Search

Recently, the development of agile-legged robots that mimic the structures and functions of biological muscles and tendons of quadruped mammals has attracted significant attention. The driving force of muscles and the elastic force of muscles and tendons can help improve the performances of these robots. Thus far, most of these robots have been developed based on the muscle-tendon complex of the lower legs. However, some mammals have a larger muscle-tendon complex on the upper legs, which contributes significantly to agile movements. In this study, a legged robot that mimics the functions of the bi-articular muscle-tendon complex of the thigh was developed, and its effects on static and dynamic movements were verified through simulations and experiments. While maintaining a static posture, the transfer of torque between two joints in the thigh mechanism reduced the power consumption by the robot’s motors by half, used for balancing the gravity of the robot. For a dynamic vertical jump, the motion trajectory generation for the robot was performed using non-linear optimization to maximize the jump height. As a result, the jump height was significantly enhanced by the effect of the thigh mechanism, and the height was more than twice the leg length.

The generalized renewal process considers repair efficiency of imperfect repair in reliability assessment of repairable systems; therefore, its evaluation results are close to real repair circumstance than the ordinary renewal process or the non-homogeneous Poisson process. Based on the asymptotic distribution of maximum likelihood estimation, a calculating method of reliability indices for repairable systems with imperfect repair is proposed. The point and approximate interval estimators of model parameters for Kijima type Weibull generalized renewal processes Models I and II, as well as reliability indices of repairable systems, such as reliability, cumulative failure number and failure intensity, etc., are all presented. Two real cases are studied using generalized renewal processes Models I and II respectively to show the validity of our method. The results show that imperfect repair makes the instantaneous failure intensity of a repairable system discretely jump either up or down at the time of each failure, and the method proposed in this paper agrees well with the other exiting methods, and can also reduce the complexity of calculation.

A dynamic population size strategy is proposed for the fireworks algorithm (FWA) to adjust the population size based to the search results of the current generation. When the currently found optimal individual is updated, a linear decreasing method is activated to maintain an efficient exploitation speed. The population size is reduced by 1 until the minimum preset population size is reached, then the population size remains unchanged. Otherwise, we randomly generate a larger population size than the initial population and expand the explosion amplitudes of all firework individuals artificially, which the expectation that we can escape current local minima. To analyze the effectiveness of the proposed strategy, we combined it with the enhanced FWA (EFWA) together, and run the EFWA and (the EFWA + our proposed strategy) on 28 CEC 2013 benchmark functions in three different dimensions. Each function is run 30 trial times independently, and the Wilcoxon signed-rank test is applied to check significant differences. The statistical results showed that the proposed dynamic population size strategy can not only achieve a faster convergence speed for the FWA but also can jump out of trapped local minima more easily to maintain a higher performance, especially for high-dimensional problems.

In this work, an improved Jaya (IJaya) algorithm is proposed and used to solve the economic load dispatch (ELD) problem considering valve-point effect and multi-fuel options. Compared with original Jaya, the convergence rate of IJaya is accelerated by distance-varying acceleration coefficient (DVAC), and the population diversity is enlarged by a self-adaptive population mechanism (SAPM). Meanwhile, to avoid premature problem, Gaussian and Cauchy mutation (GCM) is used to help IJaya jump out of local optima. The superiorities of IJaya have been validated by solving ELD cases with 6, 10, 13, 15, 40 generators, and the results are analysed in aspects of convergence rate, solution quality and robustness. The conclusion is that, in the tested ELD cases, IJaya behaves much more competitive than Jaya under the same testing conditions.

Estimation of unknown noise covariances in a Kalman filter is a problem of significant practical interest in a wide array of applications. This paper presents a single-pass stochastic gradient descent (SGD) algorithm for noise covariance estimation for use in adaptive Kalman filters applied to non-stationary systems where the noise covariances can occasionally jump up or down by an unknown magnitude. Unlike our previous batch method or our multi-pass decision-directed algorithm, the proposed streaming algorithm reads measurement data exactly once and has similar root mean square error (RMSE). The computational efficiency of the new algorithm stems from its one-pass nature, recursive fading memory estimation of the sample cross-correlations of the innovations, and the RMSprop accelerated SGD algorithm. The comparative evaluation of the proposed method on a number of test cases demonstrates its computational efficiency and accuracy.

Aiming at the problem of low accuracy of the Chicken Swarm Optimization Algorithm and falling into the local optimum easily, a self-adaptive dynamic distribution Chicken Swarm Optimization (DCSO) is proposed. Firstly, a dynamic weight strategy is proposed to solve the problem of reduced algorithm accuracy; Secondly, the learning factor of normal distribution is used to solve the problem that the algorithm is easy to fall into the local optimum; Finally, 16 benchmark functions are used to test the performance of the algorithm. And the experimental results show that the improved Chicken Swarm Optimization has better solution accuracy and it can jump out of the local optimum.

Localizing stereo boundaries is difficult because matching cues are absent in the occluded regions that are adjacent to them. We introduce an energy and level-set optimizer that improves boundaries by encoding the essential geometry of occlusions: The spatial extent of an occlusion must equal the amplitude of the disparity jump that causes it. In a collection of figure-ground scenes from Middlebury and Falling Things stereo datasets, the model provides more accurate boundaries than previous occlusion-handling techniques., Comment: ICIP 2021 Code and data: https://github.com/jialiangw/levelsetstereo

Compared to its predecessor, the 4G network, the 5G mobile network offers an increase in speeds and a significant reduction in response time. It is a standard that will help both in industry and professional usage. It will also affect the use of the smartphone of the average user. However, the introduction of 5G networks means a prospective jump in the number of transmitters. High transmission speeds only work over a very short distance and spread poorly in buildings. For this reason, the debate on health hazards is still open. In this article, we deal with the creation of a simple model and simulations of the electromagnetic field around a 5G mobile phone and its penetration into nearby tissue. The resulting values for the two modeled frequencies in the upper range of 5G frequencies 28GHz and 39GHz are presented in the text.

The LOS (line of sight) angle will jump twice before and after the infrared imaging guided missile identify the infrared decoy, which will cause the mutation of guidance command and reduce the guidance accuracy. The LOS angle jump model is established based on point source decoy, and the mechanism of LOS angle jump is derived. To solve the problem of miss distance increase caused by LOS angle jump, on the basis of traditional Kalman filter method which takes the current statistical model as the LOS motion form, a Kalman filter with variable variance is designed to extract LOS angular velocity. The simulation results show that under the influence of decoys, the accuracy of LOS angle and angular velocity output by traditional Kalman filtering method is poor, while the Kalman filtering method with variable variance can greatly improve the accuracy.

Synchronous reference frame phase-locked loop (SRF-PLL) is a classical method to achieve grid synchronization in three-phase systems. And SRF-PLL has important influence on weak grids. Many linearized models have been addressed to describe PLLs. However, they have low dynamic accuracy in the case of frequency jump. To fill this gap, an accurate linearized model of SRF-PLL is proposed and the proposed model has good dynamic accuracy in cases of frequency jump and phase angle jump. In details, a nonlinear model of SRF-PLL is established. Then, a simplified model is obtained according to the accurate linearization technology. Compared with the traditional model of SRF-PLL, the proposed model has better dynamic accuracy, especially in the case of frequency jump.

For solving the problems of low accuracy, low convergence speed and accidental premature of canonical whale optimization algorithm (WOA), a modified multi-population WOA based on orthogonal learning (MOL-WOA) is proposed. Firstly, the multi-population strategy is adopted. The diversity of the sub-populations can be improved by the generalized opposition-based learning (GOBL) strategy. Secondly, the nonlinear convergence factor and adaptive weight strategy are utilized to take account of the global search and local development. Finally, the orthogonal learning method is adopted to realize the communication among the sub-populations. At the same time, a generation jump operation is brought in for avoiding premature. Simulation experiments with 20 benchmark test functions, contrasted with the other two modified algorithms, MOL-WOA owns better performance on accuracy, convergence speed and ability to miss the local optimal.

In order to improve the ability of BBPSO to jump out of the local optimal solution and avoid it falling into the local optimal solution, this paper proposes the BBPSO with linear variable weights. First, a linearly changing adjustment factor β is introduced into the iterative formula to balance the exploration and exploitation. Secondly, the scale factor D is introduced for the fast convergence of BBPSO, so that the adjustment factor will decrease to its minimum in the first half of the iterative process. The experimental results show that BBPSO with linear variable weight has better global optimality and can effectively balance the exploration and exploitation.

Aiming at the trajectory prediction problem of hypersonic flight vehicles skip trajectory, a prediction model of trajectory reachable area based on LSTM is proposed. According to the periodic jump characteristics of the skip trajectory, firstly, the trajectory is expressed as a time-dependent approximate expression consisting of a linear attenuation term and a sine term of amplitude attenuation. Then the dynamic characteristics of the parameters in the approximate expression are analyzed and the range of the parameters is calculated. Finally, according to the parameter range identified by the LSTM network and the trajectory expression, the final predicted trajectory reachable region is calculated. Due to the consideration of the prediction error caused by the drastic change of altitude and density in the jump trajectory, the trajectory accessible region prediction algorithm based on LSTM has more advantages than the previous trajectory prediction algorithm. The simulation results show that the trajectory reachable region prediction algorithm based on LSTM requires less prior knowledge and significantly improves the accuracy of trajectory prediction.

Optimal switch-time control is the study that investigates how best to switch between different modes. In this paper, we solve an optimal switch-time control problem for a class of impulsive switched systems based on observer design. The considered system undergoes jumps at the switching times and the state is only partially known through the outputs. The control variables consist of the impulse times and a set of scalars which determine the jump amplitudes. We present a method that both guarantees that the current control variables remain optimal as the state estimates evolve, and that can be applied to real-time applications. Moreover, the optimal impulsive control strategy can be found using some numerical gradient descent algorithms. Finally, the viability of the proposed method is illustrated through a numerical example.

This paper studies the issue of finite-time stability (FTS) for stochastic impulsive systems (SIS). Based on the notion of FTS for deterministic impulsive systems in the literature, the FTS including global FTS (GFTS) is defined for SIS. By using the comparison principle of impulsive systems, two types of GFTS criteria for SIS are established. The first type of GFTS criteria requires both the flow and the jump to be stabilising for GFTS. And the settling-time is dependent on the initial state. The second type of GFTS criteria includes some state-dependent impulse conditions. From the second type of GFTS criteria, it only requires the jump to be stabilising for GFTS. And the settling-time is independent of the initial state. Finally, two examples with simulations are given to verify the effectiveness of the results.

This paper proposes a vertical jump optimization strategy for a one-legged robot with consideration of its variable reduction ratio joints. Firstly, the characteristic of the joint is derived to obtain its influence on jump motion, which is similar to the reduction ratio. Secondly, referring to the joint’s characteristic, the initial posture of jumping is optimized to maximize the initial acceleration of jumping. Then, to generate the trajectory of the center of mass (CoM) and make the jump motion more efficient, nonlinear optimization of CoM is adopted with respect to human jumping data. Full-body dynamics is considered to track the trajectory with virtual force control. For flight phase, joint PD controller is adopted to decelerate and maintain the posture. A contrast simulation is implemented to demonstrate the characteristics of the variable reduction ratio joint. Vertical jump experiment on a one-legged robot platform is realized with a height of 30 cm.

This paper mainly studies the accurate height jumping control of wheeled-bipedal robots based on torque planning and energy consumption optimization. Due to the characteristics of underactuated, nonlinear estimation, and instantaneous impact in the jumping process, accurate control of the wheeled-bipedal robot’s jumping height is complicated. In reality, robots often jump at excessive height to ensure safety, causing additional motor loss, greater ground reaction force and more energy consumption. To solve this problem, a novel wheeled-bipedal jumping dynamical model(W-JBD) is proposed to achieve accurate height control. It performs well but not suitable for the real robot because the torque has a striking step. Therefore, the Bayesian optimization for torque planning method(BOTP) is proposed, which can obtain the optimal torque planning without accurate dynamic model and within few iterations. BOTP method can reduce 82.3% height error, 26.9% energy cost with continuous torque curve. This result is validated in the Webots simulation platform. Based on the torque curve obtained in the W-JBD model to narrow the searching space, BOTP can quickly converge (40 times on average). Cooperating W-JBD model and BOTP method, it is possible to achieve the height control of real robots with reasonable times of experiments.

In order to solve the problem of hopping mechanism design of robot, we proposed a method of optimization of transmission mechanism based on parametric model. In this paper, the optimization of energy transfer efficiency of robot jump mechanism is completed by parametric model method through the mechanism simplification, parametric modeling and parameter optimization. The results show that the method can greatly optimize the energy transfer efficiency and reduce the driving torque required by 74.6%. It provides a feasible optimization method for the design of the robot hopping structure.

In order to improve the velocity regulation performance of permanent magnet linear synchronous motor (PMLSM), switched Hall sensors are used to estimate the velocity and electric angle of the mover, and the vector closed-loop control is carried out. The current closed-loop starting strategy based on current frequency control is adopted to improve the starting performance of the motor. Combined with the characteristic of “thrust power angle self-balance”, this paper puts forward a kind of technology to determine the actual installation distance of Hall sensor by Hall signal jump time after the motor velocity is stable, so as to improve the estimation accuracy of motor velocity and electric angle, and improve the closed-loop control performance. Simulation and experiment verify the effectiveness of the start-up strategy and self-tuning technology.

We present the first-ever successful jumps of a microrobot fabricated in a silicon-on-insulator (SOI) process with an energy storing spring etched into the silicon substrate. The 0.08 gram silicon robot used its onboard electrostatic inchworm motor to store 8µJ of spring energy and vertically jump more than 3mm when powered and controlled with wire tethers. The robot's vertical jump height is more than 3X higher than what has been previously demonstrated by an SOI robot.

This technical note discusses the stability and stabilization problems for Takagi-Sugeno (T-S) fuzzy semi-Markovian jump systems (S-MJSs) in the discrete-time domain subjected to matched disturbances. The disturbance observer is designed to estimate the external disturbance. And then by combining the disturbance observer with model predictive control (MPC), a composite controller is proposed, such that, the stability of the closed-loop system can be guaranteed. By constructing a proper sojourn-time-dependent Lyapunov function, in the form of linear matrix inequalities (LMI), sufficient conditions for the existence of the composite anti-disturbance controller are framed. Finally, a set of numerical simulation examples are used to prove the effectiveness and feasibility of the proposed control strategy.

International audience; In this paper, the estimation of additive inertial navigation sensor faults with unknown dynamics is considered with application to the longitudinal navigation and control of a fixed wing unmanned aerial vehicle. The faulty measurement is on the pitch angle. A jump Markov regularized particle filter is proposed for fault and state estimation of the nonlinear aircraft dynamics, with a Markovian jump strategy to manage the probabilistic transitions between the fault free and faulty modes. The jump strategy uses a small number of sentinel particles to continue testing the alternate hypothesis under both fault free and faulty modes. The proposed filter is shown to outperform the regularized particle filter for this application in terms of fault estimation accuracy and convergence time for scenarios involving both abrupt and incipient faults, without prior knowledge of the fault models. The state estimation is also more accurate and robust to faults using the proposed approach. The root-mean-square error for the altitude is reduced by 77 % using the jump Markov regularized particle filter under a pitch sensor fault amplitude of up to 10 degrees. Performance enhancement compared to the regularized particle filter was found to be more pronounced when fault amplitudes increase.

Vertical jump height is an important tool to measure athletes’ lower body power in sports science and medicine. This work improves upon a previously published self-calibrating algorithm, which determines jump height using a single smartphone camera. The algorithm uses the parabolic fall trajectory obtained by tracking a single feature in a high-speed video. Instead of tracking an ArUco marker, which must be attached to the jumping subject, this work uses the OpenPose neural network for human pose estimation in order to calculate an approximation of the body center of mass. Jump heights obtained this way are compared to the reference heights from a motion capture system and to the results of the original work. The result is a trade-off between increased ease-of-use and slightly diminished accuracy of the jump height measurement.

Roboticists have increasingly sought to incorporate mechanical compliance into legged robots to realize a range of potential benefits, from improved agility to resilience in complex environments. A promising approach for building compliance into robot legs is to utilize the pneumatic artificial muscle, a pneumatic actuator with inherent compliance due to the compressibility of air. While previous work has explored the capabilities of pneumatic-muscle driven robots in highly dynamic tasks like jumping, there is a lack of trajectory planning strategies for such robots. In this paper, we detail our approach to planning vertical jumping trajectories for a planar two-legged robot driven by four pneumatic artificial muscles using on/off "burst inflation" control. The trajectory optimization problem is represented as a factor graph and solved with the GTSAM optimizer. A hybrid dynamics approach is used to handle foot-ground contacts. The average jump height error between simulation and experiment across multiple jumping trajectories of varying heights was 9.5 cm; the average RMS error between all four joints was 5.6 deg. This work provides a basis to plan more complex jumping and leaping trajectories for pneumatic muscle-driven robots.

Research on tracking control has been on-going for many years. The accuracy and the practicality of the tracking control method has always been one of the most important aspects when designing the control strategy. Autonomous Underwater Vehicles are becoming increasingly important in the applications of ocean surveillance and military, etc. Therefore, this paper aims to develop a control method for autonomous underwater vehicles based on bioinspired neural dynamics. The proposed method practically solves the speed jump and chattering issues that are respectively in conventional backstepping and sliding mode controls with the aid of the bioinspired neural dynamics. In addition, the proposed control method also takes the dynamic uncertainties for the autonomous underwater vehicle into the consideration. The combined tracking method has relatively good overall performance for autonomous underwater vehicle against model uncertainties and disturbances.

Jumping with controllable elevation is significant for insect-scale robots to improve terrain adaptability and to escape from risks. However, jumping robots based on soft materials with low stiffness cannot transmit displacement precisely, exhibiting poor control of jumping. Here, we propose a modified two-bars catapult mechanism combined with an asynchronous sequential releasing strategy to realize elevation controllable jumping. In this work, an 80 mg prototype robot, 56 (long) × 29 mm (wide) × 3 mm (high) mm in size, is designed with the controllable elevation range from 63° to 112°. The soft robot is mainly composed of a shape memory alloy actuator and four electrostatic pads acting as the lock/release structures. Elevation control is realized by asynchronously releasing the electrostatic pads in a small time interval (about 10 ms). A maximum jump height of 62 mm and a maximum half-distance of 41 mm are also achieved.

In this paper, a jump-wheeled lunar rover is designed, which is capable of walking and jumping. The lunar rover relies on its wheels to move in flat areas, and jumps over obstacles by its inertial wheel in complex and rugged areas. Firstly, kinematics and dynamics modeling are carried out for the rover. The lunar surface is photographed by camera during the jumping process, which is then changed into a grid map. Finally, the take-off point of each step is generated by Q-Learning.

Aiming at the problem of intrusion detection, the extreme learning machine is not effective in processing imbalanced data. Using Levy flight to improve the slime mold optimization algorithm, an improved slime mold algorithm is proposed to optimize the weighted extreme learning machine intrusion detection algorithm. The improved algorithm can jump out of the local optimum with a higher probability, and the weighted extreme learning machine has the advantages of short training time and good generalization performance. It increases the weight of minority attacks and makes the detection rate of rare attacks in network attacks higher than that of traditional machines. The learning method has been greatly improved. Global optimization is performed on the input weights and biases of the hidden layer in the weighted extreme learning machine to avoid the algorithm from falling into the local optimal solution and realize the classification of the intrusion detection data set. Experiments show that the detection rate and classification accuracy of this algorithm for rare attacks are improved, and the false alarm rate is reduced.

Given a linear input/output relationship involving unknown parameters, we propose a hybrid gradient descent algorithm to estimate the unknown parameters when the inputs and the outputs are hybrid signals. These signals are allowed to change continuously during ordinary time - or flow - and to change discretely - or jump - at isolated time instances. To estimate the unknown parameters, we develop a gradient descent algorithm that updates the estimates continuously during flows and instantaneously at jumps. The proposed hybrid gradient algorithm generalizes the existing gradient descent algorithms in the continuous-time and the discrete-time settings. Under a relaxed (hybrid) version of the well-known persistence of excitation condition, the proposed hybrid gradient descent algorithm estimates the parameters exponentially fast. An illustrative example is presented, showing the capabilities of our approach while classical algorithms fails to ensure the convergence.

To overcome image degradation under the conditions of haze, and fog, a method using conditional generative adversarial defogging algorithm based on polarization characteristic is proposed. Four original images with different polarization angles were obtained from the original image, and then the polarization images were characterized using Stokes vectors. From the relationship between Stokes vector and polarization image, each polarization image with different angles is input into the same network to extract features. At the same time, the enhanced network is used to extract the characteristics of the fog area in the polarization image, and the polarization information is extracted by layer jump connection, which is fused with the image features of different angles. By constructing the loss function and obtaining the optimal solution, the fog-free image is finally reconstructed. Experimental results show that a clear image can be reconstructed in fog situations by using conditional generative adversarial network-based on polarization characteristics, and the structural similarity is improved by about 10%, the peak signal to noise ratio is increased by about 0.5.

This paper designs an $L_{1}$ fault detection filter for positive semi-Markovian jump systems with disturbances based on event-triggering scheme. First, an event-triggering strategy is presented in terms of linear inequality and a fault detection filter is proposed. Then, an augmented fault detection system is constructed. Using stochastic copositive Lyapunov function and linear programming, the filter matrices are designed for guaranteeing the positivity and stochastic $L_{1}$ stability of the considered system. One illustrative example is provided to verify the effectiveness of the results in this paper.

This paper deals with h -gain controller synthesis of positive Markov jump systems (PMJSs) with asynchronous modes. Thanks to the hidden Markov model, the closed-loop systems are modeled as hidden Markov jump systems (HMJSs). The definitions of positivity, mean stability, and h -gain are introduced for HMJSs. A necessary and sufficient condition is derived to ensure that the HMJSs are positive and mean stable with h -gain γ that can be solvable by linear programming strategy. Two numerical examples are listed to show the effectiveness of our results.

The fruit fly optimization algorithm has drawn various attention to researchers and engineers, due to the simple theory and flexible frame. For solving the complex continuous optimization problems, an improved fruit fly algorithm based on vision scanning search and extensive learning mechanism is proposed in this paper. The vision scanning search strategy is used to scan the potential area by changing the search angle of swarm center. This strategy is utilized to guide the population to jump out the local trap. The extensive learning is using the knowledge of neighboring structure to increase the diversity of population for solving the non-separable issues. Furthermore, a new mutation strategy based on difference vector is proposed to improve the search efficiency of VLFOA. Testified in CEC 2017 benchmark problems, the results show that the VLFOA has a superior performance compared with the original FOA and the state of art variants of the FOA.

I am not convinced that the challenge for self-adaptive software is how to handle unexpected changes. I believe that the real challenge is how to characterise the uncertainty that the self-adaptive software has to handle. Once a proper characterisation of the uncertainty is in place, I think handling unanticipated changes and situations becomes a lot simpler. What is difficult is to know what range of uncertainty that self-adaptive software should be designed for. To use an autonomous driving example: should we expect people to jump in front of the vehicle or can we ignore that part of the problem because we are driving in a freeway and there are supposed to be no people around? The ultimate challenge then becomes providing guarantees and legally valid contracts and understanding where the responsibility lays in case of errors.

We propose a method to improve the performance of JPS for path planning on static grid map, including a fast neighbor pruning method and a symmetry-breaking heuristic function. First, we adopt a new and effective method to identify jump points quickly through bit operations at a single time. Second, we adopt a symmetry-breaking heuristic function to pruning redundant jump points by adding angle information and node sequence number in order to further speed up path planning search. We conducted simulation experiments on grid maps of different specifications and obstacle ratios to verify the effectiveness and feasibility of the proposed algorithm, comparing with JPS. The experimental results show that our improvement has more advantages on grid maps of different specifications and obstacle ratios, both in terms of search time and number of jump points.

For the maximum penetration capacity problem of distributed PV, a computational model with power equation, node voltage, line capacity and distributed PV capacity as the main constraints is established, and an improved firework algorithm is proposed to solve the above model. Based on the semi-positive cloud generator to improve the explosion spark generation method, a large number of displacement deviations with stable propensity and randomness are obtained, increasing the population diversity and speeding up the convergence; the dimensional difference strategy is used to improve the variance operator, which is conducive to the search for the late jump out local optimum. Apply the algorithm to the CIGER6 Task Force system and verify the validity of the algorithm. The results show that the proposed algorithm is able to solve the penetration level calculation problem of distributed PV.

The article is devoted to solving the problem of improving the quality of the classification of human movements according to accelerometric data of wearable monitoring devices. An algorithm for working with accelerometric data is described, which makes it possible to reduce the measured data arrays to a single dimension, as well as generate data for training a neural network for the selection and classification of movements. The choice of the optimal parameters of the neural network for the classification of movements was substantiated, which was based on a comparative assessment of the values of the percentage of errors in the classification of movements and the parameter of cross entropy. For training and testing of the neural network, the data of the results of experimental studies of the parameters of movements during the performance of squat and jump exercises were used by 20 subjects (10 women and 10 men at the age of 18 ±3.4 years). The average value of the probability of correct classification of movements based on the data for each of the subjects was 0.94, which allows concluding that there are prospects for using the developed algorithm in biotechnical systems and dynamic biometric systems.

Jumping Rope (JR) is a consecutive jump exercise and needs upper and lower body movements skills with the rope. JR requires an effective vertical take-off from ground and landing on ground to maintain successive coordination of whole body movements. In this study, we newly developed the index of real-time feedback to improve successive coordination movement using an inertial measurement unit (IMU) during JR movements. 86 elementary-school and junior high-school children participated in this study. They performed basic jump and backward jump for 30 seconds with a IMU on their back. After the measurement, maximum value, root mean square (RMS) value and autocorrelation coefficient from vertical acceleration were calculated to confirm the relationship between vertical acceleration and autocorrelation coefficient during JR. As the results, the maximum value of vertical acceleration had strongly negative correlations with autocorrelation coefficient in basic jump (r > -0.73, p -0.50, p -0.51, p -0.52, p < 0.01).

In this paper, based on research and analysis, some improvements are made compared to the traditional LDO structure, a fast load transient response performance of capacitorless LDO is designed based on SMIC180nmCMOS process. Theoretical analysis, design procedure and simulation process are also introduced. And the design process of key technologies and implementation methods are discussed in detail. The simulation results show that when the input voltage is 1.5V, the output voltage is 1.2V, the maximum load current is 300mA, and the load current is 1mA and 300mA. The maximum overshoot voltage is 51.1mV and the response time is only 1.58μs when the jump time is 1μs. The designed LDO has fast transient response performance under load and good comprehensive performance.

In this paper, we present a conceptional design framework for jumping robots for lunar exploration. A method is developed for spring placement and sizing that maximises the onboard elastic potential energy storage, which is essential for space missions. In the class of rhomboidal linkage robots, the analysis shows that torsion springs placed at the knee joints store more energy than the commonly used extension or linear compression springs for a given charging-actuator force; torsion springs store around 80% of the idealized maximum attainable elastic energy storage, compared to only 60% for an extension spring. A physical demonstrator is developed using torsion springs, and exhibits repeatable jumps with an average jump height of 1.1m under earth gravity, equivalent to 6.7m under lunar gravity. However, preliminary trials show the choice of substrate is shown to influence the jump height. This effect is predicted to be pronounced when jumping from lunar regolith.

In this paper we address the problem of rare-event simulation for heavy-tailed Levy processes with infinite activities. We propose a strongly efficient importance sampling algorithm that builds upon the sample path large deviations for heavy-tailed Levy processes, stick-breaking approximation of extrema of Levy processes, and the randomized debiasing Monte Carlo scheme. The proposed importance sampling algorithm can be applied to a broad class of Levy processes and exhibits significant improvements in efficiency when compared to crude Monte-Carlo method in our numerical experiments.

In this paper, robust model predictive control (RMPC) problem is investigated for a class of Markovian jump systems with unstable modes under polytopic uncertainties and hard constraints. The transition probability matrix and a mode-dependent control strategy in the framework of RMPC are co-designed. Moreover, in order to design a switching rule for the mean-square stability of the jump system, an off-line design scheme is first proposed to guarantee the on-line mode-dependent model predictive controller design. For the “on-line” part, a set of mode-dependent state feedback controllers is designed to minimize an upper bound of the worst-case infinite horizon cost function in terms of the solutions to a series of linear matrix inequalities. Finally, an simulation example regarding the economic system is implemented to verify the effectiveness of the proposed design scheme.

In this paper, a dynamic random hill-climbing mechanism is proposed to solve the problems that the ant lion optimization algorithm is easy to fall into the local optimal. The algorithm adjusts the position of ant lion by hill-climbing mechanism to enhance the ability to jump out of local optimal. In addition, the global search ability of the algorithm is improved by dynamically hill-climbing mechanism by balancing exploration and development ability. The performance of the proposed algorithm is evaluated on 7 benchmark function and compared with original ALO and an improved ant lion optimization algorithm. The experimental results prove that the proposed algorithm can effectively improve the performance of the algorithm.

In the application of multidisciplinary design optimization, intelligent optimization algorithm can better avoid the problems of “falling into local optimum” or “unexpected termination” caused by conventional numerical optimization methods. In this paper, an adaptive GASA algorithm is proposed, which combines the global parallel search ability of genetic algorithm with the probability jump characteristic of simulated annealing algorithm, and uses adaptive mechanism to improve the population operation of intelligent optimization algorithm. Finally, the proposed method is verified by using a calculation example in combination with the collaborative optimization strategy. The results show that the proposed method can effectively improve the optimization efficiency and the quality of the optimization results.

We apply a novel algorithm, Harris Hawks optimization (HHO), to the optimization problem of waveguide filters. In order to investigate the efficiency, particle swarm optimization(PSO), differential evolution(DE), and self-adaptive differential optimization(SaDE) are considered to optimize a fourth-order dual-mode waveguide filter as well. Unlike standard PSO, DE, and SaDE, HHO utilizes a hybrid updating strategy to balance the exploiting and exploration phase during the optimization. Therefore, HHO can jump out of a local minimum and converge to the globally optimal solution with a higher probability. The statistical comparison between different algorithms shows that HHO converges faster than the other algorithms. The optimization result also indicates that HHO obtains the best solution, which demonstrates that HHO has the potential to solve sophisticated optimization problems of various electromagnetism devices.

Traditional genetic algorithm is prone to precocious problem. An improved genetic algorithm based on improved ring is proposed. First, the parent generation is improved and optimized by the improved circle method, then the offspring are obtained by crossing, and the inferior individuals produced after crossing are reduced by keeping the excellent genes to the maximum extent in the process of crossing, and the convergence rate is improved. The population diversity of the algorithm is guaranteed by improved ring and by choosing crossover probability and the mutation probability. Finally, the optimal search path is obtained by selecting the parent and offspring. The simulation results show that compared with the traditional Hamilton trilateral exchange algorithm, the improved genetic algorithm is simple, fast, and it can jump out of local convergence and obtain the optimal solution. It is a more reasonable UAV route method to solve the optimal search problem.

Aiming at a class of nonlinear systems with parameter jump, a neural network-based multi-model adaptive control of nonlinear systems is proposed. An adaptive control model and a neural network-based control model are established respectively, and on this basis, a robust adaptive controller and a neural network-based nonlinear controller are designed, thereby constructing a neural network-based nonlinear system adaptive controller. The simulation results show that the proposed adaptive control strategy for nonlinear systems based on neural network has good control characteristics.

Two Wheeler’s are one of the most commonly used vehicles worldwide, and the helmet is the most critical gear/safety equipment. There is no proper system that validates whether all the users are equipped with a helmet while they are riding. The existing traffic surveillance and security systems are designed only to identify the vehicles that over speed and jump over the red lights. These systems use interceptors at highways to capture the violators and consume a lot of manpower and vehicles for patrolling. An efficient traffic surveillance system to monitor whether the motorcyclist and the pillion rider is wearing a helmet is missing. This paper proposes an efficient and reliable traffic surveillance system using edge computing and transfer learning that continuously monitors the two-Wheeler’s and sends alerts and information on the non-helmet riders to the nearest police vehicles. The edge computing is being applied at the input/entry-level (CCTV camera) and helps in reducing the delay of the entire system. The classification models are created by applying Transfer Learning on ResNet50, VGG16 architectures, and applied to real-time video footage from the CCTV camera to detect non-helmet riders. The proposed system gives an accuracy of 97.50%, reduces the manpower, and enables the police interceptors to work efficiently.

With the rapid development of the service robot industry, the realization of autonomous robot indoor navigation has become the focus of research and development institutions. Due to the low cost and high precision, ultra-wideband (UWB) positioning has become an ideal indoor positioning solution. However, under non-line-of-sight (NLOS) conditions, the positioning accuracy of UWB signals will be reduced, and the deviation of the positioning solution due to signal interference will cause the trajectory of the solution to be discontinuous and jump, which makes it impossible to complete continuous measurement. To solve this problem, the UWB/INS integrated navigation method is adopted. Field tests show that the adopted method can track the navigation continuous without jumping, which greatly improves the positioning accuracy.