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A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth's gravity field elements using ultra high-order gravity field coefficient models. In the surveying and mapping industry, researchers typically rely on CPU-based systems for these calculations, which leads to limitations in execution speed and power efficiency. Although modern CPUs improve instruction execution efficiency through instruction-level parallelism, the constraints of a shared memory architecture impose further limitations on the execution speed and power efficiency. This results in exponential increases in computation time as demand rises alongside high power consumption. In this article, we present a new computational implementation of an fnALF based on the ZYNQ platform. We design a task-parallel "pipeline" architecture which converts the original serial logic into a more efficient hardware implementation, and we utilize a redundant calculation layer to handle repetitive coefficient computations separately. The experimental results demonstrate that our system achieved accurate and rapid calculations. Under the only one geocentric residual latitude condition, we measured the computation times for spherical harmonic coefficient degrees of 360, 720, and 1080 to be 0.155922 s, 0.520950 s, and 1.401609 s, respectively. In the case of the multiple geocentric residual latitudes condition, our design generally yielded efficiency gains of over three times those of MATLAB R2020b implementation. Additionally, our calculated results were used to determine the geoid height in the field with an error of less than ± 0.1 m , confirming the reliability of our computations. [ABSTRACT FROM AUTHOR]

Accurate dynamic models that update in real‐time are vital for industrial robots to adapt flexibly and robustly to evolving environments and tasks, enabling precise model‐based control, motion planning and disturbance estimation. However, the identification of a dynamic robot model involves multiple challenges. First, existing friction models cannot accurately characterize torque variance during prolonged robot operations. Second, traditional offline identification methods such as the least squares (LS) method, suffer from over‐fitting when the inertia parameters are unknown a priori. Furthermore, existing online identification approaches, such as sliding LS, fail to meet the accuracy and real‐time requirements of industrial robots. To address these challenges, this article proposes a two‐stage Bayesian framework for rapid offline identification and online updating of industrial robot dynamics. In the offline stage, we developed a recursive sparse Bayesian learning (RSBL) method to select the dominant parameters and discover a thermal‐related nonlinear dynamic friction model from data, which was then used to find a sparser and simpler inertia model. The obtained friction and inertia models were used as prior knowledge for online updating. The RSBL method significantly reduces the computational complexity and runtime, while it also accelerates online model updating. Experimental results for a 6‐DOF medium‐load robot validate dual offline improvements in prediction accuracy and model sparsity, along with higher online prediction accuracy and shorter computational time. [ABSTRACT FROM AUTHOR]

It is essential for meeting the stringent real-time demands encountered in actual production scenarios. Employing the low computational complexity of recursive algorithms, some new schemes are developed for the parameter estimation of a class of time-varying systems. The temporal evolution of parameters is characterized through the autoregressive process, facilitating the construction of the identification model with regard to the autoregressive coefficients. Based on the computational efficiency of the gradient search, a parametric autoregression-based stochastic gradient algorithm is derived with an appropriate step size, achieving a compromise between the steepest descent and convergence rate. In order to address the limitation of the low estimation accuracy in gradient algorithms, a parametric autoregression-based multi-innovation stochastic gradient algorithm is explored by making use of the favorable information for corrections. The simulation results are given to demonstrate the effectiveness of the proposed algorithms. Therefore, for a class of time-varying systems whose parameters become the further insight through the autoregressive process, the proposed gradient methods can obtain the parameter estimates faster and more accurately while ensuring the real-time performance of time-varying systems. [ABSTRACT FROM AUTHOR]

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Buffer allocation is a critical issue in the design stage of manufacturing systems, as buffer capacities may have a great impact on system performance. In this paper, we consider the problem of minimizing the total buffer capacity of a flow line to achieve a desired production rate. A recursive optimization approach is proposed to solve the problem in large production lines. Instead of optimizing a long line directly, the proposed approach decomposes it into two sub-lines, optimizes them recursively, and combines their solutions to find the optimal buffer distribution of the original line. Two different recursive algorithms are developed and their performance is demonstrated by comparing them with a gradient search algorithm. The numerical results show that the recursive algorithms are almost as accurate as the gradient algorithm, but much more efficient, especially for large production lines. [ABSTRACT FROM AUTHOR]

Color shift keying is a modulation scheme for visible light communication that uses fixtures with three or more narrow-spectral light-emitting diodes to transmit data while fulfilling the primary function of illumination. When this modulation is used indoors, the reflectivity of the walls strongly affects the inter-channel interference and illumination quality. In this paper we present an indoor channel model that takes into account multi-wavelength propagation. This model is available as an open-source Python package. The model calculates the inter-channel interference, illuminance, correlated color temperature, and color rendering index at the receiver position. The Python package includes a module for estimating the symbol error rate. To validate the model, we computed the received power at each color photodetector for four different indoor scenarios. The model demonstrated a color rendering index of less than 15 when using IEEE-based color shift keying and non-uniform illumination on a horizontal plane. The simulation determined the required luminous flux to achieve a symbol error rate of less than 10 − 5 when the photodetector is at the center of the indoor space and vertically below the light source. To maintain a symbol error rate less than 10 − 5 , the luminous flux increases when the photodetector is displaced in a diagonal direction from the center of the plane. [ABSTRACT FROM AUTHOR]

The complex underground environment of coal coalmines, influenced by lighting, coal dust, and water mist, often results in collected images with blurred details and missing textures, leading to decreased image resolution and posing significant limitations to the intelligent development of coal coalmine safety monitoring. Image super-resolution reconstruction, an essential image processing technology, aims to recover clear high-resolution images from low-resolution coalmine images, thereby significantly enhancing the reliability of intelligent monitoring and safety management in coal coalmines. To address issues such as the loss of edge texture information and blurring of details in coalmine images, a coalmine image super-resolution reconstruction method integrating multi-dimensional features and residual attention networks is proposed. First, a multi-branch network is employed to parallelly integrate dynamic convolution and channel attention mechanisms, capturing different spatial statistical characteristics through “horizontal-channel” and “vertical-channel” interactions. Secondly, a recursive sparse self-attention mechanism is designed to aggregate representative feature maps under linear complexity, adaptively selecting weight distribution and reducing information redundancy during computation. Finally, the basic unit of deep feature extraction is constructed based on the standard multi-head self-attention mechanism and residual connection, with the obtained feature information and shallow features jointly input into the reconstruction module via skip connections to complete super-resolution reconstruction of coalmine images. Experimental results indicate that the proposed method significantly outperforms existing mainstream algorithms in both objective evaluation metrics and subjective visual analysis. In tests on the coalmine dataset, LPIPS (Learned Perceptual Image Patch Similarity) decreases by an average of 10.97% and 9.91%, while PSNR (Peak Signal-to-Noise Ratio) increases by an average of 4.10% and 2.30% for 2x and 4x scaling factors, respectively, demonstrating the method's effectiveness in restoring the structure and texture details of coalmine images.

In this study, discrete event time-variant parameter of a lumped SISO dynamical system is obtained for a novel energy harvester. The harvester utilizes the resonance vibration of a coupled fluid-mechanic system, which is comprised of a reciprocating-piston, a tunable oscillator and twin electric turbine generators. The system model is simplified by an underdamped spring-mass-damper with tunable parameters subjected to periodic or arbitrary impulse forces. We developed a method that solves general problems underlining non-homogenous ordinary second order differential equation with discrete event time-variant parameters. The method uses the superposition to compile the general solution by segmenting the problem into ODE equations with constant coefficient. Each segment corresponds to a change in system, and it is solved analytically with recursively updated initial conditions. We utilized this method to tune the system's natural frequency such that the accumulated energy is maximum within desired time range. We obtained the general solution of the displacement response due to varying parameters and input impulse force. We obtained the displacement peaks and their time of occurrence that could help understand the structural design limitation. We showed system displacement responses (constructive and destructive) corresponding to various impulse repetition patterns. We derived the relationship between energy, volume displacement, and volumetric flow rate (or piston velocity) and provided illustrative case studies. One finding shows that for a given impulse condition the accumulative energy exhibits local maximum and minimum within a narrow band of natural frequency range. The simulations showed the feasibility of generating considerable power from electric turbine when the air flow is driven by a resonating piston. [ABSTRACT FROM AUTHOR]

In this paper, a novel recursive and efficient algorithm for real-time implementation of the adaptive and passivity-based controllers in model-based control of robot manipulators is proposed. Many of the previous methods on these topics involve the computation of the regressor matrix explicitly or non-recursive computations, which remains as the main challenge in practical applications. The proposed method achieves a compact and fully recursive reformulation without computing the regressor matrix or its elements. This paper is based on a comprehensive literature review of the previously proposed methods, presented in a unified mathematical framework suitable for understanding the fundamentals and making comparisons. The considered methods are implemented on several processors and their performances are compared in terms of real-time computational efficiency. Computational results show that the proposed Adaptive Newton-Euler Algorithm significantly reduces the computation time of the control law per cycle time in the implementation of adaptive control laws. In addition, using the dynamic simulation of an industrial robot with 6-DoF, trajectory tracking performances of the adaptive controllers are compared with those of non-adaptive control methods where dynamic parameters are assumed to be known. [ABSTRACT FROM AUTHOR]

A large number of fully normalized associated Legendre function (fnALF) calculations are required to compute Earth’s gravity field elements using ultra high-order gravity field coefficient models. In the surveying and mapping industry, researchers typically rely on CPU-based systems for these calculations, which leads to limitations in execution speed and power efficiency. Although modern CPUs improve instruction execution efficiency through instruction-level parallelism, the constraints of a shared memory architecture impose further limitations on the execution speed and power efficiency. This results in exponential increases in computation time as demand rises alongside high power consumption. In this article, we present a new computational implementation of an fnALF based on the ZYNQ platform. We design a task-parallel “pipeline” architecture which converts the original serial logic into a more efficient hardware implementation, and we utilize a redundant calculation layer to handle repetitive coefficient computations separately. The experimental results demonstrate that our system achieved accurate and rapid calculations. Under the only one geocentric residual latitude condition, we measured the computation times for spherical harmonic coefficient degrees of 360, 720, and 1080 to be 0.155922 s, 0.520950 s, and 1.401609 s, respectively. In the case of the multiple geocentric residual latitudes condition, our design generally yielded efficiency gains of over three times those of MATLAB R2020b implementation. Additionally, our calculated results were used to determine the geoid height in the field with an error of less than ±0.1m, confirming the reliability of our computations.

Buffer allocation is a critical issue in the design stage of manufacturing systems, as buffer capacities may have a great impact on system performance. In this paper, we consider the problem of minimizing the total buffer capacity of a flow line to achieve a desired production rate. A recursive optimization approach is proposed to solve the problem in large production lines. Instead of optimizing a long line directly, the proposed approach decomposes it into two sub-lines, optimizes them recursively, and combines their solutions to find the optimal buffer distribution of the original line. Two different recursive algorithms are developed and their performance is demonstrated by comparing them with a gradient search algorithm. The numerical results show that the recursive algorithms are almost as accurate as the gradient algorithm, but much more efficient, especially for large production lines.

Color shift keying is a modulation scheme for visible light communication that uses fixtures with three or more narrow-spectral light-emitting diodes to transmit data while fulfilling the primary function of illumination. When this modulation is used indoors, the reflectivity of the walls strongly affects the inter-channel interference and illumination quality. In this paper we present an indoor channel model that takes into account multi-wavelength propagation. This model is available as an open-source Python package. The model calculates the inter-channel interference, illuminance, correlated color temperature, and color rendering index at the receiver position. The Python package includes a module for estimating the symbol error rate. To validate the model, we computed the received power at each color photodetector for four different indoor scenarios. The model demonstrated a color rendering index of less than 15 when using IEEE-based color shift keying and non-uniform illumination on a horizontal plane. The simulation determined the required luminous flux to achieve a symbol error rate of less than 10−5 when the photodetector is at the center of the indoor space and vertically below the light source. To maintain a symbol error rate less than 10−5, the luminous flux increases when the photodetector is displaced in a diagonal direction from the center of the plane.

Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems. [ABSTRACT FROM AUTHOR]

Aiming at the problem of quantitative inertia evaluation of a new energy electric power system, the system inertia constant tracking method based on system identification is studied. The method is divided into two categories: non-recursive algorithm and recursive algorithm. The non-recursive algorithm uses a batch of data for batch processing to obtain the estimated value of the identification model parameters. The recursive algorithm is based on the estimated value of the model parameter at the previous moment and corrects the estimated value based on the new data currently obtained. From the perspective of the identification principle, the difference and internal relationship between the two in terms of calculation storage and identification speed are analyzed. The IEEE typical system is used to compare and verify the experimental examples. Theoretical analysis and experimental results show that the recursive algorithm has high identification accuracy, stable identification results and fast identification speed. It is suitable for the identification of objects with large numbers of nodes and complex structures, which is conducive to real-time monitoring and fast perception of the inertia constant of the new energy power system. [ABSTRACT FROM AUTHOR]

Non-invasive maternal electrocardiogram recording is the least unpleasant method to record a weak fetal electrocardiogram signal. The importance of this recording lies in the fact that it reveals crucial information about the fetal health state, especially during the last four weeks of pregnancy. This paper will be concerned with a new adaptive algorithm, namely the generalized recursive algorithm, to isolate and get the fetal electrocardiogram from the abdominal maternal electrocardiogram. This is achieved using a non-invasive method for bi-channel maternal electrocardiogram recordings i.e., with the thoracic maternal electrocardiogram as a reference signal, and the abdominal maternal electrocardiogram as a primary signal. Prior to this procedure, the discrete wavelet transform (DWT) method is applied to the abdominal electrocardiogram signal to clean it from any additive noise and the baseline wandering that is generally present on the raw recordings. The proposed new adaptive filter is shown to deliver improved characteristics through simulations. These simulations were performed on both synthetic and actual signals. This work was compared with the normalized least mean square algorithm. [ABSTRACT FROM AUTHOR]

As an important aspect of reliability theory, availability has now been considered a very meaningful design criterion of repairable system. This paper investigates the availability evaluation and design optimization of the multi-state k-out-of-n: G systems considering random weight threshold. The system availability is evaluated by extending the recursive algorithm (RA) and universal generating function (UGF) technique. Based on the traditional recursive algorithm, the total probability theorem is used to solve the discrete random weight threshold. Another better UGF method combines a new stochastic joint operator, which is suitable for both continuous and discrete random weight thresholds. Furthermore, we constructed two system design optimization models under availability or cost constraint respectively, and genetic algorithm (GA) programming can be applied to obtain the optimal state probability distribution and weight distribution of multi-state components of the suggested system. Finally, through numerical examples, the flexibility and effectiveness of the proposed methods for design optimization are demonstrated. In addition, two evaluation methods are compared to show that the customized UGF method features higher generality than RA in the case of continuous stochastic weight threshold, and higher operational efficiency in the case of increasing component quantity and state. The results can be helpful for engineers to optimize the design of complex systems.

Vulnerability refers to the ability of a network to continue functioning when part of the network is either naturally damaged or targeted for attack. In this paper, the rupture degree of graphs is employed to measure the vulnerability of uniform linear hypergraphs. First, we discuss the bounds of the rupture degrees of k-uniform linear hypergraphs. Then, we give a recursive algorithm for computing the rupture degree of k-uniform hypertrees. [ABSTRACT FROM AUTHOR]

Aiming at the problem of quantitative inertia evaluation of a new energy electric power system, the system inertia constant tracking method based on system identification is studied. The method is divided into two categories: non-recursive algorithm and recursive algorithm. The non-recursive algorithm uses a batch of data for batch processing to obtain the estimated value of the identification model parameters. The recursive algorithm is based on the estimated value of the model parameter at the previous moment and corrects the estimated value based on the new data currently obtained. From the perspective of the identification principle, the difference and internal relationship between the two in terms of calculation storage and identification speed are analyzed. The IEEE typical system is used to compare and verify the experimental examples. Theoretical analysis and experimental results show that the recursive algorithm has high identification accuracy, stable identification results and fast identification speed. It is suitable for the identification of objects with large numbers of nodes and complex structures, which is conducive to real-time monitoring and fast perception of the inertia constant of the new energy power system.

In recent years, research and development of robots that exist in the same space as humans and can collaborate with humans have been actively carried out. If the body of a robot is made of a hard material, it may cause injury. Therefore, attempts have been made to make a robot with a soft body using rubber or resin. In order to accelerate such research on soft robotics, it is necessary to establish fast and stable simulation algorithm for robots containing viscoelastic bodies such as rubber and resin. Therefore, in this study, we consider to approximate viscoelastic bodies with finite rigid body segments and connect them with joints and linear viscoelastic elements such as Voigt model, Maxwell model and generalized Maxwell model to approximate viscoelastic properties. The recursive dynamics algorithm is used to speed up the calculation, and the generalized-α method is used to stabilize the numerical integration. In particular, we propose a new method on how to incorporate the Maxwell model and generalized Maxwell model into recursive dynamics algorithm and generalized-α method. The effectiveness of the proposed method is confirmed by some numerical examples. In addition, the effectiveness of the proposed method is verified on a real system by applying Particle Swarm Optimization (PSO) to identify the dynamic parameters in linear viscoelastic elements.

Recently, few studies have been made on the stereophonic acoustic echo cancellation (SAEC) with nonlinear systems. To identify such nonlinear model, the functional link artificial neural network (FLANN) and the widely linear model can provide an approach to explore the SAEC with complex random variable. In this paper, a class of augmented complex-value functional link network (ACFLN) adaptive algorithms is developed. Based on the augmented complex-value functional least-mean-square (ACFLMS) algorithm, we have proposed the recursive augmented complex-value functional least-mean-square (RACFLMS) algorithm designed by a recursive structure. To further reduce its computational complexity and enhance its performance, a novel inverse square root function is employed in its structure of the RACFLMS algorithm. The results of several experiments demonstrate that our approach can effectively model the nonlinear systems and verify the improvement of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The main research object of this paper is to present a systematic computational procedure for computing the inverse of a generalized Vandermonde matrix. Short and rigorous proofs for the formulas of the determinant and the inverse of a generalized Vandermonde matrix are proposed. The computational cost of this method is O (n 2) . The proposed method can be used efficiently for hand calculation as well as for computer programming. Some examples are given for the sake of illustration. Furthermore, we present a simulation study to compare the time spent to calculate the inverse using the proposed algorithm and the inverse function in Maple. [ABSTRACT FROM AUTHOR]

We analyze the structure of a one-dimensional deep ReLU neural network (ReLU DNN) in comparison to the model of continuous piecewise linear (CPL) spline functions with arbitrary knots. In particular, we give a recursive algorithm to transfer the parameter set determining the ReLU DNN into the parameter set of a CPL spline function. Using this representation, we show that after removing the well-known parameter redundancies of the ReLU DNN, which are caused by the positive scaling property, all remaining parameters are independent. Moreover, we show that the ReLU DNN with one, two or three hidden layers can represent CPL spline functions with K arbitrarily prescribed knots (breakpoints), where K is the number of real parameters determining the normalized ReLU DNN (up to the output layer parameters). Our findings are useful to fix a priori conditions on the ReLU DNN to achieve an output with prescribed breakpoints and function values. [ABSTRACT FROM AUTHOR]

More than half a century ago Martin Gardner popularized a question leading to the benchmark problem of determining the minimum side length of a square into which the squares of sizes 1 , 2 , ⋯ , n can be packed without overlap. Constructions are known for a certain range of n, and summing up the areas yields that a packing in a square of size smaller than N : = n (n + 1) (2 n + 1) / 6) is not possible. Here we prove that an asymptotically minimal packing exists in a square of size N + c n + O (n) with c < 1 , and such a packing is achievable with guillotine-cuts. An improved construction is also given for the case where the constraint of guillotine cutting is dropped. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, we consider the problem of estimating diffraction attenuation from the approximation of terrain using a multiple bridged knife-edge model. This model can be considered as a generalization of the well-known multiple knife-edge (MKE) one where spaces between knife-edges are bridged by reflecting surfaces. A series-based-standard solution is presented in the literature but suffers from its very high computational complexity. We, thus, propose to generalize the recursive Vogler algorithm developed for the MKE model to tackle this problem. To reduce complexity, the proposed algorithm exploits the recursive form to avoid repeated calculations of the existing solution. Moreover, we provide a complexity analysis of both the standard algorithm and the proposed algorithm based on the number of computed integrals. Both theoretical and numerical results show that our proposed algorithm is faster than the original one while having identical accuracy, hence proving the effectiveness of our solution. [ABSTRACT FROM AUTHOR]

The identification of reliability-critical primary input leads (RCPIs) plays an important role in the testing and prediction of reliability boundaries of logic circuits. This article presents a gate-sensitive-attributes-based approach to estimate the criticality of the primary input leads in combinational circuits to their reliability. Oriented to the input vector, a subcircuit-based traversal method marks the critical input leads of each gate in a circuit. Gate-sensitive attributes and a reverse recursive algorithm quantify the effect of each RCPI on circuit reliability under the input vector. A parallel calculation method based on subcircuits with only one primary output reduces the computational complexity to accelerate the calculation process. Similarity-based clustering avoids unnecessary calculations, and a self-adaptive strategy is used to check convergence. Experimental results on benchmark circuits show that the average accuracy of this approach is 0.9634 with Monte Carlo (MC) as the reference and it is 3445 times faster than the MC on average while its average memory cost is 1.67 greater than the MC model. Although the fitness of the worst input vector obtained by other reference methods is 1.09 times better than that of this approach on average, this approach is approximately 21 times faster than that reference method on average. [ABSTRACT FROM AUTHOR]

Let α be a nonzero algebraic integer of degree d whose all conjugates α 1 = α , α 2 , ... , α d lie in a sector | arg ⁡ z | ≤ θ , 0 < θ < 90 °. In 2013, K. Stulov and R. Yang defined the total distance of α as td (α) = ∑ i = 1 d | | α i | − 1 |. This paper is organized in two parts. First, we compute the greatest lower bound c (θ) of the absolute total distance of α , for α belonging to twenty one subintervals of (0 , 90). Among these subintervals, eighteen are complete. Moreover, there are two sequences of seven then nine both complete and consecutive subintervals. It is the first time that these phenomena appear: such a large number of subintervals, such a large number of complete subintervals and such a large number of complete and consecutive subintervals. These phenomena are keeping with a conjecture of G. Rhin and C. J. Smyth on the nature of the function c (θ). Secondly, using the previous results and assuming that the above conjecture is true, we give upper bounds and lower bounds for the total distance involving the Mahler measure. Mostly these bounds improve those of K. Stulov and R. Yang. All computations are done by using the method of explicit auxiliary functions. The polynomials involved in these functions are found by our recursive algorithm. [ABSTRACT FROM AUTHOR]

Dynamic modelling of a multibody system is critical to analyse its behaviour under different circumstances. This paper proposes the dynamic modelling of a rigid multibody system in minimum coordinates with the help of Decoupled Natural Orthogonal Complement (DeNOC) matrices and dual algebra. The work brings DeNOC matrices into the dual algebra framework for the first time to solve the dynamics of a serial-chain rigid multibody system with benefits that were not explicitly available previously. The present work proposes properties of a matrix containing dual operator(s), namely, 'Duality Inducing Matrix (DIM)'. Utilizing these properties, one obtains the constrained dynamic equations of motion in minimum coordinates, and the constraint or reaction forces along the joint axes simultaneously. The latter information is useful for the calculation of the shaking forces that are typically used to optimize dynamic performance, etc. It is the unique feature of the dual algebra-based dynamics proposed in this paper. The formulation is illustrated with a simple example, namely, a spatial Revolute–Revolute manipulator even though the formulation is very generic and appropriate for any n -link serial-chain system. [ABSTRACT FROM AUTHOR]

The chief objective of the current research is to outline an automated technique for deriving the governing equations and also simulating the motions of the multi-closed-chain mechanisms. To this end, the behavior of a mechanical system, whose straight and rigid links are hinged together by perfect rotary joints to make an array of closed loops, will be studied in the two phases of impact and non-impact. For the non-impact phase, in which the said mechanism is only affected by the Earth's gravity, the relevant differential equations will be derived by the recursive Gibbs–Appell (G–A) methodology (which is a simple approach with fewer partial derivatives to worry about, especially when dealing with the holonomic systems); while for the impact phase, in which at least one joint of the mechanism collides with the surrounding curved wall, the algebraic equations of the system will be obtained by the Newton's kinematic impact law. One of the most formidable challenges in the current research is the presence of the holonomic constraints that govern the kinematics of the problem. The number of these constraints goes up linearly with the increase in the number of system loops. The other challenges and issues that have been successfully addressed by the presented technique include the oblique impact of the joints of the said mechanism with the surrounding curved wall and the relatively large number of the dependent generalized coordinates needed to define the system configuration. In the suggested approach, after deriving the inverse kinetic equations of the considered mechanism, they are converted to the forward dynamics form in a graphical procedure to simulate the system's dynamic responses. To demonstrate the capability and the efficacy of the presented technique in deriving the kinetic equations of multi-closed-loop systems, the dynamic behavior of a 5RRR mechanism with 4 closed loops and 10 rigid links is simulated. The simulation results are then validated by means of the work and energy principles. [ABSTRACT FROM AUTHOR]

The discretization of fractional-order differential operators is the key to the digital realization of fractional-order controllers. This paper proposes an improved second-order fractional differential equation operation method based on power series expansion. The algorithm's operation speed and accuracy performance are analyzed. The research found that the algorithm proposed in this paper is suitable for the fractional operation of arbitrary signals, including discrete data sequences whose mathematical model is unknown and the solution of linear systems.

The suitable aircraft performance monitoring(APM)parameters selection method can realize the efficient selection of domestic civil cruising APM parameters,which can provide the reliable data basis of aircraft performance analysis and calculation.The Sage-Husa noise estimator is introduced into unscented Kalman filter(UKF),and the adaptive unscented Kalman filter(AUKF)is constructed.AUKF is used to denoise the quick access recorder(QAR)data.The stable cruise parameter screening criteria are given,and the improved sliding time window algorithm is used to realize the stable cruise parameter selecting.The algorithm is verified by the sample data of the domestic ARJ21 aircraft.The results show that the adaptive unscented Kalman filter algorithm can improve the reliability of data,and the improved sliding time window algorithm can improve the selecting efficiency by about 50%.

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]

The health status of the running gear in high-speed trains changes dynamically with time in a complete life cycle. Running gear systems composed of many coupled components are complex, and health statuses of which are difficult to predict in real time through a traditional health status prediction scheme. Lately, belief rule base (BRB), which is able to combine quantitative information and expert knowledge, has shown excellent expression in modeling complex systems. In the procedure of health status prediction, expert expertise can sufficiently enhance the accuracy and efficiency of this model. Therefore, this paper puts forwards a real-time health status prediction framework based on a multi-layer BRB with priority scheduling strategies for running gears. In the first-layer BRB, a time-series prediction model of multiple module BRB considering complete features is established. In the second-layer model, grey relation analysis (GRA) is employed in priority scheduling strategies of features. The third-layer BRB is used for assessing the health status of running gears by combining the features. In addition, the initial parameters of all module BRB given by experts may not be precise. Accordingly, the initial parameters in the BRB are updated by the recursive algorithm online. Finally, the proposed method is tested on the testing platform in running gears. The results make it clear the proposed method can predict the health status of running gears with much accuracy in real time. [ABSTRACT FROM AUTHOR]

A coiled coil is a structural motif in proteins that consists of at least two α-helices wound around each other. For structural stabilization, these α-helices form interhelical contacts via their amino acid side chains. However, there are restrictions as to the distances along the amino acid sequence at which those contacts occur. As the spatial period of the α-helix is 3.6, the most frequent distances between hydrophobic contacts are 3, 4, and 7. Up to now, the multitude of possible decompositions of α-helices participating in coiled coils at these distances has not been explored systematically. Here, we present an algorithm that computes all non-redundant decompositions of sequence periods of hydrophobic amino acids into distances of 3, 4, and 7. Further, we examine which decompositions can be found in nature by analyzing the available data and taking a closer look at correlations between the properties of the coiled coil and its decomposition. We find that the availability of decompositions allowing for coiled-coil formation without putting too much strain on the α-helix geometry follows an oscillatory pattern in respect of period length. Our algorithm supplies the basis for exploring the possible decompositions of coiled coils of any period length. [ABSTRACT FROM AUTHOR]

• Laser Interferometer based surface Profilometry that is made immune to surrounding vibrations. • Capability to improve height map estimate even with wrapped phase due to tilted interference. • Improvement of dynamic range by the application of recursive algorithm. • Test and calibrated the measurement scheme with standard grade 0 slip gauges with known height. Frequency-tunable lasers allow the removal of phase ambiguities in interferometric profilometry through the synthetic wavelength longer than height variations in the sample. The subsequent measurements lowering synthetic wavelengths and updating phase difference values improves the measurement. The surface profilometry on samples with tilted interference can lead to an initial synthetic phase map, though locally unambiguous, getting globally wrapped whose unwrapping can be difficult in the presence of noise. Starting with the synthetic phase map that is locally unambiguous but globally wrapped can still update the phase values through a recursive algorithm. The artefact of 2π jumps in the initial wrapped synthetic phase just gets carried forward which can be easily removed from the final phase map obtained with the shortest possible synthetic wavelength. Choosing a point on the sample surface as the point of comparison, the proposed interferometry scheme can be made immune to surrounding vibrations while tuning wavelengths. [ABSTRACT FROM AUTHOR]

• Contact forces of frictional unilateral constraints are added to recurrence relations. • Modeling non-smooth systems by defining a frictional unilateral constraint pair. • The linear complementarity problem is constructed using the recurrence relations. • The proposed algorithm incorporates Moreau time-stepping method and recursive method. The contact impact problem in planar multibody systems can be efficiently solved by formulating it as the linear complementarity problem, which requires a complex modeling process. To simplify the process, a recursive algorithm for the dynamics of planar multibody systems with frictional unilateral constraints is proposed based on the reduced multibody system transfer matrix method. Firstly, the contact forces of frictional unilateral constraints are integrated into the recurrence relations of system components using Lagrange multipliers. Subsequently, the relative motion equations of the contact positions are discretized in time, which are then utilized to describe the linear complementarity problem for systems. The dynamics of the system is solved by the Moreau time-stepping method with the recursive method. Finally, the proposed algorithm was validated using the woodpecker toy and used to model a slider-crank mechanism with clearance, which shows its characteristics of facilitating modeling, universal, and highly programmable. This recursive algorithm provides an effective tool for solving non-smooth planar multibody systems while extending the application of the multibody system transfer matrix method. [ABSTRACT FROM AUTHOR]

Vulnerability refers to the ability of a network to continue functioning when part of the network is either naturally damaged or targeted for attack. In this paper, the rupture degree of graphs is employed to measure the vulnerability of uniform linear hypergraphs. First, we discuss the bounds of the rupture degrees of k-uniform linear hypergraphs. Then, we give a recursive algorithm for computing the rupture degree of k-uniform hypertrees.