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1
Li, Jing, Xiao Wei, Fengpin Wang, and Jinjia Wang. "IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning." Electronics 10, no. 23 (December 3, 2021): 3021. http://dx.doi.org/10.3390/electronics10233021.
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Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a sparsity term that enforces an ℓ1 penalty. Contrary to other CDL methods, in the proposed approach, the dictionary and needles are updated with an inertial force by the PGM. We obtain a novel derivative formula for the needles and dictionary with respect to the data fidelity term. At the same time, a gradient descent step is designed to add an inertial term. The proximal operation uses the thresholding operation for needles and projects the dictionary to a unit-norm sphere. We prove the convergence property of the proposed IPGM algorithm in a backtracking case. Simulation results show that the proposed IPGM achieves better performance than the PGM and slice-based methods that possess the same structure and are optimized using the alternating-direction method of multipliers (ADMM).
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Zhang, Yanwei, and James N. Moum. "Inertial-Convective Subrange Estimates of Thermal Variance Dissipation Rate from Moored Temperature Measurements." Journal of Atmospheric and Oceanic Technology 27, no. 11 (November 1, 2010): 1950–59. http://dx.doi.org/10.1175/2010jtecho746.1.
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Abstract A procedure for estimating thermal variance dissipation rate χT by scaling the inertial-convective subrange of temperature gradient spectra from thermistor measurements on a Tropical Atmosphere Ocean (TAO) equatorial mooring, maintained by NOAA’s National Data Buoy Center, is demonstrated. The inertial-convective subrange of wavenumbers/frequencies is contaminated by the vertical motion induced by the pumping of the surface float by surface gravity waves through the local vertical temperature gradient. The uncontaminated signal can be retrieved by removing the part of the measured signal that is coherent with the signal induced by surface gravity waves, which must be measured independently. An estimate of χT is then obtained by fitting corrected spectra to theoretical temperature gradient spectra over the inertial-convective subrange (0.05 < f < 0.5 Hz); this estimate is referred to as χTIC. Here χTIC was calculated over 120-min intervals and compared with estimates of χTo determined by scaling temperature gradient spectra at high wavenumbers (viscous-convective and viscous-diffusive subranges). Large differences up to a factor of 20 and of unknown origin occur infrequently, especially when both background currents and vertical temperature gradients are weak, but the results herein indicate that 75% of the data pairs are within a factor of 3 of each other. Tests on 15-, 30-, 60-, 120-min intervals demonstrate that differences between the two methods are nearly random, unbiased, and less than estimates of natural variability determined from unrelated experiments at the same location. Because the inertial-convective subrange occupies a lower-frequency range than is typically used for turbulence measurements, the potential for more routine measurements of χT exists. The evaluation of degraded signals (resampled from original measurements) indicates that a particularly important component of such a measurement is the independent resolution of the surface wave–induced signal.
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Kesornprom, Suparat, and Prasit Cholamjiak. "A modified inertial proximal gradient method for minimization problems and applications." AIMS Mathematics 7, no. 5 (2022): 8147–61. http://dx.doi.org/10.3934/math.2022453.
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<abstract><p>In this paper, the aim is to design a new proximal gradient algorithm by using the inertial technique with adaptive stepsize for solving convex minimization problems and prove convergence of the iterates under some suitable assumptions. Some numerical implementations of image deblurring are performed to show the efficiency of the proposed methods.</p></abstract>
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Sheng, Guangrun, Guowei Gao, and Boyuan Zhang. "Application of Improved Wavelet Thresholding Method and an RBF Network in the Error Compensating of an MEMS Gyroscope." Micromachines 10, no. 9 (September 13, 2019): 608. http://dx.doi.org/10.3390/mi10090608.
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The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.
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Milder, A. L., A. S. Joglekar, W. Rozmus, and D. H. Froula. "Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion." Machine Learning: Science and Technology 5, no. 1 (February 13, 2024): 015026. http://dx.doi.org/10.1088/2632-2153/ad2493.
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Abstract Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require O ( 10 3 ) parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.
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Ceng, Lu-Chuan, Adrian Petruşel, Ching-Feng Wen, and Jen-Chih Yao. "Inertial-Like Subgradient Extragradient Methods for Variational Inequalities and Fixed Points of Asymptotically Nonexpansive and Strictly Pseudocontractive Mappings." Mathematics 7, no. 9 (September 17, 2019): 860. http://dx.doi.org/10.3390/math7090860.
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Let VIP indicate the variational inequality problem with Lipschitzian and pseudomonotone operator and let CFPP denote the common fixed-point problem of an asymptotically nonexpansive mapping and a strictly pseudocontractive mapping in a real Hilbert space. Our object in this article is to establish strong convergence results for solving the VIP and CFPP by utilizing an inertial-like gradient-like extragradient method with line-search process. Via suitable assumptions, it is shown that the sequences generated by such a method converge strongly to a common solution of the VIP and CFPP, which also solves a hierarchical variational inequality (HVI).
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Kesornprom, Suparat, Papatsara Inkrong, Uamporn Witthayarat, and Prasit Cholamjiak. "A recent proximal gradient algorithm for convex minimization problem using double inertial extrapolations." AIMS Mathematics 9, no. 7 (2024): 18841–59. http://dx.doi.org/10.3934/math.2024917.
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<abstract><p>In this study, we suggest a new class of forward-backward (FB) algorithms designed to solve convex minimization problems. Our method incorporates a linesearch technique, eliminating the need to choose Lipschitz assumptions explicitly. Additionally, we apply double inertial extrapolations to enhance the algorithm's convergence rate. We establish a weak convergence theorem under some mild conditions. Furthermore, we perform numerical tests, and apply the algorithm to image restoration and data classification as a practical application. The experimental results show our approach's superior performance and effectiveness, surpassing some existing methods in the literature.</p></abstract>
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Rasmussen, John, Sebastian Skejø, and Rasmus Plenge Waagepetersen. "Predicting Tissue Loads in Running from Inertial Measurement Units." Sensors 23, no. 24 (December 15, 2023): 9836. http://dx.doi.org/10.3390/s23249836.
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Background: Runners have high incidence of repetitive load injuries, and habitual runners often use smartwatches with embedded IMU sensors to track their performance and training. If accelerometer information from such IMUs can provide information about individual tissue loads, then running watches may be used to prevent injuries. Methods: We investigate a combined physics-based simulation and data-based method. A total of 285 running trials from 76 real runners are subjected to physics-based simulation to recover forces in the Achilles tendon and patella ligament, and the collected data are used to train and test a data-based model using elastic net and gradient boosting methods. Results: Correlations of up to 0.95 and 0.71 for the patella ligament and Achilles tendon forces, respectively, are obtained, but no single best predictive algorithm can be identified. Conclusions: Prediction of tissues loads based on body-mounted IMUs appears promising but requires further investigation before deployment as a general option for users of running watches to reduce running-related injuries.
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Michielsen, M., C. Aerts, and D. M. Bowman. "Probing the temperature gradient in the core boundary layer of stars with gravito-inertial modes." Astronomy & Astrophysics 650 (June 2021): A175. http://dx.doi.org/10.1051/0004-6361/202039926.
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Aims. We investigated the thermal and chemical structure in the near-core region of stars with a convective core by means of gravito-inertial modes. We determined the probing power of different asteroseismic observables and fitting methodologies. We focus on the case of the B-type star KIC 7760680, rotating at a quarter of its critical rotation velocity. Methods. We computed grids of 1D stellar structure and evolution models for two different prescriptions of the temperature gradient and mixing profile in the near-core region. We determined which of these prescriptions is preferred according to the prograde dipole modes detected in 4 yr Kepler photometry of KIC 7760680. We considered different sets of asteroseismic observables and compared the outcomes of the regression problem for a χ2 and a Mahalanobis distance merit function, where the latter takes into account realistic uncertainties for the theoretical predictions and the former does not. Results. Period spacings of modes with consecutive radial order offer a better diagnostic than mode periods or mode frequencies for asteroseismic modelling of stars revealing only high-order gravito-inertial modes. We find KIC 7760680 to reveal a radiative temperature gradient in models with convective boundary mixing, but less complex models without such mixing are statistically preferred for this rotating star, revealing extremely low vertical envelope mixing. Conclusions. Our results strongly suggest the use of measured individual period spacing values for modes of consecutive radial order as an asteroseismic diagnostic for stellar modelling of B-type pulsators with gravito-inertial modes.
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Neuwirth, Christina, Cory Snyder, Wolfgang Kremser, Richard Brunauer, Helmut Holzer, and Thomas Stöggl. "Classification of Alpine Skiing Styles Using GNSS and Inertial Measurement Units." Sensors 20, no. 15 (July 29, 2020): 4232. http://dx.doi.org/10.3390/s20154232.
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In alpine skiing, four commonly used turning styles are snowplow, snowplow-steering, drifting and carving. They differ significantly in speed, directional control and difficulty to execute. While they are visually distinguishable, data-driven classification is underexplored. The aim of this work is to classify alpine skiing styles based on a global navigation satellite system (GNSS) and inertial measurement units (IMU). Data of 2000 turns of 20 advanced or expert skiers were collected with two IMU sensors on the upper cuff of each ski boot and a mobile phone with GNSS. After feature extraction and feature selection, turn style classification was applied separately for parallel (drifted or carved) and non-parallel (snowplow or snowplow-steering) turns. The most important features for style classification were identified via recursive feature elimination. Three different classification methods were then tested and compared: Decision trees, random forests and gradient boosted decision trees. Classification accuracies were lowest for the decision tree and similar for the random forests and gradient boosted classification trees, which both achieved accuracies of more than 93% in the parallel classification task and 88% in the non-parallel case. While the accuracy might be improved by considering slope and weather conditions, these first results suggest that IMU data can classify alpine skiing styles reasonably well.
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Tomiło, Paweł. "Classification of the Condition of Pavement with the Use of Machine Learning Methods." Transport and Telecommunication Journal 24, no. 2 (April 1, 2023): 158–66. http://dx.doi.org/10.2478/ttj-2023-0014.
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Abstract The publication includes a review of information on the methods of pavement condition recognition using various methods. Measurement system has been presented that allows to determine the condition of the pavement using the Inertial Measurement Unit (IMU) and machine learning methods. Three machine learning methods were considered: random forest, gradient boosted tree and custom architecture neural network (roadNet). Due to the developed system the set of learning and validation data was created on 3 vehicles: Opel Corsa, Honda Accord, Volkswagen Passat. All of the listed vehicles have front wheel drive. The presented machine learning methods have been compared with each other. The best accuracy on the validation set was achieved by the artificial neural network (ANN). The study showed that asphalt condition classification is possible and the developed system fulfils its task.
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Ofem, Austine Efut, Jacob Ashiwere Abuchu, Godwin Chidi Ugwunnadi, Hossam A. Nabwey, Abubakar Adamu, and Ojen Kumar Narain. "Double inertial steps extragadient-type methods for solving optimal control and image restoration problems." AIMS Mathematics 9, no. 5 (2024): 12870–905. http://dx.doi.org/10.3934/math.2024629.
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<abstract><p>In order to approximate the common solution of quasi-nonexpansive fixed point and pseudo-monotone variational inequality problems in real Hilbert spaces, this paper presented three new modified sub-gradient extragradient-type methods. Our algorithms incorporated viscosity terms and double inertial extrapolations to ensure strong convergence and to speed up convergence. No line search methods of the Armijo type were required by our algorithms. Instead, they employed a novel self-adaptive step size technique that produced a non-monotonic sequence of step sizes while also correctly incorporating a number of well-known step sizes. The step size was designed to lessen the algorithms' reliance on the initial step size. Numerical tests were performed, and the results showed that our step size is more effective and that it guarantees that our methods require less execution time. We stated and proved the strong convergence of our algorithms under mild conditions imposed on the control parameters. To show the computational advantage of the suggested methods over some well-known methods in the literature, several numerical experiments were provided. To test the applicability and efficiencies of our methods in solving real-world problems, we utilized the proposed methods to solve optimal control and image restoration problems.</p></abstract>
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Zak, Idan, Itzik Klein, and Reuven Katz. "A Feasibility Study of Machine Learning Based Coarse Alignment." Proceedings 4, no. 1 (November 14, 2018): 50. http://dx.doi.org/10.3390/ecsa-5-05735.
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Inertial navigation systems (INSs) require an initial attitude before its operation. To that end, the coarse alignment process is applied using inertial sensors readings. For low-cost inertial sensors, only the accelerometers readings are processed to yield the initial roll and pitch angles. The accuracy of the coarse alignment procedure is vitally important for the navigation solution accuracy due to the navigation solution drift accumulating over time. In this paper, we propose using machine learning (ML) approaches, instead of traditional approaches, to conduct the coarse alignment procedure. To that end, a new methodology for the alignment process is proposed, based on state-of-the-art ML algorithms such as random forest (RF) and the more advanced boosting method of gradient tree XGBoost. Results from a simulated alignment of stationary INS scenarios are presented accompanied by a feasibility study. ML results are compared with the traditional coarse alignment methods in terms of time to convergence and accuracy performance. When using the proposed approach, with the examined scenarios, results show a significant improvement of the accuracy and time required for the alignment process.
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Hu, Bo, Bing Zhou, Shanshan Bu, Xinghua Wu, and Baoping Gong. "Holistic Hydraulic Simulation for Pebble Bed Using Porous Media Approach." Energies 17, no. 14 (July 19, 2024): 3562. http://dx.doi.org/10.3390/en17143562.
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The porous media approach is regarded as an appropriate methodology for hydraulic simulations of complex pebble beds in fusion reactors. In order to determine the parameters (permeability α and inertial loss coefficient C) of the porous media accurately, two methods are proposed: the correction method and the fitting method. In this paper, a single-channel model with sequentially packed pebbles is constructed in order to obtain the pressure drop gradient against superficial velocities. Two methods, the correction method and fitting method, are employed to determine the permeability and inertial loss coefficient, and the results are evaluated with comparisons. Based on the results, both the correction method and fitting method are deemed feasible for the parameter determinations. In consideration of the consumption of resources and time for simulation, the fitting method is recommended during the preliminary design phase to shorten the duration of design, while the correction method is suggested to obtain precise results when the design is accomplished. Both of the methods would be evaluated with the data obtained from experiments in the future.
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Harris, D. Lee. "WIND TIDE AND SEICHES IN THE GREAT LAKES." Coastal Engineering Proceedings 1, no. 4 (January 1, 2000): 3. http://dx.doi.org/10.9753/icce.v4.3.
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Because of the unusually high lake stages of recent years, the Weather Bureau was called on to forecast the short period variations of lake level, which were believed to be caused by wind stress and atmospheric pressure gradient. It became necessary to investigate the feasibility of such forecasts. In a review of the available literature, many papers were found which described methods of computing the free periods of oscillation for lakes when no external forces were acting. Other papers were found which described methods of computing the steady state relation between a constant atmospheric force and the lake surface when inertial forces are neglected.
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Wan, Liang Jin, and Chun Dong. "Application of Rotation Vector in SINS Algorithms." Applied Mechanics and Materials 615 (August 2014): 229–35. http://dx.doi.org/10.4028/www.scientific.net/amm.615.229.
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Updating attitude precisely in time is the primary task of strapdown inertial navigation system(SINS) algorithms. This paper mainly studied the application of rotation vector in three different methods of data fusion respectively named linear interpolation, gradient descent and complementary filter for attitude-updating, using low-cost MEMS inertial sensors in SINS. Meanwhile, an idea that the quaternion attitude could be updated by constructing micro-rotation quaternion from rotation vector in the sampling interval is proposed. The idea is based on geometric interpretation of space rotation transformation, while the general method is the differential equations of quaternion about rotation vector. Therefore the new method is an approximation method within enough short update interval, but its best superiority is the higher speed of attitude-updating than general method with little loss of accuracy because of no necessary to solve differential equations. The experimental results also show the effectiveness and accuracy of three improved algorithms with the new idea.
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Rehman, Habib ur, Poom Kumam, Ioannis K. Argyros, Nasser Aedh Alreshidi, Wiyada Kumam, and Wachirapong Jirakitpuwapat. "A Self-Adaptive Extra-Gradient Methods for a Family of Pseudomonotone Equilibrium Programming with Application in Different Classes of Variational Inequality Problems." Symmetry 12, no. 4 (April 2, 2020): 523. http://dx.doi.org/10.3390/sym12040523.
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The main objective of this article is to propose a new method that would extend Popov’s extragradient method by changing two natural projections with two convex optimization problems. We also show the weak convergence of our designed method by taking mild assumptions on a cost bifunction. The method is evaluating only one value of the bifunction per iteration and it is uses an explicit formula for identifying the appropriate stepsize parameter for each iteration. The variable stepsize is going to be effective for enhancing iterative algorithm performance. The variable stepsize is updating for each iteration based on the previous iterations. After numerical examples, we conclude that the effect of the inertial term and variable stepsize has a significant improvement over the processing time and number of iterations.
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Kaewyong, Nattakarn, and Kanokwan Sitthithakerngkiet. "An Inertial Extragradient Direction Method with Self-Adaptive Step Size for Solving Split Minimization Problems and Its Applications to Compressed Sensing." Mathematics 10, no. 6 (March 9, 2022): 874. http://dx.doi.org/10.3390/math10060874.
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The purpose of this work is to construct iterative methods for solving a split minimization problem using a self-adaptive step size, conjugate gradient direction, and inertia technique. We introduce and prove a strong convergence theorem in the framework of Hilbert spaces. We then demonstrate numerically how the extrapolation factor (θn) in the inertia term and a step size parameter affect the performance of our proposed algorithm. Additionally, we apply our proposed algorithms to solve the signal recovery problem. Finally, we compared our algorithm’s recovery signal quality performance to that of three previously published works.
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Choi, Man Ho, Robert Porter, and Bijan Shirinzadeh. "Comparison of Attitude Determination Methodologies for Implementation with 9DOF, Low Cost Inertial Measurement Unit for Autonomous Aerial Vehicles." International Journal of Intelligent Mechatronics and Robotics 3, no. 2 (April 2013): 1–15. http://dx.doi.org/10.4018/ijimr.2013040101.
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The performances of three attitude determination algorithms are compared in this paper. The three methods are the Complementary Filter, a Quaternion-based Kalman Filter and a Quaternion-based Gradient Descent Algorithm. An analysis of their performance based on an experimental investigation was undertaken. This paper shows that the Complementary Filter requires the least computational power; Quaternion-based Kalman Filter has the best noise filtering ability; and the Quaternion-based Gradient Descent Algorithm produced estimates with the highest accuracy. As many attitude determination methodologies make use of the quaternion rotation representation, the attitude quaternion to Euler angle singularity property has been investigated. Experiments conducted show that when Y-rotation approach the singularity position (±90°), the X-rotation drifts away from the reference input. This paper proposes the use of an imaginary set of sensor measurements to replace the original sensor measurements as the Y-rotation approaches the singularity. The proposed methodology for overcoming the conversion singularity has been experimentally verified.
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Lee, Jae-Neung, Yeong-Hyeon Byeon, and Keun-Chang Kwak. "Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology." Micromachines 9, no. 8 (August 17, 2018): 411. http://dx.doi.org/10.3390/mi9080411.
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This paper discusses the classification of horse gaits for self-coaching using an ensemble stacked auto-encoder (ESAE) based on wavelet packets from the motion data of the horse rider. For this purpose, we built an ESAE and used probability values at the end of the softmax classifier. First, we initialized variables such as hidden nodes, weight, and max epoch using the options of the auto-encoder (AE). Second, the ESAE model is trained by feedforward, back propagation, and gradient calculation. Next, the parameters are updated by a gradient descent mechanism as new parameters. Finally, once the error value is satisfied, the algorithm terminates. The experiments were performed to classify horse gaits for self-coaching. We constructed the motion data of a horse rider. For the experiment, an expert horse rider of the national team wore a suit containing 16 inertial sensors based on a wireless network. To improve and quantify the performance of the classification, we used three methods (wavelet packet, statistical value, and ensemble model), as well as cross entropy with mean squared error. The experimental results revealed that the proposed method showed good performance when compared with conventional algorithms such as the support vector machine (SVM).
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Kozlov, N. V., E. A. Mosheva, and A. V. Shmyrov. "Visualization of hydrodynamic and physico-chemical processes in rotating and vibrating containers." Journal of Physics: Conference Series 2127, no. 1 (November 1, 2021): 012004. http://dx.doi.org/10.1088/1742-6596/2127/1/012004.
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Abstract Variable inertial fields are an efficient way to control the behaviour of hydrodynamic systems. Forces of inertia can be used, for example, to stabilize or destabilize systems with an interface or density gradient, to mix multiphase or non-isothermal fluids. The implementation of this approach means that liquids fill the periodically moving containers. In this paper, the situations are considered when the containers perform either rotation or translational vibrations. Methods for measuring the density and velocity fields of convective flows in reacting hydrodynamic systems are described. Interferometry is used to visualize the density distribution. Particle image velocimetry (PIV) is used to study the structure and velocity of the flows. Optical instruments are installed stationary in the laboratory system. For video recording, a camera shutter is synchronized with the motion of a container, and thus the images are captured in a fixed phase of oscillations or rotation. Constructions of the containers make it possible to illuminate the working volume through transparent walls at different angles or in different planes. They also provide a compensation for the centrifugal pressure and allow interference cells to be used in overload conditions. The successful application of the methods in experimental studies of chemo-hydrodynamic processes is demonstrated.
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Lu, Zhenglong, Jie Li, Xi Zhang, Kaiqiang Feng, Xiaokai Wei, Debiao Zhang, Jing Mi, and Yang Liu. "A New In-Flight Alignment Method with an Application to the Low-Cost SINS/GPS Integrated Navigation System." Sensors 20, no. 2 (January 16, 2020): 512. http://dx.doi.org/10.3390/s20020512.
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The optimization-based alignment (OBA) methods, which are implemented by the optimal attitude estimation using vector observations—also called double-vectors—have proven to be effective at solving the in-flight alignment (IFA) problem. However, the traditional OBA methods are not applicable for the low-cost strap-down inertial navigation system (SINS) since the error of double-vectors will be accumulated over time due to the substantial drift of micro-electronic- mechanical system (MEMS) gyroscope. Moreover, the existing optimal estimation method is subject to a large computation burden, which results in a low alignment speed. To address these issues, in this article we propose a new fast IFA method based on modified double-vectors construction and the gradient descent method. To be specific, the modified construction method is implemented by reducing the integration interval and identifying the gyroscope bias during the construction procedure, which improves the accuracy of double-vectors and IFA; the gradient descent scheme is adopted to estimate the optimal attitude of alignment without complex matrix operation, which results in the improvement of alignment speed. The effect of different sizes of mini-batch on the performance of the gradient descent method is also discussed. Extensive simulations and vehicle experiments demonstrate that the proposed method has better accuracy and faster alignment speed than the related traditional methods for the low-cost SINS/global positioning system (GPS) integrated navigation system
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Shuttleworth, Matthew Peter, Oliver Vickers, Mackenzie Smeeton, Tim Board, Graham Isaac, Peter Culmer, Sophie Williams, and Robert William Kay. "Inertial Tracking System for Monitoring Dual Mobility Hip Implants In Vitro." Sensors 23, no. 2 (January 12, 2023): 904. http://dx.doi.org/10.3390/s23020904.
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Dual mobility (DM) implants are being increasingly used for total hip arthroplasties due to the additional range of motion and joint stability they afford over more traditional implant types. Currently, there are no reported methods for monitoring their motions under realistic operating conditions while in vitro and, therefore, it is challenging to predict how they will function under clinically relevant conditions and what failure modes may exist. This study reports the development, calibration, and validation of a novel inertial tracking system that directly mounts to the mobile liner of DM implants. The tracker was custom built and based on a miniaturized, off-the-shelf inertial measurement unit (IMU) and employed a gradient-decent sensor fusion algorithm for amalgamating nine degree-of-freedom IMU readings into three-axis orientation estimates. Additionally, a novel approach to magnetic interference mitigation using a fixed solenoid and magnetic field simulation was evaluated. The system produced orientation measurements to within 1.0° of the true value under ideal conditions and 3.9° with a negligible drift while in vitro, submerged in lubricant, and without a line of sight.
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Jabbari, Aidin, Leon Boegman, Reza Valipour, Danielle Wain, and Damien Bouffard. "Dissipation of Turbulent Kinetic Energy in the Oscillating Bottom Boundary Layer of a Large Shallow Lake." Journal of Atmospheric and Oceanic Technology 37, no. 3 (March 2020): 517–31. http://dx.doi.org/10.1175/jtech-d-19-0083.1.
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AbstractMixing rates and biogeochemical fluxes are commonly estimated from the rate of dissipation of turbulent kinetic energy ε as measured with a single instrument and processing method. However, differences in measurements of ε between instruments/methods often vary by one order of magnitude. In an effort to identify error in computing ε, we have applied four common methods to data from the bottom boundary layer of Lake Erie. We applied the second-order structure function method (SFM) to velocity measurements from an acoustic Doppler current profiler, using both canonical and anisotropy-adjusted Kolmogorov constants, and compared the results with those computed from the law of the wall, Batchelor fitting to temperature gradient microstructure, and inertial subrange fitting to acoustic Doppler velocimeter data. The ε from anisotropy-adjusted constants in SFM increased by a factor of 6 or more at 0.2 m above the bed and showed a better agreement with microstructure and inertial method estimations. The maximum difference between SFM ε, computed using adjusted and canonical constants, and microstructure values was 25% and 50%, respectively. This difference was 30% and 55%, respectively, for those from inertial subrange fitting at times of high-intensity turbulence (Reynolds number at 1 m above the bed of more than 2 × 104). Comparison of the SFM ε to those from law of the wall was often poor, with errors as large as one order of magnitude. From the considerable improvement in ε estimates near the bed, anisotropy-adjusted Kolmogorov constants should be applied to compute dissipation in geophysical boundary layers.
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Ding, Lin, Sajad Razavi Bazaz, Timothy Hall, Graham Vesey, and Majid Ebrahimi Warkiani. "Giardia purification from fecal samples using rigid spiral inertial microfluidics." Biomicrofluidics 16, no. 1 (January 2022): 014105. http://dx.doi.org/10.1063/5.0069406.
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Giardia is one of the most common waterborne pathogens causing around 200 × 106 diarrheal infections annually. It is of great interest to microbiological research as it is among the oldest known eukaryotic cells. Purifying Giardia from fecal samples for both research and diagnostic purposes presents one of the most difficult challenges. Traditional purification methods rely on density gradient centrifugation, membrane-based filtration, and sedimentation methods, which suffer from low recovery rates, high costs, and poor efficiency. Here, we report on the use of microfluidics to purify Giardia cysts from mouse feces. We propose a rigid spiral microfluidic device with a trapezoidal cross section to effectively separate Giardia from surrounding debris. Our characterizations reveal that the recovery rate is concentration-dependent, and our proposed device can achieve recovery rates as high as 75% with 0.75 ml/min throughput. Moreover, this device can purify Giardia from extremely turbid samples to a level where cysts are visually distinguishable with just one round of purification. This highly scalable and versatile 3D printed microfluidic device is then capable of further purifying or enhancing the recovery rate of the samples by recirculation. This device also has the potential to purify other gastrointestinal pathogens of similar size, and throughput can be significantly increased by parallelization.
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Longo, Umile Giuseppe, Sergio De Salvatore, Martina Sassi, Arianna Carnevale, Giovanna De Luca, and Vincenzo Denaro. "Motion Tracking Algorithms Based on Wearable Inertial Sensor: A Focus on Shoulder." Electronics 11, no. 11 (May 30, 2022): 1741. http://dx.doi.org/10.3390/electronics11111741.
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Shoulder Range of Motion (ROM) has been studied with several devices and methods in recent years. Accurate tracking and assessment of shoulder movements could help us to understand the pathogenetic mechanism of specific conditions in quantifying the improvements after rehabilitation. The assessment methods can be classified as subjective and objective. However, self-reported methods are not accurate, and they do not allow the collection of specific information. Therefore, developing measurement devices that provide quantitative and objective data on shoulder function and range of motion is important. A comprehensive search of PubMed and IEEE Xplore was conducted. The sensor fusion algorithm used to analyze shoulder kinematics was described in all studies involving wearable inertial sensors. Eleven articles were included. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias (QUADAS-2). The finding showed that the Kalman filter and its variants UKF and EKF are used in the majority of studies. Alternatives based on complementary filters and gradient descent algorithms have been reported as being more computationally efficient. Many approaches and algorithms have been developed to solve this problem. It is useful to fuse data from different sensors to obtain a more accurate estimation of the 3D position and 3D orientation of a body segment. The sensor fusion technique makes this integration reliable. This systematic review aims to redact an overview of the literature on the sensor fusion algorithms used for shoulder motion tracking.
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D’Asaro, Eric A., and Ren-Chieh Lien. "Measurement of Scalar Variance Dissipation from Lagrangian Floats." Journal of Atmospheric and Oceanic Technology 24, no. 6 (June 2007): 1066–77. http://dx.doi.org/10.1175/jtech2031.1.
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Simultaneous measurements of temperature, salinity, their vertical gradients, and the vertical gradient of velocity across a 1.4-m-long Lagrangian float were used to investigate the accuracy with which the dissipation of scalar variance χ can be computed using inertial subrange methods from such a neutrally buoyant float. The float was deployed in a variety of environments in Puget Sound; χ varied by about 3.5 orders of magnitude. A previous study used an inertial subrange method to yield accurate measurements of ε, the rate of dissipation of kinetic energy, from this data. Kolmogorov scaling predicts a Lagrangian frequency spectrum for the rate of change of a scalar as ΦDσ/Dt(ω) = βsχ, where βs is a universal Kolmogorov constant. Measured spectra of the rate of change of potential density σ were nearly white at frequencies above N, the buoyancy frequency. Deviations at higher frequency could be modeled quantitatively using the measured deviations of the float from perfect Lagrangian behavior, yielding an empirical nondimensional form ΦDσ/Dt = βsχH(ω/ωL) for the measured spectra, where L is half the float length, ω3L = ε/L2, and H is a function describing the deviations of the spectrum from Kolmogorov scaling. Using this empirical form, estimates of χ were computed and compared with estimates derived from ε. The required mixing efficiency was computed from the turbulent Froude number ω0/N, where ω0 is the large-eddy frequency. The results are consistent over a range of ε from 10−8 to 3 × 10−5 W kg−1 implying that χ can be estimated from float data to an accuracy of least a factor of 2. These methods for estimating ε, χ, and the Froude number from Lagrangian floats appear to be unbiased and self-consistent for ε > 10−8 W kg−1. They are expected to fail in less energetic turbulence both for instrumental reasons and because the Reynolds number typically becomes too small to support an inertial subrange. The value of βs is estimated at 0.6 to within an uncertainty of less than a factor of 2.
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Tahir, Sheikh Badar ud din, Abdul Basit Dogar, Rubia Fatima, Affan Yasin, Muhammad Shafiq, Javed Ali Khan, Muhammad Assam, Abdullah Mohamed, and El-Awady Attia. "Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model." Sensors 22, no. 17 (September 2, 2022): 6632. http://dx.doi.org/10.3390/s22176632.
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Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time–frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.
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Bussaban, Limpapat, Attapol Kaewkhao, and Suthep Suantai. "Inertial s-iteration forward-backward algorithm for a family of nonexpansive operators with applications to image restoration problems." Filomat 35, no. 3 (2021): 771–82. http://dx.doi.org/10.2298/fil2103771b.
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Image restoration is an important branch of image processing which has been studied extensively while there are several methods to solve this problem by many authors with the challenges of computational speed and accuracy of algorithms. In this paper, we present two methods, called ?Inertial S-iteration forward-backward algorithm (ISFBA)? and ?A fast iterative shrinkage-thresholding algorithm-Siteration (FISTA-S)?, for finding an approximate solution of least absolute shrinkage and selection operator problem by using a special technique in fixed point theory and prove weak convergence of the proposed methods under some suitable conditions. Moreover, we apply our main results to solve image restoration problems. It is shown by some numerical examples that our algorithms have a good behavior compared with forward-backward algorithm (FBA), a new accelerated proximal gradient algorithm (nAGA) and a fast iterative shrinkage-thresholding algorithm (FISTA).
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Pogorilov, Sergij Yurijovich, and Valerij Lvovich Khavin. "Modeling of the thermal regime of a platformless navigation system with thermal stabilization of accelerated warm-up." Bulletin of the National Technical University «KhPI» Series: Dynamics and Strength of Machines, no. 1 (December 31, 2022): 74–80. http://dx.doi.org/10.20998/2078-9130.2022.1.265440.
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Currently, platformless inertial navigation systems (IBS) based on fiber optic gyroscopes (FOG) are widely used in modern aviation and space technology. In connection with the high sensitivity of VOG to the influence of temperature changes, ensuring stable thermal modes of operation is an urgent problem. The most important task of increasing the accuracy of system operation is the development of methods of thermal protection and thermostabilization of VOG. The work is devoted to the modeling of the temperature field of the platformless inertial unit (BIB), which is part of the BINS, in order to ensure a minimum temperature difference on the VOG platform with the use of thermal stabilization. The purpose of the work is to simulate the temperature field of the BIB measuring unit and determine the conditions that ensure a minimum of temperature differences on the VOG platform under thermal stabilization conditions. To achieve the goal, the following tasks were solved: creation of a calculation scheme and finite element model of the BIB device, modeling of the effect of changes in external temperatures on the temperature field of the BIB device, numerical determination of temperature gradients at given points of the device. According to the results of numerical modeling, the parameters of the temperature field of the device and the characteristics of the thermal stabilization system were studied. The thermal mode of the device with a thermostabilization system for accelerated heating and reduction of temperature change gradients on VOG sensors with a governing law corresponding to base temperature changes is considered. The temperature gradients at the given points of the device are numerically determined. It was established that the law of thermostabilization should ensure the stability of the temperature field (the smallness of the temperature gradient). The value of the VOG temperature itself does not have a significant effect on the amount of drift. It is advisable to use the thermal stabilization system only to bring the system to the working temperature mode for no more than 30 minutes, and not to use it in the working mode.
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Afanasyev, Y. D., P. B. Rhines, and E. G. Lindahl. "Emission of Inertial Waves by Baroclinically Unstable Flows: Laboratory Experiments with Altimetric Imaging Velocimetry." Journal of the Atmospheric Sciences 65, no. 1 (January 1, 2008): 250–62. http://dx.doi.org/10.1175/2007jas2336.1.
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Abstract Results from new experiments on baroclinic instability of a coastal jet demonstrate that this almost balanced flow spontaneously emits inertial waves when the Rossby radius of deformation is relatively small such that the characteristics of baroclinic meanders match the dispersion relation for the inertial waves. The energy of the waves is small compared to the energy of the flow. A single event of wave emission is identified in the experiment with larger radius of deformation and is interpreted in terms of vorticity dynamics. The flows are generated on a laboratory polar β plane where the Coriolis parameter varies quadratically with latitude. A new method for imaging the rotating flows, which the authors call “altimetric imaging velocimetry,” is employed. Optical color coding of slopes of the free-surface elevation field allows the authors to derive the fields of pressure, surface elevation, geostrophic velocity, or the “gradient wind” velocity with very high spatial resolution (typically several million vectors) limited largely by the pixel resolution of the available imaging sensors. The technique is particularly suited for the investigations of small-amplitude waves, which are often difficult to detect by other methods.
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Lukyanov, Alexander V., Stanislav M. Orlov, and Boris R. Romanenko. "Study of the characteristics of the ascending vortex of the cyclone and the concentration of dust along its section." Vestnik MGSU, no. 8 (August 2021): 1034–44. http://dx.doi.org/10.22227/1997-0935.2021.8.1034-1044.
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Introduction. Protection of the atmosphere is a social and economic problem inextricably linked with the task of creating comfortable conditions for human life and work. Cyclones are the most typical representatives of dry inertial dust collectors. This work is aimed at reducing energy consumption when cleaning gas with cyclones.
Materials and methods. In the course of the work, analytical and experimental research methods were applied.
Results. Analytical dependences of the aerodynamics of the ascending cyclone vortex have been obtained, which showed that the ascending vortex has a complex structure and the cyclone is an artificially created spiral structure, akin to such a natural phenomenon as a tornado. The obtained mathematical model was fully confirmed by experimental studies.
Conclusions. The studies carried out show that the ascending vortex in the cyclone has a structure consisting of two zones. In the first zone (core), the force of the radial pressure gradient exceeds the centrifugal force, and the dust rushes towards the cyclone axis. In the second, the centrifugal force exceeds the force of the pressure gradient, and the dust is thrown to the periphery. The obtained theoretical model will make it possible to reasonably choose methods for more rational use of the expended energy and increasing the efficiency of cyclones.
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Luce, Hubert, Lakshmi Kantha, Hiroyuki Hashiguchi, and Dale Lawrence. "Estimation of Turbulence Parameters in the Lower Troposphere from ShUREX (2016–2017) UAV Data." Atmosphere 10, no. 7 (July 11, 2019): 384. http://dx.doi.org/10.3390/atmos10070384.
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Turbulence parameters in the lower troposphere (up to ~4.5 km) are estimated from measurements of high-resolution and fast-response cold-wire temperature and Pitot tube velocity from sensors onboard DataHawk Unmanned Aerial Vehicles (UAVs) operated at the Shigaraki Middle and Upper atmosphere (MU) Observatory during two ShUREX (Shigaraki UAV Radar Experiment) campaigns in 2016 and 2017. The practical processing methods used for estimating turbulence kinetic energy dissipation rate ε and temperature structure function parameter C T 2 from one-dimensional wind and temperature frequency spectra are first described in detail. Both are based on the identification of inertial (−5/3) subranges in respective spectra. Using a formulation relating ε and C T 2 valid for Kolmogorov turbulence in steady state, the flux Richardson number R f and the mixing efficiency χ m are then estimated. The statistical analysis confirms the variability of R f and χ m around ~ 0.13 − 0.14 and ~ 0.16 − 0.17 , respectively, values close to the canonical values found from some earlier experimental and theoretical studies of both the atmosphere and the oceans. The relevance of the interpretation of the inertial subranges in terms of Kolmogorov turbulence is confirmed by assessing the consistency of additional parameters, the Ozmidov length scale L O , the buoyancy Reynolds number R e b , and the gradient Richardson number Ri. Finally, a case study is presented showing altitude differences between the peaks of N 2 , C T 2 and ε , suggesting turbulent stirring at the margin of a stable temperature gradient sheet. The possible contribution of this sheet and layer structure on clear air radar backscattering mechanisms is examined.
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Wang, Zihui, Xianghong Cheng, and Jingjing Du. "Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System." Sensors 20, no. 2 (January 9, 2020): 384. http://dx.doi.org/10.3390/s20020384.
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Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.
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Rabb, Ethan, and John Josiah Steckenrider. "Walking Trajectory Estimation Using Multi-Sensor Fusion and a Probabilistic Step Model." Sensors 23, no. 14 (July 18, 2023): 6494. http://dx.doi.org/10.3390/s23146494.
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This paper presents a framework for accurately and efficiently estimating a walking human’s trajectory using a computationally inexpensive non-Gaussian recursive Bayesian estimator. The proposed framework fuses global and inertial measurements with predictions from a kinematically driven step model to provide robustness in localization. A maximum a posteriori-type filter is trained on typical human kinematic parameters and updated based on live measurements. Local step size estimates are generated from inertial measurement units using the zero-velocity update (ZUPT) algorithm, while global measurements come from a wearable GPS. After each fusion event, a gradient ascent optimizer efficiently locates the highest likelihood of the individual’s location which then triggers the next estimator iteration.The proposed estimator was compared to a state-of-the-art particle filter in several Monte Carlo simulation scenarios, and the original framework was found to be comparable in accuracy and more efficient at higher resolutions. It is anticipated that the methods proposed in this work could be more useful in general real-time estimation (beyond just personal navigation) than the traditional particle filter, especially if the state is many-dimensional. Applications of this research include but are not limited to: in natura biomechanics measurement, human safety in manual fieldwork environments, and human/robot teaming.
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Kim, Yong-Gyun, Sungjoon Kim, Jae Hyeon Park, Seung Yang, Minkyu Jang, Yeo Joon Yun, Jae-sung Cho, Sungmin You, and Seong-Ho Jang. "Explainable Deep-Learning-Based Gait Analysis of Hip–Knee Cyclogram for the Prediction of Adolescent Idiopathic Scoliosis Progression." Sensors 24, no. 14 (July 12, 2024): 4504. http://dx.doi.org/10.3390/s24144504.
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Accurate prediction of scoliotic curve progression is crucial for guiding treatment decisions in adolescent idiopathic scoliosis (AIS). Traditional methods of assessing the likelihood of AIS progression are limited by variability and rely on static measurements. This study developed and validated machine learning models for classifying progressive and non-progressive scoliotic curves based on gait analysis using wearable inertial sensors. Gait data from 38 AIS patients were collected using seven inertial measurement unit (IMU) sensors, and hip–knee (HK) cyclograms representing inter-joint coordination were generated. Various machine learning algorithms, including support vector machine (SVM), random forest (RF), and novel deep convolutional neural network (DCNN) models utilizing multi-plane HK cyclograms, were developed and evaluated using 10-fold cross-validation. The DCNN model incorporating multi-plane HK cyclograms and clinical factors achieved an accuracy of 92% in predicting curve progression, outperforming SVM (55% accuracy) and RF (52% accuracy) models using handcrafted gait features. Gradient-based class activation mapping revealed that the DCNN model focused on the swing phase of the gait cycle to make predictions. This study demonstrates the potential of deep learning techniques, and DCNNs in particular, in accurately classifying scoliotic curve progression using gait data from wearable IMU sensors.
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Rehman, Habib ur, Poom Kumam, Ioannis K. Argyros, Wejdan Deebani, and Wiyada Kumam. "Inertial Extra-Gradient Method for Solving a Family of Strongly Pseudomonotone Equilibrium Problems in Real Hilbert Spaces with Application in Variational Inequality Problem." Symmetry 12, no. 4 (April 1, 2020): 503. http://dx.doi.org/10.3390/sym12040503.
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In this paper, we propose a new method, which is set up by incorporating an inertial step with the extragradient method for solving a strongly pseudomonotone equilibrium problems. This method had to comply with a strongly pseudomonotone property and a certain Lipschitz-type condition of a bifunction. A strong convergence result is provided under some mild conditions, and an iterative sequence is accomplished without previous knowledge of the Lipschitz-type constants of a cost bifunction. A sufficient explanation is that the method operates with a slow-moving stepsize sequence that converges to zero and non-summable. For numerical explanations, we analyze a well-known equilibrium model to support our well-established convergence result, and we can see that the proposed method seems to have a significant consistent improvement over the performance of the existing methods.
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Milam, Gary, Baijun Xie, Runnan Liu, Xiaoheng Zhu, Juyoun Park, Gonwoo Kim, and Chung Hyuk Park. "Trainable Quaternion Extended Kalman Filter with Multi-Head Attention for Dead Reckoning in Autonomous Ground Vehicles." Sensors 22, no. 20 (October 11, 2022): 7701. http://dx.doi.org/10.3390/s22207701.
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Extended Kalman filter (EKF) is one of the most widely used Bayesian estimation methods in the optimal control area. Recent works on mobile robot control and transportation systems have applied various EKF methods, especially for localization. However, it is difficult to obtain adequate and reliable process-noise and measurement-noise models due to the complex and dynamic surrounding environments and sensor uncertainty. Generally, the default noise values of the sensors are provided by the manufacturer, but the values may frequently change depending on the environment. Thus, this paper mainly focuses on designing a highly accurate trainable EKF-based localization framework using inertial measurement units (IMUs) for the autonomous ground vehicle (AGV) with dead reckoning, with the goal of fusing it with a laser imaging, detection, and ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM) estimation for enhancing the performance. Convolution neural networks (CNNs), backward propagation algorithms, and gradient descent methods are implemented in the system to optimize the parameters in our framework. Furthermore, we develop a unique cost function for training the models to improve EKF accuracy. The proposed work is general and applicable to diverse IMU-aided robot localization models.
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Coulaud, O., P. Morel, and J. P. Caltagirone. "Numerical modelling of nonlinear effects in laminar flow through a porous medium." Journal of Fluid Mechanics 190 (May 1988): 393–407. http://dx.doi.org/10.1017/s0022112088001375.
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This paper deals with the introduction of a nonlinear term into Darcy's equation to describe inertial effects in a porous medium. The method chosen is the numerical resolution of flow equations at a pore scale. The medium is modelled by cylinders of either equal or unequal diameters arranged in a regular pattern with a square or triangular base. For a given flow through this medium the pressure drop is evaluated numerically.The Navier-Stokes equations are discretized by the mixed finite-element method. The numerical solution is based on operator-splitting methods whose purpose is to separate the difficulties due to the nonlinear operator in the equation of motion and the necessity of taking into account the continuity equation. The associated Stokes problems are solved by a mixed formulation proposed by Glowinski & Pironneau.For Reynolds numbers lower than 1, the relationship between the global pressure gradient and the filtration velocity is linear as predicted by Darcy's law. For higher values of the Reynolds number the pressure drop is influenced by inertial effects which can be interpreted by the addition of a quadratic term in Darcy's law.On the one hand this study confirms the presence of a nonlinear term in the motion equation as experimentally predicted by several authors, and on the other hand analyses the fluid behaviour in simple media. In addition to the detailed numerical solutions, an estimation of the hydrodynamical constants in the Forchheimer equation is given in terms of porosity and the geometrical characteristics of the models studied.
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AHMAD, ABDULWAHAB, POOM KUMAM, and MURTALA HARUNA HARBAU. "Convergence Theorems for Common Solutions of Nonlinear Problems and Applications." Carpathian Journal of Mathematics 40, no. 2 (March 28, 2024): 207–41. http://dx.doi.org/10.37193/cjm.2024.02.01.
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In this work, two inertial algorithms for approximating common elements of the sets of solu- tions of three important problems are constructed. The first problem is a generalized mixed equilibrium one involving relaxed monotone mapping, the second is a zero problem of inverse strongly monotone mappings, while the third one is a fixed point problem of a family of relatively nonexpansive mappings. The first algorithm is a shrinking projection type for a common solution of all the three problems. The second is a generalized Alber projection free method for the second and the third problems. Each of the devised algorithms uses the conjugate gradient-like direction, which allows it to accelerate its iterates toward a solution of the problems. The strong convergence theorem for each of the algorithms is formulated and proved in a real 2 - uniformly convex and uniformly smooth Banach space. Additionally, the applications of our algorithms to convex optimization prob- lems and image recovery problems are studied. The advantages and computational efficiency of our methods are analyzed based on their numerical performance in comparison to some of the existing and recently proposed methods using numerical example.
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Lateef, Rana Abdulrahman, and Dr Ayad Rodhan Abbas. "A Proposed ConvXGBoost Model for Human Activity Recognition with Multi Optimizers." Webology 19, no. 1 (January 20, 2022): 1703–15. http://dx.doi.org/10.14704/web/v19i1/web19114.
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The wide use of smartphones and later smartwatches equipped with a set of sensors such as location, motion, and direction blaze the trail for researchers to better recognize human activity. However, researches on using inertial or motion sensors (i.e., accelerometer, gyroscope) for human activity recognition (HAR) has intensified and reside a great confrontation to be faced. Lately, many deep learning methods have been suggested to improve the human activity classification and discrimination performance to reach an optimal accuracy. Therefore, this paper applies a Convolutional eXtreme Gradient Boosting (ConvXGBoost), which combines Convolutional Neural Network (CNN) represented by AlexNet to learn the input features automatically, followed by XGBoost decision tree used to predict the class label and thereof recognize the performed activity. Human activities are collected from sensors as time series data. Therefore, we suggested using one-dimensional AlexNet (1D AlexNet) model instead of 2D. The AlexNet model is compiled with two optimizers Adam and Stochastic Gradient Descent (SGD) which are applied consecutively. The suggested architecture was trained and evaluated on the “WISDM Smartphone and Smartwatch Activity and Biometric Dataset” that consists of raw data for eighteen activities recorded from phone and watch. The experiments revealed that using multi optimizer with a convolutional neural network improved the accuracy of recognition by 5%. Moreover, a proposed ConvXGBoost model outperformed the performance of other models works with the dataset as mentioned above with an overall accuracy of 98-99% depends on the device used.
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Sheta, Bassem, Mohamed Elhabiby, and Naser El-Sheimy. "An Assessment of Nonlinear Optimization and Speeded up Robust Features (SURF) Algorithm for Estimating Object Space Transformation Parameters for UAV Pose Estimation." GEOMATICA 66, no. 4 (December 2012): 307–21. http://dx.doi.org/10.5623/cig2012-056.
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Localizing set of features (with known coordinates) on the ground and finding their matches in the image taken by imaging sensor on the aerial vehicle is the basic concept behind Vision Based Navigation (VBN). The number of matching points necessary for solving the collinearity equation is a critical factor to be investigated while using the VBN approach for navigation. Although a robust scale and rotation invariant image matching algorithm is important for VBN of aerial vehicles, the proper estimation of the collinearity equation object space transformation parameters improves the efficiency of the navigation process through the real-time estimation of transformation parameters. These parameters can then be used in aiding the inertial measurements data in the navigation estimation filter. The main objective of this paper is to investigate the estimation of the object space transformation parameters necessary for VBN of aerial vehicles with the assumption that the aerial vehicle experiences large values of the rotational angles, which will lead to non-linearity of the estimation model. In this case, traditional least squares approaches will fail or will take longer to estimate the object space transformation parameters, because of the expected non-linearity of the mathematical model. Five different nonlinear optimization methods are presented for estimating the transformation parameters – these include four gradient based nonlinear optimization methods; Trust region, Trust region dogleg algorithm, Levenberg-Marquardt, and Quasi-Newton line search method and one non-gradient method; Nelder-Mead simplex direct search is employed for the six transformation parameters estimation process. Assessments of the proposed nonlinear optimization approaches on the image matching algorithm necessary for the VBN approach are investigated.
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Thavitchasri, Phummarin, Dechrit Maneetham, and Padma Nyoman Crisnapati. "Intelligent Surface Recognition for Autonomous Tractors Using Ensemble Learning with BNO055 IMU Sensor Data." Agriculture 14, no. 9 (September 9, 2024): 1557. http://dx.doi.org/10.3390/agriculture14091557.
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This study aims to enhance the navigation capabilities of autonomous tractors by predicting the surface type they are traversing using data collected from BNO055 Inertial Measurement Units (IMU sensors). IMU sensor data were collected from a small mobile robot driven over seven different floor surfaces within a university environment, including tile, carpet, grass, gravel, asphalt, concrete, and sand. Several machine learning models, including Logistic Regression, K-Neighbors, SVC, Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and XGBoost, were trained and evaluated to predict the surface type based on the sensor data. The results indicate that Random Forest and XGBoost achieved the highest accuracy, with scores of 98.5% and 98.7% in K-Fold Cross-Validation, respectively, and 98.8% and 98.6% in an 80/20 Random State split. These findings demonstrate that ensemble methods are highly effective for this classification task. Accurately identifying surface types can prevent operational errors and improve the overall efficiency of autonomous systems. Integrating these models into autonomous tractor systems can significantly enhance adaptability and reliability across various terrains, ensuring safer and more efficient operations.
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MYDLARSKI, L. "Mixed velocity–passive scalar statistics in high-Reynolds-number turbulence." Journal of Fluid Mechanics 475 (January 25, 2003): 173–203. http://dx.doi.org/10.1017/s0022112002002756.
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Statistics of the mixed velocity–passive scalar field and its Reynolds number dependence are studied in quasi-isotropic decaying grid turbulence with an imposed mean temperature gradient. The turbulent Reynolds number (using the Taylor microscale as the length scale), Rλ, is varied over the range 85 [les ] Rλ [les ] 582. The passive scalar under consideration is temperature in air. The turbulence is generated by means of an active grid and the temperature fluctuations result from the action of the turbulence on the mean temperature gradient. The latter is created by differentially heating elements at the entrance to the wind tunnel plenum chamber. The mixed velocity–passive scalar field evolves slowly with Reynolds number. Inertial-range scaling exponents of the co-spectra of transverse velocity and temperature, Evθ(k1), and its real-space analogue, the ‘heat flux structure function,’ 〈Δv(r)Δθ(r)〉, show a slow evolution towards their theoretical predictions of −7/3 and 4/3, respectively. The sixth-order longitudinal mixed structure functions, 〈(Δu(r))2(Δθ(r))4〉, exhibit inertial-range structure function exponents of 1.36–1.52. However, discrepancies still exist with respect to the various methods used to estimate the scaling exponents, the value of the scalar intermittency exponent, μθ, and the effects of large-scale phenomena (namely shear, decay and turbulent production of 〈θ2〉) on 〈(Δu(r))2(Δθ(r))4〉. All the measured fine-scale statistics required to be zero in a locally isotropic flow are, or tend towards, zero in the limit of large Reynolds numbers. The probability density functions (PDFs) of Δv(r)Δθ(r) exhibit roughly exponential tails for large separations and super-exponential tails for small separations, thus displaying the effects of internal intermittency. As the Reynolds number increases, the PDFs become symmetric at the smallest scales – in accordance with local isotropy. The expectation of the transverse velocity fluctuation conditioned on the scalar fluctuation is linear for all Reynolds numbers, with slope equal to the correlation coefficient between v and θ. The expectation of (a surrogate of) the Laplacian of the scalar reveals a Reynolds number dependence when conditioned on the transverse velocity fluctuation (but displays no such dependence when conditioned on the scalar fluctuation). This former Reynolds number dependence is consistent with Taylor’s diffusivity independence hypothesis. Lastly, for the statistics measured, no violations of local isotropy were observed.
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Xu, Zhiyuan, Carissa Carlson, John Snell, Matt Eames, Arik Hananel, M. Beatriz Lopes, Prashant Raghavan, et al. "Intracranial inertial cavitation threshold and thermal ablation lesion creation using MRI-guided 220-kHz focused ultrasound surgery: preclinical investigation." Journal of Neurosurgery 122, no. 1 (January 2015): 152–61. http://dx.doi.org/10.3171/2014.9.jns14541.
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OBJECT In biological tissues, it is known that the creation of gas bubbles (cavitation) during ultrasound exposure is more likely to occur at lower rather than higher frequencies. Upon collapsing, such bubbles can induce hemorrhage. Thus, acoustic inertial cavitation secondary to a 220-kHz MRI-guided focused ultrasound (MRgFUS) surgery is a serious safety issue, and animal studies are mandatory for laying the groundwork for the use of low-frequency systems in future clinical trials. The authors investigate here the in vivo potential thresholds of MRgFUS-induced inertial cavitation and MRgFUS-induced thermal coagulation using MRI, acoustic spectroscopy, and histology. METHODS Ten female piglets that had undergone a craniectomy were sonicated using a 220-kHz transcranial MRgFUS system over an acoustic energy range of 5600–14,000 J. For each piglet, a long-duration sonication (40-second duration) was performed on the right thalamus, and a short sonication (20-second duration) was performed on the left thalamus. An acoustic power range of 140–300 W was used for long-duration sonications and 300–700 W for short-duration sonications. Signals collected by 2 passive cavitation detectors were stored in memory during each sonication, and any subsequent cavitation activity was integrated within the bandwidth of the detectors. Real-time 2D MR thermometry was performed during the sonications. T1-weighted, T2-weighted, gradient-recalled echo, and diffusion-weighted imaging MRI was performed after treatment to assess the lesions. The piglets were killed immediately after the last series of posttreatment MR images were obtained. Their brains were harvested, and histological examinations were then performed to further evaluate the lesions. RESULTS Two types of lesions were induced: thermal ablation lesions, as evidenced by an acute ischemic infarction on MRI and histology, and hemorrhagic lesions, associated with inertial cavitation. Passive cavitation signals exhibited 3 main patterns identified as follows: no cavitation, stable cavitation, and inertial cavitation. Low-power and longer sonications induced only thermal lesions, with a peak temperature threshold for lesioning of 53°C. Hemorrhagic lesions occurred only with high-power and shorter sonications. The sizes of the hemorrhages measured on macroscopic histological examinations correlated with the intensity of the cavitation activity (R2 = 0.74). The acoustic cavitation activity detected by the passive cavitation detectors exhibited a threshold of 0.09 V·Hz for the occurrence of hemorrhages. CONCLUSIONS This work demonstrates that 220-kHz ultrasound is capable of inducing a thermal lesion in the brain of living swines without hemorrhage. Although the same acoustic energy can induce either a hemorrhage or a thermal lesion, it seems that low-power, long-duration sonication is less likely to cause hemorrhage and may be safer. Although further study is needed to decrease the likelihood of ischemic infarction associated with the 220-kHz ultrasound, the threshold established in this work may allow for the detection and prevention of deleterious cavitations.
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Yang, Tao, Fang Xu, Shoujun Zhao, Tongtong Li, Zelin Yang, Yanbo Wang, and Yuwang Liu. "A High-Certainty Visual Servo Control Method for a Space Manipulator with Flexible Joints." Sensors 23, no. 15 (July 26, 2023): 6679. http://dx.doi.org/10.3390/s23156679.
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This paper introduces a novel high-certainty visual servo algorithm for a space manipulator with flexible joints, which consists of a kinematic motion planner and a Lyapunov dynamics model reference adaptive controller. To enhance kinematic certainty, a three-stage motion planner is proposed in Cartesian space to control the intermediate states and minimize the relative position error between the manipulator and the target. Moreover, a planner in joint space based on the fast gradient descent algorithm is proposed to optimize the joint’s deviation from the centrality. To improve dynamic certainty, an adaptive control algorithm based on Lyapunov stability analysis is used to enhance the system’s anti-disturbance capability. As to the basic PBVS (position-based visual servo methods) algorithm, the proposed method aims to increase the certainty of the intermediate states to avoid collision. A physical experiment is designed to validate the effectiveness of the algorithm. The experiment shows that the visual servo motion state in Cartesian space is basically consistent with the planned three-stage motion state, the average joint deviation index from the centrality is less than 40%, and the motion trajectory consistency exceeds 90% under different inertial load disturbances. Overall, this method reduces the risk of collision by enhancing the certainty of the basic PBVS algorithm.
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Xu, Hongfu, Haiyong Luo, Zijian Wu, Fan Wu, Linfeng Bao, and Fang Zhao. "Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation." Remote Sensing 14, no. 7 (March 31, 2022): 1691. http://dx.doi.org/10.3390/rs14071691.
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The tightly coupled navigation system is commonly used in UAV products and land vehicles. It adopts the Kalman filter to combine raw satellite observations, including the pseudorange, pseudorange rate and Doppler frequency, with the inertial measurements to achieve high navigational accuracy in GNSS-challenged environments. The accurate estimation of measurement noise covariance can ensure the quick convergence of the Kalman filter and the accuracy of the navigation results. Existing tightly coupled integrated navigation systems employ either constant noise covariance or simple noise covariance updating methods, which cannot accurately reflect the dynamic measurement noises. In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE), which can dynamically estimate the covariance of measurement noise. The residual module is used to solve the gradient degradation problem. The DAE is adopted to learn the essential characteristics from the noisy ephemeris data. By introducing the attention mechanism, the transformer can effectively learn the time and space dependency of long-term ephemeris data, and thus dynamically adjusts the noise covariance with the predicted factors. Extensive experimental results demonstrate that our method can achieve sub-meter positioning accuracy in the outdoor open environment. In a GNSS-degraded environment, our proposed method can still obtain about 3 m positioning accuracy. Another test on a new dataset also confirms that our proposed method has reasonable robustness and adaptability.
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Zhen, Tao, Jian-lei Kong, and Lei Yan. "Hybrid Deep-Learning Framework Based on Gaussian Fusion of Multiple Spatiotemporal Networks for Walking Gait Phase Recognition." Complexity 2020 (October 9, 2020): 1–17. http://dx.doi.org/10.1155/2020/8672431.
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Human gait phase detection is a significance technology for robotics exoskeletons control and exercise rehabilitation therapy. Inertial Measurement Units (IMUs) with accelerometer and gyroscope are convenient and inexpensive to collect gait data, which are often used to analyze gait dynamics for personal daily applications. However, current deep-learning methods that extract spatial and the isolated temporal features can easily ignore the correlation that may exist in the high-dimensional space, which limits the recognition effect of a single model. In this study, an effective hybrid deep-learning framework based on Gaussian probability fusion of multiple spatiotemporal networks (GFM-Net) is proposed to detect different gait phases from multisource IMU signals. Furthermore, it first employs the gait information acquisition system to collect IMU data fixed on lower limb. With the data preprocessing, the framework constructs a spatial feature extractor with AutoEncoder and CNN modules and a multistream temporal feature extractor with three collateral modules combining RNN, LSTM, and GRU modules. Finally, the novel Gaussian probability fusion module optimized by the Expectation-Maximum (EM) algorithm is developed to integrate the different feature maps output by the three submodels and continues to realize gait recognition. The framework proposed in this paper implements the inner loop that also contains the EM algorithm in the outer loop and optimizes the reverse gradient in the entire network. Experiments show that this method has better performance in gait classification with accuracy reaching more than 96.7%.
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Zhang, Ying, Xu Wu, Qifeng Guo, Zhaohong Zhang, and Meifeng Cai. "Nonlinear Seepage Behaviors of Pore-Fracture Sandstone under Hydro-Mechanical Coupling." Crystals 12, no. 3 (March 10, 2022): 373. http://dx.doi.org/10.3390/cryst12030373.
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This work focused on the nonlinear seepage behaviors of flow in pore-fracture media. Natural sandstones were selected to prefabricate single-fracture specimens with different inclinations (0–90°). Seepage tests of combined media were performed under different confining pressures (8–10 MPa) and different water pressures (3–7 MPa) in a triaxial pressure chamber. The fitting analysis of experimental data showed that Forchheimer’s law described the nonlinear characteristics of flow in the pore-fracture media. Linear term coefficient a and nonlinear term coefficient b of the sandstone samples with different inclinations changed more obviously with the increased inclination. When the fracture inclination was greater than 30°, a and b values had a sudden jump. The nonlinear inertial-parameter equation of fluid flow in pore-fracture media was proposed based on non-Darcy flow coefficient β and inherent permeability k. The applicability of the following methods to evaluate Darcy’s law was discussed, including normalized hydraulic conductivity, pressure gradient ratio, and discharge ratio. The three methods were able to determine critical parameters and distinguish linear and nonlinear flow. Furthermore, it was specified for the first time that when β was negative, critical nonlinear effect E was −0.1, and Forchheimer’s coefficient F0 was −0.091. In the −∇P-Q relationship, the fitting curve was convex to the −∇P axis, and the increase of Q was higher than the linear increase, presenting the nonlinearity of overflow. On the one hand, the fractures and pores were compressed under the confining pressure due to the prefabricated fractures of different shapes and different inclinations. A higher seepage water pressure was needed to stabilize the seepage system with the excessive flow rate. On the other hand, the barrier effect of the fluid inside the rock was completely lost because the fluid expanded the seepage channel. Its permeability was changed, leading to seepage instability.
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Soni, Shashank, Nitin K. Jain, and Prasad V. Joshi. "Analytical Modeling on Vibration Analysis of Cracked Functionally Graded Plate Submerged in Fluid." Recent Patents on Mechanical Engineering 12, no. 3 (September 26, 2019): 240–47. http://dx.doi.org/10.2174/2212797612666190531115429.
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Background: It is established that the vibration response of submerged structures is quite different than that calculated in vacuum. Therefore, the study of vibration characteristics of submerged plate structures is important for safety and its designing purpose. Objective: To investigate the fundamental frequency of partially cracked Functionally Graded (FG) submerged plate based on analytical approach. Methods: The governing differential equation of the cracked-submerged plate is derived based on Kirchhoff’s thin classical plate theory in conjunction with the potential flow theory. The line spring model is used to incorporate the effect of crack in the form of additional bending whereas the effect of fluid medium is incorporated in form fluids forces associated with inertial effects of its surrounding fluids. The Bernoulli’s equation and velocity potential function are used to define the fluid forces acting on plate surface. Results: An approximate solution for governing equation of coupled fluid-plate system is obtained by using the Galerkin’s method. For validation of the present results, they are compared with the existing results of the previous published work, which are in good agreements. New results for natural frequencies as affected by gradient index, crack length, level of submergence and immersed depth of plate are presented for Simply Supported (SSSS) boundary condition. Conclusion: It has been concluded that the presence of crack and fluidic medium significantly affect the natural frequencies of the plate. It is observed that the increase in the length of crack and level of submergence decreases the fundamental frequency. In this paper, few patents have been discussed.