Flywheel micro-vibration characters of a high resolution optical satellite

According to the pictures of a sub-meter resolution optical satellite which were acquired on orbit, there is a phenomenon of jitter in the process of taking pictures. As the main attitude control component of the satellite, the flywheel will produce the disturbance in its normal work, which has great influence on the high resolution optical satellite. This paper has respectively researched the flywheel components’ disturbance mechanism from four parts, including uneven rotator, rotator friction, bearing disturbance, foundation loose, and builds the mathematical model of disturbance to analyze the characteristics of disturbance. We have simulated and tested the flywheel components’ disturbance. The disturbance force of flywheel components is 2 N magnitude and the torque of disturbance is 1.5 N·m magnitude in time domain. The flywheel's infrastructure should be more inflexible especially around 90-100 Hz. For this target high resolution optical satellite, there should be effective damping measures around 48.6 Hz, 190.4 Hz and 285.4 Hz to decrease the flywheel disturbance to guarantee the high precision of the satellite. The result would offer guidance for system optimization design and vibration isolation compensation of the later type of improved satellite or other same type of satellites

Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects

We present a novel freehand sketch beautification method, which takes as input a freely drawn sketch of a man-made object and automatically beautifies it both geometrically and structurally. Beautifying a sketch is challenging because of its highly abstract and heavily diverse drawing manner. Existing methods are usually confined to the distribution of their limited training samples and thus cannot beautify freely drawn sketches with rich variations. To address this challenge, we adopt a divide-and-combine strategy. Specifically, we first parse an input sketch into semantic components, beautify individual components by a learned part beautification module based on part-level implicit manifolds, and then reassemble the beautified components through a structure beautification module. With this strategy, our method can go beyond the training samples and handle novel freehand sketches. We demonstrate the effectiveness of our system with extensive experiments and a perceptive study.Comment: 13 figure

Optimal design of the main support structure of space camera aiming at the RMS value of random response

To explore the optimal design method for main support structure of micro satellite, this paper proposed a method targeting the random acceleration response RMS value of the space camera installation position when design the main support structure of LQ-video satellite in Jilin-1 group satellites. Camera main support structure optimization mathematical model was established, and the thickness and flexible beam position of the flexible beam support structure has been optimized in the establishment of the optimization mathematical model. When the flexible beam thickness is 2.5 mm, and the distance between it and the support structure mounting surface is 94.5 mm, the camera installation point acceleration response root mean square (RMS) value is minimal. Engineering analysis showed that the maximal random response RMS of the camera installation point is 19.6 grms and the maximal relative magnification is 0.93. The camera mechanics test showed that the maximal relative error of finite element analysis and experimental measurements is 4.0 % and the maximal relative magnification of the response is 1.2 which is less than the overall index 1.5. It proved that the optimization method is effective and feasible

Differences in the link between social trait judgment and socio-emotional experience in neurotypical and autistic individuals

Neurotypical (NT) individuals and individuals with autism spectrum disorder (ASD) make different judgments of social traits from others\u27 faces; they also exhibit different social emotional responses in social interactions. A common hypothesis is that the differences in face perception in ASD compared with NT is related to distinct social behaviors. To test this hypothesis, we combined a face trait judgment task with a novel interpersonal transgression task that induces measures social emotions and behaviors. ASD and neurotypical participants viewed a large set of naturalistic facial stimuli while judging them on a comprehensive set of social traits (e.g., warm, charismatic, critical). They also completed an interpersonal transgression task where their responsibility in causing an unpleasant outcome to a social partner was manipulated. The purpose of the latter task was to measure participants\u27 emotional (e.g., guilt) and behavioral (e.g., compensation) responses to interpersonal transgression. We found that, compared with neurotypical participants, ASD participants\u27 self-reported guilt and compensation tendency was less sensitive to our responsibility manipulation. Importantly, ASD participants and neurotypical participants showed distinct associations between self-reported guilt and judgments of criticalness from others\u27 faces. These findings reveal a novel link between perception of social traits and social emotional responses in ASD

Terminology-aware Medical Dialogue Generation

Medical dialogue generation aims to generate responses according to a history of dialogue turns between doctors and patients. Unlike open-domain dialogue generation, this requires background knowledge specific to the medical domain. Existing generative frameworks for medical dialogue generation fall short of incorporating domain-specific knowledge, especially with regard to medical terminology. In this paper, we propose a novel framework to improve medical dialogue generation by considering features centered on domain-specific terminology. We leverage an attention mechanism to incorporate terminologically centred features, and fill in the semantic gap between medical background knowledge and common utterances by enforcing language models to learn terminology representations with an auxiliary terminology recognition task. Experimental results demonstrate the effectiveness of our approach, in which our proposed framework outperforms SOTA language models. Additionally, we provide a new dataset with medical terminology annotations to support the research on medical dialogue generation. Our dataset and code are available at https://github.com/tangg555/meddialog.Comment: Submitted to ICASSP 202

Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation

Incorporating external graph knowledge into neural chatbot models has been proven effective for enhancing dialogue generation. However, in conventional graph neural networks (GNNs), message passing on a graph is independent from text, resulting in the graph representation hidden space differing from that of the text. This training regime of existing models therefore leads to a semantic gap between graph knowledge and text. In this study, we propose a novel framework for knowledge graph enhanced dialogue generation. We dynamically construct a multi-hop knowledge graph with pseudo nodes to involve the language model in feature aggregation within the graph at all steps. To avoid the semantic biases caused by learning on vanilla subgraphs, the proposed framework applies hierarchical graph attention to aggregate graph features on pseudo nodes and then attains a global feature. Therefore, the framework can better utilise the heterogeneous features from both the post and external graph knowledge. Extensive experiments demonstrate that our framework outperforms state-of-the-art (SOTA) baselines on dialogue generation. Further analysis also shows that our representation learning framework can fill the semantic gap by coagulating representations of both text and graph knowledge. Moreover, the language model also learns how to better select knowledge triples for a more informative response via exploiting subgraph patterns within our feature aggregation process. Our code and resources are available at https://github.com/tangg555/SaBART.Comment: Accepted by ACL 202