Sensorimotor self-learning model based on operant conditioning for two-wheeled robot

Traditional control methods of two-wheeled robot are usually model-based and require the robot’s precise mathematic model which is hard to get. A sensorimotor self-learning model named SMM TWR is presented in this paper to handle these problems. The model consists of seven elements: the discrete learning time set, the sensory state set, the motion set, the sensorimotor mapping, the state orientation unit, the learning mechanism and the model’s entropy. The learning mechanism for SMM TWR is designed based on the theory of operant conditioning (OC), and it adjusts the sensorimotor mapping at every learning step. This helps the robot to choose motions. The leaning direction of the mechanism is decided by the state orientation unit. Simulation results show that with the sensorimotor model designed, the robot is endowed the abilities of self-learning and self-organizing, and it can learn the skills to keep itself balance through interacting with the environment.     

Compensation of pressure enthalpy effects on temperature fields for throttling of high-pressure real gas

For the pressure enthalpy of high pressure pneumatics, the computational fluid dynamics (CFD) simulation based on ideal gas assumption fails to obtain the real temperature information. Therefore, we propose a method to compensate the pressure enthalpy of throttling for CFD simulation based on ideal gas assumption. Firstly, the pressure enthalpy is calculated for the pressure range of 0.101 to 30 MPa and the temperature range of 190 to 298 K based on Soave-Redlich-Kwong (S-R-K) equation. Then, a polynomial fitting equation is applied to practical application in the above mentioned range. The basic idea of the compensation method is to convert the pressure enthalpy difference between inlet air and nodes into the compensation temperature. In the above temperature and pressure range, the compensated temperature is close to the real one, and the relative temperature drop error is below 10%. This error is mainly caused by the velocity difference of the orifice between the real and ideal gas models. Finally, this compensation method performs an icing analysis for practical high pressure slide pilot valve.     

Approximate method for reliability assessment of complex phased mission systems

Phased-mission systems (PMSs) have wide applications in engineering practices, such as manmade satellites. Certain critical parts in the system, such as cold standby, hot standby and functional standby, are designed in redundancy architecture to achieve high reliability performance. State-space models such as Markov process have been used extensively in previous studies for reliability evaluation of PMSs with dynamic behaviors. The most popular way to deal with the dynamic behaviors is Markov process, but it is well known that Markov process is limited to exponential distribution. In practice, however, the lifetime of most machinery products can follow non-exponential distributions like the Weibull distribution which cannot be handled by the Markov process. In order to solve this kind of problem, we present a semi-Markov model combined with an approximation algorithm to analyze PMS reliability subjected to non-exponential failures. Furthermore, the accuracy of the approximation algorithm is investigated by comparing to an accurate solution, and a typical PMS (attitude and orbit control system) is analyzed to demonstrate the implementation of the method.     

Hybrid process of fabricating high-quality micro wine-glass fused silica resonators

A new hybrid method, which combines improved glass-blown technology with wet etching, is reported to fabricate micro wine-glass resonators with high-quality fused silica. The optimum placement is compared to achieve the resonators with good shell shape. The typical shell diameter is about 4mm and its thickness covers from dozens to hundreds of micrometers. The etching rates in corrosion solutions with different ratios and at different thicknesses of hemispherical shells are studied. We also conclude how to precisely control the thickness. The corrosion solutions with different ratios of HF solution to NH₄F solution make the spherical shells rougher in different degrees. The best roughness is 0.581 nm in the 1: 8 ratio corrosion solution while the original roughness is 0.537 nm. This fact shows that the resonator remains atomically smooth surface. Based on the glassblowing spherical fused silica structure, the thickness of the resonator is effectively controlled by buffered oxide etch (BOE) technology according to the measured etching rate. The measured resonant frequency of the hemispherical shell at ambient pressure and room temperature is 1.75 kHz of rocking mode which is close to the simulated frequency. Using such a low-cost hybrid approach, we can fabricate high-quality microscale resonators in batch.     

Lattice-based group signature with verifier-local revocation

Among several post quantum primitives proposed in the past few decades, lattice-based cryptography is considered as the most promising one, due to its underlying rich combinatorial structure, and the worst-case to average-case reductions. The first lattice-based group signature scheme with verifier-local revocation(VLR) is treated as the first quantum-resistant scheme supported member revocation, and was put forward by Langlois et al. This VLR group signature(VLR-GS) has group public key size of O(nm log N log q), and a signature size of O(tm log N log q log β). Nguyen et al. constructed a simple efficient group signature from lattice, with significant advantages in bit-size of both the group public key and the signature. Based on their work, we present a VLR-GS scheme with group public key size of O(nm log q) and signature size of O(tm log q). Our group signature has notable advantages: support of membership revocation, and short in both the public key size and the signature size.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.     

Long-term tracking based on spatio-temporal context

Aiming at the problem that the fast tracking algorithm using spatio-temporal context (STC) will inevitably lead to drift and even lose the target in long-term tracking, a new algorithm based on spatio-temporal context that integrates long-term tracking with detecting is proposed in this paper. We track the target by the fast tracking algorithm, and the cascaded search strategy is introduced to the detecting part to relocate the target if the fast tracking fails. To a large extent, the proposed algorithm effectively improves the accuracy and stability of long-term tracking. Extensive experimental results on benchmark datasets show that the proposed algorithm can accurately track and relocate the target though the target is partially or completely occluded or reappears after being out of the scene.     

Effect of orthogonal stiffeners on the stability of axially compressed steel jacking pipe

Load conditions for steel pipe-jacking are complex during the construction stage. The stability of steel jacking pipe has been an increasingly important problem as jacking forces, pipe diameters and jacking distances increase. However, there are no standards for pipe reinforcement, for prevention of buckling, or for remedying pipe that buckles when being jacked axially. Past experience suggests that stiffeners can effectively reinforce the structure. This study analyzes the effect of different stiffeners on the stability of steel jacking pipe under axial compression using finite element analysis. The results suggest that the stability of steel jacking pipe can be significantly improved by using orthogonal stiffeners, in terms of engineering costs and construction space inside the pipe. Based on current engineering practice, the application of orthogonal stiffeners is discussed. This study provides a useful reference for the design and construction of steel jacking pipe.     

Calculation of strain amplification matrix for strain invariant failure theory based on representative volume element models with periodical boundary condition

Strain invariant failure theory (SIFT) is a micro-mechanics-based failure theory for multi-scale failure analysis of composite materials originally proposed by Gosse and Christensen. In this paper, the approach for obtaining strain amplification matrix which is a key step for the execution of SIFT is improved by adopting representative volume element (RVE) finite element models considering periodical boundary condition, based on which more actual deformation mode is reflected. The deformation modes and strain data at the characteristic points of the centroid cell of multi-cell RVE model are analyzed and taken as a reference. It can be concluded that more reasonable deformation mode and relationship between the micro-mechanical and macro-mechanical strain states are obtained by employing the new model. Finally, numerical examples are provided to illustrate the determination of strain amplification factors within the RVEs considering periodical boundary condition at the characteristic points.