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Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

The increasing pressure on natural resources and the climate has been noted by businesses and governments worldwide, who now face the difficult task of integrating paths of environmental sustainability and economic growth. One promising approach to sustainable development, reducing the pressure on natural resources and solving waste problems is circular economy. From different ways of implementing circular economy, this study focuses on the productization of industrial side streams, which carry a great deal of underutilized potential. In this article, the productization of pulp and paper industry side stream, fiber sludge, for commercial use as a dust-binding agent is studied via a descriptive, in-depth case study. Apart from inconsistent quality, fluctuating supply, cost of storage and logistics, what makes industrial side stream utilization challenging is the lack of knowledge that stakeholders have about side streams and their utilization and the lack of new value chain development for their commercial use. Therefore, it is essential for all parties involved to have a clearer vision of what is being sold for what purposes, through productization. This study found that the productization of side streams follows the same steps as the productization of any other traditional product, and the productized side streams can be added to the company's product portfolio as by-products. [ABSTRACT FROM AUTHOR]

Abstract Successful problem solving relies on the availability of suitable mental representations of the task domain. Especially for more complex problems, there might be a wide variety of possible problem representations, and it might even be beneficial to change them during problem solving. In a first part, we argue that investigating the dynamics of understanding in terms of dynamically changing problem representations is an underexplored aspect of problem solving research, and that most classic tasks even preclude the opportunity of such dynamics to occur. Continuing this theoretical discussion, as an illustrative example of a task designed for the exploration of such representational dynamics, the second part of the paper discusses a novel, complex spatial transformation and problem solving task. In this task, one is asked to repeatedly mentally cross-fold a sheet of paper, and to predict the resulting sheet geometry without the use of external aids. Through its deliberate openness and difficulty, this task requires finding new and more efficient representations – ranging from kinaesthetic and visuospatial imagery to symbolic notions. We present an overview of the task domain and discuss various ways of representing the domain as well as potential dynamics between them. [ABSTRACT FROM AUTHOR]

This paper focuses on investigating a second-order time parallel decoupled algorithm for the mixed Stokes/Darcy model. The main objective of this algorithm is to deal with the issue of low computational efficiency in solving the coupling problem directly. Our scheme not only reduces the computation scale through the decoupling method, but also realizes the parallelism in the time direction through parareal method. Furthermore, we implement a second-order spectral deferred correction method to improve accuracy. For some small time step size, we prove the stability and convergence results of the time parallel decoupled algorithm. Finally, we provide three numerical tests to demonstrate the reliability and computational efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

• The paper gives a novel combination between sparse signal recovery and moving least squares in the field of outlier detection. • This paper proposes a new method to detect outliers in scattered data for approximation. • The idea of sparse signal recovery in the proposed method provides the theoretical guarantee for its effectiveness. • The proposed method can restore the true values corresponding to outliers in high accuracy while outliers are marked. In the scattered data used for function approximation, there may be some data that deviate greatly from their true values, which are called outliers. The existence of outliers significantly affects the accuracy of some approximation methods, such as moving least squares. How to quickly and accurately find these outliers and restore their true values has not been well resolved. In this paper, a new outlier detection and recovery method is proposed for data processing. The method uses the high accuracy of moving least squares quasi-interpolation scheme, its sensitivity to outliers and the sparse distribution of outliers to construct a ł 0 -minimization problem with an inequality constrain. Under certain assumptions, it is proved theoretically that the deviation vector corresponding to the outliers is a solution to the optimization problem. The classical orthogonal matching pursuit algorithm is introduced to solve the optimization problem efficiently. By solving the problem, the outliers are marked, and the deviations are also estimated approximately, which can restore the true values of outliers. The numerical experiments demonstrate that the proposed method has high computational efficiency, very high detection accuracy, and high recovery accuracy for the scattered data used for function approximation, so it is practical. [ABSTRACT FROM AUTHOR]

[Display omitted] • Size-dependent assembly of graphene sheets constructs vertical heat pathways across the graphene paper. • GHGP exhibits both high through-plane thermal conductivity and good compressibility. • The cooling efficiency of GHGP was 2.2 times that of the state-of-the-art commercial TIM. Graphene-based papers have recently triggered considerable interests in developing the application as thermal interface materials (TIMs) for addressing the interfacial heat transfer issue, but their low through-plane thermal conductivity (κ ⊥), resulting from the layer-by-layer stacked architecture, limits the direct use as TIMs. Although various hybrid graphene papers prepared by combining the graphene sheets and the thermally conductive insertions have been proposed to solve this problem, achieving a satisfactory κ ⊥ higher than that of commercial TIMs (>5 W m−1 K−1) remains challenging. Here, a strategy aimed at the construction of heat pathways along the through-plane direction inside the graphene paper for achieving a high κ ⊥ was demonstrated through the simultaneous filtration of graphene sheets with two different lateral sizes. The as-prepared graphene paper presented a hierarchical structure composed of loosely stacked horizontal layers formed by large graphene sheets, intercalated by a random arrangement of small graphene sheets. Due to the heat pathways formed by small graphene sheets along the through-plane direction, the hierarchically structured graphene paper exhibited an improved κ ⊥ as high as 12.6 W m−1 K−1 after a common graphitization post-treatment. In the practical test, our proposed paper as an all-graphene TIM achieved an enhancement in cooling efficiency of ≈ 2.2 times compared to that of the state-of-the-art TIM, demonstrating its superior performance to meet the ever-increasing heat dissipation requirement. [ABSTRACT FROM AUTHOR]

The development of ARX-based lightweight block ciphers has been plagued by the difficulty of theoretical security analysis. SAND solves this problem better by obtaining an equivalent representation based on a synthetic S-box. This paper analyzed SAND in terms of diffusivity and found that it can be optimized. SAND has the issue of slow diffusion after the initial plaintext and key are all 0. On the other hand, it takes at least 11 rounds for SAND to reach full diffusion without AddRoundKey. Thus, this paper proposes a high-diffusion architecture SAND-2 to address the above issues. Firstly, issue 1 is solved by replacing the AND operation with a NAND operation and calling the round function dynamically. Then, in order to solve issue 2, P1 and P2 permutations are introduced into G 0 and G 1 , respectively. The full diffusion speed of SAND-2 is 63.7% increased compared to SAND. In addition, the comparison results of hardware indicators show that the hardware resources of SAND-2 are slightly lower, and the throughput is 10% higher than SAND. Finally, the security analysis shows that SAND-2 reaches the upper bound of the resisting differential analysis in fewer rounds. • SAND-2 diffusion speed is 63.7% increased by SAND. • SAND-2 throughput is 10% higher than SAND. • SAND-2 is more secure than SAND. [ABSTRACT FROM AUTHOR]

This paper studies the high telepresence control for the relay communication based space teleoperation. High telepresence requires real-time, accurate force perception and good position tracking, but all of them are destroyed or affected by time delay. To eliminate the influence of time delay, direct recursive generalized predictive control (DR-GPC) is quoted into this paper to realize position tracking. As for the other two properties, the pseudo position-force (Pseudo-PF) feedback strategy (composed of parameter identification and force reconstruction) is a good way as it can provide real-time and precise force feedback when environment change slowly and time delay is relatively small. However, complex and changeable environment and serious and complex time delay in space teleoperation make the environment force parameter available to force reconstruction is outdated and cannot realize accurate force rebuilding. To solve this problem, we propose a dual regression Pseudo-PF feedback strategy. Compared with the traditional one, its biggest change is the introduction of dual regression that consists of time delay prediction and parameter regression. The former predicts time delay and provides it to the latter to determine the forward prediction steps, while the latter regresses the actual latest and precise environment force parameter according to the forward prediction steps. With the cooperation of time delay prediction and parameter regression, the influence of fast changing environment or serious and complex time delay on force reconstruction can be effectively overcome, so as to realize real-time and accurate force feedback. Finally, DR-GPC and dual regression Pseudo-PF feedback strategies cooperate to achieve high telepresence control, and simulation is given to show its effectiveness. • A dual regression Pseudo-PF feedback strategy is proposed. • High teleopresence control for the relay communication based space teleoperation is first studied. • The introduction of dual regression makes the applicability of this method wider. [ABSTRACT FROM AUTHOR]

This paper studies the efficient methods for option pricing under multivariate rough volatility models. The characteristic functions of the asset log-price, which play important role in the option pricing under the multivariate rough volatility models, are determined by a system of parametric nonlinear fractional Riccati equations. This paper obtains the results on the existence, uniqueness and regularity of the solutions to the parametric nonlinear fractional Riccati equations, proposes a high-order scheme to solve the system and proves the high-order convergence. The option pricing problem is solved by the Fourier-cosine formula with the fast approximation of the characteristic functions. Numerical examples are carried out to confirm the theoretical results and show efficiency of the methods. [ABSTRACT FROM AUTHOR]

In this paper we discuss different transmission operators for the non-overlapping Schwarz method which are suited for solving the time-harmonic Helmholtz equation in cavities (i.e. closed domains which do not feature an outgoing wave condition). Such problems are heavily impacted by back-propagating waves which are often neglected when devising optimized transmission operators for the Schwarz method. This work explores new operators taking into account those back-propagating waves and compares them with well-established operators neglecting these contributions. Notably, this paper focuses on the case of rectangular cavities, as the optimal (non-local) transmission operator can be easily determined. Nonetheless, deviations from this ideal geometry are considered as well. In particular, computations of the acoustic noise in a three-dimensional model of the helium vessel of a beamline cryostat with optimized Schwarz schemes are discussed. Those computations show a reduction of 46% in the iteration count, when comparing an operator optimized for cavities with those optimized for unbounded problems. [ABSTRACT FROM AUTHOR]

In this paper, a novel spectral method is presented and studied for the fourth order problem with mixed boundary in a cylindrical domain. The basic idea of our approach is to reduce the original problem into a series of decoupled two-dimensional fourth-order problems first, by using the cylindrical coordinate transformation and Fourier expansion, and then adopt the standard spectral method to solve the decoupled problems. A new essential pole condition is proposed to overcome the difficulty caused by the introduction of singularity and variable coefficients in cylindrical coordinate transformation. Existence and uniqueness of the weak solution and the discrete numerical solution are proved, and error estimates of the spectral method are derived. Furthermore, the efficient implementation of our algorithm is discussed, where a set of effective basis functions are constructed to ensure the sparsity of the mass matrix and stiffness matrix. Numerical examples are presented to validate the theoretical findings and the efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamics and digital control issues of deploying a tethered satellite using constrained tether tension. To cater to the requirements of the digital techniques applied in engineering applications, unlike existing results, this study develops a digital feedback control strategy to achieve the rapid and stable tether deployment. The proposed digital control law consists of discretized and quantized tether states and is derived based on continuous-time dynamical model instead of the discrete-time one. To keep tether tension at a positive value when implementing control action, the constraint on tether tension is explicitly accounted for during controller design. Furthermore, the discretization and quantization errors are treated as the uncertain terms in the closed-loop system which can be handled by robust analysis and synthesis techniques. A Lyapunov functional, together with an integral performance index, is made to prove the stability of the system, and the desired controller is obtained by solving an optimization problem. Finally, the effectiveness of the proposed controller is demonstrated by numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a decentralized guidance and target assignment (DGTA) strategy for reconfiguring swarms made up of hundreds of modules that have limited communication capabilities. This strategy provides a decentralized onboard computation method to address both near-optimal target assignment and collision-free trajectory planning for swarms of modules. To effectively estimate the transfer cost between modules and targets for target assignment, a multilayer perceptron (MLP) is developed via the backward generation method. The decentralized target assignment problem is solved by greedy algorithm according to the result of the efficient estimating solver. A decentralized model predictive control approach is proposed to guide swarm modules towards their assigned targets, while avoiding collisions with other modules. The feasibility of this guidance framework has been demonstrated through rigorous theoretical analysis. Numerical experiment confirms that the MLP-based method shows an accurate and real-time advantage for estimating transfer cost. Furthermore, the DGTA strategy has been shown to possess robustness and generalization ability, as evidenced by its successful application in a pinpoint assembly task involving 125 and 182 modules. The simulation results confirm the feasibility of this approach for in-orbit assembly application. [ABSTRACT FROM AUTHOR]

• This paper studies the scaled consensus problem which is a generalization of usual consensus investigated in the literature. • Innovative impulsive control protocols are proposed to solve the problems of scaled consensus for multi-agent systems. • It also studied the scaled consensus problems of multi-agent systems subject to external perturbations. This paper studies scaled consensus problems of multi-agent systems under fixed and switched topologies. Firstly, the scaled consensus protocols are designed for solving consensus problems of multi-agent systems under fixed topologies. Secondly, the scaled consensus problems of multi-agent systems under switching topologies are considered. Thirdly, scaled consensus of multi-agent systems with external perturbations is investigated via impulsive mechanism. Using matrix theory, graph theory and Lyapunov algorithm, we can show that under appropriate conditions the scaled consensus problems can be solved. Moreover, our results show that the boundedness of the sampling period depending on scalar scales and degree of networks has strong impact for achieving scaled consensus of the systems. Some numerical examples are provided to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

• A computational binary characterization method for convex object(s). • A unified model for convex object(s) segmentation and convex hull computation. • Linearization technique employed to reduce the difficulty of the model solving. • A convergent proximal alternating direction method of multipliers for the linearization problem. • An interactive procedure to improve the segmentation accurac y in terms of shape distance and D ice coefficient. Convexity prior is one of the main cues for human vision and shape completion with important applications in image processing and computer vision. This paper provides a computable characterization method for shape convexity, and illustrates its applications in image segmentation and convex hull computation. We prove that the convexity of a region is equivalent to a series of quadratic inequality constraints on its indicator function. By incorporating this result, models are proposed for image segmentation with convexity prior and convex hull computation of clean and noisy data sets, respectively. Then, these models are summarized into a unified optimization problem on binary function(s) with the quadratic inequality constraints. Numerical method is proposed by linearizing the quadratic constraints. The linearization problem is solved by a proximal alternating direction method of multipliers, the convergence of which is guaranteed under some proper conditions. Numerical experiments demonstrate the efficiency and effectiveness of the proposed methods for image segmentation and convex hull computation. [ABSTRACT FROM AUTHOR]

• The trajectory planning problem is transformed into an optimal control problem. • A loosen formation constraint strategy is integrated to facilitate the convergence. • An improved APF method is constructed to warm-start the trajectory planner. In this paper, an optimal control-based method with efficient warm-start is proposed to solve the trajectory planning problem for fixed-wing unmanned aerial vehicle formations. We first formulate the trajectory planning problem as an optimal control problem by analyzing the motion equations and various constraints. Secondly, an improved artificial potential field method is designed to meet the requirements of eliminating local oscillations, inter-formation coordination, and physical constraints with the objective of a high-quality homotopic warm-start. Last, a loose formation constraint strategy is employed to expand the solution space and thus facilitate the convergence process. Numerical simulations at different complexity levels show that the proposed planner is efficient and robust and that the designed warm-start strategy contributes significantly to the convergence of the method. Also, the proposed planner exhibits promising performance in solving the problem with multiple formations. [ABSTRACT FROM AUTHOR]

In this paper we present a nonoverlapping Robin-type multi-domain decomposition method based on stabilized Q 1 – P 0 mixed approximation (RMDD-Q1P0) for incompressible flow problems. The global Stokes and Navier-Stokes equations are decomposed into a series of local problems through Robin-type domain decomposition, and local problems are solved through the local jump stabilized Q 1 – P 0 approximation. The RMDD-Q1P0 solutions are proven to converge to the standard global Q 1 – P 0 solutions for the Stokes problem. Numerical experiments for both Stokes and Navier-Stokes equations demonstrate that RMDD-Q1P0 significantly improves the scalability of the stabilized Q 1 – P 0 approximation. The codes used to generate the numerical results are available online. [ABSTRACT FROM AUTHOR]

Hate Speech Detection (HSD) aims to identify whether a text contains hate speech content, which often refers to discrimination and is even associated with a hate crime. The mainstream methods jointly train the HSD problem with relevant auxiliary problems, e.g., emotion detection and sentiment analysis, under the paradigm of Multi-Task Learning (MTL). In this paper, we improve HSD by integrating it with emotion detection, since we take inspiration from the potential correlations between hate speech and certain negative emotion states, which have been studied theoretically and empirically. To be specific, we can concatenate their hateful labels and predicted emotion states as pseudo-multiple labels for hate speech samples, formulating a pseudo-Multi-Label Learning (MLL) problem. Beyond the existing MTL-HSD methods, we further incorporate this pseudo-MLL problem and solve it by capturing the correlations between hate speech and negative emotion states, so as to improve the performance of HSD. Based on these ideas, we propose a novel HSD method named the E motion-correlated H ate S peech Detect OR (EHS or). We conduct extensive experiments to evaluate EHS or , and the results show that it can consistently outperform the existing HSD methods across benchmark datasets. • Validating correlations between hate speech and specific negative emotions. • Proposing a new MTL-based HSD method EHSOR from the perspective of MLL. • EHSOR empirically outperforms baseline methods over three HSD benchmarks. [ABSTRACT FROM AUTHOR]

This paper investigates the asteroid hovering problem using the Multiple-Overlapping-Horizon Multiple-Model Predictive Control method. The effectiveness of the predictive controllers in satisfying control constraints and minimizing the required control effort is making Model Predictive Control a desirable control method for asteroid exploration missions which consist of the asteroid hovering phase. However, the computational burden of Model Predictive Control is an obstacle to employing the asteroid's complex gravitational field model. As an alternative option, the Multiple Horizon Multiple-Model Predictive Control method has been introduced previously, which could provide a solution with the less computational burden with respect to the nonlinear Model Predictive Control. It was shown that it is not necessary to deduce the exact dynamics model to predict the system's behavior during a long period using this approach. However, the calculated control acceleration was not smooth enough because of the crisp borders of consecutive horizons, which may cause an image motion and degrades the geometric accuracy of high-resolution images in asteroid hovering missions. In this paper, the Multiple-Overlapping-Horizon Multiple-Model Predictive Control method is introduced instead to solve the problem of controlling acceleration fluctuations by overlapping consecutive horizons. Numerical simulation results are presented to validate the effectiveness of the proposed control method, and its advantage is demonstrated accordingly for the asteroid hovering problem in achieving the hovering position and velocity. [ABSTRACT FROM AUTHOR]

In this paper, we propose a lightweight asymmetric refining fusion network (LARFNet) for real-time semantic segmentation to solve the problem that some existing models cannot achieve good segmentation accuracy with real-time inference speed in mobile devices due to the huge computational overhead. Specifically, LARFNet adopts an asymmetric encoder–decoder structure. The depth-wise separable asymmetric interaction module (DSAI module) is designed in the encoding process, which effectively extracted local and surrounding information under different receptive fields with optimized convolution in the condition of ensuring communication between channels. In the decoder, we design the bilateral pyramid pooling attention module (BPPA module) and the multi-stage refinement fusion module (MRF Module). The BPPA module is used to integrate the high-level output multi-scale context information. Based on spatial and channel attention mechanisms, the MRF module is proposed to refine the feature maps of different resolutions and guide the feature fusion. Experimental results show that LARFNet achieves 69.2% mIoU and 65.6% mIoU on Cityscapes and Camvid datasets at 127 FPS and 222 FPS respectively, only using a single NVIDIA GeForce GTX2080Ti GPU and 0.72M parameters without any pre-training or pre-processing. Compared with most of the existing state-of-the-art models, the proposed method realizes the efficient use of network parameters at a faster speed, reduces the number of network parameters, and still achieves the accuracy of good segmentation. [Display omitted] • In this paper, we propose a lightweight real-time semantic segmentation network which considers inference speed, number of model parameters and segmentation accuracy as a whole, named lightweight asymmetric refining fusion network (LARFNet). The network mainly consists of three modules. The DSAI module is used to extract different features. The BPPA module is used to provide pixel-level attention for features, and the MRF module is used to guide feature fusion after optimizing features. [ABSTRACT FROM AUTHOR]

Routing is one of the most important and time-consuming stages of physical design. As the process node of semiconductors keeps scaling down, the routing process faces increasing challenges, and the traditional solutions are not sufficiently efficient. In recent years, machine learning has aroused much interest in this context, and an increasing number of algorithms have introduced advanced machine learning techniques to help solve the routing problem. In this paper, we survey the recent development of machine learning-based routing algorithms. • This paper provides an overview of recent published machine learning-based routing algorithms. • Some trends on the development of machine learning-based routing algorithms are revealed. • The possible advantages and disadvantages on machine learning-based routing methods are discussed. [ABSTRACT FROM AUTHOR]

It is intuitively clear that a circuit to be implemented by selectively utilizing standard cells of various non-integer multiple-heights (NIMH) (e.g., mixed use of 6-track, 7.5-track, and 9-track cells) is able to provide a better opportunity in optimizing power, performance, and area over that by using cells of single-height only or of integer multiple-heights only. However, from the cell placement legalization point of view, the issues to be addressed for placement legalization on NIMH designs are very complex. And this paper primarily focuses on introducing novel ideas for row placement when utilizing NIMH cells, which involves determining the row pattern. The most inter-dependent and critical tasks, which are rather unique to the NIMH cell placement legalization problem, are (task 1) for the cells of the same height, distributing and assigning them to a set of distinct rows on a die and (task 2) determining the location of the rows containing cells of the same height. We solve the legalization problem by, starting from an initial row placement, iteratively solving the two tasks by formulating task 1 into an instance of row-capacity constrained network flow problem, followed by solving task 2 which leads to an optimal vertical displacement of the cells in the rows. Meanwhile, through experiments, it is shown that our network flow driven global cell assignment to rows for NIMH cell placement problem tightly linking the optimal determination of row location is able to reduce the total amount of cell displacements from the global (initial) placement by 9.5% and 51.2% in comparison with that produced by a greedy approach and the conventional state-of-the-art NIMH cell placement legalization method, respectively. • This paper primarily focuses on introducing novel ideas for row placement when utilizing NIMH (non-integer multiple-height) cells. • The most inter-dependent and critical tasks are (task 1) for the cells of the same height, distributing and assigning them to a set of distinct rows on a die and (task 2) determining the location of the rows containing cells of the same height. • We solve the legalization problem by iteratively solving the two tasks by formulating task 1 into an instance of row-capacity constrained network flow problem, followed by solving task 2 which leads to an optimal vertical displacement of the cells in the rows. • Our network flow driven global cell assignment to rows is able to reduce the total amount of cell displacements from the global (initial) placement by 29.0% and 62.0% in comparison with that produced by a greedy approach and the conventional state-of-the-art NIMH cell placement legalization method, respectively. [ABSTRACT FROM AUTHOR]

• In order to solve the problem that the pulsewidth modulation strategy has poor dynamic characteristics at low switching frequencies, a predictive method is proposed. • Aiming at the difficulty of adjusting the weight factors, a self-adjusting strategy of the weight factors is proposed, which evaluates and adjusts the weight factors according to the harmonic component obtained by real-time calculation to meet the requirements. • In order to further improve the real-time performance, the sliding discrete Fourier analysis is applied to extract harmonic components in real time. Active front-end converters are part of high power installations for regenerative energy systems, feeding the regenerated energy into the utility. Under the island mode operation of the regenerative energy system, the load characteristics are difficult to detect, and the change of the load characteristics will cause the corresponding resonance. V/f control is usually used to ensure the harmonic quality. At high switching frequency, it has a good control effect. However, high switching frequency causes high switching loss. In order to reduce the switching loss, it will produce more harmonic distortion. Furthermore, the commonly used modulation strategies in this case face the problem of low real-time performance. Therefore, in order to avoid the harm caused by circuit resonance under the condition of reducing the switching frequency, this paper proposes a specific harmonic suppression algorithm with high real-time performance, which can be used as an optional control algorithm in the island operation state of power system. At the same time, aiming at the difficulty of adjusting the weight factors, this paper proposes a weight factor automatic tuner, which automatically adjusts the weight factors according to the harmonic component. [ABSTRACT FROM AUTHOR]

This paper presents a method for the control design of five-level DCC converters based on mixed-integer optimization and machine learning. The resulting controller is computationally simple and can be easily implemented on low-resource control hardware using simple nested "if-else" statements. The optimization problem is recalled from previous work by modifying the cost function to further enhance the dynamic performance. Additionally, and in contrast to previous works, the online implementation accomplished in this paper allows the system to cover a wider range of operating points. For this, the optimization problem is solved offline for several operating conditions, and the results are gathered into a dataset to train classification and regression trees (CARTs), which are later used online. Due to the generalization capability of the CARTs, a more flexible and less resource-intensive implementation is achieved which is capable of operating at points outside the ones considered in the training dataset. The resulting control strategy is compared in simulation and experiments with several alternative approaches found in the literature. This approach can be extended to other power converter topologies, allowing the implementation of optimized modulations. • Modulation of five-level DCC as a mixed-integer optimization problem. • Achievement of wider range of operating points. • Classification and regression trees used for implementation of optimization problem. • Guidelines for robust implementation of such tress. • In-detail comparison with existing approaches both in simulation and experiments. [ABSTRACT FROM AUTHOR]

Geographic modeling is considered as an effective way to solve geographic problems. Geographic conceptual modeling is the first step of geographic modeling, in which modelers can fully exchange modeling ideas, thereby achieving a common understanding of geographic system and guiding subsequent geographic modeling process. However, due to the diverse research backgrounds of modelers, it is challenging for them to exchange modeling ideas with one another. This paper provides a visual cognitive communication method for modelers by constructing a conceptual graph gallery, and then improves the efficiency of geographic conceptual modeling. A multidimensional description method that describes modeling cognition from multiple perspectives, the conceptual graph gallery that includes concept items and conceptual graphs, and the conceptual graph gallery-based cognitive communication methods are designed in this paper. Finally, a case study involving the construction of a hydrological conceptual graph gallery is designed to illustrate the feasibility of conceptual graph gallery-based cognitive communication. • Cognitive communication is a key component in geographic conceptual modeling. • Geographical concepts can be described from different perspectives. • Concept graphs can describe geographic concepts visually. • Conceptual graph gallery can support cognitive communication. [ABSTRACT FROM AUTHOR]

The present comment concerns some doubtful results included in the above paper. [ABSTRACT FROM AUTHOR]

The inherent brittleness of 3D-printed ceramic parts makes the surface more prone to produce defects. In the 3D-printing environment, the defect regions in the surface are easily covered by interference factors. This brings great difficulties to surface defect detection. Based on this, this paper proposes an anti-interference detection method for surface defects of ceramic parts based on deep learning. This anti-interference detection method is mainly divided into three stages: interference factors identification, interference factors repair, defect detection. Interfering factors in the surface are located and identified through the built Multimodal Feature Layer Fusion-PSP network (MFLF-PSP net) model. MFLF-PSP net's mAP for interference factor identification is up to 95.67%. Then, based on the results of the interference identification, the proposed Parallel Spatial-Channel Attention Mechanism (PSCAM)-RFR net model is used to perform pixel filling and repair in the regions where the interference factor is located. This solves the difficult problem of defect detection caused by interference factors. On this basis, the constructed Inception-SSD network model is used to perform effective defect detection on ceramic surface images. The mAP of the model for the detection of crack defects and bulge defects is 96.34% and 94.92%, respectively. Through the close cooperation of the above three stages, the problem of defect detection caused by interference factors, such as misjudgment and defect region segmentation, has been solved. It provides technical support for automatic detection of ceramic surface quality during 3D-printing. [ABSTRACT FROM AUTHOR]

In the real world, one object is usually described via multiple views or modalities. Many existing multiview clustering methods fuse the information of multiple views by learning a consensus representation. However, the feature learned in this manner is usually redundant and has neglected the distinctions among the different views. Addressing this issue, a method named nonredundancy regularization based nonnegative matrix factorization with manifold learning (NRRNMF-ML) is proposed in the paper. A novel nonredundancy regularizer defined with the Hilbert–Schmidt Independence Criterion (HSIC) is incorporated in the objective function of the proposed method. By minimizing this term, the redundant information among the multiple views can be effectively reduced and the distinct contributions of the different views can be encouraged. To further utilizing manifold structure information of the data, a manifold regularizer is also constructed and included in the objective function of the proposed method. For the proposed method, an iterative optimization strategy was designed to solve the problem; the corresponding proof is presented both theoretically and experimentally in this paper. Experimental results on five multiview data sets compared with several representative multiview clustering methods revealed the effectiveness of the proposed method. • A novel multiview clustering method named NRRNMF-ML is presented. • A nonredundant term is designed and incorporated into an NMF-based multiview clustering model. • By minimizing the nonredundant term, the redundancy among the views is suppressed. • An iterative optimizing strategy is presented to optimize the proposed method. [ABSTRACT FROM AUTHOR]

• The quantum theory is utilized to quantify the relationship between stresses and lifetime for the first time. • A novel proportional decay model is proposed. • A quantum framework for addressing how stresses impact the parameters of the lifetime distribution is provided. • The proposed model outperforms the existing models. The traditional models for the analysis of the accelerated test data cannot physically explains how the environment affects lifetime and its uncertainty. To solve this problem, this paper utilizes the theory of open quantum system, the theory about random behaviour of microparticles under the external environment, to describe the effects of stresses on lifetime uncertainty. The interaction between the system and environments motivates us to describe the accelerated test data from a quantum perspective. A quantum master equation is used as the reliability dynamics, which can be separated into a conservation term and a decay term. Then, the acceleration model is further incorporated into the decay term of the dynamic and forms a semi-quantum model. A practical procedure is proposed to obtain the initial guesses of parameters and do a graphic check on the assumption. Applications on two sets of real accelerated life test data show the proposed model outperforms the existing ones and hence verify its validity. This paper makes the first attempt to quantify the uncertainty under different environments from a quantum perspective and shows that it is feasible to utilize quantum theory to make a further exploration. [ABSTRACT FROM AUTHOR]

• The degradation model with power-law drift is established based on the fractional Levy stable motion. • The degradation trend of bearing is extracted by the characteristic factor and the variational mode decomposition. • The fractional LȨvy stable motion degradation model have good prediction performance for remaining useful life. In practice, the processes of degradation of inductive components and produced have long-range dependence characteristic, whereby the future degradation evolution is related to both current and previous states. Most known prediction models consider the degradation increments independent and the long-range dependence is not reflected. On the contrary, the heavy tail model can reveal the long-range dependence characteristic of the degradation processes.The contribution of this paper is to propose a new heavy tail degradation model, in which the fractional L e ´ vy stable motion is used as a diffusion term to establish a degradation model with power rate drift. The difference iterative form of the fractional L e ´ vy stable motion degradation model is, then, used to predict the remaining useful life. In the paper, this is applied to actual bearing degradation data and its prediction performance is analyzed. Variational mode decomposition is used to solve the problem that the degradation trend is not obvious for the vibration signals. [ABSTRACT FROM AUTHOR]

• The correlation between uncertain variables was considered in optimization model. • The feasible discriminant of interval constraints was defined to identify the feasible solutions. • A competitive strategy was proposed to identify optimal solution and achieve equilibrium design. • A direct solution framework integrating response surface model, interval analysis with genetic algorithm was established. • The proposed algorithm has advantages in robust design than other methods. To solve the design problems of engineering structures with multiple conflicting performance indices under uncertainties, a non-probabilistic optimization model which takes into consideration the correlation of uncertainties was proposed in this paper. The multidimensional parallelepiped model was utilized to describe the uncertainties and deal with their correlation. Further, a novel concept of feasible discriminant of interval constraints was proposed to identify the feasible solutions, and a competitive strategy for design vectors was proposed to achieve equilibrium design of structures. To improve the efficiency of optimization and promote its applications in engineering, a direct solution framework was constructed in this paper. This framework was based on response surface model and integrated the interval analysis with the genetic algorithm. The effectiveness and superiority of the proposed approach were illustrated by two numerical examples. Finally, the proposed method was applied to structural design of the upper and lower beams of forming machine, and the design of lightweight and equilibrium were achieved. This structural design example demonstrated the applicability and effectiveness of the proposed method in engineering. [ABSTRACT FROM AUTHOR]

• Propose a queueing model to characterize the intrinsic uncertainty of right-turn-on-red queue. • Establish an analytical framework to measure the performance of right-turn-on-red control. • Formulate an algorithm to compute the right-turn-on-red performance measures. • Develop recommendations and clarify the use of right-turn-on-red control. • Factors that could influence the performance of right-turn-on-red are quantitatively examined. The primary purpose of this study is to develop an explicit dynamical state estimation model of the right-turn-on-red queue to solve the accuracy problem of the right-turn-on-red operation assessment in a variety of conflicting environments. Our effort focuses on characterizing the dynamics and nonlinearity of the right-turn-on-red queue. The proposed model can explicitly measure the variability of the right-turn-on-red queue under various traffic conditions without requiring substantial parameters. In this paper, we provide a set of measures to formulate the right-turn-on-red operation performance. Besides some fundamental average indices, the index of the signal-state-dependent queue length that can characterize the queue evolution process of right-turn-on-red is obtained, in particular, we also obtain the variance of right-turn-on-red queue length at a given time point within a cycle. The new version of the queue length along the signal state can play an important role in understanding the accuracy of the use of right-turn-on-red operation and providing a high quality solution to properly treat the right-turn-on-red control under the different types of right-turn-on-red phasing regimes. The results obtained from the proposed model were compared with field data, which showed good agreement. In addition, a series of numerical experiments is carried out to clarify the effects of the possible factors on the dynamical state of the right-turn-on-red queue. [ABSTRACT FROM AUTHOR]

This paper considers the temporal discretization of an inverse problem subject to a time fractional diffusion equation. Firstly, the convergence of the L1 scheme is established with an arbitrary sectorial operator of spectral angle < π / 2 , that is the resolvent set of this operator contains { z ∈ C ∖ { 0 } : | Arg z | < θ } for some π / 2 < θ < π. The relationship between the time fractional order α ∈ (0 , 1) and the constants in the error estimates is precisely characterized, revealing that the L1 scheme is robust as α approaches 1. Then an inverse problem of a fractional diffusion equation is analyzed, and the convergence analysis of a temporal discretization of this inverse problem is given. Finally, numerical results are provided to confirm the theoretical results. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment analysis (ALSA) is the process of collecting, processing, analyzing, inferring, and synthesizing subjective sentiments of entities contained in texts at the aspect level. The development of social networks has been driven by the on-going appearance of vast numbers of short documents, such as those in which opinions are expressed and comments are made. The text in these documents reflects users' emotions related to entities. The ALSA of these short texts plays an important role in solving various problems in life. Particularly in e-commerce, manufacturers can use sentiment analysis to determine users' orientations, adapt their products to perfection, identify potential users, and pinpoint users that influence other users. Therefore, improving the performance of ALSA methods has recently attracted the interest of researchers. Currently, four main types of ALSA methods are available: knowledge-based, machine learning-based, hybrid-based, and most recently, graph convolutional network (GCN)-based. This study is the first survey to focus on reviewing the proposed methods for ALSA using GCN methods. In this paper, we propose a novel taxonomy to divide GCN-based ALSA models into three categories based on the types of knowledge extraction. We present and compare GCN-based ALSA methods following our taxonomy comprehensively. Common benchmark datasets and text representations that are often used in GCN-based methods are also discussed. In addition, we discuss five challenges and suggest seven future research directions for GCN-based ALSA methods. The findings of our survey are expected to provide the necessary guidelines for beginners, practitioners, and new researchers to improve the performance of ALSA methods. • A concise overview of aspect-level sentiment analysis (ALSA) and GCNs. • Proposal of first taxonomy for GCNs-based aspect-level sentiment analysis methods. • A comprehensive review of modern GCNs methods for aspect-level sentiment analysis. • Five possible current challenges of GCNs-based aspect-level sentiment analysis. • Seven suggestions for future directions of GCNs-based ALSA. [ABSTRACT FROM AUTHOR]

Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with high-dimensional decision variables, making it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this study proposes a constraint handling method combining a penalty function nested DPSA-POA and an intelligent algorithm, to determine the optimal flood control operation of cascade reservoirs in the middle reaches of the Ganjiang River, a problem with decision variables of up to 2196 dimensions. The results indicate that the constraint handling method proposed in this paper can solve high-dimensional optimization problems in three modes: continuous nesting (Mode 1), optimization with intelligence after obtaining a feasible solution (Mode 2), and optimization with the DPSA-POA after obtaining a feasible solution (Mode 3). Of the three modes, Mode 2 has the highest accuracy, but its calculation time is approximately 10 h. Although the accuracy of Mode 3 is slightly worse, can only achieve 98%–99% of Mode 2, its calculation time is only approximately 1–3 h. The comprehensive performance of Mode 1 is poor, the convergence accuracy can only reach 97% of Mode 2, which corresponds to a calculation time of approximately 4–6 h. The existing superiority of feasibility (SF), stochastic ranking (SR), penalty function (PF), ε-constraint (EC) and adaptive ε-constraint (Adaptive EC) methods cannot converge to a feasible solution stably, and the accuracy of the results of these methods under the condition of obtaining a feasible solution is significantly lower than that of Mode 2 and Mode 3, reaching only 95%. The research ideas of the articles "Study on the constraint handling method for the high-dimensional optimization of cascade reservoirs"The caption of the graphical abstract should read: Multicomplex constraints often need to be considered in the optimal operation of cascade reservoirs with high-dimensional decision variables, which makes it difficult for traditional optimization methods and modern intelligent algorithms to solve such problems. Therefore, this paper proposes a constraint handling method combined with penalty function nested DPSA-POA and the intelligent algorithm. The experimental results of applying this method to the reservoir group in the middle reaches of the Gan River demonstrate its superiority. [Display omitted] • Constraint processing method nested DPSA-POA and intelligent algorithm was proposed. • Using iteratively the random search strategy and guided search strategy. • Optimal operation of cascade reservoirs with 2196 decision variables was tested. [ABSTRACT FROM AUTHOR]

Airspace surveillance is a significant issue for many countries to control and manage their airspace. The number of radars used and their coverage rate are the main issues to consider in this case. Therefore, this paper addresses the problem of finding the best radar locations to obtain the highest coverage rate with the least possible number of radars in a certain region. The radar placement problem is considered as a multi-objective optimization problem with two objectives: the number of radars and the coverage rate. To perfectly solve this optimization problem, a set of multi-objective meta-heuristic approaches based on simulated annealing, memory-based steady-state genetic algorithm, a decomposition-based multi-objective algorithm with differential evolution, and non-dominated sorting genetic algorithm (NSGA-II) are utilized. Algorithms are tested on a dataset created using DTED-1 map elevation data for two different selected regions. Based on the results, the NSGA-II algorithm achieves the best results and the highest coverage ratios among the tested algorithms. Two improved versions of the NSGA-II algorithm are also proposed to enhance its performance and make it more suitable for solving this optimization problem. The experimental results show that a coverage rate of 98% could be achieved with a small number of radars, and by increasing the number of radars, it exceeds 99%. • This paper solves the radar placement problem using a set of metaheuristic approaches. • The problem is considered as a multi-objective optimization problem. • Two adaptive and enhanced versions of the NSGA-II algorithm are proposed. • The results show that a coverage rate of 98% can be achieved with just a few radars. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AV)s have become a trending topic nowadays since they have the potential to solve traffic problems, such as accidents and congestion. Although AV systems have greatly evolved, it still have their limitations. For example, Google reported that their AVs have been involved in several collisions and near misses. While most of these collisions and near misses were caused by third parties, the AVs should be able to predict and avoid them. Events like this show that there is still room for improvement in the AV system. This paper aims to present a review of the state-of-the-art algorithms proposed to enable AV behaviour prediction systems to predict trajectories and intentions for pedestrians and vehicles. This will be achieved by using information from previous literature review papers, recent works, and results obtained using well-known datasets. • An AV behaviour prediction system must have a long (> 3s) prediction horizon. • It is a challenging task to model interaction between agents. • Datasets from onboard sensors should be used to predict road agents' behaviour. • Deep learning techniques have shown better results to predict behaviours. • Algorithms should be evaluated using the same datasets and evaluation metrics. [ABSTRACT FROM AUTHOR]

This paper presents a simple methodology for determining the optimal design of an elastic funicular arch under point loads and selfweight for maximum in-plane buckling capacity. This class of optimization problem has hitherto not been investigated. The cross-section of the arch is assumed to be geometrically similar and constant through its entire length. The arch is assumed to be inextensible. The supporting points of the arch may be either pinned or fixed. The methodology for determining the optimal arch shape involves first finding the funicular shape of the arch under point loads and selfweight. Next, the arch is modelled by the Hencky bar chain model (HBM) for the buckling analysis. The HBM is adopted because it eliminates the requirement for formulating the governing equation for buckling and it is a straightforward model to comprehend and implement in a computer code. Each funicular arch shape is specified by a horizontal force value, and it has to be optimally selected to maximise the buckling capacity of the arch. As it is a single parameter optimization problem, the Golden Section search was used to determine the optimal horizontal force, and hence the optimal funicular arch shape. The methodology is illustrated by solving two example problems: (1) an arch under a point load and selfweight and (2) a symmetric arch under two equal point loads and selfweight. By solving the self-buckling problem of a catenary arch, the maximum spanning capacity of the catenary arch can be obtained as shown herein. • Paper outlines a method for designing elastic funicular arches to optimize buckling capacity. • Arch supportings can be pinned or fixed, offering design flexibility. • Method finds funicular arch shape under seflweight and point loads for analysis. • Paper uses Hencky bar chain model for buckling analysis, simplifying governing equations. • Method demonstrates with two examples: an arch under point load and selfweight, and a symmetric arch under two equal point loads and selfweight. [ABSTRACT FROM AUTHOR]

The Dung beetle optimization algorithm is a kind of group intelligence optimization algorithm proposed by Jiankai Xue in 2022, which has the characteristics of strong optimization-seeking ability and fast convergence but suffers from the defect of easily falling into local optimum at the late stage of optimization-seeking as other group intelligence optimization algorithms. To address this problem, this paper proposes a dung beetle search algorithm (QHDBO) based on quantum computing and a multi-strategy hybrid. The good point set strategy is used to initialize the initial population of dung beetles. That makes the initial population more evenly distributed, and reduces the likelihood of the algorithm falling into a local optimum solution. The convergence factor and dynamic balance between the number of Spawning and foraging dung beetles is proposed. That allows the algorithm to focus on the global search in the early stages and local exploration in the later stages. The quantum computing based t-distribution variation strategy is used to variate the optimal global solution, that prevents the algorithm from falling into a local optimum. To verify the performance of the QHDBO algorithm, this paper compares QHDBO with six other swarm intelligence algorithms through 37 test functions and practical engineering application problems. The experimental results show that the improved dung beetle optimization algorithm significantly improves convergence speed and optimization accuracy and has good robustness. • Good point set strategy for initializing populations. • Multi-strategy fusion balancing pre-global exploration and post-local search. • Quantum computing interferes with t-distribution variation strategies. [ABSTRACT FROM AUTHOR]

Deep Learning and its applications have cascaded impactful research and development with a diverse range of modalities present in the real-world data. More recently, this has enhanced research interests in the intersection of the Vision and Language arena with its numerous applications and fast-paced growth. In this paper, we present a detailed overview of the latest trends in research pertaining to visual and language modalities. We look at its applications in their task formulations and how to solve various problems related to semantic perception and content generation. We also address task-specific trends, along with their evaluation strategies and upcoming challenges. Moreover, we shed some light on multi-disciplinary patterns and insights that have emerged in the recent past, directing this field toward more modular and transparent intelligent systems. This survey identifies key trends gravitating recent literature in VisLang research and attempts to unearth directions that the field is heading toward. • Covers multiple tasks in Vision and Language (VisLang) Research. • Discussions on task-specific trends in VisLang. • Directions that the field of VisLang is heading toward. • Over 400 state-of-the-art papers surveyed. [ABSTRACT FROM AUTHOR]

In recent years, the research of indoor positioning technology has mainly focused on WI-FI, Bluetooth, ultra-wideband, and pseudo-satellites. Each of these indoor positioning technologies has different advantages, but also has various limitations, especially in indoor areas such as corridors, tunnels, and stairs. Due to the limitations of application scenarios, navigation and positioning accuracy and efficiency, it is difficult to form a satisfactory navigation and positioning solution for indoor special environment. In order to solve the positioning problem of indoor long and narrow areas, a multi-source fusion navigation and positioning algorithm based on pseudo-satellite assistance is proposed in this paper. Under certain map information constraints, it can use the direct arrival signal of a single pseudo-satellite to correct the location information output by mobile phone PDR(Pedestrian Dead Reckoning), and improve the navigation and positioning performance of indoor passageway and corridor areas. To realize the system, it is necessary to ensure that a direct pseudo-satellite signal can be received in any area of the corridor. The position of the pseudo-satellite can be determined according to the captured pseudo-satellite signal, and the pseudo-range change rate can be estimated by calculating the carrier phase change rate. In this paper, we deduce the relationship between the pseudo-range change rate and the change of the mobile phone PDR position, and build an optimization model based on map information constraints, which improves the positioning accuracy of the system for indoor narrow and long areas. A large number of experimental results show that the system has high positioning accuracy in the real indoor long and narrow environment. The average positioning accuracy of the algorithm in indoor environments is better than 0.4 m, and there is no significant change in the performance after a long time of cumulative positioning. [ABSTRACT FROM AUTHOR]

Quantum computing has entered its mature life thanks to the availability of cloud-based Noisy Intermediate-Scale Quantum (NISQ) technologies. These devices allow quantum researchers and practitioners to design, develop and test quantum algorithms on actual hardware, paving the way toward new approaches in solving problems intractable by classical computers. However, in spite of these achievements in quantum technologies, the size of the problems that can be actually solved by quantum algorithms is still limited by the small number of qubits and the considerable noise in computation that still characterizes NISQ devices. As a consequence, there is a strong need of introducing innovative computing architectures able to interconnect a set of NISQ devices to increase the capabilities of current quantum computers of solving hard problems in a reliable way. In this paper, the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) is introduced as a reference computational model by which designing innovative frameworks where quantum devices interact to solve a complex problem by working cooperatively. In detail, the D-NISQ model consists of a hybrid and hierarchical architecture where classical and quantum processors interact to iteratively split up a problem into a collection of sub-problems, solve each one of them by means of a proper quantum algorithm, and fuse output quantum information so as to compute the final solution to the problem posed. As demonstrated by two case studies based on the well-known Grover's algorithm, a multi-threaded implementation of the D-NISQ reference model allows for greater reliability in solving problems by quantum computation. • Quantum computing has entered its mature life thanks to NISQ technologies. • NISQ devices are still characterized by critical issues in computation. • Distributed quantum architectures can improve the capabilities of NISQ devices. • D-NISQ is a reference model for distributed quantum computation. [ABSTRACT FROM AUTHOR]

This paper proposes a general numerical methodology of inverse kinematics computation for 7-degree-of-freedom offset manipulators based on arm angle parameterization. First, analytical inverse kinematics solutions for two types of Spherical-Revolute-Spherical manipulators are derived based on arm angle parameterization. Then, the inverse kinematics of the offset manipulator is solved using a simplified manipulator. In each iteration of the algorithm, the inverse solution of the simplified manipulator is used to calculate the position and arm angle error between the actual manipulator and desired value. The error is utilized to compensate the position and arm angle of the simplified manipulator so that the inverse solution of the simplified manipulator gradually approaches the desired solution of the actual manipulator. Moreover, the problems of workspace reduction due to structure simplification and the placement of the iteration point outside of the workspace of the simplified manipulator are optimized. Additionally, the algorithm singularity problem of the offset manipulator is solved via the dual arm angle parameterization method. Finally, the method is validated by numerical simulation and physical experiments. • A general IK method for offset manipulator based on arm angle parameterization. • Approximate analytical solution for two kinds of offset manipulators. • Unsolvable points caused by workspace loss and error compensation are optimized. • Dual arm angle parameterization method to solve singularity problem. • Propose a high-efficiency algorithm without complex calculations. [ABSTRACT FROM AUTHOR]

In this paper, we consider an inverse problem of identifying a pair of function { u (x , t) , r (t) } in the time fractional diffusion equation from two kinds of observations. By virtue of the high-precision Legendre spectral collocation and mollification method in the spatial and time direction severally, we present an efficient algorithm to solve the inverse problem. Finally, two numerical examples explicate the effectiveness and stability of the proposed method. [ABSTRACT FROM AUTHOR]

• The enriched degree of freedom method is proposed to solve the problems with absorbing boundaries. • The proposed method is independent of the load frequency. • The proposed method can be coupled with the XFEM to solve the problem with multiple cracks. • The proposed method can be used to simulate the field-scale and laboratory-scale models. For elastodynamic problems, significant effort has been exerted by numerical researchers to eliminate the reflection of outgoing elastic waves at the boundary of the computational domain. Eliminating these reflection is very important for correctly analyzing the dynamic response of various structures. In this paper, the enriched degree of freedom method is first proposed to absorb outgoing elastic waves; degrees of freedom are added to the nodes in the absorbing domain by using a user-defined function. Next, the gradually increasing mass matrix is derived to further dampen the elastic waves based on the theory of the perfectly matched layer. Then, a novel damping method is proposed based on the enriched degree of freedom method, and it is appropriately coupled with the XFEM and the modified Newmark algorithm to solve elastodynamic problems with infinite boundaries. Finally, the numerical results show that the proposed method is feasible and applicable for absorbing the outgoing elastic waves. Importantly, the proposed method is independent of the frequency; it can be used to simulate the field-scale model and the laboratory-scale model, and it is effective in solving problems with multiple cracks. [ABSTRACT FROM AUTHOR]

In this paper, a new strategy to establish less time-consuming upwind compact difference method with adjusted dissipation is introduced for solving the incompressible Navier-Stokes (N-S) equations in the streamfunction-velocity form efficiently. By weighted combination of the numerical solutions calculated using the upwind term and the downwind term of the general third-order upwind compact scheme (UCD3), a new fourth-order compact formulation and a third-order upwind compact formulation with adjusted dissipation nature are proposed for computing the first derivatives. Further, they are used to approximate the biharmonic term and the convective terms in the streamfunction-velocity formulation of the N-S equations, respectively. Meanwhile, the first derivatives of the streamfunction (velocities) in the coefficients in the convective terms are solved by the newly proposed fourth-order compact formulation. Temporal discretization for the streamfunction-velocity formulation is addressed with the help of the second-order Crank-Nicolson scheme. Moreover, the newly proposed scheme for the linear models is proved to be unconditionally stable by virtue of the discrete Fourier analysis. Finally, five numerical problems, viz. the analytic solution, Taylor-Green vortex problem, doubly periodic double shear layer flow problem, lid-driven square cavity flow problem and two-sided square cavity flow problem are solved numerically to verify the efficiency and accuracy of the present method. Results solved by the present method match well with the analytic solutions and the existing results proving the accuracy of it. What is more, it is less time-consuming and has lower dissipation than the existing method [20]. [ABSTRACT FROM AUTHOR]

Fatigue driving is an important cause of traffic accidents, so it is particularly important to achieve effective fatigue driving detection. The current fatigue driving detection algorithm has the problems of poor comfort, vulnerable to external factors (light intensity, wearing masks and sunglasses), and poor real-time performance. To solve these problems, we design a system for detecting driver's facial features (FFD-System) and an algorithm for judging driver's fatigue state (MF-Algorithm). In FFD-System, we designed three networks M1-FDNet, M2-PENet and M3-SJNet for driver face detection, head pose estimation and eye-mouth state judgment respectively. M1-FDNet is a lightweight face detection network. We design a new loss function to allow the network to output five key face points in addition to the output face. M2-PENet converts the regression problem into a classification problem, and the head pose can be estimated only by a single face image. M3-SJNet can use the global information of eyes and mouth to determine its state. Wearing sunglasses or masks while driving can cause the driver's eyes or mouth to be obscured, which makes it impossible to accurately detect all of the driver's facial features. MF-Algorithm can solve this problem well. MF-Algorithm uses the output of FFD-System to calculate single eye closure time, blinking frequency, eye closure rate, mouth opening frequency and head misalignment time, and combines these parameters to comprehensively judge the driver's fatigue state. Thus, even if a single facial feature cannot be accurately identified, our algorithm can accurately determine the driver's fatigue status based on other features. The experimental results show that the algorithm meets the requirements of real-time detection in terms of detection speed and accuracy. [Display omitted] • Two lightweight neural networks are designed to detect the driver' s facial features. • A fatigue state judgment algorithm based on multiple facial factors of the driver is proposed. • This paper uses multiple modules to interact with each other to reduce the interference of external factors. [ABSTRACT FROM AUTHOR]

This paper examines a time fractional version of the 2D Fokker-Planck equation involved with the Atangana-Baleanu-Caputo fractional derivative, under the Dirichlet boundary conditions. A fully discretization approach based on the meshless local Petrov-Galerkin method and (3 − β) -order approximation is proposed for this equation. More precisely, we apply the meshless local Petrov-Galerkin method based on the Moving Kriging interpolation to discretize the space domain, and utilize the (3 − β) -order approximation together with the θ -weighted finite difference method to discretize the temporal domain. By implementing this method, we get a solution for the problem by solving a system of algebraic equations. The validity of the method is investigated by solving four examples. [ABSTRACT FROM AUTHOR]

Up to now, the problem of synchronization of mutual coupled integer order systems has been tackled by several researchers. However, few works have been done to investigate such context on fractional order systems. In this context, this paper presents a complete methodology to solve this problem. On the other hand, to the best of our knowledge, among all the existing works dealing with the synchronization of mutual coupled fractional order systems, no paper has treated the special class of one-sided Lipschitz systems. In order to validate the theoretical results, two numerical examples are studied in the simulation section. • The problem of synchronization of mutual coupled fractional order systems via the partial stability theory has been presented. • The special class of one-sided Lipschitz systems is presented and detailed. • Two criteria are given. The first is without perturbation and the other is under vanishing perturbations. • A numerical example is studied in the simulation section to validate the theoretical contribution. [ABSTRACT FROM AUTHOR]

• A variable order fractional accumulation generating operator is proposed. • A variable order derivative is introduced into a grey model. • The effect of the new model is better than that of similar competitive models. • The new model can be used to predict problems with variable order characteristics. The use of constant order differential equations to describe the evolution of complex systems is often unable to describe some of the changing characteristics of the systems accurately. Variable order fractional derivatives provide us with new tools to solve such problems. In this paper, the accumulation and derivative orders of the classic grey model are expanded from constants to functions, and a variable order fractional grey model is established to describe the evolution process of complex systems. Firstly, this paper defines the variable order fractional accumulation generation sequence. On the basis of this sequence, a variable order fractional derivative grey model is established, the parameters of the model are estimated using the least square method, and the quantum particle swarm optimization algorithm is used to solve the order of fractional derivative and accumulation. Sadik transform and Laplace transform are adopted to obtain the analytical solution of the new model. Lastly, the effectiveness of the new model is verified through four cases. Compared with other models, the variable order fractional model can describe the development process of complex systems more accurately. [ABSTRACT FROM AUTHOR]