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The fast-inertial-relaxation engine (FIRE) has proven to efficiently find local minima of potential energies or related penalty functions although its implementation requires only few, additional lines of code in a molecular-dynamics or steepest-descent program. So far, FIRE has been predominantly applied to particle-based or low-dimensional problems. In this work, we demonstrate that it can also benefit the solution of boundary-value problems. Towards this end, we study the mechanical contact between an elastic body and a rigid indenter of varying complexity by augmenting Green's function molecular dynamics (GFMD) with FIRE. We find a rather remarkable speedup, which can be further enhanced when choosing the masses associated with the eigenmodes of the free elastic solid appropriately. For the investigated adhesive and randomly rough indenter with typical system size, 100 mass-weighted FIRE-GFMD iterations suffice to relax the excess energy to ${10}^{\ensuremath{-}3}$ of its original value. The standard GFMD method needs 25 times more iterations. For the investigated problems, FIRE-GFMD even appears to slightly outperform conjugate-gradient based optimization.

In this article a global method for master-slave synchronization of nonlinear systems is provided. Based on a diffeomorphic transformation of the original dynamics, a reduced observer is designed such that the nonlinear state feedback law is independent of the inertial frame. Generally, the method requires non trivial computations but leads to global convergence results for the invariant tracking error. We illustrate the design procedure in detail for a master-slave synchronization of a bacterial growth model, which may escape to infinity in finite time.

This paper investigates global dynamics in fractional-order dual inertial neural networks with time lags. Firstly, according to some crucial features of Mittag-Leffler functions and Banach contracting mapping principle, the existence and uniqueness of $ S $-asymptotically $ \omega $-periodic oscillation of the model are gained. Secondly, by using the comparison principle and the stability criteria of delayed Caputo fractional-order differential equations, global asymptotical stability of the model is studied. In the end, the feasibility and effectiveness of the obtained conclusions are supported by two numerical examples. There are few papers focus on $ S $-asymptotically $ \omega $-periodic dynamics in fractional-order dual inertial neural networks with time-varying lags, apparently, the works in this paper fill some of the gaps.

In this paper we discuss the model of fractional oscillator where the inertial and restoring force terms maintains their usual expression but the damping term involves a fractional derivative of Caputo type, the so called fractional Kelvin-Voigt oscillator. The transient solution of this model is given in terms of the so called bivariate Mittag-Leffler function, while the steady-state solution in response to a sinusoidal force involves a 4-variate Mittag-Leffler function. We give numerical examples comparing the solutions for different values of the order α of the fractional derivative (0

This study investigates the weak convergence of the sequences generated by the inertial technique combining the parallel monotone hybrid method for finding a common fixed point of a finite family of $ G $-nonexpansive mappings under suitable conditions in Hilbert spaces endowed with graphs. Some numerical examples are also presented, providing applications to signal recovery under situations without knowing the type of noises. Besides, numerical experiments of the proposed algorithms, defined by different types of blurred matrices and noises on the algorithm, are able to show the efficiency and the implementation for LASSO problem in signal recovery.

In this paper, we consider a new class of quasi-variational inequalities, which is called the general quasi-variational inequality. Using the projection operator technique, we establish the equivalence between the general quasi-variational inequalities and the fixed point problems. We use this alternate formulation to propose some new inertial iterative schemes for solving the general quasi-variational inequalities. The convergence criteria of the new inertial projection methods under some appropriate conditions is investigated. Since the general quasi-variational inequalities include the quasi-variational inequalities, variational inequalities, complementarity problems and the related optimization problems as special cases, our results continue to hold for these problems. It is an interesting problem to compare the efficiency of the proposed methods with other known methods.

In this paper, we introduce a new iterative forward-backward splitting method with an error for solving the variational inclusion problem of the sum of two monotone operators in real Hilbert spaces. We suggest and analyze this method under some mild appropriate conditions imposed on the parameters such that another strong convergence theorem for these problem is obtained. We also apply our main result to improve the fast iterative shrinkage thresholding algorithm (IFISTA) with an error for solving the image deblurring problem. Finally, we provide numerical experiments to illustrate the convergence behavior and show the effectiveness of the sequence constructed by the inertial technique to the fast processing with high performance and the fast convergence with good performance of IFISTA.

The central concern of this paper is to study leader‐following quasi‐bipartite synchronisation of a multiple inertial signed neural networks with varying time‐delay by utilising distributed event‐triggered impulsive control scheme, where connections between adjacent nodes of the neural networks either positive or negative. The second‐order neural networks, called inertial neural networks, can be transformed into differential equations of first‐order by implementing suitable variable substitution. Under certain hypothesis about the node dynamics, signed graph theory and balanced topology of networks, some conditions are derived in terms of lower‐dimensional linear matrix inequalities (LMIs) to achieve leader‐following quasi‐bipartite synchronisation. In addition, a basic algebraic condition is derived to estimate the theoretical upper bound for the error node. Finally, some numerical simulations are provided to illustrate the correctness of the theoretical results.

Theaimof the work is to study the possibility and features of the use of inertial storage devices in the traction electric drive of multi unit train with a power plant based on fuel cells.Methodology.The principle of power flow control in traction electric drives in the modes of acceleration and braking of rolling stock is proposed. The mathematical model of the traction electric drive in the form of the combination of three components: the train, the traction unit and the battery of fuel cells is developed. It was used to study the operation of a traction electric drive when solving a test traction task for rolling stock.Results.It is established that the use of inertial energy storage reduces hydrogen consumption by at least 25 %, which increases the mileage of rolling stock between equipment by more than 30 %.Originality.The traction electric drive on the basis of fuel elements and the inertial energy storage for the multi unit train is offered. The work of the proposed traction electric drive in solving the test traction problem for rolling stock is investigated.Practical significance.A mathematical model of the traction electric drive has been developed. The test traction problem for rolling stock is solved.

Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM systems have attained high levels of accuracy and robustness through the inclusion of inertial measurements in a tightly-coupled fusion. Inspired by this performance, we propose the first tightly-coupled dense RGB-D-inertial SLAM system. Our system has real-time capability while running on a GPU. It jointly optimises for the camera pose, velocity, IMU biases and gravity direction while building up a globally consistent, fully dense surfel-based 3D reconstruction of the environment. Through a series of experiments on both synthetic and real world datasets, we show that our dense visual-inertial SLAM system is more robust to fast motions and periods of low texture and low geometric variation than a related RGB-D-only SLAM system., Accepted at IROS 2017; supplementary video available at https://youtu.be/-gUdQ0cxDh0

Virtual synchronous generator (VSG) has been a grid-friendly integration control technique for the integration of grid-connected inverters. However, the emulated inertia and damping of VSG control technique can also be used in the field of DC systems. In this paper, a virtual synchronous control is proposed to dampen the wideband oscillation of DC voltage in a DC microgrid. The proposed control strategy contributes to maintaining synchronous operation of DC converter with the network. Besides, the relationships among damping, inertia, wideband oscillation, rate of change of voltage (RoCoV) as well as DC voltage nadir (DCVN) are studied. It is concluded that the RoCoV and DCVN are similarly as the oscillation frequency and fluctuation ranges of poorly-damped oscillation, respectively. A unified concept is proposed by connecting the oscillation-related stability with inertial transient response originated from the imbalanced powers/mismatched currents. Besides of this, the inertia plays the same role as damping because the inertia contributes to maintaining the original state and damping to impeding further change. A new feedback analytical method is proposed to clarify the important role of RoCoV and DCVN on the motion of DC voltage. Finally, the theoretical results are compared with simulations and experiments.

This paper focuses on the design and comparison of different deep neural networks for the real-time prediction of locomotor and transition intentions of one osseointegrated transfemoral amputee using only data from inertial measurement units. The deep neural networks are based on convolutional neural networks, recurrent neural networks, and convolutional recurrent neural networks. The architectures’ input are features in both the time domain and the time-frequency domain, which are derived from either one inertial measurement unit (placed above the prosthetic knee) or two inertial measurement units (placed above and below the prosthetic knee). The prediction of eight different locomotion modes (i.e., sitting, standing, level ground walking, stair ascent and descent, ramp ascent and descent, walking on uneven terrain) and the twenty-four transitions among them is investigated. The study shows that a recurrent neural network, realized with four layers of gated recurrent unit networks, achieves (with a 5-fold cross-validation) a mean F1 score of 84.78% and 86.50% using one inertial measurement unit, and 93.06% and 89.99% using two inertial measurement units, with or without sitting, respectively.

Herein we shall consider Lorentz boosts and Wigner rotations from a (complexified) quaternionic point of view. We shall demonstrate that for a suitably defined self-adjoint complex quaternionic 4-velocity, pure Lorentz boosts can be phrased in terms of the quaternion square root of the relative 4-velocity connecting the two inertial frames. Straightforward computations then lead to quite explicit and relatively simple algebraic formulae for the composition of 4-velocities and the Wigner angle. We subsequently relate the Wigner rotation to the generic non-associativity of the composition of three 4-velocities, and develop a necessary and sufficient condition for associativity to hold. Finally, we relate the composition of 4-velocities to a specific implementation of the Baker-Campbell-Hausdorff theorem. As compared to ordinary 4x4 Lorentz transformations, the use of self-adjoint complexified quaternions leads, from a computational view, to storage savings and more rapid computations, and from a pedagogical view to to relatively simple and explicit formulae., Comment: 21 pages; no figures

This paper focuses on the finite-time anti-synchronization for a class of delayed master-slave inertial neural networks. By means of using the property of quadratic inequality of one variable and designing the fractional and polynomial controllers of time variable, two sufficient conditions to assure the finite-time anti-synchronization for the master-slave delayed inertial neural networks are established. Our controllers designed related to time variable t and the study method on the finite-time anti-synchronization are different from these in the existing papers.

The Abraham-Lorentz-Dirac theory predicts vanishing radiation reaction for uniformly accelerated charges. However, since an accelerating observer should detect thermal radiation, the charge should be seen absorbing photons in the accelerated frame which, if nothing else occurs, would influence its motion. This means that either there is radiation reaction seen in an inertial frame or there should be an additional phenomenon seen in the accelerated frame countering the effect of absorption. In this paper I rederive the Abraham--Lorentz--Dirac force from quantum electrodynamics, then I study the case of a uniformly accelerated charge. I show that in the accelerated frame, in addition to the absorption of photons due to the Unruh effect there should also be stimulated emission. The net effect of these phenomena on the motion of the charge is found to be zero., Comment: 26 pages

The satellite LARES 2 is designed to test dragging of inertial frames, or frame-dragging, predicted by Einstein’s theory of General Relativity, with accuracy of a few parts in a thousand. For this purpose, besides the typical requirements for a space construction, a high density alloy must be used. In this paper are reported the studies performed on a nickel alloy, the Haynes 242, that is considered a possible candidate for manufacturing all the metallic parts of LARES 2 and other passive geodetic satellites. Haynes 242 density and mechanical properties are compliant with the requirements of the mission. Three different casting with the nominal composition of the alloy have been prepared and tested along with a commercial bar of Haynes 242. The results of tensile and hardness tests on several specimens with different aging time are reported, along with the relevant metallographic analysis. Furthermore, a test on the machinability, performed on a screw, which is the most demanding item from the manufacturing point of view, is reported.

Microfluidics exploiting the phenomenon of inertial focusing have attracted much attention in the last decade as they provide the means to facilitate the detection and analysis of rare particles of interest in complex fluids such as blood and natural water. Although many interesting applications have been demonstrated, the systems remain difficult to engineer. A recently presented line of the technology, inertial focusing in High Aspect Ratio Curved microfluidics, has the potential to change this and make the benefits of inertial focusing more accessible to the community. In this paper, with experimental evidence and fluid simulations, we provide the two necessary equations to design the systems and successfully focus the targets in a single, stable, and high-quality position. The experiments also revealed an interesting scaling law of the lift force, which we believe provides a valuable insight into the phenomenon of inertial focusing.

In this paper, the relative sliding motion between the target and the manipulator’s end-effector is considered and characterized as a unilateral contact constraint. A new possible solution is presented to estimate the inertial parameters of a non-cooperative target while the relative sliding motion exists. First, the detailed analysis of the dynamical model is presented, and a parameter-explicit linear time-varying model is obtained. Then, an extended state observer is constructed based on the new model, which can effectively estimate the unknown inertial parameters of the target when relative sliding motion exists. As the modified reactionless controller requires the knowledge of inertial parameters, a hybrid post-capture control scheme is also established based on the switch law between different controllers. The correctness and efficiency of the proposed algorithm are validated by numerical simulation, which proves a potential framework for the non-cooperative target post-capture operation.

We propose an inertial KM-type extragradient scheme to approximate a common solution of a variational inequality problem and a hierarchical fixed point problem for nonexpansive mappings. This scheme generalizes and unifies a number of known iterative schemes. Furthermore, we discuss the weak convergence for the proposed scheme. We also discuss an example to illustrate the main theorem.

In this paper, stabilization control of a class of delayed inertial neural networks (INN) is investigated. Employing matrix measure method and two Halanay-type inequalities, some succinct stabilization criteria in terms of algebraic inequalities are derived for the INN with time-varying delays under periodically intermittent control (PIMC) strategy. Moreover, more precise results are obtained to stabilize the INN with time-invariant delays by using comparison principle. Specifically, the criteria of matrix measure form proposed in this paper can be converted into LMI-type condition for the case of 2-norm, which provides a bridge between the matrix-measure method and the Lyapunov function method. Finally, two numerical examples validate the efficacy of the derived results. The comparative research shows that the proposed methods generalize and develop some known results.

In this article, the concurrent optimal design of a planar five-bar parallel robot for a high-speed pick and place task is considered. A trade-off between trajectory tracking, energy consumption and deformation of the flexible links is sought. Due to the high-speed operation, the minimization of the vibratory effect is considered since the design stage. Thus, the design is stated as a non-linear multi-objective dynamic optimization problem that is solved through the Differential Evolution (DE) algorithm, as well as, feasibility rules for constraints handling. Mechanical structural variables that modify the inertial parameters of the rigid and flexible links, as well as, control variables related to gains of a PID controller are considered as independent variables. This problem is subject to maximum torque that each motor could provide; inherent constraints for link manufacture; dimensional synthesis; Grashof’s criterion and initial and boundary conditions. Results show that it is possible, despite the vibrational phenomenon, to reduce the energy consumption without loss of precision through an appropriate mass balance of the actuated links.

In this paper, we propose an inertial derivative-free projection method for solving convex constrained nonlinear monotone operator equations (CNME). The method incorporates the inertial step with an existing method called derivative-free projection (DFPI) method for solving CNME. The reason is to improve the convergence speed of DFPI as it has been shown and reported in several works that indeed the inertial step can speed up convergence. The global convergence of the proposed method is proved under some mild assumptions. Finally, numerical results reported clearly show that the proposed method is more efficient than the DFPI.

This paper is devoted to studying global dynamic behaviours of periodic solutions of discrete-time inertial neural networks with delays by applying Mawhin's continuation theorem and some innovative mathematical analysis techniques. Finally, an numerical example is given to illustrate our theoretical results.

In this paper, an adaptive virtual inertia-damping system based on model predictive control (MPC) is proposed to enhance the frequency dynamic performance of islanded microgrids (MGs) considering a high penetration level of renewable energy sources (RESs). Recently, a large amount of RESs is replacing traditional generating units, causing an undesirable effect on the MG frequency stability and system inertia, and thus weakening the MG. Therefore, the proposed control system handles this challenge to enhance the robust performance and stability of the MG with high RESs penetration during contingencies. The proposed online MPC strategy estimates the gains of the virtual inertia control (VIC) system (i.e., inertia and damping coefficients) in high RESs MG. The performance of the proposed adaptive VIC system is compared with the conventional VIC system (i.e., constant values of inertia and damping coefficients) using MATLAB/Simulink under numerous disturbances and system uncertainties. Moreover, the effectiveness of the proposed adaptive VIC system based on the online MPC strategy is verified by comparing its performance with the adaptive VIC system based on fuzzy logic control, which is designed to estimate only the inertial gain. The results highlight that the frequency stability is upgraded, and the adaptive virtual inertia system based on MPC successfully supports low-inertia islanded MGs with RESs and load fluctuations.

In this paper, we propose a robust method for estimating the parameters of robot manipulators using the torque separation technique, which was developed previously by the authors, to extract the inertial, gravitational, and frictional components from the input torques of multiple sinusoidal joint motions. The separated components of the input torque produce a set of reduced linear regression equations where the dynamic parameters of the robot manipulators appear decoupled or minimally coupled. A mathematical analysis is presented to show that the set of reduced regression equations tends to yield more robust parameter estimation than the conventional way where a large dimensional full regression equation is solved simultaneously. An iterative scheme to alleviate the possibility of parameter estimation error caused by the deviation from the assumed ideal sinusoidal motions is also proposed. Experimentation with two robot manipulators is used to verify the proposed approach and related claims. The proposed method is simple and pragmatic without requiring any specialized signal filtering and technical know-hows which numerous previous methods often demanded for explicitly or implicitly.

It is extremely challenging to apply an Inertial Navigation System (INS) to a gun-launched rolling projectile effectively. Overcoming the negative effects produced by high angular rolling and overloading requires innovative solutions. This paper introduces a unique inertial system that differs from a traditional gimballed INS and a strapdown INS. A Partial Strapdown Platform (PSP) was developed based on the principle of a pendulum to mitigate the adverse effects of projectile rolling motion. The anti-overloading performance of the PSP was improved by using a pair of specially designed alloy steel head-on hemispheres. Force condition analysis was conducted on the projectile and the PSP’s inner cylinder to establish the mechanical dynamic model. Computer simulations and turntable experiments were performed to verify the stabilization function of the PSP. The pitch angle was determined to be associated with the stability of the PSP based on simulations and experiments. Gun firing tests were performed to verify the overload-burdening and stabilization functions of the PSP.

In baseball pitching, biomechanical parameters have been linked to ball velocity and potential injury risk. However, although the features of a biomechanical model have a significant influence on the kinematics and kinetics of a motion, this influence have not been assessed for pitching. The aim of this study was to evaluate the choice of the trunk and shoulder features, by comparing two models using the same input. The models differed in thoraco-humeral joint definition (moving or fixed with the thorax), joint centre estimation, values of the inertial parameters and computational framework. One professional pitcher participated in the study. We found that the different features of the biomechanical models have a substantial influence on the kinematics and kinetics of the pitchers. With a fixed thoraco-humeral joint the peak average thorax angular velocity was delayed and underestimated by 17% and the shoulder internal rotation velocity was overestimated by 7%. The use of a thoraco-humeral joint fixed to the thorax will lead to an overestimation of the rotational power at the shoulder and will neglect the power produced by the forward and upward translation of the shoulder girdle. These findings have direct implications for the interpretation of shoulder muscle contributions to the pitch.

The aims of this study were to i) compare a foot-mounted inertial system (PlayerMaker™) to three commercially available Global Positioning Systems (GPS) for measurement of velocity-based metrics during team sport movements and ii) evaluate the inter-unit reliability of the PlayerMaker™. Twelve soccer players completed a soccer simulation, whilst wearing a PlayerMaker™ and three GPS (GPS#1, #2 and #3). A sub-sample (n = 7) also wore two PlayerMaker™ systems concurrently. The PlayerMaker™ measured higher (p < 0.05) total distance (518 ± 15 m) compared to GPS#1 (488 ± 15 m), GPS#2 (486 ± 15 m), and GPS#3 (501 ± 14 m). This was explained by greater (p < 0.05) distances in the 1.5-3.5 m/s zone (356 ± 24 m vs. 326 ± 26 m vs. 324 ± 18 m vs. 335 ± 24 m) and the 3.51-5.5 m/s zone (64 ± 18 m vs. 35 ± 5 vs. 43 ± 8 m vs. 41 ± 8 m) between the PlayerMaker™, GPS#1, GPS#2 and GPS#3, respectively. The PlayerMaker™ recorded higher (p < 0.05) distances while changing speed. There were no systematic differences (p > 0.05) between the two PlayerMaker™ systems. The PlayerMaker™ is reliable and records higher velocity and distances compared to GPS.

Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs., Current wearable motion capture technologies are unable to accurately detect dynamic motion of human limbs due to drift and instability problems. Here, the authors report a wearable motion capture device combining tri-axis velocity sensor and inertial sensors for accurate 3D limb motion capture.

In this work, we discuss the possibility of inertial sensing with positronium in the 2$^{3}$S metastable state for the measurement of optical dipole, relativistic and gravitational forces on a purely leptonic matter-antimatter system. Starting from the characteristics of an available 2$^{3}$S beam, we estimate the time necessary to measure accelerations ranging from ~10$^{5}$ m/s$^{2}$ to 9.1 m/s$^{2}$ with two different inertial sensitive devices: a classical moiré deflectometer and a Mach–Zehnder interferometer. The sensitivity of the Mach–Zehnder interferometer has been estimated to be several tens of times better than that of the moiré deflectometer, for the same measurement time. Different strategies to strengthen the 2$^{3}$S beam flux and to improve the sensitivity of the devices are proposed and analyzed. Among them, the most promising are reducing the divergence of the positronium beam through 2D laser Doppler cooling and coherent positronium Raman excitation from the ground state to the 2$^{3}$S level. If implemented, these improvements promise to result in the time required to measure an acceleration of 9.1 m/s$^{2}$ of few weeks and 100 m/s$^{2}$ of a few hours. Different detection schemes for resolving the fringe pattern shift generated on 2$^{3}$S positronium crossing the deflectometer/interferometer are also discussed. In this work, we discuss the possibility of inertial sensing with positronium in the $2^3 S$ metastable state for the measurement of optical dipole, relativistic and gravitational forces on a purely leptonic matter-antimatter system. Starting from the characteristics of an available $2^3 S$ beam, we estimate the time necessary to measure accelerations ranging from $\sim10^5$$m/s^2$ to 9.1 $m/s^2$ with two different inertial sensitive devices: a classical moiré deflectometer and a Mach-Zehnder interferometer. The sensitivity of the Mach-Zehnder interferometer has been estimated to be several tens of times better than that of the moiré deflectometer, for the same measurement time.\\ Different strategies to strengthen the $2^3 S$ beam flux and to improve the sensitivity of the devices are proposed and analyzed. Among them, the most promising are reducing the divergence of the positronium beam through 2D laser Doppler cooling and coherent positronium Raman excitation from the ground state to the $2^3 S$ level. If implemented, these improvements promise to result in the time required to measure an acceleration of 9.1 $m/s^2$ of few weeks and 100 $m/s^2$ of a few hours. Different detection schemes for resolving the fringe pattern shift generated on $2^3 S$ positronium crossing the deflectometer/interferometer are also discussed.

Inertial measurement units (IMUs) have been widely used to provide accurate location and movement measurement solutions, along with the advances of modern manufacturing technologies. The scale factors of accelerometers and gyroscopes are linear when the range of the sensors are reasonably small, but the factor becomes nonlinear when the range gets much bigger. Based on this observation, this article presents a calibration method for low-cost IMU by effectively deriving the nonlinear scale factors of the sensors. Two motion patterns of the sensor on a rigid object are moved to collect data for calibration: One motion pattern is to upcast and rotate the rigid object, and another pattern is to place the rigid object on a stable base in different attitudes. The rotation motion produces centripetal and Coriolis force, which increases the measurement range of accelerometers. Four cost functions with different weight factors and two sets of data are utilized to optimize the IMU parameters. The weight factor comes from derived formula with input values which are the variance of the noise of the sampled data. The proposed approach was validated and evaluated on both synthetic and real-world data sets, and the experimental results demonstrated the superiority of the proposed approach in improving the accuracy of IMU for long-range use. In particular, the errors of acceleration and angular velocity led by our algorithm are significantly smaller than those resulted from the existing approaches using the same testing data sets, demonstrating a remarkable improvement of 64.12% and 47.90%, respectively.

Falls are a major concern for society. They may result in death or in several injuries that require motor assistance, representing an economic burden. To overcome these problems, a diversity of fall prevention strategies implemented on assistive devices such as smart walkers, have been widely explored. This study presents a novel strategy by using exclusively information from wearable sensors to detect near-falls while the subject uses a conventional rollator. A comparative analysis was performed to identify the most suitable classifier and the most relevant subset of features for detecting near-fall events. Ten able-bodied subjects performed 240 trials and simulated 180 near-falls with the rollator. The Ensemble Learning with the first 51 ranked features by the mRMR presented the best performance results (Accuracy = 95.18%; Detection time before recovery= 1.48 ± 0.68 s). The results show that this strategy is suitable for use with conventional rollators, which are more used than smart walkers., This work has been supported by the FCT - Fundação para a Ciência e Tecnologia - with the scholarship reference PD/BD/141515/2018, by the FEDER funds through the Programa Operacional Regional do Norte and national funds from FCT with the SmartOs project under Grant NORTE-01-0145-FEDER-030386, and under the national support to RD units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020.

The motion of non-interacting Brownian particles in the washboard potential is investigated in inertial regimes, when the overdamped approximation does not yield accurate predictions. The analysis is based on homogenization methods, deriving closed-form expressions for the long-term transport properties, i.e. effective velocity and diffusion coefficient. Different reduced models of increasing complexity, improving the overdamped approximation, are developed starting from the basic assumption that the velocity variable can be split into an “almost” deterministic and a fully stochastic contribution. The almost deterministic velocity term can be estimated from a fully deterministic or from a stochastic slow inertial manifold. The latter approach provides accurate predictions for all the asymptotic transport properties.

The quasi-dynamic mooring line model is a new approach to assess the mooring line tension that considers the static solution of the catenary shape, but updates the distributed vertical force along the mooring line as a function of the external forces, drag and inertial, acting on the line. In this way, an updated apparent weight is applied to the static solution of the catenary shape that improves significantly the solution obtained by the quasi-static approach, with an equivalent computational cost. Then, the model improves the tension solution of the quasi-static models by using that updated weight of the mooring, achieving results much closer to the dynamic models than the standard quasi-static ones. To appraise the improvement of the results, a parametric analysis within a wide range of mooring physical parameters is conducted. The error of the quasi-static tension of one cycle is less than 20% only for the 18% of the simulations, while the quasi-dynamic model produces an error less than 20% for the 84% of simulations. In addition, the model can predict the slack phenomenon.

International audience; To improve the turbine operational life, the interaction between flow properties and turbine performance needs to be elucidated. We then propose to examine the physical origin of the power-law scaling in the inertial range of turbine power outputs by experimentally exploring the spectral content of a 1:20 scaled model of a three-bladed horizontal-axis turbine positioned in a 3D turbulent flow. First, measurements confirm that the turbine power frequency spectra exhibit a power law decay proportional to −11/3 in the inertial range. Knowing that the turbine power fluctuations are linearly dependent on the incoming velocity fluctuations, PIV measurements are carried out to study the effect of the spatially integrated velocity onto its resulted spectrum. It is demonstrated that in inhomogeneous anisotropic turbulent flow, the velocity spectrum of its spatial average along N direction(s) has an inertial slope of −5/3 − 2N/3. This information is used to physically interpret the power-law scaling in the inertial range of the turbine power spectra. The previously observed f −11/3 scaling results from a 2D-spatial average velocity field coupled with a spectral average over blades. This physical explanation confirms previous works in which a transfer function was developed between incoming turbulence and the turbine power outputs.

The Richardson-scaling law states that the mean square separation of a fluid particle pair grows according to t3 within the inertial range and at intermediate times. The theories predicting this scaling regime assume that the pair separation is within the inertial range and that the dispersion is local, which means that only eddies at the scale of the separation contribute. These assumptions ignore the structural organization of the turbulent flow into large-scale shear layers, where the intense small-scale motions are bounded by the large-scale energetic motions. Therefore, the large scales contribute to the velocity difference across the small-scale structures. It is shown that, indeed, the pair dispersion inside these layers is highly non-local and approaches Taylor dispersion in a way that is fundamentally different from the Richardson-scaling law. Also, the layer's contribution to the overall mean square separation remains significant as the Reynolds number increases. This calls into question the validity of the theoretical assumptions. Moreover, a literature survey reveals that, so far, t3 scaling is not observed for initial separations within the inertial range. We propose that the intermediate pair dispersion regime is a transition region that connects the initial Batchelor- with the final Taylor-dispersion regime. Such a simple interpretation is shown to be consistent with observations and is able to explain why t3 scaling is found only for one specific initial separation outside the inertial range. Moreover, the model incorporates the observed non-local contribution to the dispersion, because it requires only small-time-scale properties and large-scale properties.

We introduce a new isometric strain model for the study of the dynamics of cloth garments in a moderate stress environment, such as robotic manipulation in the neighborhood of humans. This model treats textiles as surfaces that are inextensible, admitting only isometric motions. Inextensibility is derived in a continuous setting, prior to any discretization, which gives consistency with respect to remeshing and prevents the problem of locking even with coarse meshes. The simulations of robotic manipulation using the model are compared to the actual manipulation in the real world, finding that the difference between the simulated and the real position of each point in the garment is lower than 1cm in average even when a coarse mesh is used. Aerodynamic contributions to motion are incorporated to the model through the virtual uncoupling of the inertial and gravitational mass of the garment. This approach results in an accurate, when compared to the recorded dynamics of real textiles, description of cloth motion incorporating aerodynamic effects by using only two parameters., This project was developed in the context of the project CLOTHILDE (”CLOTH manIpulation Learning from DEmonstrations”) which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 741930) and is also supported by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). Maria Alberich-Carramiñana is also with the Barcelona Graduate School of Mathematics (BGSMath) and the Institut de Matemàtiques de la UPC-BarcelonaTech (IMTech). Maria Alberich-Carramiñana and Jaume Amorós are partially supported by the Spanish Research Agency PID2019-103849GB-I00 / AEI / 10.13039/501100011033 grant, and by Generalitat de Catalunya 2017SGR-932 grant.

Aiming at the problem that the traditional Unscented Kalman Filtering (UKF) algorithm can’t solve the problem that the measurement covariance matrix is unknown and the measured value contains outliers, this paper proposes a robust adaptive UKF algorithm based on Support Vector Regression (SVR). The algorithm combines the advantages of support vector regression with small samples, nonlinear learning ability and online estimation capability of adaptive algorithm based on innovation. Firstly, the SVR model is trained by using the innovation in the sliding window, and the new innovation is monitored. If the deviation between the estimated innovation and the measured innovation exceeds a given threshold, then measured innovation will be replaced by the predicted innovation, and then the processed innovation is used to calculate the measurement noise covariance matrix using the adaptive estimation algorithm. Simulation experiments and measured data experiments show that SVRUKF is significantly better than the traditional UKF, robust UKF and adaptive UKF algorithms for the case where the covariance matrix is unknown and the measured values have outliers.

In article approaches to creation of the complex inertial and optical navigation system of the short-range tactical unmanned aerial vehicle are considered. Algorithms constant and periodic (in intermediate points of a route) are offered correction of the platformless onboard inertial navigation system. At integration of information on parameters of the movement of the unmanned aerial vehicle (received from the considered systems) the invariant loosely coupled scheme of data processing on the basis of the expanded filter of Kallman was used that allowed to lower significantly a systematic component of an error of the platformless inertial navigation system. Advantages of the complex inertial and optical navigation system when ensuring flight of the unmanned aerial vehicle in an area of coverage of means of radio-electronic fight of the opponent are shown. The results of modeling confirming a possibility of ensuring precision characteristics of the inertial and optical navigation system in the absence of signals of satellite radio navigational systems are presented.

This paper deals with a class of high-order inertial Hopfield neural networks involving mixed delays. Utilizing differential inequality techniques and the Lyapunov function method, we obtain a sufficient assertion to ensure the existence and global exponential stability of anti-periodic solutions of the proposed networks. Moreover, an example with a numerical simulation is furnished to illustrate the effectiveness and feasibility of the theoretical results.

In this paper, we introduce a self-adaptive inertial subgradient extragradient method for solving pseudomonotone equilibrium problem and common fixed point problem in real Hilbert spaces. The algorithm consists of an inertial extrapolation process for speeding the rate of its convergence, a monotone nonincreasing stepsize rule, and a viscosity approximation method which guaranteed its strong convergence. More so, a strong convergence theorem is proved for the sequence generated by the algorithm under some mild conditions and without prior knowledge of the Lipschitz-like constants of the equilibrium bifunction. We further provide some numerical examples to illustrate the performance and accuracy of our method.

We analyze a gedanken experiment in which a spinning particle that also possesses an extrinsic orbital angular momentum is captured by a spinning Kerr black hole. The gravitational spin-orbit interaction decreases the energy of the particle, thus allowing one to test the validity of the Penrose weak cosmic censorship conjecture in extreme situations that have not been analyzed thus far. It is explicitly shown that, to leading order in the black-hole-particle interactions, the linearized test particle can over-spin the black hole, thus exposing its inner spacetime singularity to external observers. However, we prove that the general relativistic effect of dragging of inertial frames by the orbiting particle contributes to the energy budget of the system a non-linear black-hole-particle interaction term that ultimately ensures the validity of the Penrose cosmic censorship conjecture in this type of gedanken experiments.

Nonprehensile manipulation produces underconstraint motions that are sensitive to environmental dynamics. Legged locomotion constitutes a floating-based movement, whose dynamic is underactuated with respect to the inertial frame. When these two tasks are combined, system motion planning and control are complex due to their inherent underactuated features. This article presents a motion planning framework for a legged robot that uses its limbs for nonprehensile manipulation, as well as locomoting motions. First, issues related to the description of the robot–object–environment system and the task are presented. The velocity constraint that prevents separation and the force constraint that restricts interactive forces are then integrated into the system dynamic model to produce bounds on the system acceleration as a function of the system state. Then, we solve the motion planning problem by reducing the system dimensions in operational space and programming feasible trajectories within the phase plane. This approach is employed to control the quadruped robot TITAN-VIII to manipulate objects and locomote itself using Drive Mode , Inchworm Mode , Scoot Mode , and Throw Mode . Experimental results obtained through simulations and physical tests are reported to demonstrate the effectiveness of our approach.

A new method is introduced by using high precision accelerometer and gyroscope micro-electromechanical sensors (MEMS), which can record Lagrangian observations of sediments and shed light into the dynamics of sediment transport processes at above threshold conditions. The sensor can be used under a range of well-controlled flow conditions and can record measurements at high frequency (200 Hz), which can be used at the field. The smart sphere performance was evaluated by comparing its rotation and acceleration readings from the sensors to the video recordings of both top and underwater high-speed camera for a range of flow rates and sphere densities. Furthermore, an initial attempt to compare the smart-sphere’s velocity is achieved, by transforming the particle’s velocity from the Lagrangian frame of reference, obtained from the inertial sensor, to its velocity at the Eularian frame, obtained from the top camera.