Online stiffness estimation for robotic tasks with force observers

In this paper we propose an online stiffness estimation technique for robotic tasks based only on force data, therefore, not requiring contact position information. This allows estimations to be obtained in robotic tasks involving interactions with unstructured and unknown environments where geometrical data is unavailable or unreliable. Our technique – the Candidate Observer Based Algorithm (COBA) – uses two force observers, configured with different candidate stiffnesses, to estimate online the actual target object stiffness. COBA is embedded in a force control architecture with computed torque in the task space. The theoretical presentation of the algorithm, as well as simulation tests and experimental results with a lightweight robot arm are also presented.     

Fault diagnosis of power transformer based on support vector machine with genetic algorithm

Diagnosis of potential faults concealed inside power transformers is the key of ensuring stable electrical power supply to consumers. Support vector machine (SVM) is a new machine learning method based on the statistical learning theory, which is a powerful tool for solving the problem with small sampling, nonlinearity and high dimension. The selection of SVM parameters has an important influence on the classification accuracy of SVM. However, it is very difficult to select appropriate SVM parameters. In this study, support vector machine with genetic algorithm (SVMG) is applied to fault diagnosis of a power transformer, in which genetic algorithm (GA) is used to select appropriate free parameters of SVM. The experimental data from several electric power companies in China are used to illustrate the performance of the proposed SVMG model. The experimental results indicate that the SVMG method can achieve higher diagnostic accuracy than IEC three ratios, normal SVM classifier and artificial neural network.     

Dynamic neural network method-based improved PSO and BR algorithms for transient probabilistic analysis of flexible mechanism

To improve the computing efficiency and precision of transient probabilistic analysis of flexible mechanism, dynamic neural network method (DNNM)-based improved particle swarm optimization (PSO)/Bayesian regularization (BR) (called as PSO/BR-DNNM) is proposed based on the developed DNNM with the integration of extremum response surface method (ERSM) and artificial neural network (ANN). The mathematical model of DNNM is established based on ANN on the foundation of investigating ERSM. Aiming at the high nonlinearity and strong coupling characteristics of limit state function of flexible mechanism, accurate weights and thresholds of PSO/BR-DNNM function are discussed by searching initial weights and thresholds based on the improved PSO and training final weights and thresholds by the BR-based training performance function. The probabilistic analysis of two-link flexible robot manipulator (TFRM) was investigated with the proposed method. Reliability degree, distribution characteristics and major factors (section sizes of link-2) of TFRM are obtained, which provides a useful reference for a more effective TFRM design. Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of flexible mechanism and thereby also enriches the theory and method of mechanical reliability design.     

Performance of dual-throat supersonic separation device with porous wall structure

In this paper, a dual-throat supersonic separation device with porous wall has been proposed to solve the starting problem of supersonic separator, and the feasibility of the proposed device has been tested numerically and experimentally. Its flow characteristics have been investigated and the effect of some important parameters includ-ing nozzle pressure ratio （RNP）, inlet temperature and swirl intensity were examined. In the device, the supersonic flow state and strong centrifugal acceleration of 240000g can be obtained, which are necessary for the condensation and separation of water vapor. The supersonic region in the device enlarged and the shock wave shifted downstream along with the increasing RNP. The separation performance was improved with the increasing RNP and the inlet temperature. The best separation performance in this study was obtained withΔTd=28 K.     

Measurement of the smoke layer interface in fires

In the normal case, as we all know, when there is a fire, smoke will arise along with the fire. The height of the smoke layer interface is an important parameter to assess a fire hazard. A smoke generator was used in a hot smoke test. The test verified the feasibility of two methods of measuring the smoke layer interface: ocular estimate and thermocouple measurement. The findings show that the ocular estimate is less accurate because subjective factors of the observer lead to changes of the data while the thermocouple measurement is more accurate. It has a delay in measurement, so revision of the delay should be taken into account and the two methods can be complementary.     

Dual-quaternion based fault-tolerant control for spacecraft formation flying with finite-time convergence

Study results of developing control system for spacecraft formation proximity operations between a target and a chaser are presented. In particular, a coupled model using dual quaternion is employed to describe the proximity problem of spacecraft formation, and a nonlinear adaptive fault-tolerant feedback control law is developed to enable the chaser spacecraft to track the position and attitude of the target even though its actuator occurs fault. Multiple-task capability of the proposed control system is further demonstrated in the presence of disturbances and parametric uncertainties as well. In addition, the practical finite-time stability feature of the closed-loop system is guaranteed theoretically under the designed control law. Numerical simulation of the proposed method is presented to demonstrate the advantages with respect to interference suppression, fast tracking, fault tolerant and practical finite-time stability.     

Flexible distributed heterogeneous computing in traffic noise mapping

In China, fast city rebuilding poses the challenge of frequent refresh cycle of urban traffic noise mapping. Computational complexity and lack of resources are the primary bottleneck in traffic noise mapping. In this paper, we present a flexible distributed heterogeneous computing method based on GPU-CPU cooperation, which reduces the overhead, improves the efficiency of parallel computing and consistently generates good quality results for traffic noise mapping. A genetic algorithm based large-scale task partition algorithm is employed to solve load balancing problem in distributed noise mapping calculation. The methodology is evaluated by an example, whose results show that the proposed task partition method can significantly improve running efficiency. Parallel efficiency increases from 54% to 78%. In addition, test speed is further improved by 21% with the GPU-CPU collaborative computing, even with only low-end type GPUs.     

Damage development of a woven SiCf/SiC composite during multi-step fatigue tests at room temperature

The monotonic tensile and multi-step fatigue tests of 2D woven SiC_f/SiC composite were performed to explore the damage development, respectively. The acoustic emission-based technique was used to analyze the damage state and select the peak stresses for fatigue tests. The damage evolution after specific mechanical tests was characterized by optical microscopy and scanning electron microscopy. Cracks are prone to occur in the vicinity of flaws and boundaries of different matrix components under relatively low fatigue stress. The cyclic fatigue stress can do much harm to the interfaces and facilitate the interfacial debonding. The damage characteristics of five types of cracking, fiber breakage and pull-out, and interfacial debonding of the composite after specific mechanical tests are concluded and discussed in detail, which can offer help for deeper analysis of the oxidation mechanism in service and more reasonable design of SiC_f/SiC composite.     

Are electric self-balancing scooters safe in vehicle crash accidents?

With the pressing demand of environmentally friendly personal transportation vehicles, mobility scooters become more and more popular for the short-distance transportation. Similar to pedestrians and bicyclists, scooter riders are vulnerable road users and are expected to receive severe injuries during traffic accidents. In this research, a MADYMO model of vehicle–scooter crash scenarios is numerically set up. The model of the vehicle with the scenario is validated in pedestrian–vehicle accident investigation with previous literatures in terms of throwing distance and HIC₁₅ value. HIC₁₅ values gained at systematic parametric studies. Injury information from various vehicle crashing speeds (i.e. from 10 m/s to 24 m/s), angles (i.e. from 0 to 360°), scooter's speeds (i.e. from 0 m/s to 4 m/s), contact positions (i.e. left, middle and right bumper positions) are extracted, analyzed and then compared with those from widely studied pedestrian–vehicle and bicycle–vehicle accidents. Results show that the ESS provides better impact protection for the riders. Riding ESS would not increase the risk higher than walking at the same impact conditions in terms of head injury. The responsible reasons should be the smaller friction coefficient between the wheel-road than the heel-road interactions, different body gestures leading to different contact positions, forces and timing. Results may shed lights upon the future research of mobility scooter safety analysis and also the safety design guidance for the scooters.     

A modified ant colony system for finding the expected shortest path in networks with variable arc lengths and probabilistic nodes

The problem of finding the expected shortest path in stochastic networks, where the presence of each node is probabilistic and the arc lengths are random variables, have numerous applications, especially in communication networks. The problem being NP-hard we use an ant colony system (ACS) to propose a metaheuristic algorithm for finding the expected shortest path. A new local heuristic is formulated for the proposed algorithm to consider the probabilistic nodes. The arc lengths are randomly generated based on the arc length distribution functions. Examples are worked out to illustrate the applicability of the proposed approach.