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The pulsed electro-acoustic (PEA) method is widely applied for space charge measurement in solid dielectrics. The signals, however, can be seriously distorted during transmission, especially in non-planar specimens. The aim of this work is to find an efficient algorithm to correctly recover the space charge profile for different types of specimens.

The distortion can be associated with both geometry and material (attenuation and dispersion). Hence the recovery algorithm consists of two parts respectively. The influences of geometries, causing the divergences of electric force and acoustic waveform, can be corrected by sets of factors. The attenuation and dispersion of the material can be suppressed based on the transfer function matrix in frequency domain, which could be obtained from calibration.

A general algorithm applicable to three kinds of specimens (single-layer, multi-layer and coaxial-geometry dielectrics) has been proposed. Compared with the other two algorithms in literature, the present one offers the most accurate solution while taking relatively shorter time. In addition, this algorithm is applied on signals measured from a planar LDPE sample and the results show that the new algorithm is fairly effective with excellent stability in a real system.

As one of the most accurate algorithms, the present one is theoretically one third quicker than the others. This algorithm would be helpful in applications calling for large calculations, i.e. 3-D imaging of space charge distribution in XLPE cable.

This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge.

A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed.

Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability.

This research can provide a theoretical and engineering basis for lane detection on unmanned robots.

Mobile robots with independent wheel control face challenges in steering precision, motion stability and robustness across various wheel and steering system types. This paper aims to propose a coordinated torque distribution control approach that compensates for tracking deviations using the longitudinal moment generated by active steering.

Building upon a two-degree-of-freedom robot model, an adaptive robust controller is used to compute the total longitudinal moment, while the robot actuator is regulated based on the difference between autonomous steering and the longitudinal moment. An adaptive robust control scheme is developed to achieve accurate and stable generation of the desired total moment value. Furthermore, quadratic programming is used for torque allocation, optimizing maneuverability and tracking precision by considering the robot’s dynamic model, tire load rate and maximum motor torque output.

Comparative evaluations with autonomous steering Ackermann speed control and the average torque method validate the superior performance of the proposed control strategy, demonstrating improved tracking accuracy and robot stability under diverse driving conditions.

When designing adaptive algorithms, using models with higher degrees of freedom can enhance accuracy. Furthermore, incorporating additional objective functions in moment distribution can be explored to enhance adaptability, particularly in extreme environments.

By combining this method with the path-tracking algorithm, the robot’s structural path-tracking capabilities and ability to navigate a variety of difficult terrains can be optimized and improved.