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Abstract: This paper treats controller design and tuning for systems with two input signals and two output signals in the process industry. Two design methods that can be combined to form a core in an algorithm for automatic design and tuning for the considered systems are presented. The proposed controller consists of a decoupler and a diagonal PID controller. The decoupler has the property that as little dynamics as possible is introduced in the decoupler. The PID design method is based on exhaustive search. The methods are combined and tested in an industrial environment in a paper mill. [Copyright &y& Elsevier]

Some of the most common defects that can be generated during the production of solid propellant are voids and porosity, usually associated with the casting process, and cracks and debonding, typically initiated by the high stresses caused by the curing process. This paper presents the development of an algorithm capable of evaluating the burning surface regression of a solid rocket booster when inclusions are present within the grain. The effects produced by the cavities are evaluated both in terms of performance (i.e., comparison with the behavior of the nominal combustion surface), and in terms of safety (i.e., evaluation of the thermal protection increased exposure). The paper also documents the influence of uncertainties in the knowledge of the real dimension and position of the inclusions detected within the motor. The radiography inspection of the motor is able to detect the presence of cavities within a certain level of accuracy, and the worst combination of these uncertainties has to be determined in order to guarantee, even under such circumstances, the safe and successful firing of the motor. The methodology developed in the paper is adapted in order to identify the worst uncertainty combination, and to subsequently determine the corresponding performance deviation. • Algorithm able to evaluate the effects of grain inclusions on solid rocket motors. • Numerical simulations to evaluate the geometric regression of the burning surface when inclusions are present. • Inclusion effects evaluated both in terms of performance and safety (thermal protections exposure time). • Evaluation of the worst uncertainty combination, and determination of the corresponding performance deviation. [ABSTRACT FROM AUTHOR]

Process monitoring based on neural networks is getting more and more attention. Compared with classical neural networks, high-order neural networks have natural advantages in dealing with heteroscedastic data. However, high-order neural networks might bring the risk of overfitting, which learning both the key information from original data and noises or anomalies. Orthogonal constraints can greatly reduce correlations between extracted features, thereby reducing the overfitting risk. This paper proposes a novel fault detection method called second-order component analysis (SCA). SCA rules out the heteroscedasticity of process data by optimizing a second-order autoencoder with orthogonal constraints. In order to deal with this constrained optimization problem, a geometric conjugate gradient algorithm is adopted in this paper, which performs geometric optimization on the combination of Stiefel manifold and Euclidean manifold. Extensive experiments on the Tennessee-Eastman benchmark process show that SCA outperforms the compared state-of-the-art methods with missed detection rate (MDR) and false alarm rate (FAR). • Second-order component analysis (SCA) is proposed for fault detection. • SCA uses the structure of autoencoder and adds second-order terms to improve nonlinear mapping capabilities. • SCA adopts orthogonal constraints to reduce the overfitting problem. • SCA adopts a geometric conjugate gradient (GCG) algorithm to deal with constrained optimization problem. [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]

• The reduction of CIT approach to FBP algorithm. • The rapid 2-D CIT algorithm of electron density profile. • The possibility of detection small ionospheric irregularities. The usage of a satellite cluster of satellite-to-satellite sounding of the ionosphere makes it possible to obtain the global height distribution of an electron density between 200 and 500 km height range in the orbital plane of the cluster. This paper introduces a novel rapid two-dimensional ionospheric radio tomography algorithm that uses total electron content (TEC) data measured between low Earth orbit (LEO) satellites. This algorithm reduces the problem of the ionospheric radio tomography to the rapid filtered back-projection (FBP) algorithm, which was not previously used in the computerized tomography of the ionosphere, and which allows to obtain a high resolution nowcast. Data sets of TEC measurements were simulated via IRI-2016 model and used as an input for the developed algorithm. The reconstructed profiles of the electron density were compared with those profiles from IRI-2016 model to calculate the difference between them. The obtained accuracy is comparable to the accuracy of algebraic reconstruction algorithms (ART), which are classical in the ionospheric radio tomography, and makes it possible to use the developed method to detect local disturbances in the ionosphere that is demonstrated on a test case in this paper. [ABSTRACT FROM AUTHOR]

Multi-exposure image fusion (MEF) is an important area in computer vision and has attracted increasing interests in recent years. Apart from conventional algorithms, deep learning techniques have also been applied to MEF. However, although many efforts have been made on developing MEF algorithms, the lack of benchmarking studies makes it difficult to perform fair and comprehensive performance comparison among MEF algorithms, thus hindering the development of this field significantly. In this paper, we fill this gap by proposing a benchmark of multi-exposure image fusion (MEFB), which consists of a test set of 100 image pairs, a code library of 21 algorithms, 20 evaluation metrics, 2100 fused images, and a software toolkit. To the best of our knowledge, this is the first benchmarking study in the field of MEF. This paper also gives a literature review on MEF methods with a focus on deep learning-based algorithms. Extensive experiments have been conducted using MEFB for comprehensive performance evaluation and for identifying effective algorithms. We expect that MEFB will serve as an effective platform for researchers to compare the performance of MEF algorithms. • A literature review on multi-exposure image fusion is presented. • A multi-exposure image fusion benchmark is proposed. • The benchmark integrates a test set, algorithms, evaluation metrics, and a toolkit. • Comprehensive performance comparison is conducted and presented. [ABSTRACT FROM AUTHOR]

Soft sensor technology is an important solution for timely prediction of some difficult-to-measure variables in the chemical process. For the data-driven soft sensor models, the modeling process needs a certain amount of labeled samples. In some actual processes, the number of unlabeled samples is very large, but the labeling process of unlabeled samples is very time-consuming and laborious. When soft sensor modeling is performed in the case of sparse labeled samples, the generalization ability of the model is bound to be poor. To address this problem, a semi-supervised soft sensor method based on vine copula regression and tri-training algorithm (tri-training VCR) is proposed in this paper. Semi-supervised learning is to enhance the performance of supervised learning models by using unlabeled samples. The tri-training algorithm is used to expand the labeled samples through the cooperation of three vine copula regression models with different structures and a new method to judge the confidence degree of pseudo-labeled samples, so as to improve the prediction accuracy of the soft sensor model. A numerical example and an industrial example are used to demonstrate the effectiveness of the proposed method. • A semi-supervised style algorithm based on vine copula regression and tri-training algorithm has been developed. • A new confidence evaluation method is proposed to more accurately judge the reliability of pseudo-label samples. • Multiple vine copula regression models are reasonably integrated through variance information. • Two examples are used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The ionospheric effect on radio wave propagation error has a significant impact on navigation, positioning, and real-time measurements. It is the primary source of errors in high precision aviation and measurement systems. A set of eight coefficients is used to correct ionospheric errors for Indian Regional Navigation Satellite System (IRNSS) users. Currently, the Klobuchar model is an effective low computational mathematical model for ionospheric range error mitigation through a set of eight coefficients. It has low accuracy in Equatorial Ionization Anomaly (EIA) regions and can correct up to ∼60% of the ionospheric range error. This paper proposes an ionospheric error correction model that can be applied to such measurements as well as an associated error model to compute accuracy with a differential interval of observations 5 m, 30 m and 60 m. The numerical simulation results show that compared to widely used algorithms, the proposed strategy can enhance the 83.13% ionospheric correction with Klobuchar- like coefficients at a sampling rate of 5 m. The proposed solution could be effective for Regional Navigational Satellite Systems (RNSS) for ionospheric delay corrections in low-latitude regions. [ABSTRACT FROM AUTHOR]

Nowadays, secure digital data in store and transmission is an important issue. Cryptographic methods are widely used to provide optimal security for multimedia data. In this paper, a good performance implementation of a strong block-cipher system is proposed. This article's contribution is to develop a new method to block-cipher hardware system based on DNA biological properties and various 3D chaotic maps. In this scheme, the high-level security is achieved by chaotic sequences generated by a robust chaos-based PRNG based on the Lorenz, Chua, Rossler, and Chen chaotic maps. The latter is used to generates high-quality keys that are applied for encryption. Thus, a high-security block cipher approach for encrypting and decrypting images has been developed. To increase the confusion process complexity, several biological operations, such as DNA-XOR, are added to the encryption process. Furthermore, a novel hardware architecture of the proposed block-cipher system is put forward. The latter achieves a low power consumption, good frequency of 192.813 MHz and high throughput of 24,576,153 Mbps built. The security analysis demonstrates that the cryptosystem provides effective security. The proposed PRNG validated successfully both the NIST SP 800–22 test suite and the U01-Test. Various tests are performed, such as statical tests, phase analysis and differential attacks applied to different images. A comparison of the proposed algorithm with several newly developed encryption algorithms demonstrates that our system generates good results. • Creating a robust PRNG based on multiple chaotic systems. • Using confusion and diffusion features to design a fast and safe block-cipher algorithm. • Proposing and FPGA-zynq implementation of the block-cipher hardware architecture. • Experimenting with ordinary images with the aim to assess the effectiveness of the suggested block-cipher system against the most known attacks. • Evaluating the performances of the hardware cryptosystem and comparing the obtained results to comparable related works. [ABSTRACT FROM AUTHOR]

This paper develops a model for describing a stochastic age-dependent population system (SADPS) with Lévy noise in a polluted environment. As the model includes a nonlinear drift term and Lévy noise, it is difficult, if not impossible, to derive an analytical solution of the model. This study thus aims at developing a numerical algorithm that is based on a modified truncated Euler-Maruyama (EM) method and positivity preserving. We first study global positivity for the solution of the SADPS model in a polluted environment. Subsequently, we present a semi-discrete Galerkin finite element method in an age variable, and construct a full-discrete numerical approximation using modified truncated EM scheme on time to preserve positivity. Under certain suitable regularity conditions, estimates of convergence error of the full-discrete and a semi-discrete scheme are presented. Finally, a numerical example is given to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

• An algorithm is proposed for time-dependent reliability with hybrid uncertainties; • The algorithm combines the Kriging modeling and envelope function; • The expansion points are located by a surrogate method; • The algorithm can assess the structure reliability over a time interval. This paper proposes a novel envelope-function-based algorithm for time-dependent reliability analysis considering random and interval uncertainties. First, the envelope function of the bound of the limit-state function is approximated with assistance of the first-order reliability method, thereby transforming time-dependent reliability problem into a time-independent one. Here, the Kriging modeling method is utilized to construct the expansion point determination function for reducing the most probable point search, which significantly accelerates the expansion point determination in constructing the envelope function. After the envelope function is found, the time-dependent reliability in terms of random and interval uncertainties is efficiently calculated by a multivariate Gaussian integral. Finally, five case studies are used to demonstrate the effectiveness of the proposed algorithm. The results indicate it can provide accurate and efficient time-dependent reliability analysis for structures where the random and interval uncertainties coexist. [ABSTRACT FROM AUTHOR]

• The new algorithm is explicit for both displacement and velocity. • The new algorithm is unconditionally stable for linear and nonlinear softening stiffness systems. • The new algorithm has extremely small period error. • The new algorithm has no energy dissipation. • There still exist overshoot of the new algorithm. Several model-based or structure-dependent integration algorithms with explicit displacement formulation and unconditional stability are recently developed. According to the difference equations, these algorithms can be divided into dual-explicit and semi-explicit, where the first category algorithms are explicit for both displacement and velocity, while the second category are only explicit for displacement. The well-known Chen-Ricles (CR) and Chang algorithms are two representatives of dual-explicit and semi-explicit model-based algorithms, respectively. The model-based integration algorithms are promising with excellent computing efficiency, but most of them are second-order accurate. In this paper, a new dual-explicit (NDE) algorithm with higher-order accuracy is developed. The integration parameters of the NDE algorithm are obtained by using the pole mapping and second-order Pade approximation. Numerous important numerical characteristics including the consistency and local truncation error, stability, numerical dispersion and energy dissipation and overshoot are systematically analyzed for the new algorithm. It is found that the NDE algorithm is fourth-order accurate for free vibration without damping. As for forced vibration with/without damping and free vibration with damping, the NDE algorithm is no longer fourth-order accurate, but it is still more accurate than the CR and Chang algorithms. In addition, the period error of the algorithm is extremely small. Three representative numerical examples are finally provided to demonstrate the high accuracy of the NDE algorithm. Several classical and latest model-based integration algorithms and three fourth-order accurate algorithms are also adopted for comparisons. It has been proved that the NDE algorithm has much better performance than other selected model-based algorithms. [ABSTRACT FROM AUTHOR]

In this paper we intend to construct a structure preserving difference scheme for two-dimensional space-fractional nonlinear Schrödinger (2D SFNS) equation with the integral fractional Laplacian. The temporal direction is discretized by the modified Crank-Nicolson method, and the spatial variable is approximated by a novel fractional central difference method. The mass and energy conservations and the convergence are rigorously proved for the proposed scheme. For 1D SFNS equation, the convergence relies heavily on the L ∞ -norm boundness of the numerical solution of the proposed scheme. However, we cannot obtain the L ∞ -norm boundness of the numerical solution by using the similar process for the 2D SFNS equation. One of the major significance of this paper is that we first obtain the L ∞ -norm boundness of the numerical solution and L 2 -norm error estimate via the popular "cut-off" function for the 2D SFNS equation. Further, we reveal that the spatial discretization generates a block-Toeplitz coefficient matrix, and it will be ill-conditioned as the spatial grid mesh number M and the fractional order α increase. Thus, we exploit an linearized iteration algorithm for the nonlinear system, so that it can be efficiently solved by the Krylov subspace solver with a suitable preconditioner, where the 2D fast Fourier transform (2D FFT) is applied in the solver to accelerate the matrix-vector product, and the standard orthogonal projection approach is used to eliminate the drift of mass and energy. Extensive numerical results are reported to confirm the theoretical analysis and high efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the estimation of zonal geomagnetic indices of two super geomagnetic activities of 2015 in the 24th solar cycle. It estimates the zonal geomagnetic indices (Dst, ap, AE) of 17 March and 22 June 2015 super storms with an artificial neural network model. The activities that happened in March and June are considered on the solar wind parameters (B z , E, P, N, v, T) and zonal geomagnetic indices obtained from NASA coordinated data analysis web. Descriptive values of the variables are indicated, binary correlations of the data are shown with the covariance matrix and the hierarchical cluster of the data are presented by the dendrogram. In the paper, the physical principles govern the artificial neural network model. The model utilizes solar wind parameters as inputs and zonal geomagnetic indices as outputs. The causality principle shapes the models by cause-effect relationship. Back propagation algorithm is specified as Levenberg–Marquardt (trainlm) and 30 neural numbers are used in the artificial neural network. The neural network model estimates the Dst, ap, and AE indices of 17 March and 22 June activities with reliable accuracy. The geomagnetic activity estimation can support interplanetary studies. [ABSTRACT FROM AUTHOR]

The Asteroid (469219) 2016 HO 3 , one of the targets of the potential small celestial body missions, is a typical small-sized stable quasi-satellite of the Earth. Compared with the targets of previous impact missions, it is more challenging for a hypervelocity impact on 2016 HO 3 due to its much smaller size. Besides, the time durations for autonomous navigation and maneuver correction are greatly narrowed because the impactor can hardly capture the small-sized target until they are close enough. This paper suggests a high-precision autonomous navigation system and proposes a minimum-impact-error correction strategy for the targeting maneuver to solve the challenges. The navigation system utilizes traditional optical navigation assisted by a radar ranging measurement unit, which has been proved of high precision and short convergence time during the approach phase (better than 10 m in 100.0 s). After obtaining the relative positions and velocities, the targeting maneuvers are needed to correct the impactor's orbit for a final successful impact. Therefore, a minimum-impact-error correction strategy is proposed to determine the optimal number and correction schedule of targeting maneuvers, which can minimize the impact error under the condition that the total velocity increments are limited. In the case of the 2016 HO 3 impactor, this paper conducts numerical Monte-Carlo simulations for validation. The results are encouraging, and the proportion of successful impact is above 99.1 percent. The correction strategy proposed in this paper is meaningful for future hypervelocity impact on small celestial bodies. • The navigation algorithm can converge fast and have a high precision. • A minimum-impact-error correction strategy is proposed with the limited delta-v. • The research is meaningful for hypervelocity impact missions for small-sized asteroids. [ABSTRACT FROM AUTHOR]

The tether-net system is a promising technique for active debris removal. The capture process of a tether-net system for debris exhibits the nonlinear dynamics of very high dimensions and requires the dynamic simulation of high accuracy and efficiency in the design stage. This paper presents an integrated approach of both method of discrete elastic rods and algorithm of energy-conserving integration for the above purpose. The study shows that the method of discrete elastic rods is more suitable for modeling the extremely flexible threads of a woven net than the slender beam elements with C 1 continuity. Besides, the energy-conserving integrator embedded in the present approach well conserves the total energy, and does not produce any numerical dissipations. The paper gives three numerical examples, including the capturing process of a hexagonal tether-net system for a space target, to demonstrate the advantages of the present approach, as well as the influence of some initial control parameters on the deployment phase of the tether-net system. • The method of discrete elastic rod is used for modeling the thread of a net. • An energy-conserving integrator is applied to solve the dynamic equations. • Influence of some control parameters on the deployment phase are discussed. [ABSTRACT FROM AUTHOR]

• The current 3D human body reconstruction technology requires a large number of 3D scanning equipment and a complex dynamic capture process, which significantly limits its rapid adoption in these application domains. The purpose of this article is to simplify the process of 3D reconstruction. • By combining deep learning methods, an end-to-end 3D human body reconstruction framework is proposed to achieve the goal of reconstructing a high-precision 3D human body model based on RGB images. [Display omitted] In recent years, with the improvement of artificial intelligence technology, it has become possible to reconstruct high-precision 3D human body models based on ordinary RGB images. The current 3D human body reconstruction technology requires complex external equipment to scan all angles of the human body, which is complicated to be implemented and cannot be popularized. In order to solve this problem, this paper applies deep learning models on reconstructing 3D human body based on monocular images. First of all, this paper uses Stacked Hourglass network to perform convolution operations on monocular images collected from different views. Then Multi-Layer Perceptrons (MLPs) are used to decode the encoded high-level images. The feature codes in the two views(main and side) are fused, and the interior and exterior points are classified by the fusion features, so as to obtain the corresponding 3D occupancy field. At last, the Marching Cube algorithm is used for 3D reconstruction with a specific threshold and then we use Laplace smoothing algorithm to remove artifacts. This paper proposes a dense sampling strategy based on the important joint points of the human body, which has a certain optimization effect on the realization of high-precision 3D reconstruction. The performance of the proposed scheme has been validated on the open source datasets, MGN dataset and the THuman dataset, provided by Tsinghua University. The proposed scheme can reconstruct features such as clothing folds, color textures, and facial details,and has great potential to be applied in different applications. [ABSTRACT FROM AUTHOR]

• This paper presents an enhanced salp swarm algorithm for solving global optimization problems. • The random replacement strategy is used to accelerate the convergence speed of the basic salp swarm algorithm. • The double adaptive weighting strategy enhances the algorithm's global exploration and local exploitation capabilities. • The optimization performance of the improved algorithm is evaluated on basic test functions and practical problems. The salp swarm algorithm is a newly population-based search algorithm. Because the original salp swarm algorithm has low search efficiency and is easy to fall into local optimum, in this paper, we propose an enhanced salp swarm algorithm, which combines two strategies with the original salp swarm algorithm. One is the random replacement strategy, which can replace the current position with the optimal solution position with a certain probability of speeding up the convergence rate. The other tactic is double adaptive weight, which can expand the search scope throughout the early stages and enhance exploitation capability in the later stages. With the cooperation and guidance of the two mechanisms, the algorithm's convergence speed is accelerated, and the exploitation capacity is meritoriously increased. The proposed method's performance is compared with three mainstream meta-heuristics and four advanced algorithms on four necessary test cases. The extensive analysis and recorded results indicate that the proposed method outperforms these algorithms in terms of the accuracy of the solution and convergence speed. Finally, we apply the developed method to four well-known engineering design problems (welded beam design problem; cantilever beam design; I-beam design; and multiple disk clutch brake) to validate the algorithm's effectiveness for some constrained challenge. The results show that our algorithm has significant advantages in solving practical problems with constraints and unknown search spaces. [ABSTRACT FROM AUTHOR]

Floorplanning is an ever-emerging field in the Very Large Scale Integration (VLSI) circuit design automation since it deals with essential design metrics of a floorplan, such as chip area, total wirelength, and temperature. This paper aims to optimize these design metrics to obtain an optimal floorplan with reduced computation time. A Skewed B* (SKB) tree based Enhanced Memetic Algorithm (EMA) is proposed for temperature-driven fixed-outline non-sliceable floorplanning. The proposed EMA has two search phases: a global search based on Genetic Algorithm (GA), and a local search based on the Adaptive Fast Simulated Annealing (AFSA) algorithm. A novel dynamic threshold bias search strategy is implemented to balance the search phases, thus increasing the convergence speed to obtain an optimal solution. The performance of the proposed algorithm is tested on MCNC (Microelectronic Centre for North Carolina), and GSRC (Gigascale System Research Centre) state-of-the-art benchmark circuits. Experimental results show that the EMA produces highly optimal design metrics on all tested benchmark circuits. • A global search based on Genetic Algorithm, and a local search based on Adaptive Fast Simulated Annealing algorithm. • A novel dynamic threshold bias search strategy is used to balance the search phases, increasing the convergence speed. • The selection operator is implemented using one of the famous selection techniques called fitness-proportionate method. • The SKB-tree enhances the Memetic Algorithm with four new perturbations to produce greater diversity in the population. • Simultaneous optimization of chip area, wirelength and temperature with fixed-outline constraints. [ABSTRACT FROM AUTHOR]

The electrodynamic tether (EDT) system is used as one of the end-of-life disposal technologies to deorbit defunct satellites with the benefit of Earth's magnetic field and plasma environment. However, due to the flexibility of the conductive tether, the orbit-attitude coupling effects as well as the influence of Multiphysics fields, this system suffers from time-consuming computations using complicated mathematical model in long-term simulations. Therefore, this paper proposes a high-fidelity high-efficiency dynamics model for the EDT system, in which the tether libration and transverse motions are described by the articulated model, and the system equations are formulated using recursive dynamics algorithm. Computational cost of the recursive method is compared with a nonrecursive method, and the result shows that with the number of tether elements larger than 15, the recursive method would show better computational efficiency. Since the conductive tether tends to be kilometers long in application, this new model would definitely contribute to the numerical efficiency. In addition, simulations are conducted with 2 and 16 tether elements solutions. By comparing the obtained results, it is found that the two cases show large differences over time. For a long tether, it is reasonable to use a larger number of elements in the dynamics analysis. Besides, in order to suppress the libration motion and avoid unstable states in the EDT system, a current control strategy is proposed and verified by a deorbit simulation in low Earth orbit. • A high-fidelity high-efficiency model is proposed for the electrodynamic tether system. • The recursive method shows better efficiency with the number of elements larger than 15. • Different tether elements would result in large differences in simulation. • A current control strategy is proposed to stabilize the libration motion. [ABSTRACT FROM AUTHOR]

Detecting anomalies in telemetry data captured on-board a spacecraft is critical to ensure its safe operation. Although there exist various techniques for automatically detecting point, contextual, and collective anomalies from time-series data, quantifying their performance remains under-researched. In this paper, we thoroughly validate our approach for the task of anomalous event detection that is built upon a two-stage technique, in which the telemetry signal is predicted using a long short-term memory network based on the historical data, and then the prediction is compared with the actual (captured) data. If the difference between those two is sufficiently large, we can infer that an anomalous event has happened. To evaluate the capabilities of such detection techniques over the simulated and benchmark time-series data, we investigate a set of commonly used metrics obtained for a range of anomaly detection approaches, and present their shortcomings, especially related to their inability of capturing the temporal aspects of the detectors. We tackle this issue by introducing new quality metrics which enable us to objectively verify if the detectors can timely spot the anomalies in sequential data. The experimental study showed that inferring the conclusions based on a subset of metrics can lead to biased observations, as the best algorithms determined based on the overlap metrics, including the Dice coefficient, do not necessarily correspond to the algorithms that offer the fastest detection. Finally, we discuss the Antelope Toolbox—our software tool for simulating nominal telemetry data of given characteristics, alongside well-defined anomalous events, and to perform the quantitative and qualitative analysis of anomaly detection algorithms over such simulated events. • We investigate the quality metrics to verify anomaly detection in telemetry data. • We introduce new measures that capture temporal aspects of anomaly detectors. • We present an end-to-end machine learning pipeline for anomaly detection. • We objectively assess 15 anomaly detection algorithms using all metrics. • Exploiting only a subset of all metrics can easily lead to biased conclusions. [ABSTRACT FROM AUTHOR]

Spacecraft are required to achieve fast attitude reorientation in many space missions. However, the attitude maneuver will be limited by the bounded and pointing constraints. The presence of these complex multiaxis constraints will largely reduce the feasible attitude space and greatly degrade the solving efficiency of the planning algorithm. For the time-optimal spacecraft reorientation problem under multiaxis constraints, a new heuristic decomposition planning on the virtual domain (HDPV) method is proposed in this paper. The parameterized attitude path is first determined on the virtual domain. Then the virtual-domain-based variable-step approach is presented to rapidly check the pointing constraints and generate the sets of candidate rotational-path decomposition nodes. The composite-position heuristics is developed to select the best node during the decomposition optimization, which considers the maneuver time and connection smoothness of paths. The total continuous parameterized path under keep-out and keep-in constraints is yielded through recursive planning. Finally, the time-optimal path parameterization approach is adopted to minimize the maneuver time along the total path and generate a fast attitude trajectory under bounded constraints on the time domain. Simulation results demonstrate the high computational efficiency and good optimization effect of the proposed method. • Propose the heuristic decomposition planning on the virtual domain method. • New virtual-domain-based variable-step approach and composite-position heuristics. • Address the time-optimal problem under multiaxis constraints. • The HDPV method has a high computational efficiency. • Generate a fast spacecraft reorientation trajectory. [ABSTRACT FROM AUTHOR]

In this paper, new and effective methods are provided for modeling and numerical simulation of viscoelastic plate, respectively. Viscoelastic plate is modeled by a variable fractional derivative model with better fitting effect and is numerically analyzed directly in time domain using shifted Chebyshev wavelets algorithm for the first time. A governing equation with three independent variables is established. The feasibility and accuracy of proposed algorithm are verified by convergence analysis and error estimations of mathematical example. Under different conditions, the ternary variable fractional differential equation of plate is solved numerically by shifted Chebyshev wavelets algorithm directly in time domain. The vibrations of plate are analyzed under different loads, loading times and temperatures. Numerical solutions for the stress of plate with two materials are also calculated and analyzed under the same load. The displacement of plate increases with increasing load, loading time and temperature. The bending resistance of PET plate is better than that of polyurea plate. These results are consistent with the existing references and actual practice. Therefore, the variable fractional model and shifted Chebyshev wavelets algorithm are accurate for the study of plate. They can be applied to complex problems in engineering. All obtained conclusions can provide theoretical basis for the protection and design of load-bearing structures in engineering. [ABSTRACT FROM AUTHOR]

In recent years, the application of structural control strategies to attenuate the dynamic response of civil engineering structures subjected to human-induced and environmental loads has once again received much attention. The hardware involved in providing effective control, the stochastic nature of the dynamic excitation, the number of distinct variables required to determine the dissipating forces, and the nature of the control approach are a few of the parameters involved in determining the efficacy of structural control systems. However, the primary parameter to determine the overall performance of a smart adaptive structure is the implementation of a control algorithm that would conveniently counteract the external known and/or unknown excitation by providing additional force input through control devices. The control algorithm to be adopted should be robust, multi-purpose, and simple to design and implement. Moreover, the control algorithm should provide the flexibility in selecting performance objectives for a holistically effective response reduction. This paper focuses on providing a comprehensive review of control algorithms implemented in structural control engineering. This article first provides an overview of the fundamental concepts pertaining to the vibration control of structures subjected to dynamic excitations. Next, an exhaustive review of different types of control algorithms that feed the input signal to active-type control devices is conducted. Thereafter, important benchmark applications in the field of structural control in civil engineering are presented. Finally, conclusions are presented on the advantages and drawbacks of each control strategy and recommendations are presented for further research in this area. [ABSTRACT FROM AUTHOR]

• Algorithm for PLIC-based interfacial area concentration on 3D non-orthogonal grids. • Thermal phase change model with geometrical basis and free of empirical input. • Oscillating behavior of GOX jet plume in LOX crossflow is reproduced in simulation. • Imbalance between the mass transfer rate and the supply causes the oscillation. • Methodology developed is verified by the Stefan problem and the bubble experiment. In order to enhance the physical basis of the mass transfer model in numerical simulation of gas-liquid direct contact condensation and improve the fidelity, a set of methods and models are developed. Based on the concept of the piecewise-linear interface calculation (PLIC), an interface reconstruction algorithm is proposed to calculate the interfacial area concentration in three-dimensional non-orthogonal structured grids. The actual interface in the cell is replaced by a plane, whose position is determined iteratively to satisfy the local volume fraction and orientation of the actual interface. The area of the polygonal cut by the plane is taken as the interfacial area in the cell. The proposed algorithm is validated by calculating the surface area of a series of basic geometries. Obvious superiority of the proposed algorithm over the traditional models (gradient model and algebraic model) is seen. Then, the reconstructed area is used to determine the interfacial area concentration in the thermal phase change model. The process is implemented by ANSYS Fluent user-defined functions. The Stefan problem and steam bubble condensation experiment are used to test the performance of the developed methods and models. The simulation results agree well with the theoretical and experimental results with the quantitative relative errors no more than 4.3 % and 15 %, respectively. The oxygen jets condensation experiments are also used as test cases. The primary dominant frequencies predicted by the simulations are 163.01 Hz and 131.65 Hz, which agree well with the 139.72 Hz and 125.75 Hz in the experiments. The relative errors are only 17 % and 4.7 %. The unstable flow pattern of oxygen jet in liquid oxygen crossflow is explained based on the dynamic characteristics of turbulence, heat and mass transfer. The periodic variation of mass transfer rate is considered as the cause of condensation oscillation. The method for calculating the interfacial area proposed in this paper can serve as an optional and practical improvement to the numerical simulation of direct contact condensation. [ABSTRACT FROM AUTHOR]

The smooth execution of tasks in the cloud manufacturing (CMfg) process can be significantly affected by various real-world uncertainties, resulting in inefficiencies or even failures of the composite manufacturing service (CMS). Nevertheless, current research primarily concentrates on post-exception emergency handling methods, heavily dependent on the planned CMS and real-time status of manufacturing resources, often leading to suboptimal temporary adjustments or even the inability to find a viable solution. Hence, there is an urgent necessity to enhance the robustness of the planned CMS during the service composition and optimal selection (SCOS) phase. In pursuit of this goal, the paper initially presents an adaptive robust service composition and optimization selection (ARSCOS) model. This model dynamically determines whether to await exception resolution or invoke an alternative service, considering the circumstances when a service exception arises and employing task delay time to characterize the robustness of a specific CMS. Subsequently, an enhanced multi-objective artificial hummingbird algorithm (EMOAHA) is devised for this model. Finally, the efficacy of EMOAHA is demonstrated by applying it to nine different scales of civilian unmanned aerial vehicle manufacturing SCOS problems and 17 benchmark functions. The results illustrate that the proposed EMOAHA exhibits superior convergence and diversity in comparison to the other 11 classical intelligent optimization algorithms. [ABSTRACT FROM AUTHOR]

The lightning clustering algorithm for spaceborne lightning observations clusters events into flash products and plays a crucial role in lightning-related research. This paper modified the lightning clustering algorithm based on the routine algorithm of Lightning Mapping Imager (LMI) and applied it to lightning events recorded by the LMI in 2019 to study the flash duration and flash area and specially to reveal the extreme lightning. To reveal the temporal and spatial features of lightning, the MCA (modified cluster algorithm) flash products defined flash duration and flash area. The results showed that continental regions have higher lightning frequencies but smaller events per flash, flash durations and flash areas than the ocean. The Tibetan Plateau has a low lightning frequency and smaller flash duration and area than eastern China. The flash count, events per flash, flash duration and flash area are higher during nighttime than during daytime. The relationship between flash duration and flash area showed distinct regional differences. In the MCA flash product, about 0.6% (0.1%) flashes have duration (area) that exceeds 1.0 s (5,000 km2). Finally, we analyzed two extreme lightning cases in eastern China, where the longest flash duration was 4.748 s and the largest flash area was 17,100 km2. This research contributes to advancing our understanding in the features of space-based lightning in the Eastern Hemisphere and applications to convection study. • Clustering algorithm was modified to study lightning with extreme duration and area. • Lightning duration and area both implied regional differences. • Quantitative relationship of duration and area was established in different regions. • The longest flash duration/largest flash area was 4.75 s/17100 km2 in eastern China. [ABSTRACT FROM AUTHOR]

• An improved method for calculating energy leakage intensity based on frequency intervals is proposed. • An optimal method is proposed to increase accuracy of decomposition. • The wavelet function obtained by proposed method can achieve an excellent denoising effect. • The proposed method can achieve an accelerated road load spectrum effectively. Discrete wavelet transform (DWT) can be used to edit the vehicle road load signals for accelerating durability analyses. Nevertheless, it should be noted that one of inherent deficiencies of DWT is the obvious energy leakage. This paper proposes an improved compressed editing method for vehicle road load signals, which is to achieve more accurate results, based on the energy leakage of DWT to select the optimal wavelet parameters. A wavelet parameters optimal method is proposed by investigating the severity of parameters on energy leakage. Daubechies wavelet functions with different order are performed on signals under the same decomposition level. Then, the intensity of energy leakage in wavelet components in each level is calculated and compared with adjacent levels. Thus, the optimal wavelet parameters will be extracted taking into account the time–frequency resolutions. The effectiveness of this method has been validated by numerical signal, and the optimal method is applied to a series of load signal to achieve compressed compilation. [ABSTRACT FROM AUTHOR]

The use of subdivision schemes in applied and real-world contexts requires the development of conceptually simple algorithms that can be converted into fast and efficient implementation procedures. In the domain of interpolatory subdivision schemes, there is a demand for developing an algorithm capable of (i) reproducing all types of conic sections whenever the input data (in our case point-normal pairs) are arbitrarily sampled from them, (ii) generating a visually pleasing limit curve without creating unwanted oscillations, and (iii) having the potential to be naturally and easily extended to the bivariate case. In this paper we focus on the construction of an interpolatory subdivision scheme that meets all these conditions simultaneously. At the centre of our construction lies a conic fitting algorithm that requires as few as four point-normal pairs for finding new edge points (and associated normals) in a subdivision step. Several numerical results are included to showcase the validity of our algorithm. • We construct a univariate interpolatory subdivision scheme that reproduces conic sections. • It is a geometric subdivision scheme based on point-normal pairs. • We demonstrate our new scheme on several examples. [ABSTRACT FROM AUTHOR]

The generalization of barycentric coordinates to arbitrary simple polygons with more than three vertices has been a subject of study for a long time. Among the different constructions proposed, mean value coordinates have emerged as a popular choice, particularly due to their suitability for the non-convex setting. Since their introduction, they have found applications in numerous fields, and several equivalent formulas for their evaluation have been presented in the literature. However, so far, there has been no study regarding their numerical stability. In this paper, we aim to investigate the numerical stability of the algorithms that compute mean value coordinates. We show that all the known methods exhibit instability in some regions of the domain. To address this problem, we introduce a new formula for computing mean value coordinates, explain how to implement it, and formally prove that our new algorithm provides a stable evaluation of mean value coordinates. We validate our results through numerical experiments. • A new algorithm for evaluating mean value coordinates. • A formal proof that this algorithm is numerically stable. • A comparison of the numerical stability of all known algorithms for mean value coordinates. [ABSTRACT FROM AUTHOR]

The reentry trajectory planning problem of hypersonic vehicles is generally a continuous and nonconvex optimization problem. The solution efficiency and convergence properties of the planning algorithm become an essential point when considering the practical application scenarios. In this paper, an augmented-Lagrange-based improved sequential convex programming algorithm is proposed to solve it and achieve superior solutions with higher accuracy and efficiency. Firstly, the mapped Chebyshev pseudo-spectral method is utilized to convert the continuous optimal control problem into a highly approximately discretized one. In this framework, the discretization technique is employed to discretize the states and control variables at the time interval, which leads to the improvement of ill-conditioned differential matrix and better approximation properties of the variables. Moreover, by introducing slack variables, automatically updated penalty parameters and Lagrange multipliers during the convexification processing, the original nonconvex problem is transformed into an augmented-Lagrange-function-formed relaxed convex one. Then, the arrived convex subproblem can be solved iteratively by the matured interior-point method until the predetermined precision is satisfied. Based on this transformation, it can effectively alleviate the numerical difficulty and improve the convergence property of the proposed improved sequential convex programming algorithm. Finally, Rigorous theoretical analysis is conducted to prove that the solution sequence generated by the proposed algorithm could converge to an approximate global solution of the original problem. Numerical simulations are also presented, and it is demonstrated that the solution time of the proposed algorithm is more than 95% less than that of the traditional nonlinear programming methods without losing the merits of high precision. [ABSTRACT FROM AUTHOR]

To enable organizations to achieve carbon neutrality through agile capabilities, this paper establishes an integrative framework of carbon-zero agility consisting of three dimensions: search scope agility, search locus agility, and search pace agility. However, applying common agile methodologies like Scrum, Kanban, and Lean to cultivate these capabilities inevitably introduces feedback ambiguity, which can paralyze decision-making and increase errors due to inherent human cognitive limitations. To address this, tailored carbon-zero feedback algorithms are proposed to complement human judgment in agile workflows. Specifically, prescriptive analytics, federated learning, and probabilistic programming are injected into Scrum, Kanban, and Lean respectively to restore clarity amidst ambiguity. The framework is grounded in cases from the textile industry to demonstrate applicability in practical settings. By targeting the roots of distortions with human-algorithm collaborations, it provides an actionable roadmap to implement carbon-zero agility. • Establishes carbon-zero agility framework: search scope, locus, and pace agility. • Identifies behavioral challenges, and feedback ambiguity in agile for carbon-zero. • Proposes tailored carbon-zero feedback algorithms to enhance agility. • Connects agile processes with broader organizational sustainability capabilities. [ABSTRACT FROM AUTHOR]

In previous studies, ranging errors in the line of sight (LOS) environment were often overlooked, resulting in an increase in positioning errors. In non-line of sight (NLOS) conditions, the measuring distance hardly participates in the location calculation, leading to the loss of useful positioning information. Thus, this paper proposes a WiFi RTT positioning approach based on LOS compensation and trusted NLOS recognition, which improves the performance of RTT positioning by using the compensated LOS distances and trusted NLOS ranges to estimate location. In the proposed approach, NLOS and LOS identification need to be implemented first. A support vector machine algorithm is used to construct a real-time model that recognizes NLOS and LOS distances based on the proposed classification features. Then, the distances under NLOS and LOS environments are evaluated and compensated, respectively, to obtain reliable NLOS ranges and calibrated LOS measurements. A least squares algorithm is utilized to build the LOS distance compensation model, and Bayesian theorem is applied to recognize the trusted NLOS ranges. The compensated LOS distances and credible NLOS ranges are used for estimating the location. The experimental results demonstrate that the proposed algorithm achieves excellent positioning accuracy, with a mean absolute error of 1.082 m. This signifies a substantial enhancement of 53.34% when compared to the least squares algorithm, a remarkable 60.24% improvement over the weighted centroid positioning algorithm, a significant 36.11% progress in comparison to the RTNLIA algorithm, and a noteworthy 32.82% boost relative to the NLRNB approach. • Real-time and high-precision NLOS and LOS recognition using measuring range and RSS. • A lightweight LOS ranging calibration model is built to improve LOS ranging accuracy. • Trusted NLOS distances are recognized by leveraging RSS and distance measurements. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we present a novel clustering approach that eliminates the need for predefined cluster centres and cluster counts, addressing common limitations in traditional clustering algorithms. We achieve this by allowing each data point within the problem space to serve as a potential cluster centre, determined by the presence of nearby data points. Three distinct algorithms are introduced: i) Identifying valid data points for clustering by assessing their presence within the specified vicinity of each potential cluster centre, allowing data points to belong to multiple clusters, ii) Calculating the fuzzy membership of shared data points among the clusters and iii) Retaining shared data points in the cluster with the highest membership and removing them from others. A comprehensive evaluation of this method using synthetic data and benchmark datasets from well-known problems demonstrates promising performance. Our approach offers increased flexibility, ease of use, and enhances the fidelity of cluster centres to the underlying data distribution. [ABSTRACT FROM AUTHOR]

Accurate estimation of grid voltage parameters such as amplitude, phase angle and frequency is crucial for advance control and protection in power systems. There are different estimation techniques in the specialized bibliography, so the selection for their implementation involves analyzing a trade-off of operating characteristics such as accuracy, response time or complexity. This paper proposes a three-phase estimation algorithm with high fidelity performance during abnormal grid conditions. The proposed algorithm combines a new Two-Sample Sequence Extractor (TSSE) and a Recursive Least Mean Square (RLMS) frequency estimator in a mutual feedback mode to improve its operation. To estimate the amplitude and phase of the positive and negative sequence components, the TSSE performs a linear approximation between two voltage samples. Furthermore, a slide window approach RLMS is used to estimate the system's frequency. Simulation and experimental results are presented to analyze and validate the performance of the TSSE-RLMS. In addition, the proposed algorithm is compared with other four kinds of estimation methods under different grid conditions. • A 3 Φ voltage parameter estimator with high fidelity performance is presented. • The algorithm combines a Two-Sample Sequence Extractor and a RLMS frequency estimator. • The proposed algorithm is compared with other four kinds of estimation methods. • The TSSE- RLMS presents a fast and accurate estimation of the sequence voltages. [ABSTRACT FROM AUTHOR]

In hybrid ac/multi-terminal medium-voltage dc (AC/MT-MVDC) systems with multiple voltage source converters (VSCs) and dc/dc converters, it is essential to attain accurate initial power flow (PF) and post-event power distribution solutions efficiently. This paper proposes: (i) a novel sequential PF algorithm for hybrid AC/MT-MVDC systems by a Fibonacci search-based Newton–Raphson (FSNR) approach with uniform MVDC bus type definition, while considering power losses and varied control schemes of different converters; (ii) a zero error steady-state post-event power distribution calculation method under droop control by the introduction of dynamic I/V droop coefficients based on the FS algorithm. The FSNR approach simplifies the MVDC PF derivation by only requiring the definition of the dc current bus type, effectively eliminating the need to solve multiple sub-Jacobian matrices. Furthermore, the post-event power distribution analysis offers precise power redistribution calculation approach following disturbances by considering both open- and closed-loop operation in an MT-MVDC distribution system. The computational efficiency and validity of the proposed PF algorithm, along with the accuracy of presented post-event power distribution calculation method are verified through Python and RTDS real-time simulators in an extended MT-MVDC distribution network incorporated with the IEEE 14/33/69 bus transmission/distribution systems. • PF derivation of hybrid AC/MT-MVDC networks by a novel sequential PF algorithm. • Uniform MVDC bus type definition by an FSNR approach. • Post-event power distribution analysis method for MT-MVDC distribution networks. • Zero error power redistribution calculation by an FS method-based dynamic I/V droop coefficients. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel unit-cell based algorithm for generating amorphous SiOC:H structures. • Algorithm automates atomic coordinates, valency, connectivity and cell periodicity. • Algorithm effectively removes dangling bonds, enhancing structure reliability. • Validates by comparing calculated mechanical properties with experimental dataset. Atomistic modeling of amorphous SiOC:H structures is crucial for understanding structure–property relationship and designing of new low- k materials. While a cubic-grid algorithm was previously developed to automatically construct amorphous SiOC:H structures, it required pre-treatment of the cubic grid to meet atomic valency and connectivity criteria. Additionally, the resulting structures suffered from persistent dangling bonds. In this paper, we introduce a novel unit-cell algorithm of generating amorphous SiOC:H structures. It takes the numbers of different types of silicon atoms (Q, T, D, M, and V) and the dimension of the supercell as input parameters. The algorithm ensures automatic handling of atomic coordinates, valency, connectivity, and cell periodicity by modeling the structures on the α-quartz unit cell. Throughout the process, these features are maintained by restricting the types of allowable structural modifications. Amorphous characteristics are introduced by randomly distributing various types of silicon atoms in the supercell. Our algorithm produces structures with amorphous traits, as shown by radial distribution function plots. To validate the effectiveness of the approach, we compare calculated properties of structures generated by the new algorithm to an experimental dataset of SiOC:H films. The good agreement between the two suggests that the new unit-cell algorithm is capable of generating realistic low- k amorphous SiOC:H structures. [ABSTRACT FROM AUTHOR]

• An effective screening algorithm is derived by extracting angle measurements. • An exclusion strategy of association results is proposed by building statistics. • Angle association is calculated by least square to get coordinates of fused measurements. • Another exclusion strategy is developed using unique position measurements. Multi-target tracking among different types of sensors is facing great challenge in fully utilizing various types of measurements. To this end, this paper presents a measurement fusion algorithm of single active and multi-passive sensors (SAMPS) based on angle association (AA), named SAMPS-AA algorithm, for multi-target tracking. Firstly, in order to narrow down the association range, the common angle measurements of two types of sensors are extracted by the proposed effective screening algorithm. Then, an exclusion strategy of wrong association groups is developed by building statistics, which is based on angle measurements. Subsequently, coordinates of fused measurements are obtained by angle association, based on least squares (LS). Finally, another exclusion strategy of wrong measurement points is proposed via measurement characteristics of active sensor. Experimental results indicate that the proposed SAMPS-AA algorithm can fully combine advantages of these two types of sensors, effectively exclude as many wrong association groups as possible, efficiently reduce the computational complexity, and obviously improve the tracking accuracy. [ABSTRACT FROM AUTHOR]

Social network analysis has become an important research area in recent years. Community detection, as a fundamental task in social network analysis, aims to discover the hidden community structure. However, most real-world social networks are unweighted and undirected without edge attributes. Existing community detection methods cannot fully utilize the network topology information. In this paper, we propose a new community detection algorithm based on graph embedding and node intimacy. Firstly, we design an algorithm to calculate the edge weights from the unweighted and undirected social network structure. Then, we apply the Node2vec model to learn the low-dimensional representations of nodes by preserving both weight matrix and global network structures. After that, a node intimacy algorithm is adopted to calculate the intimacy between each node pair based on their embeddings. Finally, the Fuzzy Clustering Means algorithm is used to group nodes into communities by considering the learned node intimacy. Extensive experiments are conducted on both real-world and artificial benchmark networks. The results demonstrate that our proposed algorithm can capture the community structure more accurately by making better use of the network topology, compared with other state-of-the-art methods. In summary, the algorithm integrates graph embedding and node intimacy for community detection, which provides an effective approach for analyzing unweighted social networks. [ABSTRACT FROM AUTHOR]

Online ride-hailing has become one of the most important transportation ways. In the ride-hailing system, how to efficiently match orders with vehicles and dispatch idle vehicles are key issues. The ride-hailing platform needs to match orders with vehicles and dispatch idle vehicles efficiently to maximize social welfare. However, the matching and dispatching decisions at the current round may affect the supply and demand of ride-hailing in the future rounds since they will affect the future vehicle distributions in different geographical zones. In fact, vehicles in different zones at different times may have different values for the matching and dispatching results. In this paper, we use the vehicle value function to characterize the spatio-temporal value of vehicles in each zone and then use it to design the order matching and idle vehicle dispatching algorithm to improve the long-term social welfare. In addition, in the order matching, passengers may untruthfully report the maximum price they are willing to pay to maximize their own profits, which can affect the order matching and thus may result in the losses of the long-term social welfare. Therefore, we design a VCG based pricing algorithm to prevent the strategic behavior of passengers. We further run experiments to evaluate the proposed algorithm. The experimental results show that our algorithm can outperform the state-of-the-art algorithm in terms of social welfare by 11.7% and service ratio by 11.1%. This work can provide some useful insights for the online ride-hailing platform to design practical order matching and pricing strategies. [Display omitted] • We design a vehicle value function to optimize order matching and improve the long-term social welfare by using a reinforcement learning method. • We consider the dispatching of idle vehicles to a zone as a virtual order and process idle vehicle dispatching issue as (virtual) order matching as well. • We design a VCG based pricing method to prevent the strategic behavior of passengers and ensure positive profits for the platform. [ABSTRACT FROM AUTHOR]

Feature extraction is one of the fundamental problems in pattern recognition research. For image recognition, extracting effective image features is the key to accomplish the recognition task. In this paper, a partial differential equations-based multi-rank two-dimensional canonical correlation analysis (PDEs-MR2DC2A) is proposed for multi-view feature extraction and pattern classification. Unlike most of the previous researches on multi-view algorithms that work directly on the original 2D representation, in our approach, we first utilize the evolution process of PDEs to extract the feature matrix of per-view. In addition, we employ multi-rank left and right projecting matrices to maximize the correlation. The computational complexity of PDEs-MR2DC2A is also analyzed. To evaluate the effectiveness of the proposed algorithm, we conducted a series of performance comparisons with some existing methods on several popular datasets. The experimental results showed that our proposed algorithm performed very well on these datasets and outperformed the existing related methods on some metrics. • A novel multi-rank multi-view algorithm based on PDEs is proposed. • This approach can extract rotation and translation invariant features. • Numerous experimental results demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In order to make multiobjective evolutionary algorithm based on decomposition (MOEA/D) have better performance in dealing with many-objective optimization problems (MaOPs) with different Pareto fronts (PFs), this paper proposes a MOEA/D with adaptive external population guided weight vector adjustment (MaOEA/D-AEW). The algorithm modifies the MOEA/D method of constructing mating pools by calculating the utility function value of individuals in the population to produce high-quality offspring. For the maintenance of non-dominated solutions in the external population (EP), solutions exceeding the capacity of the EP are removed by the method of minimum Euclidean distance combined with the aggregation function value to ensure the convergence and diversity of non-dominated solutions in the EP. For the addition of weight vectors, a new sparsity evaluating method (SL+) is proposed to determine the sparsity of weight vectors by multiplying parallel distances between individuals. For the deletion of the weight vector, the minimum Euclidean distance combined with the aggregation function value is used to delete the weight vector corresponding to invalid or crowded individuals in the population. Comparing MaOEA/D-AEW with six state-of-the-art algorithms on regular PF and irregular PF test problems respectively, simulation results show that the proposed algorithm can adapt well to MaOPs with different PFs and significantly outperforms other comparative algorithms in terms of convergence and diversity of solution sets. [ABSTRACT FROM AUTHOR]

The analysis and control of stability of passive biped robot has been the subject of an in-depth study because of its unique characteristics. This work gives a new perspective aiming at a force applied on foot of collision phase of walking process of passive robot, which is a completely new way of applying impulse force that is different from the previous. Therefore, the new equation has been derived due to this force which is not parallel to the support leg to provide further analysis for the study. Besides, this paper combines the characteristics of passive walking with the process of reinforcement learning, and an improved algorithm thus was designed to calculate the value of the control force. The simulation results show that control method designed in this article can not only make the unsteady passive robot achieve stable walking, but also have a quick control speed. Furthermore, the selection range of initial values is expanded by using this method, which provides a convenient reference for the later research work. • A new dynamic model of passive robot is present based on pulse control. • A modified Q-learning algorithm is applied in control method. • Stable walking comes true quickly under the introduced method. • The amount of work required to calculate the initial value for a stable walk is reduced. [ABSTRACT FROM AUTHOR]

The mineralogical composition and microstructure of crystalline rock affect its mechanical properties. However, it is challenging to analyze and quantify the associated mechanisms using experimental methods. A potential solution to this problem is to apply microscopic simulation methods, such as a combination of the discrete element method and grain-based model (GBM). A prerequisite for microscopic simulations is to accurately reproduce the microscopic features, especially the interactions between grains. Inspired by the crystallization of polycrystalline rocks, a novel grain growth algorithm that constructs a GBM by simulating the sequential crystallization process is proposed in this paper. The results of comprehensive analyses show that the proposed modeling method can accurately reproduce the mineralogical compositions, euhedral-to-anhedral morphology transitions, and grain size distributions during different crystallization processes. By accurately reproducing the interlocking structure between grains, the proposed method can simulate typical mechanical behaviors, such as the nonlinear strength envelope, uniaxial compressive strength-to-tensile strength ratio, and bimodularity, which are consistent with the experimental findings. The effectiveness of this method in modeling porphyritic structures and grain-scale anisotropic textures is demonstrated, and the microscopic mechanism of fracture mode transition associated with the difference in grain structures is discussed. [ABSTRACT FROM AUTHOR]

This paper introduces a deep learning method for image classification that leverages knowledge formalized as a graph created from information represented by pairs attribute/value. The proposed method investigates a loss function that adaptively combines the classical cross-entropy commonly used in deep learning with a novel penalty function. The novel loss function is derived from the representation of nodes after embedding the knowledge graph and incorporates the proximity between class and image nodes. Its formulation enables the model to focus on identifying the boundary between the most challenging classes to distinguish. Experimental results on several image databases demonstrate improved performance compared to state-of-the-art methods, including classical deep learning algorithms and recent algorithms that incorporate knowledge represented by a graph. [ABSTRACT FROM AUTHOR]

The analysis of two-dimensional images enables the detection of structures, objects and their further classification. In the Internet of Things, images are data obtained from cameras or projections of signals. In this paper, we propose a hybridization of the classic RANSAC approach with a selected heuristic algorithm like Grey Wolf Optimizer. This solution allows for increasing the ability to adjust images by analyzing the detected key points of the image. Such hybridization is interesting in terms of the possibility of obtaining stitching accuracy. Moreover, we have introduced the automation of parameter selection to increase the accuracy and speed of the real-time algorithm. For this purpose, an analysis of the results of the algorithm for combining video frames into one large image allowed for a significant reduction in the amount of data processing time. The proposed algorithm was tested on various heuristic algorithms, where their differences and impact on the operation of the method were indicated and discussed. Moreover, the proposition was tested in practical application by automatically creating an image based on video frames recorded by the drone. • Hybridization of RANSAC with a heuristic solution for image stitching purposes. • A method of automating the selection of parameters in the algorithm. • Framework for minimizing the number of calculations in smart devices. • Practical application of the proposed method in IoT solution using a drone. [ABSTRACT FROM AUTHOR]