PourIt!: Weakly-supervised Liquid Perception from a Single Image for Visual Closed-Loop Robotic Pouring

Liquid perception is critical for robotic pouring tasks. It usually requires the robust visual detection of flowing liquid. However, while recent works have shown promising results in liquid perception, they typically require labeled data for model training, a process that is both time-consuming and reliant on human labor. To this end, this paper proposes a simple yet effective framework PourIt!, to serve as a tool for robotic pouring tasks. We design a simple data collection pipeline that only needs image-level labels to reduce the reliance on tedious pixel-wise annotations. Then, a binary classification model is trained to generate Class Activation Map (CAM) that focuses on the visual difference between these two kinds of collected data, i.e., the existence of liquid drop or not. We also devise a feature contrast strategy to improve the quality of the CAM, thus entirely and tightly covering the actual liquid regions. Then, the container pose is further utilized to facilitate the 3D point cloud recovery of the detected liquid region. Finally, the liquid-to-container distance is calculated for visual closed-loop control of the physical robot. To validate the effectiveness of our proposed method, we also contribute a novel dataset for our task and name it PourIt! dataset. Extensive results on this dataset and physical Franka robot have shown the utility and effectiveness of our method in the robotic pouring tasks. Our dataset, code and pre-trained models will be available on the project page.Comment: ICCV202

An approach to calculate radiation noise of gear system

A rigid-flexible coupling multi-body dynamic model which contains the structure system and transmission system of gear device is developed taking account of the internal excitations such as the time-varying mesh stiffness, tooth backlash and bearing stiffness and the external torque and speed excitation. Then the dynamic meshing forces of gear pairs and bearing reaction forces are calculated based on the dynamic theory of multi-body system. Afterwards, a vibro-acoustic coupling model of the gear system is established by taking the frequency histories of bearing reaction forces as the boundary conditions, and then the surface sound pressure of gearbox and the radiation noise of outer sound field are calculated. In fact, the proposed model would provide a quicker approach to analyze the radiation noise of the gear system during the design phase. Finally, the radiation noise experimental study is performed on the experimental prototype to verify the rationality of the analysis. The comparison analysis shows that computational results are in good agreement with the data of experiment test

Structure-Preservation Model Aggregation for Two-Stage Inverters Based Large-Scale Photovoltaic System

With the increasing penetration level of large-scale photovoltaic (PV) generator connected to the grid, an accurate simulation model is required for the dynamic analysis of the PV system. However, the detailed electromagnetic simulation of the large-scale system is complex and the dynamic response capability is estimated with obstacle caused by large computational burdens. Therefore, a precise dynamic aggregated model is indispensable for the displacement of the large-scale PV system. The structure-preservation based aggregated model with comprehensive equivalent parameters for large-scale PV system is proposed in this paper. A complete two-stage PV system model is established to analyze the dynamics of the system. Then, the aggregation method is obtained by comparing the dynamic equations of the detailed model with the aggregated model, which is based on the energy relationship in the PV system. Furthermore, four different case studies are considered including the aggregation of identical and different ten parallel-connected PV units both under the same irradiance condition, and the aggregation of different ten parallel-connected PV units under different irradiance and weak grid scenarios, where the aggregation models are obtained through the proposed equivalent modeling method. Finally, the effectiveness of the proposed aggregation method is verified by the simulation results from PSCAD/EMTDC platform, and the consistency between the aggregated model and the detailed model is confirmed under different disturbances of irradiance variation, and continuous symmetric and asymmetric grid faults.Published versio

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.Comment: Accepted by IEEE Transaction on Visualization and Computer Graphics (TVCG

Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real sensation) and somatosensory attentional orientation (SAO) provides a way to decode covert somatic attention. Subjects were instructed to sense the tactile stimulation, which was applied to the left (SS-L) or the right (SS-R) wrist. The BCI system was calibrated with the sensation data and then applied for online SAO decoding. Results: Both SS and SAO showed oscillatory activation concentrated on the contralateral somatosensory hemisphere. Offline analysis showed that the proposed calibration method led to greater accuracy than the traditional calibration method based on SAO only. This is confirmed by online experiments, where the online accuracy on 15 subjects was 78.8±13.1%, with 12 subjects >70% and 4 subject >90%. Conclusion: By integrating the stimulus-induced oscillatory dynamics from sensory cortex, covert somatosensory attention can be reliably decoded by a BCI system calibrated with tactile sensation. Significance: Indeed, real tactile sensation is more consistent during calibration than SAO. This brain-computer interfacing approach may find application for stroke and completely locked-in patients with preserved somatic sensation.University Starter Grant of the University of Waterloo (No. 203859) National Natural Science Foundation of China (Grant No. 51620105002