Preliminary Exploration of Areal Density of Angular Momentum for Spiral Galaxies

The specific angular momenta ($j_t$) of stars, baryons as a whole and dark matter haloes contain clues of vital importance about how galaxies form and evolve. Using a sample of 70 spiral galaxies, we perform a preliminary analysis of $j_t$, and introduce a new quantity, e.g., areal density of angular momentum (ADAM) ($j_t~M_\star/4R_d^2$) as an indication for the existence of jet in spiral galaxies. The percentage of spiral galaxies having jet(s) shows strong correlation with the ADAM, although the present sample is incomplete.Comment: 7 pages, 2 figure

X-ray Astronomy in the Laboratory with a Miniature Compact Object Produced by Laser-Driven Implosion

Laboratory spectroscopy of non-thermal equilibrium plasmas photoionized by intense radiation is a key to understanding compact objects, such as black holes, based on astronomical observations. This paper describes an experiment to study photoionizing plasmas in laboratory under well-defined and genuine conditions. Photoionized plasma is here generated using a 0.5-keV Planckian x-ray source created by means of a laser-driven implosion. The measured x-ray spectrum from the photoionized silicon plasma resembles those observed from the binary stars Cygnus X-3 and Vela X-1 with the Chandra x-ray satellite. This demonstrates that an extreme radiation field was produced in the laboratory, however, the theoretical interpretation of the laboratory spectrum significantly contradicts the generally accepted explanations in x-ray astronomy. This model experiment offers a novel test bed for validation and verification of computational codes used in x-ray astronomy.Comment: 5 pages, 4 figures are included. This is the original submitted version of the manuscript to be published in Nature Physic

Three-Dimensional Force Prediction of a Flexible Tactile Sensor Based on Radial Basis Function Neural Networks

A flexible tactile sensor array with 6×6 N-type sensitive elements made of conductive rubber is presented in this paper. The property and principle of the tactile sensor are analyzed in detail. Based on the piezoresistivity of conductive rubber, this paper takes full advantage of the nonlinear approximation ability of the radial basis function neural network (RBFNN) method to approach the high-dimensional mapping relation between the resistance values of the N-type sensitive element and the three-dimensional (3D) force and to accomplish the accurate prediction of the magnitude of 3D force loaded on the sensor. In the prediction process, the k-means algorithm and recursive least square (RLS) method are used to optimize the RBFNN, and the k-fold cross-validation method is conducted to build the training set and testing set to improve the prediction precision of the 3D force. The optimized RBFNN with different spreads is used to verify its influence on the performance of 3D force prediction, and the results indicate that the spread value plays a very important role in the prediction process. Then, sliding window technology is introduced to build the RBFNN model. Experimental results show that setting the size of the sliding window appropriately can effectively reduce the prediction error of the 3D force exerted on the sensor and improve the performance of the RBFNN predictor, which means that the sliding window technology is very feasible and valid in 3D force prediction for the flexible tactile sensor. All of the results indicate that the optimized RBFNN with high robustness can be well applied to the 3D force prediction research of the flexible tactile sensor

Decoupling Research of a Novel Three-Dimensional Force Flexible Tactile Sensor Based on an Improved BP Algorithm

Decoupling research on flexible tactile sensors play a very important role in the intelligent robot skin and tactile-sensing fields. In this paper, an efficient machine learning method based on the improved back-propagation (BP) algorithm is proposed to decouple the mapping relationship between the resistances of force-sensitive conductive pillars and three-dimensional forces for the 6 × 6 novel flexible tactile sensor array. Tactile-sensing principles and numerical experiments are analyzed. The tactile sensor array model accomplishes the decomposition of the force components by its delicate structure, and avoids direct interference among the electrodes of the sensor array. The force components loaded on the tactile sensor are decoupled with a very high precision from the resistance signal by the improved BP algorithm. The decoupling results show that the k-cross validation (k-CV) algorithm is a highly effective method to improve the decoupling precision of force components for the novel tactile sensor. The large dataset with the k-CV method obtains a better decoupling accuracy of the force components than the small dataset. All of the decoupling results are fairly good, and they indicate that the improved BP model with a strong non-linear approaching ability has an efficient and valid performance in decoupling force components for the tactile sensor

Structure Analysis and Decoupling Research of a Novel Flexible Tactile Sensor Array

This paper presents a novel flexible tactile sensor structure and proposes an efficient decoupling algorithm for the tactile sensor. Firstly, structure of the sensor model is introduced, and the sensing mechanism of the sensor array based on force-sensitive conductive rubber is analyzed. Then the mapping relation between the resistances of conductive pillars and the three-dimensional force is deduced. After that, the force applied on the tactile sensor is decoupled from the resistance information by the improved Back Propagation Neural Network (BPNN) algorithm with the number of hidden layer nodes optimized. The flexible tactile sensor model achieves the decomposition of the three-dimensional information from the structure with its unique design, avoids the direct interference between electrodes of the sensor array, reduces the structural complexity and the nonlinear degree, improves the decoupling accuracy, and accelerates the decoupling rate

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm

Recognizing different contact patterns imposed on tactile sensors plays a very important role in human–machine interaction. In this paper, a flexible tactile sensor with great dynamic response characteristics is designed and manufactured based on polyvinylidene fluoride (PVDF) material. Four contact patterns (stroking, patting, kneading, and scratching) are applied to the tactile sensor, and time sequence data of the four contact patterns are collected. After that, a fusion model based on the convolutional neural network (CNN) and the long-short term memory (LSTM) neural network named CNN-LSTM is constructed. It is used to classify and recognize the four contact patterns loaded on the tactile sensor, and the recognition accuracies of the four patterns are 99.60%, 99.67%, 99.07%, and 99.40%, respectively. At last, a CNN model and a random forest (RF) algorithm model are constructed to recognize the four contact patterns based on the same dataset as those for the CNN-LSTM model. The average accuracies of the four contact patterns based on the CNN-LSTM, the CNN, and the RF algorithm are 99.43%, 96.67%, and 91.39%, respectively. All of the experimental results indicate that the CNN-LSTM constructed in this paper has very efficient performance in recognizing and classifying the contact patterns for the flexible tactile sensor

Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array

Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human–machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 object types, based on the tactile time sequence signals collected by the novel sensor array; the average accuracies of hardness and type recognition are 100% and 99.7%, respectively. To further verify the classification ability of the ResNet model for the tactile feature information detected by the sensor array, the Multilayer Perceptron (MLP), LeNet, Multi-Channel Deep Convolutional Neural Network (MCDCNN), and ENCODER models are built based on the same dataset used for the ResNet model. The average recognition accuracies of the 4hardness categories, based on those four models, are 93.6%, 98.3%, 93.3%, and 98.1%. Meanwhile, the average recognition accuracies of the 12 object types, based on the four models, are 94.7%, 98.9%, 85.0%, and 96.4%. All of the results demonstrate that the novel porous graphene tactile sensor array has excellent perceptual performance and the ResNet model can very effectively and precisely complete the hardness and type recognition of objects for the flexible tactile sensor array

Hongqing Pan,

Abstract: Research on tactile sensors to enhance their flexibility and ability of multi-dimensional information detection is a key issue to develop humanoid robots. In view of that the tactile sensor is often affected by noise, this paper adds different white Gaussian noises (WGN) into the ideal model of flexible tactile sensors based on conductive rubber purposely, then improves the standard radial basis function neural network (RNFNN) to deal with the noises. The modified RBFNN is applied to approximate and decouple the mapping relationship between row-column resistance with WGNs and three-dimensional deformation. Numerical experiments demonstrate that the decoupling result of the deformation for the sensor is quite good. The results show that the improved RBFNN which doesn’t rely on the mathematical model of the system has good anti-noise ability and robustness. Copyright © 2014 IFSA Publishing, S. L

Decoupling Research on Flexible Tactile Sensors Interfered by White Gaussian Noise Using Improved Radical Basis Function Neural Network

Research on tactile sensors to enhance their flexibility and ability of multi- dimensional information detection is a key issue to develop humanoid robots. In view of that the tactile sensor is often affected by noise, this paper adds different white Gaussian noises (WGN) into the ideal model of flexible tactile sensors based on conductive rubber purposely, then improves the standard radial basis function neural network (RNFNN) to deal with the noises. The modified RBFNN is applied to approximate and decouple the mapping relationship between row-column resistance with WGNs and three-dimensional deformation. Numerical experiments demonstrate that the decoupling result of the deformation for the sensor is quite good. The results show that the improved RBFNN which doesn’t rely on the mathematical model of the system has good anti-noise ability and robustness