABSTRACT FROM AUTHOR]

Estimating the pose of an uncooperative spacecraft is an important computer vision problem for enabling the deployment of automatic vision-based systems in orbit, with applications ranging from on-orbit servicing to space debris removal. Following the general trend in computer vision, more and more works have been focusing on leveraging Deep Learning (DL) methods to address this problem. However and despite promising research-stage results, major challenges preventing the use of such methods in real-life missions still stand in the way. In particular, the deployment of such computation-intensive algorithms is still under-investigated, while the performance drop when training on synthetic and testing on real images remains to mitigate. The primary goal of this survey is to describe the current DL-based methods for spacecraft pose estimation in a comprehensive manner. The secondary goal is to help define the limitations towards the effective deployment of DL-based spacecraft pose estimation solutions for reliable autonomous vision-based applications. To this end, the survey first summarises the existing algorithms according to two approaches: hybrid modular pipelines and direct end-to-end regression methods. A comparison of algorithms is presented not only in terms of pose accuracy but also with a focus on network architectures and models' sizes keeping potential deployment in mind. Then, current monocular spacecraft pose estimation datasets used to train and test these methods are discussed. The data generation methods: simulators and testbeds, the domain gap and the performance drop between synthetically generated and lab/space collected images and the potential solutions are also discussed. Finally, the paper presents open research questions and future directions in the field, drawing parallels with other computer vision applications. • Deep Learning is used extensively for monocular spacecraft pose estimation. • Deployability is an important limitation of the current DL-based solutions. • Lack of space-borne images during training leads to the domain gap problem. [ABSTRACT FROM AUTHOR]

The tensor t-product is a powerful tool for the analysis of and computation with third-order tensors. This paper discusses properties and the computation of eigentubes and eigenslices of third-order tensors under the t-product; the eigentubes and eigenslices are analogues of eigenvalues and eigenvectors for matrices. The computational methods considered and analysed include the tensor power method, tensor subspace iteration, and the tensor QR algorithm. Computed examples illustrate the performance of these methods. [ABSTRACT FROM AUTHOR]

In this paper, we develop an inexact quasi-Newton algorithm for ℓ 1 -regularization optimization problems subject to box constraints. The algorithm uses the identification technique of the proximal gradient algorithm to estimate the active set and free variables. To accelerate the convergence, we utilize the inexact quasi-Newton algorithm to update free variables. Under certain conditions, we show that the sequence generated by the algorithm converges R -linearly to a first-order optimality point of the problem. Moreover, the corresponding sequence of objective function values is also linearly convergent. Experiment results demonstrate the competitiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper focuses on the computation of a rule in minimum cost spanning tree problems with groups, which coincides with the Owen value of the cooperative game for the irreducible cost matrix. To this aim, two polynomial-time algorithms are provided. The first of them is based on the steps of Kruskal algorithm whereas the other is based on a painting procedure. [ABSTRACT FROM AUTHOR]

In a graph, G = (V , E) without an isolated vertex, a dominating set D ⊆ V , is called a semitotal dominating set if for every vertex u ∈ D there is another vertex v ∈ D such that distance between u and v is at most two in G. Given a graph G = (V , E) without an isolated vertex, the Minimum Semitotal Domination problem is to find a minimum cardinality semitotal dominating set of G. The semitotal domination number , denoted by γ t 2 (G) , is the minimum cardinality of a semitotal dominating set of G. It is known that the decision version of the problem remains NP-complete even when restricted to chordal graphs, chordal bipartite graphs, and planar graphs. Galby et al. (2020) proved that the problem can be solved in polynomial time for bounded MIM-width graphs, which include many well known graph classes, but left the complexity of the problem in strongly chordal graphs unresolved. Henning and Pandey (2019) also asked to resolve the complexity status of the problem in strongly chordal graphs. In this paper, we resolve the complexity of the problem in strongly chordal graphs by designing a linear-time algorithm for the problem. [ABSTRACT FROM AUTHOR]

Facial information plays an important role in human communication, e.g., rich and nuanced facial expressions effectively convey emotions. Traditional data such as 2D facial images and videos are susceptible to perturbation from lighting and occlusion, while 3D static data such as mesh models lack the temporal information which is necessary to describe facial dynamics. Therefore, researches on 4D facial data (3D facial models together with time as the fourth dimension) have received considerable attention in recent years. 4D data can simultaneously reflect the complex facial temporal and space information. To fully explore these characteristics, we present a systematic overview of the 4D facial research in this paper. We give a review in terms of historical development of 4D facial datasets, acquisition process of these datasets, related algorithms and applications, and discussion of outstanding issues. We also analyze the 4D facial research works by summarizing and comparing them; in particular, we present the results on three kinds of tasks conducted on the 4D facial datasets, i.e., expression-related tasks (AU detection, expression recognition and retrieval), generation tasks (3D facial reconstruction, facial expression and facial animation generation), and other tasks (facial registration, facial recognition and facial disease diagnosis). Finally, we summarize the future research directions. [Display omitted] • We provide a comprehensive overview of a 4D facial dataset. • We classified and elaborated on the different facial studies conducted using 4D facial datasets. • We summarize the future research direction. [ABSTRACT FROM AUTHOR]

Multimodal image matching is a core basis for information fusion, change detection, and image-based navigation. However, multimodal images may simultaneously suffer from severe nonlinear radiation distortion (NRD) and complex geometric differences, which pose great challenges to existing methods. Although deep learning-based methods had shown potential in image matching, they mainly focus on same-source images or single types of multimodal images such as optical-synthetic aperture radar (SAR). One of the main obstacles is the lack of public data for different types of multimodal images. In this paper, we make two major contributions to the community of multimodal image matching: First, we collect six typical types of images, including optical-optical, optical-infrared, optical-SAR, optical-depth, optical-map, and nighttime, to construct a multimodal image dataset with a total of 1200 pairs. This dataset has good diversity in image categories, feature classes, resolutions, geometric variations, etc. Second, we propose a scale and rotation invariant feature transform (SRIF) method, which achieves good matching performance without relying on data characteristics. This is one of the advantages of our SRIF over deep learning methods. SRIF obtains the scales of FAST keypoints by projecting them into a simple pyramid scale space, which is based on the study that methods with/without scale space have similar performance under small scale change factors. This strategy largely reduces the complexity compared to traditional Gaussian scale space. SRIF also proposes a local intensity binary transform (LIBT) for SIFT-like feature description, which can largely enhance the structure information inside multimodal images. Extensive experiments on these 1200 image pairs show that our SRIF outperforms current state-of-the-arts by a large margin, including RIFT, CoFSM, LNIFT, and MS-HLMO. Both the created dataset and the code of SRIF will be publicly available in https://github.com/LJY-RS/SRIF. [ABSTRACT FROM AUTHOR]

Accurate prediction of re-entry time and location of an uncontrolled re-entering space object is crucial but challenging. This paper presents in-house re-entry prediction algorithms: RSMGA, STKOptim, STKLTOptim, and ABPro. Case studies on re-entry prediction of Starlink-26 (COSPAR ID 2019-029F or NORAD ID 44240) and CZ-5B (COSPAR ID 2021-035B or NORAD ID 48275) during the IADC re-entry test campaigns 2021/1 and 2021/2, respectively are presented. Simultaneous application of these re-entry prediction algorithms facilitates decision-making on automation of switching between these algorithms during various phases (short term or long-term) of the re-entry campaign. The effectiveness and applicability of these algorithms are studied based on percentage error. The study focuses on the overall and time-based performance of the four methods. Six more re-entered objects are selected (three Geosynchronous transfer orbit (GTO) objects and 3 Low-Earth orbit (LEO) objects), and their re-entry time prediction has been carried out using the four methods for different time instances before re-entry. Prediction estimates are compared among the methods for all the time instances in terms of percentage error. Also, the study helps in fixing the suitability of the re-entry time prediction algorithm for objects from GTO and LEO. [ABSTRACT FROM AUTHOR]

Conjugate gradient methods are much effective and widely used for large-scale unconstrained optimization problems by their simple computation, low memory requirement and strong global convergence property. Spectral gradient methods are also effective for large-scale problems. In this paper, a class of new descent spectral three-term conjugate gradient algorithms are proposed which automatically have the sufficient descent property and satisfy the Dai-Liao conjugate condition. Under the Wolfe line search technique and some standard conditions, the proposed methods are globally convergent for strongly convex functions and general nonlinear functions with the help of the modified secant equations. In numerical part, 732 problems with dimensions varying from 1500 to 150000 and three image restoration problems with three noise levels are considered. Numerical results indicate that the proposed algorithms are more efficient, reliable and robust than the other methods for the testing problems. • A class of spectral descent three-term conjugate gradient methods are proposed. • The proposed methods own the sufficient descent property and satisfy the Dai-Liao conjugate condition automatically. • The proposed methods are globally convergent for general functions without convex assumptions. • The proposed methods are applied to large-scale unconstrained optimization problems and some image restoration problems. [ABSTRACT FROM AUTHOR]

Highlights Although fabrication of colorimetric paper-based analytical devices (PADs) has drawn increasing attention recently, the signal readout method is still a crucial challenge on the way to practical exploitations. We herein introduce an integration of digital image processing with a model PAD for easing and improving the signal readout procedure. The colorimetric detection mechanism of PAD relies on in-situ induced yellowish silver nanoparticles via the interaction of silver ions, poly(vinyl alcohol), and ammonia with isoniazid. The observed color value is related with the concentration of isoniazid. The proposed algorithm is based on mathematical morphology recognition, and minimizes the errors arising from manual area selection. Besides, it allows the recognition of both circle and square shapes in 96-well plate and A4 size array designs. Since this algorithm automatically provides the blank-corrected numerical matrixes and image profiles of red, green and blue channels, further investigations such as outlier classification, construction of regression/prediction models and calculation of detection limit can be easily performed. Comparison of signal readout results of the developed algorithm with ImageJ software demonstrates significant improvements in analysis speed, reproducibility, accuracy and color values. Therefore, application of the proposed algorithm is promising as a robust technique for practical applications. [ABSTRACT FROM AUTHOR]

The present paper studies digital topological rough set structures associated with Khalimsky (K -, for brevity) topology. We further explore their properties related to topological (closure and interior) operators. In addition, we find that the K -topological rough set structures have their own features different from certain theoretical properties of the H -rough set structure in [9] w.r.t. the topological operators. More precisely, whereas the H -rough set structure is a topological operator, K -topological concept approximations are proved not to be topological operators. Thus, this paper proposes an alternative approach to the K -topological concept approximations so that we finally have new types of topological operators. Furthermore, it appears that this new ones are smoothly matched with the earlier H -rough set structure from the viewpoint of topological operators. Therefore, they can facilitate the study of continuous-oriented or digital-oriented rough sets, which can be used in applied sciences without any limitations of the cardinalities of the universe and a target set. Besides, they can support certain decision rules with no restriction to anything continuous or discrete (or digital). In addition, we introduce the notion of K -definability of a target subset of a universal set and write an algorithm for estimating the roughness of the above new concept approximations. In this paper each set need not be finite and a covering is locally finite. [ABSTRACT FROM AUTHOR]

Objective: Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support.Methods: Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab(®)-a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated.Results: The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4-6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1-4.6).Discussion: Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab(®), user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant.Conclusion: Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia. [ABSTRACT FROM AUTHOR]

• This paper constructs and analyzes a composite s -sub-step explicit method. • Seven explicit schemes in the present method are developed and compared. • Each explicit scheme is second-order accurate and provides maximal stability bound. • Each explicit scheme (s > 1) can control numerical dissipation at the bifurcation point. This paper constructs a composite s -sub-step explicit method and analyzes second-order accuracy, conditional stability, and dissipation control at the bifurcation point. In the present s -sub-step method, each explicit scheme achieves identical second-order accuracy for analyzing general structures and provides maximal stability bound, that is 2 × s where s denotes the number of sub-steps. Except for the single-sub-step case, each explicit scheme achieves dissipation control at the bifurcation point. After registering second-order accuracy and controllable numerical dissipation, the composite multi-sub-step explicit method should be well-designed to reach maximal stability bound. The analysis reveals that as the number of sub-steps increases, the developed explicit schemes can reduce numerical low-frequency dissipation and enlarge stability. Under the same computational cost, the advantage of reducing low-frequency dissipation and enlarging stability is gradually weakened with the increase of sub-steps, so the first seven explicit schemes are only developed and compared in this paper. Some typical experiments are provided to confirm the methods' numerical performance. The proposed explicit schemes are more accurate and efficient for some models than existing second-order algorithms of that class. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a three-operator splitting algorithm with deviations for solving the minimization problem composed of the sum of two convex functions minus a convex and smooth function in a real Hilbert space. The main feature of the proposed method is that two per-iteration deviation vectors provide additional degrees of freedom. We propose one-step and two step inertial three-operator splitting algorithms by selecting the deviations along a momentum direction. A numerical experiment for DC regularized sparse recovery problems shows that the proposed algorithms have better performance than the original three-operator splitting algorithm. [ABSTRACT FROM AUTHOR]

During the spacecraft attitude maneuvers for the observation of mission targets, multiple instruments may face different pointing constraints, and they are rarely considered by the current attitude maneuver planning methods. In this case, this paper proposes an improved rapidly–exploring random trees star (RRT*) algorithm to obtain discrete quaternion path nodes while satisfying the pointing constraints of multiple sensors. Next, the temporal properties of these path nodes are defined by a trapezoidal path planning method, and bounded constraints on the angular velocity and control moment during the attitude maneuver are introduced. Then, the continuous quaternion curves through the discrete quaternion path nodes are calculated by a segmented cubic spline interpolation function. Finally, the angular velocity and control moment are calculated through the inverse dynamics. The simulation results verify the feasibility of the method. It can satisfy complex constraints and obtain the attitude optimization path, and alleviate the large control moment variations in the start and stop phases of the rest–to–rest maneuver. [ABSTRACT FROM AUTHOR]

Node elimination is a numerical approach for obtaining cubature rules for the approximation of multivariate integrals. Beginning with a known cubature rule, nodes are selected for elimination, and a new, more efficient rule is constructed by iteratively solving the moment equations. This paper introduces a new criterion for selecting which nodes to eliminate that is based on a linearization of the moment equation. In addition, a penalized iterative solver is introduced, that ensures that weights are positive and nodes are inside the integration domain. A strategy for constructing an initial cubature rule for various polytopes in several space dimensions is described. High efficiency rules are presented for two, three and four dimensional polytopes. The new rules are compared with rules that are obtained by combining tensor products of one dimensional quadrature rules and domain transformations, as well as with known analytically constructed cubature rules. [ABSTRACT FROM AUTHOR]

This paper introduces a two-stage approach that automatically generates 3D CAD models from 2D orthographic drawings. First, a pattern-matching algorithm is proposed to reconstruct a network of 3D edges by matching 2D edge features extracted from the multiple views of the 2D drawing. Second, starting from the resulting 3D wireframe and generated graph, a loop detection algorithm allows identifying possible loops of faces. Then, a clustering algorithm recognizes the faces from the set of detected loops. The reconstruction ends while trimming and stitching the faces to get a watertight ready-to-use 3D CAD model. This approach has been validated on a public dataset composed of several thousands of 3D shapes, and it achieved 99.59% of well-reconstructed models in F-score. • A rule-based approach to automatically reconstruct 3D CAD models from 2D drawings. • A pattern-matching algorithm to generate 3D wireframes from 2D drawings. • A loop detection algorithm using co-planarity criterion to reduce the search space. • A clustering algorithm to identify the faces from a set of 3D loops in a wireframe. [ABSTRACT FROM AUTHOR]

Under the framework of a hybrid-index model, this paper investigates safe control problems of state-dependent random impulsive logical control networks (RILCNs) on both finite and infinite horizons, respectively. By using the ξ -domain method and the constructed transition probability matrix, the necessary and sufficient conditions for the solvability of safe control problems have been established. Further, based on the technique of state-space partition, two algorithms are proposed to design feedback controllers such that RILCNs can achieve the goal of safe control. Finally, two examples are shared to demonstrate the main results. [ABSTRACT FROM AUTHOR]

While the handling for temporally-varying linear equation (TVLE) has received extensive attention, most methods focused on trading off the conflict between computational precision and convergence rate. Different from previous studies, this paper proposes two complete adaptive zeroing neural dynamics (ZND) schemes, including a novel adaptive continuous ZND (ACZND) model, two general variable time discretization techniques, and two resultant adaptive discrete ZND (ADZND) algorithms, to essentially eliminate the conflict. Specifically, an error-related varying-parameter ACZND model with global and exponential convergence is first designed and proposed. To further adapt to the digital hardware, two novel variable time discretization techniques are proposed to discretize the ACZND model into two ADZND algorithms. The convergence properties with respect to the convergence rate and precision of ADZND algorithms are proved via rigorous mathematical analyses. By comparing with the traditional discrete ZND (TDZND) algorithms, the superiority of ADZND algorithms in convergence rate and computational precision is shown theoretically and experimentally. Finally, simulative experiments, including numerical experiments on a specific TVLE solving as well as four application experiments on arm path following and target motion positioning are successfully conducted to substantiate the efficacy, superiority, and practicability of ADZND algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the decentralized optimization problem, where agents in a network cooperate to minimize the sum of their local objective functions by communication and local computation. We propose a decentralized second-order communication-efficient algorithm called communication-censored and communication-compressed quadratically approximated alternating direction method of multipliers (ADMM), termed as CC-DQM, by combining event-triggered communication with compressed communication. In CC-DQM, agents are allowed to transmit the compressed message only when the current primal variables have changed greatly compared to its last estimate. Moreover, to relieve the computation cost, the update of Hessian is also scheduled by the trigger condition. Theoretical analysis shows that the proposed algorithm can still maintain an exact linear convergence, despite the existence of compression error and intermittent communication, if the local objective functions are strongly convex and smooth. Finally, numerical experiments demonstrate its satisfactory communication efficiency. [ABSTRACT FROM AUTHOR]

• Syncretic K-shell is developed to identify the node importance of urban rail transit network. • The invulnerability of urban rail transit network is evaluated. • Cumulative core value is proposed considering both global and local centrality of nodes. • Syncretic core value is the node importance identification criterion of the syncretic K-shell. • Relative size of maximal connected subgraph and relative network efficiency are applied to evaluate the invulnerability performance. In this paper, the Syncretic K-shell algorithm is developed and applied to identify the node importance and evaluate the invulnerability in an urban rail transit network, which is based on the idea that the nodes removed later are more important than those removed former, the nodes with the same core value also have different importance. It has a very important strategic significance for optimizing network structure, improving operational efficiency, and providing targeted protection and designated accident prevention measures for stations with different importance. Syncretic Core Value, as the node importance recognition criterion of Syncretic K-shell algorithm, considers the influence of node degree, cumulative core value and syncretic weight of adjacent nodes on node importance. Firstly, we use space L to establish the indirect and non-weighted urban rail transit network. Then, the node importance recognition steps of Syncretic K-shell algorithm are introduced. Then, a numerical study of the proposed Syncretic K-shell algorithm is conducted, and the Degree Centrality, Betweenness Centrality, Closeness Centrality, Mixed Degree Decomposition and Classical K-shell are used as comparison approaches. The node anti-destruction under node failure (including intentional attack and random attack) is evaluated, and finally, an example study is conducted with the background of Chengdu Metro Network. The results show that the important nodes identified by Syncretic K-shell have a greater impact on anti-destruction performance. The research found that Chengdu Metro Network is more vulnerable to attack and easy to be destroyed under intentional attack. Finally, two coping strategies are provided. According to the research results, we can conclude that compared with the previous node importance recognition methods, Syncretic K-shell algorithm has five advantages: (i) it can distinguish the importance between nodes with the same core value; (ii) considering the global and local centrality of nodes, the Syncretic Core Value of nodes can better reflect the importance of nodes; (iii) it can recognize the importance of nodes in networks with the same degree value or without obvious aggregation characteristics; (iv) the time complexity of Syncretic K-shell algorithm can be used for the recognition of node importance in large-scale networks; (v) it can better distinguish and amplify the differences between nodes, and quickly and effectively rank the importance of nodes. [ABSTRACT FROM AUTHOR]

A depth map fusion algorithm fuses depth maps from different perspectives into a unified coordinate framework and performs surface calculations to generate dense point clouds of the entire scene. The existing algorithms ensure the quality of these dense point clouds by eliminating inconsistencies between depth maps, but the problem of many redundant calculations often arises. In this paper, a depth map fusion algorithm based on pixel region prediction is proposed. First, the image combination is calculated to select a set of candidate neighbor images for each reference image in the scene. Second, voxels and measure estimates are constructed on a coarse scale, and an inference strategy and a corrector are proposed to merge pixel regions at a fine scale guided by the coarse scale. Finally, the deduced pixel regions at the fine scale are used as the image-space constraints for depth fusion. Public and actual oblique images datasets are used for experimental verification. Compared with the famous COLMAP, OPENMVS, Gipuma and ACMP methods, the number of redundant calculations is significantly reduced; according to Data1 ∼ Data9 in the experiment, as the number of images increases, the fusion efficiency is increased by 47.5% to 156.6%; at the same time, the point cloud accuracy is comparable to other methods. [ABSTRACT FROM AUTHOR]

In this paper, a fully discrete finite element scheme is established with L 1 approximation for solving the nonlinear time-fractional wave equation with a variable coefficient. The stability of the scheme is analyzed and the optimal error estimates are derived. To improve the computational efficiency, we propose a two-grid algorithm based on the fully discrete finite element scheme. With the proposed technique, a small-scale nonlinear problem is calculated by iteration in a coarse-grid space with mesh size H , and then a linearized problem is solved in a fine-grid space with mesh size h , H ≫ h. This technique not only maintains the numerical precision, but also saves a lot of computing time. The stability and optimal convergence order are considered. Using the Taylor expansion, the second two-grid algorithm is constructed to improve the optimal convergence order. The similar properties are discussed in detail. Numerical experiments are provided to illustrate the theoretical analysis and test the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a general inertial smoothing proximal gradient algorithm for solving the Capped- ℓ 1 exact continuous relaxation regularization model proposed by Yu and Zhang (2022) [29]. The proposed algorithm incorporates different extrapolations into the gradient and proximal steps. It is proved that, under some general parameter constraints, the singular values of any accumulation point of the sequence generated by the proposed algorithm have the common support set, and the zero singular values can be achieved in a finite number of iterations. Furthermore, any accumulation point is a lifted stationary point of the relaxation model. Numerical experiments illustrate the efficiency of the proposed algorithm on synthetic and real data, respectively. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive three-term conjugate gradient method is proposed based on the Perry family by using a modified secant condition. The new method depends on a positive parameter which has a critical role in the efficiency of the algorithm. We propose an adaptive choice for this parameter to control the condition number of the direction matrix by minimizing the distance between the smallest and largest eigenvalue. Global convergence of the new algorithm is proved for general functions, under standard assumptions. Experimental results verify the expected numerical behavior of our algorithm. [ABSTRACT FROM AUTHOR]