Author name cluster

Xi Xu

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6 papers

2 author rows

IROS Conference 2025 Conference Paper

SMA-TENG Actuator with Tactile Sensing Capability

Yiping Zhang
Zihe Liu
Xi Xu
Jiaqi Jin
Boan Yang
Li Wen
Ziyu Ren

Shape memory alloy (SMA) is widely employed in developing actuators. However, the lack of sensing capabilities limits its application. This study presents a sensing-actuation integrated device based on SMA and triboelectric nanogenerator (TENG), achieving tactile sensing while maintaining the actuation performance. The proposed core-shell structure not only repurposes the SMA spring as a key component of actuation and sensing, but also effectively isolates the actuation current to prevent interference with the sensing signal. The aerogel-modified silicone composite layer is applied to the SMA to reduce temperature rise by 30. 56%, ensuring the sensing performance. With a rapid response time of less than 31 ms and stable sensing performance exceeding 2000 cycles, the SMA-TENG actuator reliably detects dynamically varying forces and bending. Additionally, it generates a maximum actuation force of 3. 21 N, which represents a 12. 2% increase compared to a standard SMA spring, due to the pre-stress introduced by the composite layer. Moreover, it can actuate a displacement of 7. 7 cm and exhibiting a power density of 7. 15 × 10 3 W/m 3 (at 0. 84 V, 6 A). Finally, we validate its haptic sensing capability during actuation, demonstrating its potential towards interactive robotic systems.

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JBHI Journal 2024 Journal Article

A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection

Wenxin Wang
Zhuo-Xu Cui
Guanxun Cheng
Chentao Cao
Xi Xu
Ziwei Liu
Haifeng Wang
Yulong Qi

Accuratedetection and segmentation of brain tumors is critical for medical diagnosis. However, current supervised learning methods require extensively annotated images and the state-of-the-art generative models used in unsupervised methods often have limitations in covering the whole data distribution. In this paper, we propose a novel framework T wo- S tage G enerative M odel (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and V ariance E xploding stochastic differential equation using j oint p robability (VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is trained on unpaired data to generate abnormal images from healthy images as data prior. Then VE-JP is implemented to reconstruct healthy images using synthetic paired abnormal images as a guide, which alters only pathological regions but not regions of healthy. Notably, our method directly learned the joint probability distribution for conditional generation. The residual between input and reconstructed images suggests the abnormalities and a thresholding method is subsequently applied to obtain segmentation results. Furthermore, the multimodal results are weighted with different weights to improve the segmentation accuracy further. We validated our method on three datasets, and compared with other unsupervised methods for anomaly detection and segmentation. The DSC score of 0. 8590 in BraTs2020 dataset, 0. 6226 in ITCS dataset and 0. 7403 in In-house dataset show that our method achieves better segmentation performance and has better generalization.

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AAAI Conference 2023 Conference Paper

Unsupervised Paraphrasing under Syntax Knowledge

Tianyuan Liu
Yuqing Sun
Jiaqi Wu
Xi Xu
Yuchen Han
Cheng Li
Bin Gong

The soundness of syntax is an important issue for the paraphrase generation task. Most methods control the syntax of paraphrases by embedding the syntax and semantics in the generation process, which cannot guarantee the syntactical correctness of the results. Different from them, in this paper we investigate the structural patterns of word usages termed as the word composable knowledge and integrate it into the paraphrase generation to control the syntax in an explicit way. This syntax knowledge is pretrained on a large corpus with the dependency relationships and formed as the probabilistic functions on the word-level syntactical soundness. For the sentence-level correctness, we design a hierarchical syntax structure loss to quantitatively verify the syntactical soundness of the paraphrase against the given dependency template. Thus, the generation process can select the appropriate words with consideration on both semantics and syntax. The proposed method is evaluated on a few paraphrase datasets. The experimental results show that the quality of paraphrases by our proposed method outperforms the compared methods, especially in terms of syntax correctness.

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IROS Conference 2022 Conference Paper

Fisheye object detection based on standard image datasets with 24-points regression strategy

Xi Xu
Yu Gao 0040
Hao Liang 0016
Yi Yang 0009
Mengyin Fu

Fisheye object detection is a difficult task in robotics and autonomous driving. One of the reasons is that the fisheye datasets are inferior to standard image datasets in scale and quantity, which inspires the idea of using standard image datasets for fisheye object detection. However, the models trained on standard image datasets do not perform well with fisheye data. In this work, we explore the effect of fisheye images on different stages of the YOLOX with published weights generated by standard image datasets. We also propose a new regression strategy for 24-points object representation method, which is insensitive to image distortion. The experiments show that the feature extraction part is robust to fisheye image features, while the regression part of location and category performs poorly. The strategy can achieve the position of discrete points without calculating the IOU of irregular-shaped boxes. Theoretically, the strategy can be widely adopted to regress the irregular bounding boxes composed of discrete points. Source code is at https://github.com/IN2-ViAUn/Exploration-of-Potential.

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JBHI Journal 2020 Journal Article

A Hybrid Global-Local Representation CNN Model for Automatic Cataract Grading

Xi Xu
Linglin Zhang
Jianqiang Li
Yu Guan
Li Zhang

Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for the widespread use of cataract detection to prevent blindness. Artificial intelligence assisted diagnosis based on medical images has attracted more and more attention of researchers. Many studies have focused on the use of pre-defined feature sets for cataract classification, but the predefined feature sets may be incomplete or redundant. On account of the aforementioned issues, some studies have proposed deep learning methods to automatically extract image features, but all based on global features and none has analyzed the layer-by-layer transformation process of the middle-tier features. This paper uses convolutional neural networks (CNN) to learn useful features directly from input data, and deconvolution network method is employed to investigate how CNN characterizes cataract layer-by-layer. We found that compared to the global feature set, the detail vascular information, which is lost after multi-layer convolution calculation also plays an important role in cataract grading task. And this finding fits with the morphological definition of fundus image. Through the finding, we gained insights into the design of hybrid global-local feature representation model to improve the recognition performance of automatic cataract grading.

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JBHI Journal 2019 Journal Article

Efficient Mining Template of Predictive Temporal Clinical Event Patterns From Patient Electronic Medical Records

Jianqiang Li
Xiyue Tan
Xi Xu
Fei Wang

Exploring the temporal relationship among events in patient electronic medical records (EMR) is an important problem in biomedical informatics and the results can reveal patients’ impending disease conditions. In this paper, we investigate the problem of mining patterns from a sequence of point events, i. e. , we only have the information on when the event happens but no duration or numerical value available. We propose a whole pipeline, including event preprocessing, pattern mining, and outcome analysis to mine the patterns and evaluate their effectiveness and discriminative power. Finally, we treat those mined patterns as additional features and evaluate them in a predictive modeling task for the early detection of congestive heart failure. On a real-world EMR data warehouse, we found that by adding those sequential pattern features, the prediction performance could be significantly improved approximately 0. 1.

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