Prediction-Based Human-Robot Collaboration in Assembly Tasks Using a Learning from Demonstration Model.

Most robots are programmed to carry out specific tasks routinely with minor variations. However, more and more applications from SMEs require robots work alongside their counterpart human workers. To smooth the collaboration task flow and improve the collaboration efficiency, a better way is to formulate the robot to surmise what kind of assistance a human coworker needs and naturally take the right action at the right time.

An Anti-UAV Long-Term Tracking Method with Hybrid Attention Mechanism and Hierarchical Discriminator.

To prevent unmanned aerial vehicles (UAVs) from threatening public security, anti-UAV object tracking has become a critical issue in industrial and military applications. However, tracking UAV objects stably is still a challenging issue because the scenarios are complicated and the targets are generally small. In this article, a novel long-term tracking architecture composed of a Siamese network and re-detection (SiamAD) is proposed to efficiently locate UAV targets in diverse surroundings.

A base-mediated aerobic oxidative synthesis of cyclopent-2-enol derivatives from doubly activated cyclopropanes and substituted acetonitriles.

We report here that polysubstituted cyclopent-2-enols can be constructed by the one-pot reaction of doubly activated cyclopropanes and α-EWG substituted acetonitriles under mild basic conditions a domino-ring-opening-cyclization/deacylation/oxidation sequence. Moreover, the synthetic applications of these cyclopent-2-enols have been demonstrated in the late-stage derivatization into functionalized cyclopentapyrimidin-4-ones and 2-hydroxy cyclopentanones with good yields.

WDA: An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method.

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components.

Novel synthesis of fused spiro piperidone-cyclopropanes from cyclopropyl amides and electron-deficient alkenes.

We report here that a series of bridged O,O-ketal fused spiro piperidone-cyclopropane derivatives 3 can be constructed with excellent yields and good diastereoselectivity by the one-pot reaction of 1-acylcyclopropanecarboxamides 1 with electron-deficient alkene 2a (EWG = CHO) via the domino process involving [4 + 2] annulation/intermolecular electrophilic addition/intramolecular cyclization. Furthermore, reactions of 1 with 2b/2c (EWG = CN, COOMe), leading to spiro piperidone-cyclopropane derivatives 4 or 5 by base catalyst selection, were also presented.

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds.

When performing fault diagnosis tasks on bearings, the change of any bearing's rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions.

A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals.

Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of their multilayer nonlinear mapping ability. This paper proposes a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN).